Prevention of arthritis by interleukin 10-producing B cells

In this study we have shown that activation of arthritogenic splenocytes with antigen and agonistic anti-CD40 gives raise to a B cell population that produce high levels of interleukin (IL)-10 and low levels of interferon (IFN)-{gamma}. Transfer of these B cells into DBA/1-TcR-ß-Tg mice, immunized with bovine collagen (CII) emulsified in complete Freund's adjuvant inhibited T helper type 1 differentiation, prevented arthritis development, and was also effective in ameliorating established disease. IL-10 is essential for the regulatory function of this subset of B cells, as the B cells population isolated from IL-10 knockout mice failed to mediate this protective function. Furthermore, B cells isolated from arthritogenic splenocytes treated in vitro with anti–IL-10/anti–IL-10R were unable to protect recipient mice from developing arthritis. Our results suggest a new role of a subset of B cells in controlling T cell differentiation and autoimmune disorders

Wannier interpolation of the electron-phonon matrix elements in polar semiconductors: Polar-optical coupling in GaAs

We generalize the Wannier interpolation of the electron-phonon matrix elements to the case of polar-optical coupling in polar semiconductors. We verify our methodological developments against experiments, by calculating the widths of the electronic bands due to electron-phonon scattering in GaAs, the prototype polar semiconductor. The calculated widths are then used to estimate the broadenings of excitons at critical points in GaAs and the electron-phonon relaxation times of hot electrons. Our findings are in good agreement with available experimental data. Finally, we demonstrate that while the Fr\"ohlich interaction is the dominant scattering process for electrons/holes close to the valley minima, in agreement with low-field transport results, at higher energies, the intervalley scattering dominates the relaxation dynamics of hot electrons or holes. The capability of interpolating the polar-optical coupling opens new perspectives in the calculation of optical absorption and transport properties in semiconductors and thermoelectrics.Comment: To appear on Phys. Rev.

Nonlocal pseudopotentials and magnetic fields

We show how to describe the coupling of electrons to non-uniform magnetic fields in the framework of the widely used norm-conserving pseudopotential appro ximation for electronic structure calculations. Our derivation applies to magnetic fields that are smooth on the scale of the core region. The method is validated by application to the calculation of the magnetic susceptibility of molecules. Our results are compared with high quality all electron quantum chemical results, and another recently proposed formalism.Comment: 4 pages, submitted to Physical Review Letter

Theory of double-resonant Raman spectra in graphene: intensity and line shape of defect-induced and two-phonon bands

We calculate the double resonant (DR) Raman spectrum of graphene, and determine the lines associated to both phonon-defect processes, and two-phonons ones. Phonon and electronic dispersions reproduce calculations based on density functional theory corrected with GW. Electron-light, -phonon, and -defect scattering matrix elements and the electronic linewidth are explicitly calculated. Defect-induced processes are simulated by considering different kind of idealized defects. For an excitation energy of $\epsilon_L=2.4$ eV, the agreement with measurements is very good and calculations reproduce: the relative intensities among phonon-defect or among two-phonon lines; the measured small widths of the D, $D'$, 2D and $2D'$ lines; the line shapes; the presence of small intensity lines in the 1800, 2000 cm$^{-1}$ range. We determine how the spectra depend on the excitation energy, on the light polarization, on the electronic linewidth, on the kind of defects and on their concentration. According to the present findings, the intensity ratio between the $2D'$ and 2D lines can be used to determine experimentally the electronic linewidth. The intensity ratio between the $D$ and $D'$ lines depends on the kind of model defect, suggesting that this ratio could possibly be used to identify the kind of defects present in actual samples. Charged impurities outside the graphene plane provide an almost undetectable contribution to the Raman signal

Electron Transport and Hot Phonons in Carbon Nanotubes

We demonstrate the key role of phonon occupation in limiting the high-field ballistic transport in metallic carbon nanotubes. In particular, we provide a simple analytic formula for the electron transport scattering length, that we validate by accurate first principles calculations on (6,6) and (11,11) nanotubes. The comparison of our results with the scattering lengths fitted from experimental I-V curves indicates the presence of a non-equilibrium optical phonon heating induced by electron transport. We predict an effective temperature for optical phonons of thousands Kelvin.Comment: 4 pages, 1 figur

