Alternative search strategies for a BSM resonance fitting the ATLAS diboson excess

We study an s-channel resonance R as a viable candidate to fit the diboson excess reported by ATLAS. We compute the contribution of the similar to 2 TeV resonance R to semileptonic and leptonic final states at the 13 TeV LHC. To explain the absence of an excess in the semileptonic channel, we explore the possibility where the particle R decays to additional light scalars X, X or X, Y. A modified analysis strategy has been proposed to study the three-particle final state of the resonance decay and to identify decay channels of X. Associated production of R with gauge bosons has been studied in detail to identify the production mechanism of R. We construct comprehensive categories for vector and scalar beyond-standard-model particles which may play the role of particles R, X, Y and find alternate channels to fix the new couplings and search for these particles.

Object attributes combine additively in visual search

We perceive objects as containing a variety of attributes: local features, relations between features, internal details, and global properties. But we know little about how they combine. Here, we report a remarkably simple additive rule that governs how these diverse object attributes combine in vision. The perceived dissimilarity between two objects was accurately explained as a sum of (a) spatially tuned local contour-matching processes modulated by part decomposition; (b) differences in internal details, such as texture; (c) differences in emergent attributes, such as symmetry; and (d) differences in global properties, such as orientation or overall configuration of parts. Our results elucidate an enduring question in object vision by showing that the whole object is not a sum of its parts but a sum of its many attributes.

CROWD FLOW SEGMENTATION IN COMPRESSED DOMAIN USING CRF

Crowd flow segmentation is an important step in many video surveillance tasks. In this work, we propose an algorithm for segmenting flows in H.264 compressed videos in a completely unsupervised manner. Our algorithm works on motion vectors which can be obtained by partially decoding the compressed video without extracting any additional features. Our approach is based on modelling the motion vector field as a Conditional Random Field (CRF) and obtaining oriented motion segments by finding the optimal labelling which minimises the global energy of CRF. These oriented motion segments are recursively merged based on gradient across their boundaries to obtain the final flow segments. This work in compressed domain can be easily extended to pixel domain by substituting motion vectors with motion based features like optical flow. The proposed algorithm is experimentally evaluated on a standard crowd flow dataset and its superior performance in both accuracy and computational time are demonstrated through quantitative results.