When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-kk Multi-Label Learning

With the great success of deep neural networks, adversarial learning has received widespread attention in various studies, ranging from multi-class learning to multi-label learning. However, existing adversarial attacks toward multi-label learning only pursue the traditional visual imperceptibility but ignore the new perceptible problem coming from measures such as Precision@$k$ and mAP@$k$. Specifically, when a well-trained multi-label classifier performs far below the expectation on some samples, the victim can easily realize that this performance degeneration stems from attack, rather than the model itself. Therefore, an ideal multi-labeling adversarial attack should manage to not only deceive visual perception but also evade monitoring of measures. To this end, this paper first proposes the concept of measure imperceptibility. Then, a novel loss function is devised to generate such adversarial perturbations that could achieve both visual and measure imperceptibility. Furthermore, an efficient algorithm, which enjoys a convex objective, is established to optimize this objective. Finally, extensive experiments on large-scale benchmark datasets, such as PASCAL VOC 2012, MS COCO, and NUS WIDE, demonstrate the superiority of our proposed method in attacking the top-$k$ multi-label systems.Comment: 22 pages, 7 figures, accepted by ACM MM 202

NOVEL NANOSTRUCTURED HIGH-PERFORMANCE ANION EXCHANGE IONOMERS FOR ANION EXCHANGE MEMBRANE FUEL CELLS

poster abstractA novel block copolymer, styrene-ethylene/butylene-styrene (SEBS), was chosen as the starting material to prepare pendant quaternary ammonium-based ionomers with an ion-exchange-capacity (IEC) of 0.66, 1.30, and 1.54 meq g-1, denoted by QSEBS-L, QSEBS-M, and QSEBS-H, respectively. These QSEBS ionomers were demonstrated to have excellent dimensional stability against hydration without significantly sacrificing the ionic conductivity as compared to the widely studied polysulfone (PSf) based ionomers. The water uptake of the QSEBS-based ionomers depended on their functionality; a higher IEC in the ionomer resulted in more water uptake and a higher ionic conductivity. The MEAs fabricated with the QSEBS-M and QSEBS-H ionomers showed the best H2/O2 fuel cell performance with peak power densities reaching 210 mW cm-2 at 50 °C, which was significantly higher than that of the PSf-based ionomers (~30 mW cm-2). Electrochemical impedance spec-troscopy (EIS) analysis indicated that the superior fuel cell performance ob-served with the QSEBS-based ionomers can be attributed to: (1) the low in-ternal cell resistance due to good comparability of the QSEBS-based ionomers with the membranes and (2) the low mass transport and charge transport in both the anode and the cathode due to the excellent dimension-al stability and balanced conductivity-hydrophobicity originated by the unique morphology of the QSEBS-based ionomers. AFM phase imaging measurements of the QSEBS-based ionomers revealed unique nanostruc-tures containing isolated hydrophobic and continuous anion conducting hy-drophilic domains. By further optimizing the chemistry and morphology of the ionomers and the membranes, the resistance of the anode and cathode of the AEMFCs will be further reduced

A Margin-based MLE for Crowdsourced Partial Ranking

A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data. In this case, the conventional total order ranking can not capture such uncertainty with mere global ranking or utility scores. In this paper, we are specifically interested in the recent surge in crowdsourcing applications to predict partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a novel framework to learn some probabilistic models of partial orders as a \emph{margin-based Maximum Likelihood Estimate} (MLE) method. We prove that the induced MLE is a joint convex optimization problem with respect to all the parameters, including the global ranking scores and margin parameter. Moreover, three kinds of generalized linear models are studied, including the basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model, equipped with some theoretical analysis on FDR and Power control for the proposed methods. The validity of these models are supported by experiments with both simulated and real-world datasets, which shows that the proposed models exhibit improvements compared with traditional state-of-the-art algorithms.Comment: 9 pages, Accepted by ACM Multimedia 2018 as a full pape

Probing the Direct Factor for Superconductivity in FeSe-Based Superconductors by Raman Scattering

The FeSe-based superconductors exhibit a wide range of critical temperature Tcunder a variety of material and physical conditions, but extensive studies to date have yet to produce a consensus view on the underlying mechanism. Here we report on a systematic Raman-scattering work on intercalated FeSe superconductors Lix(NH3)yFe2Se2 and (Li,Fe)OHFeSe compared to pristine FeSe. All three crystals show an anomalous power-law temperature dependence of phonon linewidths, deviating from the standard anharmonic behavior. This intriguing phenomenon is attributed to electron-phonon coupling effects enhanced by electron correlation, as evidenced by the evolution of the A1g Raman mode. Meanwhile, an analysis of the B1g mode, which probes the out-of-plane vibration of Fe, reveals a lack of influence by previously suggested structural parameters, and instead indicates a crucial role of the joint density of states in determining Tc. These findings identify carrier doping as the direct factor driving and modulating superconductivity in FeSe-based compounds