Searches for TeV-scale particles at the LHC using jet shapes

New particles at the TeV scale can decay hadronically with strongly collimated jets, thus the standard reconstruction methods based on invariant-masses of well-separated jets can fail. We discuss how to identify such particles in pp collisions at the LHC using jet shapes which help to reduce the contribution of QCD-induced events. We focus on a rather generic example X to ttbar to hadrons, with X being a heavy particle, but the approach is well suited for reconstruction of other decay channels characterized by a cascade decay of known states.Comment: 14 pages, 6 figure

High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that Maximum Lik elihood inference is deeply related to Principal Component Analysis when the amp litude of the pattern components, xi, is negligible compared to N^1/2. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in xi/N^1/2. We stress that it is important to generalize the Hopfield model and include both attractive and repulsive patterns, to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size, N and of the amplitude, xi. The inference approach is illustrated on synthetic and biological data.Comment: Physical Review E: Statistical, Nonlinear, and Soft Matter Physics (2011) to appea

On the suitability of combining feature selection and resampling to manage data complexity

The effectiveness of a learning task depends on data com- plexity (class overlap, class imbalance, irrelevant features, etc.). When more than one complexity factor appears, two or more preprocessing techniques should be applied. Nevertheless, no much effort has been de- voted to investigate the importance of the order in which they can be used. This paper focuses on the joint use of feature reduction and bal- ancing techniques, and studies which could be the application order that leads to the best classification results. This analysis was made on a spe- cific problem whose aim was to identify the melodic track given a MIDI file. Several experiments were performed from different imbalanced 38- dimensional training sets with many more accompaniment tracks than melodic tracks, and where features were aggregated without any correla- tion study. Results showed that the most effective combination was the ordered use of resampling and feature reduction techniques

Screwing process analysis using multivariate statistical process control

Screws are widely used for parts joining in industry. The definition of effective monitoring strategies for screwing processes can help to prevent or significantly reduce ineffective procedures, defective screwing and downtime. Monitoring several correlated variables simultaneously in order to detect relevant changes in manufacturing processes is an increasingly frequent practice furthered by advanced data acquisition systems. However, the monitoring approaches currently used do not consider the multivariate nature of the screwing processes. This paper presents the results of a study performed in an automotive electronics assembly line. Screwing process data concerning torque and rotation angle were analyzed using multivariate statistical process control based on principal component analysis (MSPC-PCA). The main purpose was to extract relevant information from a high number of correlated variables in order to early detect undesirable changes in the process performance. A PCA model was defined based on three principal components. The physical meaning of each component was identified, and underlying causes were inferred based on technical knowledge about the process. Monitoring tools, such as score plots and multivariate control charts allowed to detect the defective screwing cases included in the analyzed data set. Furthermore, eight periods of instability were identified. Considering that the out-of-control signals detected in these periods mainly correspond to delays at the beginning of the tightening operation, four potential causes to explain this behavior were ascertained and analyzed. This research allowed to acquire a deeper understanding on the screwing process behavior and about the causes with higher impact on its stability. Due to its flexibility and versatility, it is considered that this approach can be applied to effectively monitor screwing pCEC - Clinical Excellence Commission(undefined

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Spatially Resolved Mapping of Local Polarization Dynamics in an Ergodic Phase of Ferroelectric Relaxor

Spatial variability of polarization relaxation kinetics in relaxor ferroelectric 0.9Pb(Mg1/3Nb2/3)O3-0.1PbTiO3 is studied using time-resolved Piezoresponse Force Microscopy. Local relaxation attributed to the reorientation of polar nanoregions is shown to follow stretched exponential dependence, exp(-(t/tau)^beta), with beta~~0.4, much larger than the macroscopic value determined from dielectric spectra (beta~~0.09). The spatial inhomogeneity of relaxation time distributions with the presence of 100-200 nm "fast" and "slow" regions is observed. The results are analyzed to map the Vogel-Fulcher temperatures on the nanoscale.Comment: 23 pages, 4 figures, supplementary materials attached; to be submitted to Phys. Rev. Let

Ultrafast ring opening in CHD investigated by simplex-based spectral unmixing

We use spectral unmixing to determine the number of transient photoproducts and to track their evolution following the photo- excitation of 1,3-cyclohexadiene (CHD) to form 1,3,5-hexatriene (HT) in the gas phase. The ring opening is initiated with a 266 nm ultraviolet laser pulse and probed via fragmentation with a delayed intense infrared 800 nm laser pulse. The ion time-of-flight (TOF) spectra are analyzed with a simplex-based spectral unmixing technique. We find that at least three independent spectra are needed to model the transient TOF spectra. Guided by mathematical and physical constraints, we decompose the transient TOF spectra into three spectra associated with the presence of CHD, CHD+, and HT, and show how these three products appear at different times during the ring opening

