823 research outputs found
Governance Structures, Voluntary Disclosures and Public Accountability: The Case of UK Higher Education Institutions
Purpose: We investigate the extent of voluntary disclosures in UK higher education institutionsâ (HEIs) annual reports and examine whether internal governance structures influence disclosure in the period following major reform and funding constraints.
Design/methodology/approach: We adopt a modified version of Coy and Dixonâs (2004) public accountability index, referred to in this paper as a public accountability and transparency index (PATI), to measure the extent of voluntary disclosures in 130 UK HEIsâ annual reports. Informed by a multi-theoretical framework drawn from public accountability, legitimacy, resource dependence and stakeholder perspectives, we propose that the characteristics of governing and executive structures in UK universities influence the extent of their voluntary disclosures.
Findings: We find a large degree of variability in the level of voluntary disclosures by universities and an overall relatively low level of PATI (44%), particularly with regards to the disclosure of teaching/research outcomes. We also find that audit committee quality, governing board diversity, governor independence, and the presence of a governance committee are associated with the level of disclosure. Finally, we find that the interaction between executive team characteristics and governance variables enhances the level of voluntary disclosures, thereby providing support for the continued relevance of a âsharedâ leadership in the HEIsâ sector towards enhancing accountability and transparency in HEIs.
Research limitations/implications: In spite of significant funding cuts, regulatory reforms and competitive challenges, the level of voluntary disclosure by UK HEIs remains low. Whilst the role of selected governance mechanisms and âshared leadershipâ in improving disclosure, is asserted, the varying level and selective basis of the disclosures across the surveyed HEIs suggest that the public accountability motive is weaker relative to the other motives underpinned by stakeholder, legitimacy and resource dependence perspectives.
Originality/value: This is the first study which explores the association between HEI governance structures, managerial characteristics and the level of disclosure in UK HEIs
Deep Hierarchical Parsing for Semantic Segmentation
This paper proposes a learning-based approach to scene parsing inspired by
the deep Recursive Context Propagation Network (RCPN). RCPN is a deep
feed-forward neural network that utilizes the contextual information from the
entire image, through bottom-up followed by top-down context propagation via
random binary parse trees. This improves the feature representation of every
super-pixel in the image for better classification into semantic categories. We
analyze RCPN and propose two novel contributions to further improve the model.
We first analyze the learning of RCPN parameters and discover the presence of
bypass error paths in the computation graph of RCPN that can hinder contextual
propagation. We propose to tackle this problem by including the classification
loss of the internal nodes of the random parse trees in the original RCPN loss
function. Secondly, we use an MRF on the parse tree nodes to model the
hierarchical dependency present in the output. Both modifications provide
performance boosts over the original RCPN and the new system achieves
state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler
urban datasets.Comment: IEEE CVPR 201
Radiometric Model and Inter-Comparison Results of the SGLI-VNR On-Board Calibration
The Second Generation Global Imager (SGLI) on Global Change Observation Mission Climate (GCOM-C) satellite empowers surface and atmospheric measurements related to the carbon cycle and radiation budget, with two radiometers of Visible and Near Infrared Radiometer (SGLI-VNR) and Infrared Scanning Radiometer (SGLI-IRS) that perform a wide-band (380 nm12 m) optical observation not only with as wide as a 11501400 km field of view (FOV), but also with as high as 0.250.5 km resolution. Additionally, polarization and along-track slant view observations are quite characteristic of SGLI. It is important to calibrate radiometers to provide the sensor data records for more than 28 standard products and 23 research products including clouds, aerosols, ocean color, vegetation, snow and ice, and other applications. In this paper, the radiometric model and the first results of on-board calibrations on the SGLI-VNR, which include weekly solar and light-emitting diode (LED) calibration and monthly lunar calibration, will be described. Each calibration data was obtained with corrections, where beta angle correction and avoidance of reflection from multilayer insulation (MLI) were applied for solar calibration; LED temperature correction was performed for LED calibration; and the GIRO (GSICS (Global Space-based Inter-Calibration System) Implementation of the ROLO (RObotic Lunar Observatory) model) model was used for lunar calibration. Results show that the inter-comparison of the relative degradation amount between these three calibrations agreed to within 1% or less
Robust Whole-Body Motion Control of Legged Robots
We introduce a robust control architecture for the whole-body motion control
of torque controlled robots with arms and legs. The method is based on the
robust control of contact forces in order to track a planned Center of Mass
trajectory. Its appeal lies in the ability to guarantee robust stability and
performance despite rigid body model mismatch, actuator dynamics, delays,
contact surface stiffness, and unobserved ground profiles. Furthermore, we
introduce a task space decomposition approach which removes the coupling
effects between contact force controller and the other non-contact controllers.
Finally, we verify our control performance on a quadruped robot and compare its
performance to a standard inverse dynamics approach on hardware.Comment: 8 Page
A Proper Orthogonal Decomposition Approach to Approximate Balanced Truncation of Infinite Dimensional Linear Systems
We extend a method for approximate balanced reduced order model derivation for finite dimensional linear systems developed by Rowley (Int. J. Bifur. Chaos Appl. Sci. Eng. 15(3) (2005), pp. 997-1013) to infinite dimensional systems. The algorithm is related to standard balanced truncation, but includes aspects of the proper orthogonal decomposition in its computational approach. The method can be also applied to nonlinear systems. Numerical results are presented for a convection diffusion system
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