Learning SO(3) Equivariant Representations with Spherical CNNs

We address the problem of 3D rotation equivariance in convolutional neural networks. 3D rotations have been a challenging nuisance in 3D classification tasks requiring higher capacity and extended data augmentation in order to tackle it. We model 3D data with multi-valued spherical functions and we propose a novel spherical convolutional network that implements exact convolutions on the sphere by realizing them in the spherical harmonic domain. Resulting filters have local symmetry and are localized by enforcing smooth spectra. We apply a novel pooling on the spectral domain and our operations are independent of the underlying spherical resolution throughout the network. We show that networks with much lower capacity and without requiring data augmentation can exhibit performance comparable to the state of the art in standard retrieval and classification benchmarks.Comment: Camera-ready. Accepted to ECCV'18 as oral presentatio

Detecting Sarcasm in Multimodal Social Platforms

Sarcasm is a peculiar form of sentiment expression, where the surface sentiment differs from the implied sentiment. The detection of sarcasm in social media platforms has been applied in the past mainly to textual utterances where lexical indicators (such as interjections and intensifiers), linguistic markers, and contextual information (such as user profiles, or past conversations) were used to detect the sarcastic tone. However, modern social media platforms allow to create multimodal messages where audiovisual content is integrated with the text, making the analysis of a mode in isolation partial. In our work, we first study the relationship between the textual and visual aspects in multimodal posts from three major social media platforms, i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to quantify the extent to which images are perceived as necessary by human annotators. Moreover, we propose two different computational frameworks to detect sarcasm that integrate the textual and visual modalities. The first approach exploits visual semantics trained on an external dataset, and concatenates the semantics features with state-of-the-art textual features. The second method adapts a visual neural network initialized with parameters trained on ImageNet to multimodal sarcastic posts. Results show the positive effect of combining modalities for the detection of sarcasm across platforms and methods.Comment: 10 pages, 3 figures, final version published in the Proceedings of ACM Multimedia 201

Zero-Shot Hashing via Transferring Supervised Knowledge

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge e.g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels i.e. 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.Comment: 11 page

SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds. Due to the nature of point clouds, i.e. unstructured, sparse and noisy, some features benefit-ting multi-class discrimination are underexploited, such as shape information. In this paper, we propose a novel 3D shape signature to explore the shape information from point clouds. By incorporating operations of symmetry, convex hull and chebyshev fitting, the proposed shape sig-nature is not only compact and effective but also robust to the noise, which serves as a soft constraint to improve the feature capability of multi-class discrimination. Based on the proposed shape signature, we develop the shape signature networks (SSN) for 3D object detection, which consist of pyramid feature encoding part, shape-aware grouping heads and explicit shape encoding objective. Experiments show that the proposed method performs remarkably better than existing methods on two large-scale datasets. Furthermore, our shape signature can act as a plug-and-play component and ablation study shows its effectiveness and good scalabilityComment: Code is available at https://github.com/xinge008/SS

Using Weibull Distribution Analysis to Evaluate ALARA Performance

Abstract -As Low as Reasonably Achievable (ALARA) is the underlying principle for protecting nuclear workers from potential health outcomes related to occupational radiation exposure. Radiation protection performance is currently evaluated by measures such as collective dose and average measurable dose, which do not indicate ALARA performance. The purpose of this work is to show how statistical modeling of individual doses using the Weibull distribution can provide objective supplemental performance indicators for comparing ALARA implementation among sites and for insights into ALARA practices within a site. Maximum likelihood methods were employed to estimate the Weibull shape and scale parameters used for performance indicators. The shape parameter reflects the effectiveness of maximizing the number of workers receiving lower doses and is represented as the slope of the fitted line on a Weibull probability plot. Additional performance indicators derived from the model parameters include the 99 th percentile and the exceedance fraction. When grouping sites by collective total effective dose equivalent (TEDE) and ranking by 99 th percentile with confidence intervals, differences in performance among sites can be readily identified. Applying this methodology will enable more efficient and complete evaluation of the effectiveness of ALARA implementation

Using Weibull Distribution Analysis to Evaluate ALARA Performance

As Low as Reasonably Achievable (ALARA) is the underlying principle for protecting nuclear workers from potential health outcomes related to occupational radiation exposure. Radiation protection performance is currently evaluated by measures such as collective dose and average measurable dose, which do not indicate ALARA performance. The purpose of this work is to show how statistical modeling of individual doses using the Weibull distribution can provide objective supplemental performance indicators for comparing ALARA implementation among sites and for insights into ALARA practices within a site. Maximum likelihood methods were employed to estimate the Weibull shape and scale parameters used for performance indicators. The shape parameter reflects the effectiveness of maximizing the number of workers receiving lower doses and is represented as the slope of the fitted line on a Weibull probability plot. Additional performance indicators derived from the model parameters include the 99th percentile and the exceedance fraction. When grouping sites by collective total effective dose equivalent (TEDE) and ranking by 99th percentile with confidence intervals, differences in performance among sites can be readily identified. Applying this methodology will enable more efficient and complete evaluation of the effectiveness of ALARA implementation

Low-cost, Transportable Hydrogen Fueling Station for Early FCEV Adoption

Thousands of public hydrogen fueling stations are needed to support the early Fuel Cell Electric Vehicle (FCEV) market in the U.S.; there are currently 12. The California state government has been the largest investor of the hydrogen fueling infrastructure funding 9 permanent stations currently open to the public with 48 more in development costing anywhere from $1.8M-$5.5M each. To attract private investors and decrease dependence on government funding, a low-cost, mobile hydrogen dispensing system must be developed. This paper describes a transportable hydrogen fueling station that has been designed for $423,000 using off-the-shelf components, less than 23$9.62/kg. This paper presents the mechanical design and operation of the fueling station. A complete report including an economic analysis and safety features is available at: http://hydrogencontest.org/pdf/2014/WSU_2014_HEF_CONTEST.pdf

A reference relative time-scale as an alternative to chronological age for cohorts with long follow-up

Background: Epidemiologists have debated the appropriate time-scale for cohort survival studies; chronological age or time-on-study being two such time-scales. Importantly, assessment of risk factors may depend on the choice of time-scale. Recently, chronological or attained age has gained support but a case can be made for a ‘reference relative time-scale’ as an alternative which circumvents difficulties that arise with this and other scales. The reference relative time of an individual participant is the integral of a reference population hazard function between time of entry and time of exit of the individual. The objective here is to describe the reference relative time-scale, illustrate its use, make comparison with attained age by simulation and explain its relationship to modern and traditional epidemiologic methods. Results: A comparison was made between two models; a stratified Cox model with age as the time-scale versus an un-stratified Cox model using the reference relative time-scale. The illustrative comparison used a UK cohort of cotton workers, with differing ages at entry to the study, with accrual over a time period and with long follow-up. Additionally, exponential and Weibull models were fitted since the reference relative time-scale analysis need not be restricted to the Cox model. A simulation study showed that analysis using the reference relative time-scale and analysis using chronological age had very similar power to detect a significant risk factor and both were equally unbiased. Further, the analysis using the reference relative time-scale supported fully-parametric survival modelling and allowed percentile predictions and mortality curves to be constructed. Conclusions: The reference relative time-scale was a viable alternative to chronological age, led to simplification of the modelling process and possessed the defined features of a good time-scale as defined in reliability theory. The reference relative time-scale has several interpretations and provides a unifying concept that links contemporary approaches in survival and reliability analysis to the traditional epidemiologic methods of Poisson regression and standardised mortality ratios. The community of practitioners has not previously made this connection