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

Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks

Deep generative models provide a powerful set of tools to understand real-world data. But as these models improve, they increase in size and complexity, so their computational cost in memory and execution time grows. Using binary weights in neural networks is one method which has shown promise in reducing this cost. However, whether binary neural networks can be used in generative models is an open problem. In this work we show, for the first time, that we can successfully train generative models which utilize binary neural networks. This reduces the computational cost of the models massively. We develop a new class of binary weight normalization, and provide insights for architecture designs of these binarized generative models. We demonstrate that two state-of-the-art deep generative models, the ResNet VAE and Flow++ models, can be binarized effectively using these techniques. We train binary models that achieve loss values close to those of the regular models but are 90%-94% smaller in size, and also allow significant speed-ups in execution time

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

Deep Bilateral Learning for Real-Time Image Enhancement

Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.Comment: 12 pages, 14 figures, Siggraph 201

An Online Decision-Theoretic Pipeline for Responder Dispatch

The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.Comment: Appeared in ICCPS 201

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure

Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939, arXiv:1606.08117 by other author

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