PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing

Depth estimation and scene parsing are two particularly important tasks in visual scene understanding. In this paper we tackle the problem of simultaneous depth estimation and scene parsing in a joint CNN. The task can be typically treated as a deep multi-task learning problem [42]. Different from previous methods directly optimizing multiple tasks given the input training data, this paper proposes a novel multi-task guided prediction-and-distillation network (PAD-Net), which first predicts a set of intermediate auxiliary tasks ranging from low level to high level, and then the predictions from these intermediate auxiliary tasks are utilized as multi-modal input via our proposed multi-modal distillation modules for the final tasks. During the joint learning, the intermediate tasks not only act as supervision for learning more robust deep representations but also provide rich multi-modal information for improving the final tasks. Extensive experiments are conducted on two challenging datasets (i.e. NYUD-v2 and Cityscapes) for both the depth estimation and scene parsing tasks, demonstrating the effectiveness of the proposed approach.Comment: Accepted at CVPR 201

Reverse electrodialysis – Multi effect distillation heat engine fed by lithium chloride solutions

Salinity Gradient Heat Engines (SG-HEs) have been proposed as a promising technology for converting low-temperature heat into electricity. The SG-HE includes two different processes: (i) a salinity gradient process where the salinity gradient between two solutions is converted into electricity and (ii) a thermal regeneration process where low-grade heat (T<100°C) is used to re-establish the original salinity gradient of the two streams. Among the proposed working solutions, aqueous solution of lithium chloride has been identified as one of the most promising thanks to its remarkable solubility and activity. In this work, a process model to study the performance of a SG-HE constituted by a Reverse ElectroDialysis (RED) unit coupled with a Multi Effect Distillation (MED) unit fed with lithium chloride solution is presented. The influence of the concentration of the inlet solution in the RED unit and the temperature difference in the evaporators of the MED unit on the performance were evaluated by considering ideal membranes. Furthermore, the impact of membrane permselectivity and resistance on the system performance was evaluated. Results showed promising system efficiencies, making this technology attractive for conversion of low-grade heat (<100°C) into electricity, but membrane properties should be enhanced

Energy-water-environment nexus underpinning future desalination sustainability

Energy-water-environment nexus is very important to attain COP21 goal, maintaining environment temperature increase below 2 °C, but unfortunately two third share of CO2 emission has already been used and the remaining will be exhausted by 2050. A number of technological developments in power and desalination sectors improved their efficiencies to save energy and carbon emission but still they are operating at 35% and 10% of their thermodynamic limits. Research in desalination processes contributing to fuel World population for their improved living standard and to reduce specific energy consumption and to protect environment. Recently developed highly efficient nature-inspired membranes (aquaporin & graphene) and trend in thermally driven cycle's hybridization could potentially lower then energy requirement for water purification. This paper presents a state of art review on energy, water and environment interconnection and future energy efficient desalination possibilities to save energy and protect environment

Distillation of mixed-state continuous-variable entanglement by photon subtraction

We present a detailed theoretical analysis for the distillation of one copy of a mixed two-mode continuous-variable entangled state using beamsplitters and coherent photon-detection techniques, including conventional on-off detectors and photon number resolving detectors. The initial Gaussian mixed-entangled states are generated by transmitting a two-mode squeezed state through a lossy bosonic channel, corresponding to the primary source of errors in current approaches to optical quantum communication. We provide explicit formulas to calculate the entanglement in terms of logarithmic negativity before and after distillation, including losses in the channel and the photon detection, and show that one-copy distillation is still possible even for losses near the typical fiber channel attenuation length. A lower bound for the transmission coefficient of the photon-subtraction beamsplitter is derived, representing the minimal value that still allows to enhance the entanglement.Comment: 13 pages, 8 figure