Timing and Dose of Upper Limb Motor Intervention After Stroke: A Systematic Review

This systematic review aimed to investigate timing, dose, and efficacy of upper limb intervention during the first 6 months poststroke. Three online databases were searched up to July 2020. Titles/abstracts/full-text were reviewed independently by 2 authors. Randomized and nonrandomized studies that enrolled people within the first 6 months poststroke, aimed to improve upper limb recovery, and completed preintervention and postintervention assessments were included. Risk of bias was assessed using Cochrane reporting tools. Studies were examined by timing (recovery epoch), dose, and intervention type. Two hundred and sixty-one studies were included, representing 228 (n=9704 participants) unique data sets. The number of studies completed increased from one (n=37 participants) between 1980 and 1984 to 91 (n=4417 participants) between 2015 and 2019. Timing of intervention start has not changed (median 38 days, interquartile range [IQR], 22–66) and study sample size remains small (median n=30, IQR 20–48). Most studies were rated high risk of bias (62%). Study participants were enrolled at different recovery epochs: 1 hyperacute (<24 hours), 13 acute (1–7 days), 176 early subacute (8–90 days), 34 late subacute (91–180 days), and 4 were unable to be classified to an epoch. For both the intervention and control groups, the median dose was 45 (IQR, 600–1430) min/session, 1 (IQR, 1–1) session/d, 5 (IQR, 5–5) d/wk for 4 (IQR, 3–5) weeks. The most common interventions tested were electromechanical (n=55 studies), electrical stimulation (n=38 studies), and constraint-induced movement (n=28 studies) therapies. Despite a large and growing body of research, intervention dose and sample size of included studies were often too small to detect clinically important effects. Furthermore, interventions remain focused on subacute stroke recovery with little change in recent decades. A united research agenda that establishes a clear biological understanding of timing, dose, and intervention type is needed to progress stroke recovery research. Prospective Register of Systematic Reviews ID: CRD42018019367/CRD42018111629

The L-sigma Relation of Local HII Galaxies

We present for the first time a new data set of emission line widths for 118 star-forming regions in HII galaxies (HIIGs). This homogeneous set is used to investigate the L-sigma relation in conjunction with optical spectrophotometric observations. Peculiarities in the line profiles such as sharp lines, wings, asymmetries, and in some cases more than one component in emission were verified. From a new independent homogeneous set of spectrophotometric data we derived physical condition parameters and performed the statistical principal component analysis. We have investigated the potential role of metallicity (O/H), Hbeta equivalent width (WHbeta) and ionization ratio [OIII]/[OII] to account for the observational scatter of L-sigma relation. Our results indicate that the L-sigma relation for HIIGs is more sensitive to the evolution of the current starburst event (short-term evolution) and dated by WHbeta or even the [OIII]/[OII] ratio. The long-term evolution measured by O/H also plays a potential role in determining the luminosity of the current burst for a given velocity dispersion and age as previously suggested. Additionally, galaxies showing Gaussian line profiles present more tight correlations indicating that they are best targets for the application of the parametric relations as an extragalactic cosmological distance indicator. Best fits for a restricted homogeneous sample of 45 HIIGs provide us a set of new extragalactic distance indicators with an RMS scatter compatible with observational errors of Delta_log(LHalpha) = 0.2 dex or 0.5 mag. Improvements may still come from future optimized observational programs to reduce the observational uncertainties on the predicted luminosities of HIIGs in order to achieve the precision required for the application of these relations as tests of cosmological models.Comment: 53 pages, 15 figures, 4 complete tables Accepted for publication in The Astrophysical Journa

Study protocol: a randomised controlled trial investigating the effect of exercise training on peripheral blood gene expression in patients with stable angina

Background: Exercise training has been shown to reduce angina and promote collateral vessel development in patients with coronary artery disease. However, the mechanism whereby exercise exerts these beneficial effects is unclear. There has been increasing interest in the use of whole genome peripheral blood gene expression in a wide range of conditions to attempt to identify both novel mechanisms of disease and transcriptional biomarkers. This protocol describes a study in which we will assess the effect of a structured exercise programme on peripheral blood gene expression in patients with stable angina, and correlate this with changes in angina level, anxiety, depression, and exercise capacity. Methods/Design: Sixty patients with stable angina will be recruited and randomised 1: 1 to exercise training or conventional care. Patients randomised to exercise training will attend an exercise physiology laboratory up to three times weekly for supervised aerobic interval training sessions of one hour in total duration. Patients will undergo assessments of angina, anxiety, depression, and peripheral blood gene expression at baseline, after six and twelve weeks of training, and twelve weeks after formal exercise training ceases. Discussion: This study will provide comprehensive data on the effect of exercise training on peripheral blood gene expression in patients with angina. By correlating this with improvement in angina status we will identify candidate peripheral blood transcriptional markers predictive of improvements in angina level in response to exercise training