Sequential Recommendation with Self-Attentive Multi-Adversarial Network

Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained with Maximum Likelihood Estimation (MLE). When context information (called factor) is involved, it is difficult to analyze when and how each individual factor would affect the final recommendation performance. For this purpose, we take a new perspective and introduce adversarial learning to sequential recommendation. In this paper, we present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Specifically, our proposed MFGAN has two kinds of modules: a Transformer-based generator taking user behavior sequences as input to recommend the possible next items, and multiple factor-specific discriminators to evaluate the generated sub-sequence from the perspectives of different factors. To learn the parameters, we adopt the classic policy gradient method, and utilize the reward signal of discriminators for guiding the learning of the generator. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods, in terms of effectiveness and interpretability

Low Back Demand of Equipment Carriage Tasks in Golf

Low back injury is a common concern in golfers and caddies. The literature often points to the biomechanics of the golf swing as the major contributing factor; however, golfers and caddies can regularly walk upward of 10,000 steps on uneven terrain while carrying equipment, which can contribute to the cumulative load on the low back. PURPOSE: To examine the low back biomechanical demands during golf equipment carriage and pick-up tasks. METHODS: Sixteen golfers (11M/5F, 27.4±3.5yrs) participated in 3D motion capture of a golf bag pick-up task and 3 walking tasks (1. unloaded walking [UW], 2. unilateral carrying on the right shoulder [SS], and 3. carrying the golf bag across both shoulders [DS]); using an 11.3 kg duo-strap carry bag. Kinematic and Kinetic data were collected at 60Hz and 1500Hz, respectively, and filtered with a 6Hz lowpass filter. The lower extremity, pelvis, and trunk were modeled as ridgid segments, and lumbosacral (L5S1) kinematics and kinetics were calculated in Visual 3D. Peak internal moments were calculated via inverse dynamics and normalized to body weight. Descriptive statistics were calculated for sagittal and frontal plane peak L5S1 moments. Hedge’s g effect sizes were calculated for frontal plane bilateral peak moment differences in the pick-up task. RESULTS: The pick-up task yielded peak extensor moment of 1.14 ± 0.32 and peak left lateral flexor moment of 1.08 ± 0.27 Nm/Kg. Compared with peak right lateral flexor moment (0.09 ± 0.07 Nm/Kg), peak left lateral flexor was 12 times higher with a large effect size (g=2.77). In the walking conditions, peak extensor moment was highest in UW (0.28 ± 0.14 Nm/Kg) follow by SS (0.25 ± 0.2 Nm/Kg) then DS (0.07 ± 0.19 Nm/Kg). In contrast, peak flexor moments were highest in DS, followed by SS then UW. In the frontal plane, SS yielded peak left lateral flexor moment of 0.62 ± 0.14 Nm/Kg, which was 3 times higher than UW and more than twice higher than DS. CONCLUSION: The pick-up task is unilateral and yielded comparable peak L5S1 moments to the golf swing. Carrying the golf bag while walking alters the position of center of mass, which alters low back demands compared to unloaded walking. Due to the large number of cycles performed, equipment carriage tasks should be considered when estimating cumulative low back demand in golfers and caddies

The incidence of bifid c7 spinous processes

For posterior cervical surgery, if the operation only involves the lower cervical area, counting from C2 is impractical and the level may not be visible on X-rays. In such cases, we usually place a marker at the top of the incision and also rely on the size and monofid shape of the C7 spinous process. Relying on the C7 morphology, however, we initially instrumented the wrong levels in a case where the patient had a bifid C7 spinous process. We therefore sought to determine the frequency of bifid cervicothoracic spinous processes. Computed tomography axial images of C6, C7, and T1 from 516 patients were evaluated. The spinous processes were classified into three categories: “bifid,” “partially bifid,” and “monofid.” C6 spinous process was monofid in 47.9% of cases, partially bifid in 4.2% of cases, and bifid in 47.9% of cases. C7 spinous process was monofid in 99.2% of cases, partially bifid in 0.5% of cases, and bifid in 0.3% of cases. T1 was monofid in all cases. A truly bifid C7 spinous process occurs 0.3% of the time and therefore is not a reliable landmark for choosing fusion levels. This knowledge hopefully helps prevent the type of wrong-level instrumentation that we performed

Direction-dependent Optical Modes in Nanoscale Silicon Waveguides

On-chip photonic networks have the potential to transmit and route information more efficiently than electronic circuits. Recently, a number of silicon-based optical devices including modulators, buffers, and wavelength converts have been reported. However, a number of technical challenges need to be overcome before these devices can be combined into network-level architectures. In particular, due to the high refractive index contrast between the core and cladding of semiconductor waveguides, nanoscale defects along the waveguide often scatter light into the backward-propagating mode. These reflections could result in unwanted feedback to optical sources or crosstalk in bidirectional interconnects such as those employed in fiber-optic networks. It is often assumed that these reflected waves spatially overlap the forward-propagating waves making it difficult to implement optical circulators or isolators which separate or attenuate light based on its propagation direction. Here, we individually identify and map the near-field mode profiles of forward-propagating and reflected light in a single-mode silicon waveguide using Transmission-based near-field scanning optical microscopy (TraNSOM). We show that unlike fiber-optic waveguides, the high-index-contrast and nanoscale dimensions of semiconductor waveguides create counter propagating waves with distinct spatial near-field profiles. These near-field differences are a previously-unobserved consequence of nanoscale light confinement and could provide a basis for novel elements to filter forward-propagating from reflected light