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

Laplacian-Steered Neural Style Transfer

Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to synthesize a new image that retains the high-level structure of a content image, rendered in the low-level texture of a style image. This is achieved by constraining the new image to have high-level CNN features similar to the content image, and lower-level CNN features similar to the style image. However in the traditional optimization objective, low-level features of the content image are absent, and the low-level features of the style image dominate the low-level detail structures of the new image. Hence in the synthesized image, many details of the content image are lost, and a lot of inconsistent and unpleasing artifacts appear. As a remedy, we propose to steer image synthesis with a novel loss function: the Laplacian loss. The Laplacian matrix ("Laplacian" in short), produced by a Laplacian operator, is widely used in computer vision to detect edges and contours. The Laplacian loss measures the difference of the Laplacians, and correspondingly the difference of the detail structures, between the content image and a new image. It is flexible and compatible with the traditional style transfer constraints. By incorporating the Laplacian loss, we obtain a new optimization objective for neural style transfer named Lapstyle. Minimizing this objective will produce a stylized image that better preserves the detail structures of the content image and eliminates the artifacts. Experiments show that Lapstyle produces more appealing stylized images with less artifacts, without compromising their "stylishness".Comment: Accepted by the ACM Multimedia Conference (MM) 2017. 9 pages, 65 figure

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Phalangeal fractures of the hand:An analysis of gender and age-related incidence and aetiology

The incidence and aetiology of 6,857 phalangeal fractures of the hand have been reviewed in a series of 235,427 patients, looking for an age-specific vulnerability to fracture. We found sports to be the main cause of fracture in the 10-29 years age groups and accidental falls to be the leading cause in those aged 70 years or older. We made a new observation that the highest incidence occurs in the male 40-69 age group and machinery was the dominant cause of fracture in this group. Recognition of the frequency of industrial trauma is needed, and public expenditure should be invested in its prevention and treatment.</p

Intraoperative cone beam computed tomography for detecting residual stones in percutaneous nephrolithotomy:a feasibility study

Cone beam computed tomography (CBCT) provides multiplanar cross-sectional imaging and three-dimensional reconstructions and can be used intraoperatively in a hybrid operating room. In this study, we investigated the feasibility of using a CBCT-scanner for detecting residual stones during percutaneous nephrolithotomy (PCNL). Intraoperative CBCT-scans were made during PCNL procedures from November 2018 until March 2019 in a university hospital. At the point where the urologist would have otherwise ended the procedure, a CBCT-scan was made to image any residual fragments that could not be detected by either nephroscopy or conventional C-arm fluoroscopy. Residual fragments that were visualized on the CBCT-scan were attempted to be extracted additionally. To evaluate the effect of this additional extraction, each CBCT-scan was compared with a regular follow-up CT-scan that was made 4 weeks postoperatively. A total of 19 procedures were analyzed in this study. The mean duration of performing the CBCT-scan, including preparation and interpretation, was 8 min. Additional stone extraction, if applicable, had a mean duration of 11 min. The mean effective dose per CBCT-scan was 7.25 mSv. Additional extraction of residual fragments as imaged on the CBCT-scan occurred in nine procedures (47%). Of the follow-up CT-scans, 63% showed a stone-free status as compared to 47% of the intraoperative CBCT-scans. We conclude that the use of CBCT for the detection of residual stones in PCNL is meaningful, safe, and feasible

Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201

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/