CMS Preshower in-situ Absolute Calibration with Physics Events

This note describes the in-situ absolute calibration of the Preshower detector of CMS. The Preshower is based on silicon strip sensors that will be installed in the endcaps of CMS in front of the crystal Calorimeter. Energy deposited in the lead of the Preshower is estimated by the silicon sensors, allowing a re-scaling of the energy measured by the endcap crystals. Measurement of the energy deposited in the lead absorbers to 5% accuracy is required over a very large dynamic range (1-400 MIPs equivalent), thus a maximum accuracy of 1% on the measurement of the charge deposited in the silicon will be sufficient. There are two principle sources of response variation at startup (sensor-to-sensor and channel-to-channel): sensor thickness (RMS of 1-2%) and gain uniformity of the electronics (RMS ~3%). These will be measured and thus taken into account. Radiation damage to the sensors (decrease in charge collection efficiency by up to 17% over 10 years) and the electronics (decrease in gain by up to 2% over 10 years) will need to be assessed by periodic in-situ calibrations. A precise in-situ absolute calibration using minimum ionizing particle signals from physics events is examined. For the calibration method the full simulation framework of CMS has been used (CMSIM/CMKIN, OSCAR and ORCA). It is shown that sufficient calibration accuracy can be obtained by using muon or pion events, and that the time required for the calibration is of the order of a few days at initial LHC luminosity and at least a factor of two less for nominal LHC luminosity

Η κρυοθεραπεία στο σύγχρονο ποδόσφαιρο

Σκοπός της παρούσας μελέτης είναι να ερευνήσει την σημαντικότητα της κρυοθεραπείας στην αποκατάσταση των αθλητών του ποδοσφαίρου μετά από έναν αγώνα ή κατά την διάρκεια ενός τουρνουά ή μιας προπονητικής μονάδας, ώστε να φανεί η ωφελιμότητά της και να γίνει κατανοητή από τους προπονητές για να την εντάξουν στο προπονητικό τους πρόγραμμα. Για τον σκοπό αυτόν χρησιμοποιήθηκε η μέθοδος της βιβλιογραφικής ανασκόπησης, όπου αναλύθηκαν άρθρα, που ασχολούνται με την μέθοδο της κρυοθεραπείας και διερευνήθηκαν: οι βιοχημικοί δείκτες, οι αντιληπτές μετρήσεις, οι σωματικές και οι φυσιολογικές μετρήσεις. Τα άρθρα που μελετήθηκαν ανακτήθηκαν από την μηχανή αναζήτησης Google Scholar και τοποθετούνται χρονικά από το 2008 έως το 2017. Τα αποτελέσματα έδειξαν ότι τα επιλεχθέντα άρθρα δεν συμφωνούσαν σχετικά με τους παραπάνω δείκτες. Αυτό οφείλεται στο γεγονός ότι ο κάθε ερευνητής χρησιμοποίησε διαφορετικές παραμέτρους, όπως διαφορετική διάρκεια της αποθεραπείας, διαφορετικά χρονικά διαστήματα που έγιναν οι μετρήσεις, εξωτερικές συνθήκες. Καθώς επίσης και διαφορετικό τρόπο της χρήσης της κρυοθεραπείας, όπως η χρήση της αποθεραπείας σε συνδυασμό με άλλη μέθοδο αποκατάστασης. Για τους παραπάνω δείκτες, υπήρξαν είτε θετικά, είτε αρνητικά, είτε μηδαμινά αποτελέσματα. Σύμφωνα με τα αποτελέσματα της παρούσας ανασκόπησης χρειάζεται περαιτέρω διερεύνηση στην αποκατάσταση των ποδοσφαιριστών με την μέθοδο της κρυοθεραπείας, όσο αφορά τον τρόπο που γίνεται η κρυοθεραπεία, την διάρκεια και την θερμοκρασία. Συμπερασματικά από τα αποτελέσματα της παρούσας μελέτης φάνηκε ότι η κρυοθεραπεία επηρεάζει θετικά τον ποδοσφαιριστή και πως η χρήση της μπορεί να ενισχύσει και να διατηρήσει την απόδοση του.The aim of this research is to investigate via the review method the significance of cryotherapy for the recovery of the football players after a match or during a tournament or a training, in order to show its benefits and help coaches to include it at their training programs. For this purpose the review method was used and articles on cryotherapy were examined, while biochemical markers, perceived measurements, physical and physiological measurements were investigated. The articles were found in Google scholar and they were published between 2008 and 2017. From the conclusion it is obvious that there was no agreement between all the articles about the above markers. That is due to the fact that every investigator used different parameters, such as different duration of the treatment, different moments that the measurements took place, external conditions. Furthermore, the different use of cryotherapy, such as the use of cryotherapy combined with another method of recovery. For these markers, there were either positive, negative or no results at all. According to the conclusions of the essay, more investigation on cryotherapy as a means of the football player’s recovery is needed in order to define the way cryotherapy is provided, the duration and the temperature. In conclusion, this essay showed that cryotherapy is beneficial for the football player and that its use can enhance and maintain his performance

