On the Aliphatic versus Aromatic Content of the Carriers of the "Unidentified" Infrared Emission Features

Although it is generally accepted that the so-called "unidentified" infrared emission (UIE) features at 3.3, 6.2, 7.7, 8.6, and 11.3 micrometer are characteristic of the stretching and bending vibrations of aromatic hydrocarbon materials, the exact nature of their carriers remains unknown: whether they are free-flying, predominantly aromatic gas-phase molecules, or amorphous solids with a mixed aromatic/aliphatic composition are being debated. Recently, the 3.3 and 3.4 micrometer features which are commonly respectively attributed to aromatic and aliphatic C-H stretches have been used to place an upper limit of ~2\% on the aliphatic fraction of the UIE carriers (i.e. the number of C atoms in aliphatic chains to that in aromatic rings). Here we further explore the aliphatic versus aromatic content of the UIE carriers by examining the ratio of the observed intensity of the 6.2 micrometer aromatic C-C feature (I6.2) to that of the 6.85 micrometer aliphatic C-H deformation feature (I6.85). To derive the intrinsic oscillator strengths of the 6.2 micrometer stretch (A6.2) and the 6.85 micrometer deformation (A6.85), we employ density functional theory to compute the vibrational spectra of seven methylated polycyclic aromatic hydrocarbon molecules and their cations. By comparing I6.85/I6.2 with A6.85/A6.2, we derive the fraction of C atoms in methyl(ene) aliphatic form to be at most ~10\%, confirming the earlier finding that the UIE emitters are predominantly aromatic. We have also computed the intrinsic strength of the 7.25 micrometer feature (A7.25), another aliphatic C-H deformation band. We find that A6.85 appreciably exceeds A7.25. This explains why the 6.85 micrometer feature is more frequently detected in space than the 7.25 micrometer feature.Comment: 18 pages, 10 figures, 3 tables; accepted for publication in MNRA

Challenges and framework of life cycle management of small WEEE in China

As the biggest ITC manufacturers and consumers in the world market, China’s management strategy of WEEE (Waste Electric and Electronic Equipment) will definitely affect the global WEEE flows. The improper treatment activities by the informal sectors in China had led to some environmental damages and resources lost. This study is trying to develop a systematic framework of sustainable management of small WEEE to identify the main challenges of WEEE management in China from the life cycle perspective. This framework, covering the whole life cycle of small WEEE from discard to final treatment, consists of such aspects as the definition, scope, classification, and material flow analysis, as well as the environmental risk assessment. The Chinese government had established the WEEE management mechanism based on the EPR principle. The laws and regulations associated with WEEE constitute a policy system for promoting the sustainable management of e-waste in China and cover the entire life cycles of e-products, from design, production and use, to recycling and disposal. However, it only focuses on five large-sized product categories as TV set, air conditioner, refrigerator, wash machines and personal computers. The large quantity of small WEEE was not on the list of WEEE management. Some important life cycle stages of WEEE like eco-design, use/reuse, recycling and final disposal was reviewed and analyzed based on the China’s domestic WEEE flow. It is shown that eco-design of ITC products in China is just in the very young age. There is no available data and tools for EEE designers and manufacturers to implement eco-design. Reuse of EEE products and component is very common. It is also mixed with the recycling practices, which makes the WEEE flow pathway more complicated. There are no specific disposal facilities for the final residuals from WEEE treatment. The eco-efficiency method and life cycle assessment tools are used to model and compare different strategies within the life span of small WEEE like retired mobile phone. The results show that the informal manual collection and components-reuse strategy is higher in eco-efficiency than the formal motor powered vehicle collection and disassembling strategy. The main challenges of managing small WEEEs were identified as five aspects. 1) The discordant policies between environmental protection and resources reservation led to losing of some valuable and hazardous materials, as well as residuals in recycling. 2) Collection system is not linked with formal recycling sector, thus main small WEEE stream went to informal recyclers without pollution control facilities. 3) The formal recyclers can’t make profitable business without WEEE fund, because they have to buy the small WEEE from the collectors by higher price. 4) Conflict of reusing and recycling. Reuse of parts or components of WEEE in China is quite popular and out of control. 5) WEEE fund implementation. In conclusion, the sustainable management of small WEEE should integrate all life cycle stages and consider the efficiency of materials recovery and the environmental risk

Parameterized Complexity of Multi-winner Determination: More Effort Towards Fixed-Parameter Tractability

We study the parameterized complexity of Winners Determination for three prevalent $k$-committee selection rules, namely the minimax approval voting (MAV), the proportional approval voting (PAV), and the Chamberlin-Courant's approval voting (CCAV). It is known that Winners Determination for these rules is NP-hard. Moreover, these problems have been studied from the parameterized complexity point of view with respect to some natural parameters recently. However, many results turned out to be W[1]-hard or W[2]-hard. Aiming at deriving more fixed-parameter algorithms, we revisit these problems by considering more natural and important single parameters, combined parameters, and structural parameters.Comment: 31 pages, 2 figures, AAMAS 201

Person Re-identification with Correspondence Structure Learning

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Experimental results on various datasets demonstrate the effectiveness of our approach

Learning Correspondence Structures for Person Re-identification

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global constraint-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair, so as to handle the spatial misalignments between individual images in a more precise way. Experimental results on various datasets demonstrate the effectiveness of our approach.Comment: IEEE Trans. Image Processing, vol. 26, no. 5, pp. 2438-2453, 2017. The project page for this paper is available at http://min.sjtu.edu.cn/lwydemo/personReID.htm arXiv admin note: text overlap with arXiv:1504.0624