Event-by-event fluctuations in the elliptic-flow coefficient v2 are studied in PbPb collisions at sNN=5.02 TeV using the CMS detector at the CERN LHC. Elliptic-flow probability distributions p(v2) ...for charged particles with transverse momentum 0.3<pT<3.0GeV/c and pseudorapidity |η|<1.0 are determined for different collision centrality classes. The moments of the p(v2) distributions are used to calculate the v2 coefficients based on cumulant orders 2, 4, 6, and 8. A rank ordering of the higher-order cumulant results and nonzero standardized skewness values obtained for the p(v2) distributions indicate non-Gaussian initial-state fluctuations. Bessel–Gaussian and elliptic power fits to the flow distributions are studied to characterize the initial-state spatial anisotropy.
•Define a new tensor unfolding to unfold an N-way tensor into a three-way tensor.•Propose a novel tensor rank for N-way tensors based on the new tensor unfolding.•Establish a convex relaxation for ...efficiently minimizing the proposed tensor rank.•Apply the proposed relaxation to tensor recovery problems with ADMM-based solver.
The recent popular tensor tubal rank, defined based on tensor singular value decomposition (t-SVD), yields promising results. However, its framework is applicable only to three-way tensors and lacks the flexibility necessary tohandle different correlations along different modes. To tackle these two issues, we define a new tensor unfolding operator, named mode-k1k2 tensor unfolding, as the process of lexicographically stacking all mode-k1k2 slices of an N-way tensor into a three-way tensor, which is a three-way extension of the well-known mode-k tensor matricization. On this basis, we define a novel tensor rank, named the tensor N-tubal rank, as a vector consisting of the tubal ranks of all mode-k1k2 unfolding tensors, to depict the correlations along different modes. To efficiently minimize the proposed N-tubal rank, we establish its convex relaxation: the weighted sum of the tensor nuclear norm (WSTNN). Then, we apply the WSTNN to low-rank tensor completion (LRTC) and tensor robust principal component analysis (TRPCA). The corresponding WSTNN-based LRTC and TRPCA models are proposed, and two efficient alternating direction method of multipliers (ADMM)-based algorithms are developed to solve the proposed models. Numerical experiments demonstrate that the proposed models significantly outperform the compared ones.
Study of methods of resolved top quarks kinematic reconstruction in the tt̄→ℓ+jets channel is presented at the particle level as well as the fast-simulation detector level. Previous and current ...pseudo-top quark reconstruction algorithms are compared with suggestions presented on how to improve the reconstructed top-quark mass line shape, including the check of performance on physics observables in terms of correlations between detector, particle and parton levels, and in unfolding, with implications for current high energy physics experiments.
This study presents the development of a novel unfolding program based on a Bayesian method for Bonner spheres spectrometry. The program incorporates a priori information on the neutron spectrum, ...employing a linear superposition of three non-negative parametric functions: thermal-Maxwellian distribution, an intermediate region represented by a straight line in lethargy space (1/E behavior), and the Watt fission distribution for the fast region. The effectiveness of the developed program was validated through its application to two reference cases from the EURADOS 2018 exercise (LINAC and Skyshine), followed by its implementation in characterizing the neutron field around an OB26 neutron irradiator at the Secondary Standards Dosimetry Laboratory (SSDL) of the Nuclear Research Center of Algiers (CRNA), Algeria. While validation results for the LINAC case were promising, achieving comparable neutron spectrum shape and integral quantities to the reference, challenges were encountered with the Skyshine case. However, these challenges were partially addressed through sensitivity analysis, resulting in slightly improved outcomes compared to the previous participation and to the reference. Application of the program to characterize the neutron field around the OB26 irradiator yielded satisfactory results when compared to reference data. A two-step sensitivity analysis further enhanced the outcomes, initially substituting the Watt fission distribution with the evaporation distribution, thereby improving spectrum shape and integral quantities. Subsequently, utilizing the obtained neutron spectra as prior information for unfolding codes MAXED and GRAVEL ensured stability and agreement with reference data and preliminary results.
•Development of a novel unfolding program tailored for Bonner spheres spectrometry.•Utilization of the R2OpenBUGS package to apply the Bayesian method in the program.•Validation outcomes demonstrate globally good agreement with reference scenarios.•Application of MAXED and GRAVEL codes for conducting sensitivity analyses.•The Bayesian program enabled the assessment of OB26 neutron field characteristics.
Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blogs, and implementation guides. However, in most ...articles, the inference formulas for the LSTM network and its parent, RNN, are stated axiomatically, while the training formulas are omitted altogether. In addition, the technique of “unrolling” an RNN is routinely presented without justification throughout the literature. The goal of this tutorial is to explain the essential RNN and LSTM fundamentals in a single document. Drawing from concepts in Signal Processing, we formally derive the canonical RNN formulation from differential equations. We then propose and prove a precise statement, which yields the RNN unrolling technique. We also review the difficulties with training the standard RNN and address them by transforming the RNN into the “Vanilla LSTM”11The nickname “Vanilla LSTM” symbolizes this model’s flexibility and generality (Greff et al., 2015). network through a series of logical arguments. We provide all equations pertaining to the LSTM system together with detailed descriptions of its constituent entities. Albeit unconventional, our choice of notation and the method for presenting the LSTM system emphasizes ease of understanding. As part of the analysis, we identify new opportunities to enrich the LSTM system and incorporate these extensions into the Vanilla LSTM network, producing the most general LSTM variant to date. The target reader has already been exposed to RNNs and LSTM networks through numerous available resources and is open to an alternative pedagogical approach. A Machine Learning practitioner seeking guidance for implementing our new augmented LSTM model in software for experimentation and research will find the insights and derivations in this treatise valuable as well.
