MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes

Attribute recognition, particularly facial, extracts many labels for each image. While some multi-task vision problems can be decomposed into separate tasks and stages, e.g., training independent models for each task, for a growing set of problems joint optimization across all tasks has been shown to improve performance. We show that for deep convolutional neural network (DCNN) facial attribute extraction, multi-task optimization is better. Unfortunately, it can be difficult to apply joint optimization to DCNNs when training data is imbalanced, and re-balancing multi-label data directly is structurally infeasible, since adding/removing data to balance one label will change the sampling of the other labels. This paper addresses the multi-label imbalance problem by introducing a novel mixed objective optimization network (MOON) with a loss function that mixes multiple task objectives with domain adaptive re-weighting of propagated loss. Experiments demonstrate that not only does MOON advance the state of the art in facial attribute recognition, but it also outperforms independently trained DCNNs using the same data. When using facial attributes for the LFW face recognition task, we show that our balanced (domain adapted) network outperforms the unbalanced trained network.Comment: Post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016 http://link.springer.com/chapter/10.1007%2F978-3-319-46454-1_

High-speed noise-free optical quantum memory

Quantum networks promise to revolutionise computing, simulation, and communication. Light is the ideal information carrier for quantum networks, as its properties are not degraded by noise in ambient conditions, and it can support large bandwidths enabling fast operations and a large information capacity. Quantum memories, devices that store, manipulate, and release on demand quantum light, have been identified as critical components of photonic quantum networks, because they facilitate scalability. However, any noise introduced by the memory can render the device classical by destroying the quantum character of the light. Here we introduce an intrinsically noise-free memory protocol based on two-photon off-resonant cascaded absorption (ORCA). We consequently demonstrate for the first time successful storage of GHz-bandwidth heralded single photons in a warm atomic vapour with no added noise; confirmed by the unaltered photon statistics upon recall. Our ORCA memory platform meets the stringent noise-requirements for quantum memories whilst offering technical simplicity and high-speed operation, and therefore is immediately applicable to low-latency quantum networks

Statistics of randomly branched polymers in a semi-space

We investigate the statistical properties of a randomly branched 3--functional $N$--link polymer chain without excluded volume, whose one point is fixed at the distance $d$ from the impenetrable surface in a 3--dimensional space. Exactly solving the Dyson-type equation for the partition function $Z(N,d)=N^{-\theta} e^{\gamma N}$ in 3D, we find the "surface" critical exponent $\theta={5/2}$, as well as the density profiles of 3--functional units and of dead ends. Our approach enables to compute also the pairwise correlation function of a randomly branched polymer in a 3D semi-space.Comment: 15 pages 7 figsures; section VII is slightly reorganized, discussion is revise

Time series cluster kernels to exploit informative missingness and incomplete label information

The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without resorting to imputation and the ensemble strategy ensures robustness to hyperparameters, making it particularly well suited for unsupervised learning. However, TCK assumes missing at random and that the underlying missingness mechanism is ignorable, i.e. uninformative, an assumption that does not hold in many real-world applications, such as e.g. medicine. To overcome this limitation, we present a kernel capable of exploiting the potentially rich information in the missing values and patterns, as well as the information from the observed data. In our approach, we create a representation of the missing pattern, which is incorporated into mixed mode mixture models in such a way that the information provided by the missing patterns is effectively exploited. Moreover, we also propose a semi-supervised kernel, capable of taking advantage of incomplete label information to learn more accurate similarities. Experiments on benchmark data, as well as a real-world case study of patients described by longitudinal electronic health record data who potentially suffer from hospital-acquired infections, demonstrate the effectiveness of the proposed method

Regionalized cost supply potential of bioenergy crops and residues in Colombia:A hybrid statistical balance and land suitability allocation scenario analysis

The Colombian agricultural sector has the capacity and ambition to reduce its land use and GHG emissions through sustainable intensification of livestock production. However, the impact of such pathway on the availability of land for bioenergy crops production has not been thoroughly investigated. Moreover, previous assessments of the role bioenergy in Colombia have mostly focused on residues, in isolation of land use policies. To address this gap, we propose a hybrid statistical land balancing and suitability allocation approach to estimate long term projections of the cost–supply potential of bioenergy crops and residues. Regionalized to the 32 Colombian departments (administrative divisions), this approach could provide higher resolution than global assessments, while avoiding the complexity of spatially explicit methods. We investigated three scenarios covering the uncertainty of socioeconomic drivers and agricultural and livestock productivity factors. Our results suggest that pursuing progressive land use policies (SSP1 scenario) could release up to 14 Mha of land by 2050, which could be available to produce perennial bioenergy crops. The cumulative potential of crops in SSP1 could reach up to 2200 PJ, where about half of this potential could be attained at 7 $ GJ 1 or less. Potential supply centers could be identified in Orinoquía, Andean, and Caribbean regions for energy crops and the Pacific region for residues. Our findings indicate that there could be an opportunity to create synergy between the low carbon development strategies of the land use and energy sectors in Colombia

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research