Speech-driven Animation with Meaningful Behaviors

Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table

Mapping monojet constraints onto Simplified Dark Matter Models

The move towards simplified models for Run II of the LHC will allow for stronger and more robust constraints on the dark sector. However there already exists a wealth of Run I data which should not be ignored in the run-up to Run II. Here we reinterpret public constraints on generic beyond-standard-model cross sections to place new constraints on a simplified model. We make use of an ATLAS search in the monojet $+$ missing energy channel to constrain a representative simplified model with the dark matter coupling to an axial-vector $Z'$. We scan the entire parameter space of our chosen model to set the strongest current collider constraints on our model using the full 20.3 fb$^{-1}$ ATLAS 8 TeV dataset and provide predictions for constraints that can be set with 20 fb$^{-1}$ of 14 TeV data. Our technique can also be used for the interpretation of Run II data and provides a broad benchmark for comparing future constraints on simplified models.Comment: 19 pages, 6 figures; v2: added references, corrected mistake in discussion of previous results; v3: major content additions, version accepted by JHE

Automatic generation of large-scale paraphrases

Research on paraphrase has mostly focussed on lexical or syntactic variation within individual sentences. Our concern is with larger-scale paraphrases, from multiple sentences or paragraphs to entire documents. In this paper we address the problem of generating paraphrases of large chunks of texts. We ground our discussion through a worked example of extending an existing NLG system to accept as input a source text, and to generate a range of fluent semantically-equivalent alternatives, varying not only at the lexical and syntactic levels, but also in document structure and layout

Toward Full LHC Coverage of Natural Supersymmetry

We argue that combining just a handful of searches for new physics at Run I of the LHC is sufficient to exclude most supersymmetric extensions of the Standard Model in which the gluino is kinematically accessible and the spectrum is natural. Such models typically give rise to significant MET, top quarks and/or high object multiplicity, and we show that having even one of these signatures generally results in stringent limits. We also identify, among models that lack these signatures, the few gaps in coverage remaining, and propose search strategies to close these gaps. Our results are general and independent of the details of the spectrum, assumptions about minimality, R-parity, etc. Our analysis strategy should remain applicable when the LHC moves to higher energy. Central to our argument are ATLAS and CMS searches for many jets and low MET, a proposed lepton + many jets search, an ATLAS search for 6-7 high-pT jets, and a reexamination of the control and signal regions of the CMS black hole search.Comment: 53 pages, 16 figures, journal versio