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A new geometrically-motivated algorithm for nonnegative matrix factorization is developed and applied to the discovery of latent "topics" for text and image "document" corpora. The algorithm is based on robustly finding and clustering…

Machine Learning · Statistics 2016-11-17 Weicong Ding , Mohammad H. Rohban , Prakash Ishwar , Venkatesh Saligrama

We show that "accidental" supersymmetry is a beyond-the-Standard Model framework that naturally accommodates a thermal relic dark matter candidate and successful electroweak baryogenesis, including the needed strongly first-order character…

High Energy Physics - Phenomenology · Physics 2015-06-11 Jonathan Kozaczuk , Stefano Profumo , Carroll L. Wainwright

A singlet fermion which interacts only with a new singlet scalar provides a viable and minimal scenario that can explain the dark matter. The singlet fermion is the dark matter particle whereas the new scalar mixes with the Higgs boson…

High Energy Physics - Phenomenology · Physics 2013-10-30 Sonja Esch , Michael Klasen , Carlos E. Yaguna

The empirical success of machine learning models with many more parameters than measurements has generated an interest in the theory of overparameterisation, i.e., underdetermined models. This paradigm has recently been studied in domains…

Hidden Markov models have successfully been applied as models of discrete time series in many fields. Often, when applied in practice, the parameters of these models have to be estimated. The currently predominating identification methods,…

Machine Learning · Statistics 2015-07-24 Robert Mattila , Cristian R. Rojas , Bo Wahlberg

Models of physics beyond the Standard Model often contain a large number of parameters. These form a high-dimensional space that is computationally intractable to fully explore. Experimental constraints project onto a subspace of viable…

High Energy Physics - Theory · Physics 2022-01-05 Jacob Hollingsworth , Michael Ratz , Philip Tanedo , Daniel Whiteson

Expensive multi-objective optimization problems (EMOPs) are common in real-world scenarios where evaluating objective functions is costly and involves extensive computations or physical experiments. Current Pareto set learning methods for…

Machine Learning · Computer Science 2025-03-18 Minh-Duc Nguyen , Phuong Mai Dinh , Quang-Huy Nguyen , Long P. Hoang , Dung D. Le

Multiobjective blackbox optimization deals with problems where the objective and constraint functions are the outputs of a numerical simulation. In this context, no derivatives are available, nor can they be approximated by finite…

Optimization and Control · Mathematics 2025-04-07 Sébastien Le Digabel , Antoine Lesage-Landry , Ludovic Salomon , Christophe Tribes

Useful materials are often statistically exceptional and they might be overlooked by AI models that attempt to describe all materials simultaneously. These global models perform well for the majority of (useless) materials, but they do not…

Materials Science · Physics 2024-09-26 Lucas Foppa , Matthias Scheffler

Selection of perefect parameters for low-pass filters can sometimes be an expensive problem with no analytical solution or differentiability of cost function. In this paper, we introduce a new PSO-inspired algorithm, that incorporates the…

Optimization and Control · Mathematics 2024-02-20 Dmytro Shchyrba , Izabela Paniczek

Studies of dark matter lie at the interface of collider physics, astrophysics and cosmology. Constraining models featuring dark matter candidates entails the capability to provide accurate predictions for large sets of observables and…

Materials with bespoke properties have long been identified by computational searches, and their experimental realisation is now coming within reach through autonomous laboratories. Scattering experiments are central to verifying the atomic…

Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of system parameters, such as hyper parameter tuning of…

Machine Learning · Computer Science 2018-09-19 Kouhei Nishida , Hernan Aguirre , Shota Saito , Shinichi Shirakawa , Youhei Akimoto

We study a class of supersymmetric models with non-universal gaugino masses that could arise from F-terms in a general combination of the singlet and adjoint representations of SU(5). We explore models that satisfy present Large Hadron…

High Energy Physics - Phenomenology · Physics 2013-05-30 James E. Younkin , Stephen P. Martin

The Singlet-Doublet model of dark matter is a minimal extension of the Standard Model with dark matter that is a mixture of a singlet and a non-chiral pair of electroweak doublet fermions. The stability of dark matter is ensured by the…

High Energy Physics - Phenomenology · Physics 2015-09-15 Lorenzo Calibbi , Alberto Mariotti , Pantelis Tziveloglou

Modular symmetries have been impeccable in neutrino and quark sectors. This motivated us, therefore, to propose a variant of scotogenic model based on modular $A_4$ symmetry to realize the neutrino mass generation at one-loop level through…

High Energy Physics - Phenomenology · Physics 2020-09-04 Mitesh Kumar Behera , Shivaramakrishna Singirala , Subhasmita Mishra , Rukmani Mohanta

In this paper, we investigate the mutual impact between the dark matter (DM) requirements and the scalar sector in the scale invariant (SI) scotogenic model. The model is motivated by the neutrino mass and DM within a classically SI…

High Energy Physics - Phenomenology · Physics 2022-04-06 Rachik Soualah , Amine Ahriche

We scrutinise the widely studied minimal scotogenic model of dark matter (DM) and radiative neutrino mass from the requirement of a strong first order electroweak phase transition (EWPT) and observable gravitational waves at future planned…

High Energy Physics - Phenomenology · Physics 2020-09-02 Debasish Borah , Arnab Dasgupta , Kohei Fujikura , Sin Kyu Kang , Devabrat Mahanta

We present a new method for searching optimal hyperparameters among several tasks and several criteria. Multi-Task Multi Criteria method (MTMC) provides several Pareto-optimal solutions, among which one solution is selected with given…

Machine Learning · Computer Science 2020-02-18 Kirill Akhmetzyanov , Alexander Yuzhakov

Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation…

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