Generalization of the density-matrix method to a non-orthogonal basis

We present a generalization of the Li, Nunes and Vanderbilt density-matrix method to the case of a non-orthogonal set of basis functions. A representation of the real-space density matrix is chosen in such a way that only the overlap matrix, and not its inverse, appears in the energy functional. The generalized energy functional is shown to be variational with respect to the elements of the density matrix, which typically remains well localized.Comment: 11 pages + 2 postcript figures at the end (search for -cut here

Progame:event-based machine learning approach for in-game marketing

Abstract. There’s been a significant growth in the gaming industry, which has lead to an increased number of collected player and usage data, including game events, player interactions, the connections between players and individual preferences. Such big data has many use cases such as the identification of gaming bottlenecks, detection and prediction of anomalies and suspicious usage patterns for security, and real time offer specification via fine-grained user profiling based on their interest profiles. Offering personalized offer timing could reduce product cannibalization, and ethical methods increase the trust of customers. The goal of this thesis is to predict the value and time of the next in-game purchase in a mobile game. Using data aggregation, event-based purchase data, daily in-game behaviour metrics and session data are combined into a single data table, from which samples of 50 000 data points are taken. The features are analyzed for linear correlation with the labels, and their combinations are used as input for three machine learning algorithms: Random Forest, Support Vector Machine and Multi-Layer Perceptron. Both purchase value and purchase time are correlated with features related to previous purchase behaviour. Multi-Layer Perceptron showed the lowest error in predicting both labels, showing an improvement of 22,0% for value in USD and 20,7% for days until purchase compared to a trivial baseline predictor. For ethical customer behaviour prediction, sharing of research knowledge and customer involvement in the data analysis process is suggested to build awareness.Progame : tapahtumapohjainen koneoppimisjärjestelmä pelinsisäiseen markkinointiin. Tiivistelmä. Peliteollisuuden kasvu on johtanut kerättävän pelaaja- ja käyttödatan määrään nousuun, koostuen mm. pelitapahtumista, interaktiodatasta, pelaajien välisistä yhteyksistä ja henkilökohtaisista mieltymyksistä. Tällaisella massadatalla on monia käyttötarkoituksia kuten tietoliikenteen teknisten rajoitusten tunnistaminen pelikäytössä, käyttäjien tavallisuudesta poikkeavan käytöksen tunnistaminen ja ennustaminen tietoturvatarkoituksiin, sekä reaaliaikainen tarjousten määrittäminen hienovaraisella käyttäjien mieltymysten profiloinnilla. Ostotarjousten henkilökohtaistaminen voi vähentää uusien tuotteiden aiheuttamaa vanhojen tuotteiden myynnin laskua, ja eettiset menetelmät parantavat asiakkaiden luottamusta. Tässä työssä ennustetaan asiakkaan seuraavan pelinsisäisen oston arvoa ja aikaa mobiilipelissä. Tapahtumapohjainen ostodata, päivittäiset pelin sisäiset metriikat ja sessiodata yhdistetään yhdeksi datataulukoksi, josta otetaan kerrallaan 50 000:n datarivin näytteitä. Jokaisen selittävän muuttujan lineaarinen korrelaatio ennustettavan muuttujan kanssa analysoidaan, ja niiden yhdistelmiä käytetään syötteenä kolmelle eri koneoppimismallille: satunnainen metsä (Random Forest), tukivektorikone (Support Vector Machine) ja monikerroksinen perseptroniverkko (Multi-Layer Perceptron). Tutkimuksessa havaittiin, että sekä tulevan oston arvo että ajankohta korreloivat aiemman ostokäyttäytymisen kanssa. Monikerroksisella perseptroniverkolla oli pienin virhe molemmille ennustettaville muuttujille, ja verrattuna triviaaliin vertailuennustimeen, se vähensi virhettä 22,0% arvon ennustamisessa ja 20,7% seuraavaan ostoon jäljellä olevien päivien ennustamisessa. Eettisen asiakkaiden käyttäytymisen ennustamisen varmistamiseksi ja tietoisuuden lisäämiseksi ehdotetaan tutkimustiedon jakamista ja asiakkaan ottamista mukaan analyysin tekemiseen