Are gravitational wave ringdown echoes always equal-interval ?

Gravitational wave (GW) ringdown waveforms may contain "echoes" that encode new physics in the strong gravity regime. It is commonly assumed that the new physics gives rise to the GW echoes whose intervals are constant. We point out that this assumption is not always applicable. In particular, if the post-merger object is initially a wormhole, which slowly pinches off and eventually collapses into a black hole, the late-time ringdown waveform exhibit a series of echoes whose intervals are increasing with time. We also assess how this affects the ability of Advanced LIGO/Virgo to detect these new signals.Comment: 10 pages,5 figure

Rigid vortices in MgB2

Magnetic relaxation of high-pressure synthesized MgB$_2$ bulks with different thickness is investigated. It is found that the superconducting dia-magnetic moment depends on time in a logarithmic way; the flux-creep activation energy decreases linearly with the current density (as expected by Kim-Anderson model); and the activation energy increases linearly with the thickness of sample when it is thinner than about 1 mm. These features suggest that the vortices in the MgB$_2$ are rather rigid, and the pinning and creep can be well described by Kim-Anderson model.Comment: Typo corrected & reference adde

Redetermined structure of oxaline: absolute configuration using Cu Kα radiation

In the title compound, C24H25N5O4, the stereogenic C atom bonded to three N atoms and one C atom has an S configuration and its directly bonded neighbour has an R configuration. An intra­molecular N—H⋯O hydrogen bond supports the near coplanarity of the two C3N2-five-membered rings [dihedral angle = 5.64 (10)°]. In the crystal, mol­ecules are linked by N—H⋯N hydrogen bonds, forming a C(8) chain propagating in [001]. The chains are connected by C—H⋯O inter­actions, generating a three-dimensional network. The previous study [Nagel et al. (1974 ▶). Chem. Commun. pp. 1021–1022] did not establish the absolute structure and no atomic coordinates were published or deposited

Addressing Complete New Item Cold-Start Recommendation: A Niche Item-Based Collaborative Filtering via Interrelationship Mining

Recommender system (RS) can be used to provide personalized recommendations based on the different tastes of users. Item-based collaborative filtering (IBCF) has been successfully applied to modern RSs because of its excellent performance, but it is susceptible to the new item cold-start problem, especially when a new item has no rating records (complete new item cold-start). Motivated by this, we propose a niche approach which applies interrelationship mining into IBCF in this paper. The proposed approach utilizes interrelationship mining to extract new binary relations between each pair of item attributes, and constructs interrelated attributes to rich the available information on a new item. Further, similarity, computed using interrelated attributes, can reflect characteristics between new items and others more accurately. Some significant properties, as well as the usage of interrelated attributes, are provided in detail. Experimental results obtained suggest that the proposed approach can effectively solve the complete new item cold-start problem of IBCF and can be used to provide new item recommendations with satisfactory accuracy and diversity in modern RSs.

Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting

Recommender systems (RS) analyze user rating information and recommend items that may interest users. Item-based collaborative filtering (IBCF) is widely used in RSs. However, traditional IBCF often cannot provide recommendations with good predictive and classification accuracy at the same time because it assigns equal weights to all items when computing similarity and prediction. However, some items are more relevant and should be assigned greater weight. To address this problem, we propose a niche approach to realize item-variance weighting in IBCF in this paper. In the proposed approach, to improve the predictive accuracy, a novel time-related correlation degree is proposed and applied to form time-aware similarity computation, which can estimate the relationship between two items and reduce the weight of the item rated over a long period. Furthermore, a covering-based rating prediction is proposed to increase classification accuracy, which combines the relationship between items and the target user’s preference into the predicted rating scores. Experimental results suggest that the proposed approach outperforms traditional IBCF and other existing work and can provide recommendations with satisfactory predictive and classification accuracy simultaneously.

Ferrocen­yl(meth­yl)diphenyl­silane

In the title mol­ecule, [Fe(C5H5)(C18H17Si)], the distances of the Fe atom from the centroids of the unsubstituted and substituted cyclo­penta­dienyl (Cp) rings are 1.651 (1) and 1.646 (1) Å, respectively. The dihedral angle between the two Cp rings is 3.20 (17)°. The crystal packing is mainly stabilized by van der Waals forces

cis-Bis[2-(1,3-benzothia­zol-2-yl)-1-(4-fluoro­phen­yl)ethen­yl](pentane-2,4-dionato-κ2 O,O′)iridium(III)

In the title compound, [Ir(C15H9FNS)2(C5H7O2)], the Ir atom is hexa­coordinated by three chelating ligands, with two cyclo­metalated 2-(1,3-benzothia­zol-2-yl)-1-(4-fluoro­phen­yl)ethenyl ligands showing N,C-bidentate coordination and an O,O′-bidenate pentane-2,4-dionate anion, thereby forming a distorted octa­hedral enviroment

4-[4,5-Bis(pyridin-2-yl)-1H-imidazol-2-yl]phenol monohydrate

In the title hydrate, C19H14N4O·H2O, the dihedral angle between the two pyridine rings is 38.0 (2)°. The dihedral angle between the imidazole and benzene rings is 25.3 (2)°. The crystal structure is stabilized by inter­molecular O—H⋯O, O—H⋯N and N—H⋯O hydrogen bonds

Deep Neural Network for Robust Speech Recognition With Auxiliary Features From Laser-Doppler Vibrometer Sensor

Recently, the signal captured from a laser Doppler vibrometer (LDV) sensor been used to improve the noise robustness automatic speech recognition (ASR) systems by enhancing the acoustic signal prior to feature extraction. This study proposes another approach in which auxiliary features extracted from the LDV signal are used alongside conventional acoustic features to further improve ASR performance based on the use of a deep neural network (DNN) as the acoustic model. While this approach is promising, the best training data sets for ASR do not include LDV data in parallel with the acoustic signal. Thus, to leverage such existing large-scale speech databases, a regres- sion DNN is designed to map acoustic features to LDV features. This regression DNN is well trained from a limited size parallel signal data set, then used to form pseudo-LDV features from a massive speech data set for parallel training of an ASR system. Our experiments show that both the features from the limited scale LDV data set as well as the massive scale pseudo-LDV features are able to train an ASR system that significantly outperforms one using acoustic features alone, in both quiet and noisy environments