The early Pliocene Titiokura Formation: stratigraphy of a thick, mixed carbonate-siliciclastic shelf succession in Hawke's Bay Basin, New Zealand

This paper presents a systematic stratigraphic description of the architecture of the early Pliocene Titiokura Formation (emended) in the Te Waka and Maungaharuru Ranges of western Hawke's Bay, and presents a facies, sequence stratigraphic, and paleoenvironmental analysis of the sedimentary succession. The Titiokura Formation is of early Pliocene (Opoitian-Waipipian) age, and unconformably overlies Mokonui Formation, which is a regressive late Miocene and early Pliocene (Kapitean to early Opoitian) succession. In the Te Waka Range and the southern parts of the Maungaharuru Range, the Titiokura Formation comprises a single limestone sheet 20-50 m thick, with calcareous sandstone parts. In the vicinity of Taraponui Trig, and to the northeast, the results of 1:50 000 mapping show that the limestone gradually partitions into five members, which thicken markedly to the northeast to total thicknesses of c. 730 m, and concomitantly become dominated by siliciclastic sandstone. The members (all new) from lower to upper are: Naumai Member, Te Rangi Member, Taraponui Member, Bellbird Bush Member, and Opouahi Member. The lower four members are inferred to each comprise an obliquity-controlled 41 000 yr 6th order sequence, and the Opouahi Member at least two such sequences. The sequences typically have the following architectural elements from bottom to top: disconformable sequence boundary that formed as a transgressive surface of erosion; thin transgressive systems tracts (TSTs) with onlap and backlap shellbeds, or alternatively, a single compound shellbed; downlap surface; and very thick (70-200 m) highstand (HST) and regressive systems tracts (RST) composed of fine sandstone. The sequences in the Opouahi Member have cryptic TSTs, sandy siltstone to silty sandstone HSTs, and cross-bedded, differentially cemented, fine sandstone RSTs; a separate variant is an 11 m thick bioclastic limestone (grainstone and packstone) at the top of the member that crops out in the vicinity of Lake Opouahi. Lithostratigraphic correlations along the crest of the ranges suggest that the Titiokura Formation, and its correlatives to the south around Puketitiri, represent a shoreline-to-shelf linked depositional system. Carbonate production was focused around a rocky seascape as the system onlapped basement in the south, with dispersal and deposition of the comminuted carbonate on an inner shelf to the north, which was devoid of siliciclastic sediment input. The rates of both subsidence and siliciclastic sediment flux increased rapidly to the northeast of the carbonate "platform", with active progradation and offlap of the depositional system into more axial parts of Hawke's Bay Basin

Controlling Fairness and Bias in Dynamic Learning-to-Rank

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users, as done by virtually all learning-to-rank algorithms, can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.Comment: First two authors contributed equally. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval 202

The effects of ergonomic interventions on low back moments are attenuated by changes in lifting behaviour

This study investigated the effects of ergonomic interventions involving a reduction of the mass (from 16 to 11 and 6 kg) and an increase in the initial lifting height (from pallet height to 90 cm above the ground) of building blocks in a mock-up of an industrial depalletizing task, investigating lifting behaviour as well as low back moments (calculated using a 3-D linked segment model). Nine experienced construction workers participated in the experiment, in which they removed building blocks from a pallet in the way they normally did during their work. Most of the changes in lifting behaviour that were found would attenuate the effect of the investigated interventions on low back moments. When block mass was reduced from 16 to 6 kg, subjects chose to lift the building block from a 10 (SD 10) cm greater distance from the front edge of the pallet and with a 100 (SD 66) degrees/

Ab initio studies of phonon softening and high pressure phase transitions of alpha-quartz SiO2

Density functional perturbation theory calculations of alpha-quartz using extended norm conserving pseudopotentials have been used to study the elastic properties and phonon dispersion relations along various high symmetry directions as a function of bulk, uniaxial and non-hydrostatic pressure. The computed equation of state, elastic constants and phonon frequencies are found to be in good agreement with available experimental data. A zone boundary (1/3, 1/3, 0) K-point phonon mode becomes soft for pressures above P=32 GPa. Around the same pressure, studies of the Born stability criteria reveal that the structure is mechanically unstable. The phonon and elastic softening are related to the high pressure phase transitions and amorphization of quartz and these studies suggest that the mean transition pressure is lowered under non-hydrostatic conditions. Application of uniaxial pressure, results in a post-quartz crystalline monoclinic C2 structural transition in the vicinity of the K-point instability. This structure, intermediate between quartz and stishovite has two-thirds of the silicon atoms in octahedral coordination while the remaining silicon atoms remain tetrahedrally coordinated. This novel monoclinic C2 polymorph of silica, which is found to be metastable under ambient conditions, is possibly one of the several competing dense forms of silica containing octahedrally coordinated silicon. The possible role of high pressure ferroelastic phases in causing pressure induced amorphization in silica are discussed.Comment: 17 pages, 8 figs., 8 Table

OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering

We present a method for simultaneously learning, in an unsupervised manner, (i) a conditional image generator, (ii) foreground extraction and segmentation, (iii) clustering into a two-level class hierarchy, and (iv) object removal and background completion, all done without any use of annotation. The method combines a Generative Adversarial Network and a Variational Auto-Encoder, with multiple encoders, generators and discriminators, and benefits from solving all tasks at once. The input to the training scheme is a varied collection of unlabeled images from the same domain, as well as a set of background images without a foreground object. In addition, the image generator can mix the background from one image, with a foreground that is conditioned either on that of a second image or on the index of a desired cluster. The method obtains state of the art results in comparison to the literature methods, when compared to the current state of the art in each of the tasks.Comment: To be published in the European Conference on Computer Vision (ECCV) 202

SchNet - a deep learning architecture for molecules and materials

Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics

Neural Networks for Information Retrieval

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many different approaches for many different IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research. It covers key architectures, as well as the most promising future directions.Comment: Overview of full-day tutorial at SIGIR 201

Differential dispersal costs and sex-biased dispersal distance in a cooperatively breeding bird

In most bird species, dispersal distance from the natal territory to a breeding territory is greater for females than for males. Two main hypotheses have been proposed to explain sex-biased dispersal: 1) it serves as an inbreeding-avoidance mechanism or 2) it is linked to a sex difference in resource-holding potential and territory establishment. Additionally, in species where individuals delay dispersal and become subordinates in a natal territory, differences in benefits of philopatry (e.g. territory inheritance, own reproduction) may also affect sex-biased dispersal. We show that in the group-living Seychelles warbler, Acrocephalus sechellensis, females disperse further to obtain a breeding position than males do. However, we found no evidence that female-biased dispersal distance can be explained by the above-mentioned hypotheses: further dispersal does not lead to less-related partners, both sexes defend and can inherit a territory, and subordinate females are more likely to obtain some reproduction than subordinate males. Instead, we provide evidence for a little-explored hypothesis based on sex differences in dispersal costs: namely that extra-territorial forays, pursued to search for limited vacancies, are more costly for males in terms of increased mortality, although the exact mechanism for this is unclear. In line with differential dispersal costs, males foray less far than females and often wait for local dispersal opportunities, ultimately resulting in a shorter average dispersal distance. Our results may help future studies in explaining sex-biased dispersal in social and perhaps also non-social species, and we suggest some mechanisms that may explain why sex-biased dispersal differs between species