Polytypism and Unexpected Strong Interlayer Coupling of two-Dimensional Layered ReS2

The anisotropic two-dimensional (2D) van der Waals (vdW) layered materials, with both scientific interest and potential application, have one more dimension to tune the properties than the isotropic 2D materials. The interlayer vdW coupling determines the properties of 2D multi-layer materials by varying stacking orders. As an important representative anisotropic 2D materials, multilayer rhenium disulfide (ReS2) was expected to be random stacking and lack of interlayer coupling. Here, we demonstrate two stable stacking orders (aa and a-b) of N layer (NL, N>1) ReS2 from ultralow-frequency and high-frequency Raman spectroscopy, photoluminescence spectroscopy and first-principles density functional theory calculation. Two interlayer shear modes are observed in aa-stacked NL-ReS2 while only one interlayer shear mode appears in a-b-stacked NL-ReS2, suggesting anisotropic-like and isotropic-like stacking orders in aa- and a-b-stacked NL-ReS2, respectively. The frequency of the interlayer shear and breathing modes reveals unexpected strong interlayer coupling in aa- and a-b-NL-ReS2, the force constants of which are 55-90% to those of multilayer MoS2. The observation of strong interlayer coupling and polytypism in multi-layer ReS2 stimulate future studies on the structure, electronic and optical properties of other 2D anisotropic materials

Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved a sufficient security level (> 80 bit) and reasonable classification accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images (> 8,000) without extra overhead

Perceptions and Barriers of Survivorship Care in Asia: Perceptions From Asian Breast Cancer Survivors.

PurposeWith the long-term goal to optimize post-treatment cancer care in Asia, we conducted a qualitative study to gather in-depth descriptions from multiethnic Asian breast cancer survivors on their perceptions and experiences of cancer survivorship and their perceived barriers to post-treatment follow-up.MethodsTwenty-four breast cancer survivors in Singapore participated in six structured focus group discussions. The focus group discussions were voice recorded, transcribed verbatim, and analyzed by thematic analysis.ResultsBreast cancer survivors were unfamiliar with and disliked the term "survivorship," because it implies that survivors had undergone hardship during their treatment. Cognitive impairment and peripheral neuropathy were physical symptoms that bothered survivors the most, and many indicated that they experienced emotional distress during survivorship, for which they turned to religion and peers as coping strategies. Survivors indicated lack of consultation time and fear of unplanned hospitalization as main barriers to optimal survivorship care. Furthermore, survivors indicated that they preferred receipt of survivorship care at the specialty cancer center.ConclusionBudding survivorship programs in Asia must take survivor perspectives into consideration to ensure that survivorship care is fully optimized within the community

Gender Gaps in the Measurement of Public Opinion About Homosexuality in Cross-national Surveys: A Question-Wording Experiment

Measures of attitudes towards homosexuality in cross-national studies have received criticism for not being ‘gender-sensitive’. The current study used a split-ballot design allowing for separate analyses of the attitudes towards ‘gay men and lesbian women’, ‘gay men’, and ‘lesbian women’ in a pooled sample of 3,381 participants from Great Britain, Hungary, and Portugal. Analyses controlling for sociodemographics showed that differences in attitudes towards male and female targets were generally small and did not interact with the gender of the rater. In addition, results showed that men’s attitudes towards homosexuality were more strongly related to their gender ideology than women’s attitudes. Implications of these findings for cross-national studies measuring attitudes towards homosexuality are discussed

Bis(4-amino­benzene­sulfonato-κN)diaqua­bis(dimethyl­formamide-κO)nickel(II) dihydrate

In the title compound, [Ni(C6H6NO3S)2(C3H7NO)2(H2O)2]·2H2O, the NiII ion (site symmetry ) is coordinated by two –NH2 groups from two 4-amino­benzene­sulfonate anions, two O atoms from two dimethyl­formamide mol­ecules and two water mol­ecules, forming a slightly distorted trans-NiN2O4 octa­hedral geometry. In the crystal structure, inter­molecular O—H⋯O, O—H⋯(O,O) and N—H⋯O hydrogen bonds link the components into a three-dimensional network. The O atoms of the sulfonate group are disordered over two sets of sites in a 0.833 (4):0.167 (4) ratio and the O atom of the uncoordinated water mol­ecule is disordered over two sites in a 0.637 (18):0.363 (18) ratio

Probing the isospin dependent mean field and nucleon nucleon cross section in the medium by the nucleon emissions

We study the isospin effects of the mean field and two-body collision on the nucleon emissions at the intermediate energy heavy ion collisions by using an isospin dependent transport theory. The calculated results show that the nucleon emission number $N_{n}$ depends sensitively the isospin effect of nucleon nucleon cross section and weakly on the isospin dependent mean field for neutron-poor system in higher beam energy region . In particular, the correlation between the medium correction of two-body collision and the momentum dependent interaction enhances the dependence of nucleon emission number $N_{n}$ on the isospin effect of nucleon nucleon cross section. On the contrary, the ratio of the neutron proton ratio of the gas phase to the neutron proton ratio of the liquid phase, i.e., the degree of isospin fractionation $_{b}/_{b}$ depends sensitively on the isospin dependent mean field and weakly on the isospin effect of two-body collision for neutron-rich system in the lower beam energy region. In this case, $N_{n}$ and $_{b}/_{b}$ are the probes for extracting the information about the isospin dependent nucleon nucleon cross section in the medium and the isospin dependent mean field,respectively.Comment: 4 pages,4 figure

CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate the masks for every single image. Although great progress has been achieved by these methods, they treat each image independently and do not take account of the relationships across different images. In this paper, however, we argue that the cross-image relationship is vital for weakly supervised segmentation. Because it connects related regions across images, where supplementary representations can be propagated to obtain more consistent and integral regions. To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only image-level labels. By means of this, our approach achieves 64.3% and 65.3% mIoU on Pascal VOC 2012 validation and test set respectively, which is a new state-of-the-art result by only using image-level labels for weakly supervised semantic segmentation, demonstrating the superiority of our approach.Comment: 9 pages, 4 figures, AAAI 202