•Recurrent Neural Network (RNN) definition follows from Delay Differential Equations.•RNN unfolding technique is formally justified as approximating an infinite sequence.•Long Short-Term Memory Network (LSTM) can be logically rationalized from RNN.•System diagrams with complete derivation of LSTM training equations are provided.•New LSTM extensions: external input gate and convolutional input context windows.
The scientific study of protein surfactant interactions goes back more than a century, and has been put to practical uses in everything from the estimation of protein molecular weights to efficient ...washing powder enzymes and products for personal hygiene. After a burst of activity in the late 1960s and early 1970s that established the general principles of how charged surfactants bind to and denature proteins, the field has kept a relatively low profile until the last decade. Within this period there has been a maturation of techniques for more accurate and sophisticated analyses of protein–surfactant complexes such as calorimetry and small angle scattering techniques. In this review I provide an overview of different useful approaches to study these complexes and identify eight different issues which define central concepts in the field. (1) Are proteins denatured by monomeric surfactant molecules, micelles or both? (2) How does unfolding of proteins in surfactant compare with “proper” unfolding in chemical denaturants? Recent work has highlighted the role of shared micelles, rather than monomers, below the critical micelle concentration (cmc) in promoting both protein denaturation and formation of higher order structures. Kinetic studies have extended the experimentally accessible range of surfactant concentrations to far above the cmc, revealing numerous different modes of denaturation by ionic surfactants below and above the cmc which reflect micellar properties as much as protein unfolding pathways. Uncharged surfactants follow a completely different denaturation strategy involving synergy between monomers and micelles. The high affinity of charged surfactants for proteins means that unfolding pathways are generally different in surfactants versus chemical denaturants, although there are common traits. Other issues are as follows: (3) Are there non-denaturing roles for SDS? (4) How reversible is unfolding in SDS? (5) How do solvent conditions affect the way in which surfactants denature proteins? The last three issues compare SDS with “proper” membranes. (6) Do anionic surfactants such as SDS mimic biological membranes? (7) How do mixed micelles interact with globular proteins? (8) How can mixed micelles be used to measure the stability of membrane proteins? The growing efforts to understand the unique features of membrane proteins have encouraged the development of mixed micelles to study the equilibria and kinetics of this class of proteins, and traits which unite globular and membrane proteins have also emerged. These issues emphasise the amazing power of surfactants to both extend the protein conformational landscape and at the same time provide convenient and reversible short-cuts between the native and denatured state for otherwise obdurate membrane proteins.
► This review summarizes advances in protein surfactant interactions since the 1990s. ► Shared micelles are key to protein denaturation below the cmc. ► Numerous micellar denaturation mechanisms occur above the cmc. ► Mixed micelles are highly useful tools to reversibly unfold membrane proteins. ► Chain length and micelle composition both modulate protein denaturation mechanisms.
Our group is currently developing a multilayer neutron spectrometer based on moderator combination selection to evaluate neutron energy spectra in irradiation fields for boron neutron capture therapy ...(BNCT). In a previous study, we optimally selected (based on simulations) liquid moderator combinations of the spectrometer to achieve high accuracy in the evaluated neutron energy spectra. According to the optimization result, we conducted a spectrometer verification test in the BNCT irradiation field at the Heavy Water Neutron Irradiation Facility of Kyoto University Reactor (KUR-HWNIF). The neutron energy spectrum result was evaluated, and the uncertainty was found to be less than 15%. The experimental results prove that the multilayer neutron spectrometer based on moderator combination selection effectively evaluates the neutron energy spectrum in a BNCT irradiation field.
The CYlindrical neutron SPectrometer (CYSP) is a polyethylene cylinder with diameter 50 cm and height 65 cm equipped with a collimated aperture and an internal capsule embedding multiple thermal ...neutron detectors. Analogously to Bonner Spheres, CYSP responds from thermal up to GeV neutrons and the spectrum is obtained via few-channel unfolding methods. Due to the specific design and the use of borated materials, the internal detectors only respond to neutrons coming from the desired direction. By changing the type and sensitivity of the internal detectors, the CYSP was adapted to respond over a variety of fluence rates. This work describes HELIOCYSP, an updated design of CYSP, including Helium-3 proportional counters as internal detectors and a more convenient design of the internal capsule. Due to the increased sensitivity, HELIOCYSP is suited for time-effective measurements in very low fluence rate scenarios, as those encountered in cosmic-ray-induced neutron studies at ground level.
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