Hypercalcemia in a patient with cholangiocarcinoma: a case report

<p>Abstract</p> <p>Background</p> <p>Humoral hypercalcemia of malignancy is rarely associated with cholangiocarcinoma (CC).</p> <p>Case report</p> <p>A 77-year-old man was admitted with confusion. Computer tomography showed a large multinodular mass in the right lobe of the liver and smaller lesions in the right lung. Liver histology confirmed the diagnosis of CC. Elevated calcium levels and suppressed intact parathyroid hormone in the absence of skeletal metastases or parathyroid gland pathology suggested the diagnosis of humoral hypercalcemia of malignancy (HHM). Treatment of hypercalcemia with saline infusion, loop diuretics, biphosphonate and calcitonin was effective in normalizing calcium levels and consciousness state within 48 hours, but a relapse occurred 4 weeks later and the patient succumbed to his disease.</p> <p>Conclusion</p> <p>Clinicians should be aware of this rare manifestation of CC as prompt and aggressive correction of hypercalcemia alleviates symptoms and improves patient's quality of life, despite the poor overall prognosis.</p

Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling

We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We identify a parameter-dependent effective stochastic differential equation (eSDE) in terms of physically meaningful coarse mean-field variables through a deep-learning ResNet architecture inspired by numerical stochastic integrators. We construct an approximate effective bifurcation diagram based on the identified drift term of the eSDE and contrast it with the mean-field SIS model bifurcation diagram. We observe a subcritical Hopf bifurcation in the evolving network's effective SIS dynamics, that causes the tipping point behavior; this takes the form of large amplitude collective oscillations that spontaneously -- yet rarely -- arise from the neighborhood of a (noisy) stationary state. We study the statistics of these rare events both through repeated brute force simulations and by using established mathematical/computational tools exploiting the right-hand-side of the identified SDE. We demonstrate that such a collective SDE can also be identified (and the rare events computations also performed) in terms of data-driven coarse observables, obtained here via manifold learning techniques, in particular Diffusion Maps. The workflow of our study is straightforwardly applicable to other complex dynamics problems exhibiting tipping point dynamics.Comment: 22 pages, 12 figure

Double Diffusion Maps and their Latent Harmonics for scientific computations in latent space

We introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning technique) on time series data. A second round of Diffusion Maps on those latent coordinates allows the approximation of the reduced dynamical models. This second round enables mapping the latent space coordinates back to the full ambient space (what is called lifting); it also enables the approximation of full state functions of interest in terms of the reduced coordinates. In our work, we develop and test three different reduced numerical simulation methodologies, either through pre-tabulation in the latent space and integration on the fly or by going back and forth between the ambient space and the latent space. The data-driven latent space simulation results, based on the three different approaches, are validated through (a) the latent space observation of the full simulation through the Nyström Extension formula, or through (b) lifting the reduced trajectory back to the full ambient space, via Latent Harmonics. Latent space modeling often involves additional regularization to favor certain properties of the space over others, and the mapping back to the ambient space is then constructed mostly independently from these properties; here, we use the same data-driven approach to construct the latent space and then map back to the ambient space

Machine Learning for the identification of phase-transitions in interacting agent-based systems

Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and show that they are in one-to-one correspondence with the expected theoretical order parameter of the ABM. We then utilize a deep learning framework to obtain a conformal reparametrization of the data-driven coordinates that facilitates, in our example, the identification of a single parameter-dependent ODE in these coordinates. We identify this ODE through a residual neural network inspired by a numerical integration scheme (forward Euler). We then use the identified ODE -- enabled through an odd symmetry transformation -- to construct the bifurcation diagram exhibiting the phase transition.Comment: 14 pages, 9 Figure

Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era

This study presents a collection of purely data-driven workflows for constructing reduced-order models (ROMs) for distributed dynamical systems. The ROMs we focus on, are data-assisted models inspired by, and templated upon, the theory of Approximate Inertial Manifolds (AIMs); the particular motivation is the so-called post-processing Galerkin method of Garcia-Archilla, Novo and Titi. Its applicability can be extended: the need for accurate truncated Galerkin projections and for deriving closed-formed corrections can be circumvented using machine learning tools. When the right latent variables are not a priori known, we illustrate how autoencoders as well as Diffusion Maps (a manifold learning scheme) can be used to discover good sets of latent variables and test their explainability. The proposed methodology can express the ROMs in terms of (a) theoretical (Fourier coefficients), (b) linear data-driven (POD modes) and/or (c) nonlinear data-driven (Diffusion Maps) coordinates. Both Black-Box and (theoretically-informed and data-corrected) Gray-Box models are described; the necessity for the latter arises when truncated Galerkin projections are so inaccurate as to not be amenable to post-processing. We use the Chafee-Infante reaction-diffusion and the Kuramoto-Sivashinsky dissipative partial differential equations to illustrate and successfully test the overall framework.Comment: 27 pages, 22 figure

Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learning

We identify effective stochastic differential equations (SDE) for coarse observables of fine-grained particle- or agent-based simulations; these SDE then provide coarse surrogate models of the fine scale dynamics. We approximate the drift and diffusivity functions in these effective SDE through neural networks, which can be thought of as effective stochastic ResNets. The loss function is inspired by, and embodies, the structure of established stochastic numerical integrators (here, Euler-Maruyama and Milstein); our approximations can thus benefit from error analysis of these underlying numerical schemes. They also lend themselves naturally to "physics-informed" gray-box identification when approximate coarse models, such as mean field equations, are available. Our approach does not require long trajectories, works on scattered snapshot data, and is designed to naturally handle different time steps per snapshot. We consider both the case where the coarse collective observables are known in advance, as well as the case where they must be found in a data-driven manner.Comment: 19 pages, includes supplemental materia