Identifying Student Discussion in Computer-Mediated Problem Solving Chat

The COMPS project employs computer chat for students working in small groups solving classroom problems. This summer’s project aims to build computer classifiers that could effectively “look over the shoulders” of the students while working, to approximately recognize whether the students are engaging in productive discussion. Research questions are: can we write machine classifiers that can recognize reasoning, agreement, and disagreement in student discussions? Can we achieve this using only a common English vocabulary? Several thousand lines of COMPS transcripts were manually annotated. A topic modelling program was used to determine 10 main topics which appeared in the transcripts and the words in those topics. A Linear Classifier and a Support Vector Machine Classifier used the topic model to predict the annotation of each line of dialogue. To address the common English vocabulary research question, an intersection of many transcripts from different sources was combined with Google word lists and modified to accommodate text-chat conventions. In the normal vocabulary, we found f1 scores of 0.7 and above for reasoning. Using only common vocabulary, the scores were slightly lower. The next step is to train our topic model on a combination of transcripts and apply it to other transcripts from different student discussions

Observation of proton-tagged, central (semi)exclusive production of high-mass lepton pairs in pp collisions at 13 TeV with the CMS-TOTEM precision proton spectrometer

The process pp -> pl(+)l(-)p(()*()), with l(+)l(-) a muon or an electron pair produced at midrapidity with mass larger than 110 GeV, has been observed for the first time at the LHC in pp collisions at root s = 13 TeV. One of the two scattered protons is measured in the CMS-TOTEM precision proton spectrometer (CT-PPS), which operated for the first time in 2016. The second proton either remains intact or is excited and then dissociates into a low-mass state p*, which is undetected. The measurement is based on an integrated luminosity of 9.4 fb(-1) collected during standard, high-luminosity LHC operation. A total of 12 mu(+)/mu(-) and 8 e(+)e(-) pairs with m(l(+)l(-)) > 110 GeV, and matching forward proton kinematics, are observed, with expected backgrounds of 1.49 +/- 0.07 (stat) +/- 0.53 (syst) and 2.36 +/- 0.09 (stat) +/- 0.47(syst), respectively. This corresponds to an excess of more than five standard deviations over the expected background. The present result constitutes the first observation of proton-tagged gamma gamma collisions at the electroweak scale. This measurement also demonstrates that CT-PPS performs according to the design specifications.Peer reviewe

Measurement of single-diffractive dijet production in proton-proton collisions at root s=8 TeV with the CMS and TOTEM experiments

A Publisher's Erratum to this article was published on 03 May 2021. https://doi.org/10.1140/epjc/s10052-021-08863-wPeer reviewe

Measurement of single-diffractive dijet production in proton–proton collisions at s=8 TeV\sqrt{s} = 8\,\text {Te}\text {V} with the CMS and TOTEM experiments

Measurements are presented of the single-diffractive dijet cross section and the diffractive cross section as a function of the proton fractional momentum loss ξ ξ and the four-momentum transfer squared t. Both processes p p → p X p p → p X and p p → X p p p → X p , i.e. with the proton scattering to either side of the interaction point, are measured, where X X includes at least two jets; the results of the two processes are averaged. The analyses are based on data collected simultaneously with the CMS and TOTEM detectors at the LHC in proton–proton collisions at s √ =8TeV s=8TeV during a dedicated run with β ∗ =90m β∗=90m at low instantaneous luminosity and correspond to an integrated luminosity of 37.5nb −1 37.5nb−1 . The single-diffractive dijet cross section σ p X jj σjj p X , in the kinematic region ξ<0.1 ξ<0.1 , 0.03<|t|<1GeV 2 0.03<|t|<1GeV2 , with at least two jets with transverse momentum p T >40GeV pT>40GeV , and pseudorapidity |η|<4.4 |η|<4.4 , is 21.7±0.9(stat) +3.0 −3.3 (syst)±0.9(lumi)nb 21.7±0.9(stat)−3.3+3.0(syst)±0.9(lumi)nb . The ratio of the single-diffractive to inclusive dijet yields, normalised per unit of ξ ξ , is presented as a function of x, the longitudinal momentum fraction of the proton carried by the struck parton. The ratio in the kinematic region defined above, for x values in the range −2.9≤log 10 x≤−1.6 −2.9≤log10⁡x≤−1.6 , is R=(σ p X jj /Δξ)/σ jj =0.025±0.001(stat)±0.003(syst) R=(σjj p X /Δξ)/σjj=0.025±0.001(stat)±0.003(syst) , where σ p X jj σjj p X and σ jj σjj are the single-diffractive and inclusive dijet cross sections, respectively. The results are compared with predictions from models of diffractive and nondiffractive interactions. Monte Carlo predictions based on the HERA diffractive parton distribution functions agree well with the data when corrected for the effect of soft rescattering between the spectator partons

Evaluation of the remote-sensing-based DIFFUSE model for estimating photosynthesis of vegetation

Vegetation captures carbon from the atmosphere through photosynthesis, the rate of which varies across space, through time and is determined by both physical and biological factors. Methods for estimating photosynthesis (A) vary in their complexity and in which driving processes they capture. Whilst the effect of diffuse shortwave irradiance on A is well understood, few models have explicitly incorporated the diffuse effect into estimates of A. Here we present the DIFFUSE model, a simple, generic, diffuse-light-based method for estimating A at the monthly time scale. This model is based on the assumption that, at the monthly time scale, the majority of variability in A can be explained by the variability in total and diffuse irradiance and in the fraction of shortwave irradiance absorbed by foliage (f). Comparison of model estimates to eddy flux tower-derived monthly A showed that the majority (83%) of variability in observed A could be explained by the DIFFUSE model. The diffuse fraction contributed 5 to 10% of the model's accuracy across many of Australia's coastal regions, but contributed up to 50% in the monsoonal north. Various aspects of the DIFFUSE model were tested including its performance relative to an example of the more commonly used "stress-scalar" type of photosynthesis model. In all tests, the DIFFUSE model performed at least as well as more complex alternative models, and often outperformed them. The strengths of DIFFUSE are its physical basis, its simplicity and transparency, and its minimalist data requirements - all of which are expected to make it useful to a wide variety of contexts and applications