Semantic Object Parsing with Graph LSTM

By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LSTM from sequential data or multi-dimensional data to general graph-structured data. Particularly, instead of evenly and fixedly dividing an image to pixels or patches in existing multi-dimensional LSTM structures (e.g., Row, Grid and Diagonal LSTMs), we take each arbitrary-shaped superpixel as a semantically consistent node, and adaptively construct an undirected graph for each image, where the spatial relations of the superpixels are naturally used as edges. Constructed on such an adaptive graph topology, the Graph LSTM is more naturally aligned with the visual patterns in the image (e.g., object boundaries or appearance similarities) and provides a more economical information propagation route. Furthermore, for each optimization step over Graph LSTM, we propose to use a confidence-driven scheme to update the hidden and memory states of nodes progressively till all nodes are updated. In addition, for each node, the forgets gates are adaptively learned to capture different degrees of semantic correlation with neighboring nodes. Comprehensive evaluations on four diverse semantic object parsing datasets well demonstrate the significant superiority of our Graph LSTM over other state-of-the-art solutions.Comment: 18 page

Identifying cross country skiing techniques using power meters in ski poles

Power meters are becoming a widely used tool for measuring training and racing effort in cycling, and are now spreading also to other sports. This means that increasing volumes of data can be collected from athletes, with the aim of helping coaches and athletes analyse and understanding training load, racing efforts, technique etc. In this project, we have collaborated with Skisens AB, a company producing handles for cross country ski poles equipped with power meters. We have conducted a pilot study in the use of machine learning techniques on data from Skisens poles to identify which "gear" a skier is using (double poling or gears 2-4 in skating), based only on the sensor data from the ski poles. The dataset for this pilot study contained labelled time-series data from three individual skiers using four different gears recorded in varied locations and varied terrain. We systematically evaluated a number of machine learning techniques based on neural networks with best results obtained by a LSTM network (accuracy of 95% correctly classified strokes), when a subset of data from all three skiers was used for training. As expected, accuracy dropped to 78% when the model was trained on data from only two skiers and tested on the third. To achieve better generalisation to individuals not appearing in the training set more data is required, which is ongoing work.Comment: Presented at the Norwegian Artificial Intelligence Symposium 201

Effect of periodic parametric excitation on an ensemble of force-coupled self-oscillators

We report the synchronization behavior in a one-dimensional chain of identical limit cycle oscillators coupled to a mass-spring load via a force relation. We consider the effect of periodic parametric modulation on the final synchronization states of the system. Two types of external parametric excitations are investigated numerically: periodic modulation of the stiffness of the inertial oscillator and periodic excitation of the frequency of the self-oscillatory element. We show that the synchronization scenarios are ruled not only by the choice of parameters of the excitation force but depend on the initial collective state in the ensemble. We give detailed analysis of entrainment behavior for initially homogeneous and inhomogeneous states. Among other results, we describe a regime of partial synchronization. This regime is characterized by the frequency of collective oscillation being entrained to the stimulation frequency but different from the average individual oscillators frequency.Comment: Comments and suggestions are welcom

Assessing Students\u27 Acquisition of Scientific Reasoning in an Experimental Psychology Class

This pilot study is an initial exploration of a theoretical rubric proposed to describe the progress of students’ acquisition of scientific inquiry (Halonen et al., 2003, p. 196), and an application of the utility of the rubric. Twenty-two undergraduates from a woman’s college participated in two sections of experimental psychology. Students consisted of sophomores, juniors, and seniors who completed general psychology courses. Consistent with the Halonen et al. (2003) model, results indicated that authentic research experiences in the first phase of the course were positively correlated with changes in scientific thinking in a second phase. In turn, experiences in the second phase were positively correlated with evidence of advanced thinking skills in a third phase. The findings suggest that much of the basic skill knowledge acquired in the beginning lectures, textbook readings, and writing instruction of the course enhanced students’ ability to apply that knowledge in later classes and the lab components. Further, the authentic learning experiences were instrumental in fine-tuning the skills learned from the lectures and textbooks readings. As a result, the current authors advocate the use of authentic experiences in teaching research methods, as a way for teachers to transform such classes in a beneficial and systematic way, in order to enhance acquisition of scientific thinking skills and to examine changes in scientific thinking as explicated in the Halonen et al. (2003) model

The Peak Brightness and Spatial Distribution of AGB Stars Near the Nucleus of M32

The bright stellar content near the center of the Local Group elliptical galaxy M32 is investigated with 0.12 arcsec FWHM H and K images obtained with the Gemini Mauna Kea telescope. Stars with K = 15.5, which are likely evolving near the tip of the asymptotic giant branch (AGB), are resolved to within 2 arcsec of the nucleus, and it is concluded that the peak stellar brightness near the center of M32 is similar to that in the outer regions of the galaxy. Moreover, the projected density of bright AGB stars follows the visible light profile to within 2 arcsec of the nucleus, indicating that the brightest stars are well mixed throughout the galaxy. Thus, there is no evidence for an age gradient, and the radial variations in spectroscopic indices and ultraviolet colors that have been detected previously must be due to metallicity and/or some other parameter. We suggest that either the bright AGB stars formed as part of a highly uniform and coherent galaxy-wide episode of star formation, or they originated in a separate system that merged with M32.Comment: 9 pages of text, 3 figures. ApJ (Letters) in pres

Do knee pain phenotypes have different risks of total knee replacement?

Pain is the main impetus for osteoarthritis (OA) patients to seek healthcare including joint replacement. The pain experience in OA is heterogeneous and affected by factors across multiple domains-peripheral, psychological, and neurological. This indicates the existence of homogenous subgroups/phenotypes within OA patients with pain. We recently identified three pain phenotypes using a wide spectrum of pain-related factors, including structural damage on magnetic resonance imaging (MRI), emotional problems, number of painful sites, sex, body mass index, education level and comorbidities (i.e., Class 1: high prevalence of emotional problems and low prevalence of structural damage (25%); Class 2: low prevalence of emotional problems and high prevalence of structural damage (20%); Class 3: low prevalence of emotional problems and low prevalence of structural damage (55%)). This study was to examine whether the total knee replacement (TKR) risk over 12 years was different among these three pain phenotypes. Data on 963 participants (mean age 62.8 ± 7.4 years) from a population-based cohort study were utilised. Data on socio-demographic, psychological and comorbidities were collected. MRI of the right knee structural pathology was performed. TKR history was ascertained by linking to the Australian Orthopedic Association National Joint Replacement Registry. Latent class analysis and the Cox proportional hazards model were applied for the analysis. During the follow-up period, 41 right and 44 left TKRs in 67 participants were identified. In multivariable analyses, participants in Class 1 and 2 had a higher risk of having a TKR (Class 1 vs. Class 3, HR (hazard ratio) 4.81, 95%CI (confidence interval) 2.33-9.93; Class 2 vs. Class 3, HR 9.23, 95%CI 4.66-18.30). These associations were stronger in the imaged right knee but were also significant in the left knee. Participants within distinct pain phenotypes have different risks of TKR, suggesting that the identified phenotypes reflect distinct clinical subgroups with different prognoses. The risk for TKR was higher in Class 1 than that in Class 3, suggesting that pain/emotional status is a stronger driver for TKR than structural damage, and that selecting patients for TKR needs to be optimized in clinical practice

Production and novel radiochemical separation of 194Au from Pt for use in multi-modality nanoparticles: Production and novel radiochemical separation of 194Au from Pt for use in multi-modality nanoparticles

Introduction Gold nanoparticles (AuNPs) have demonstrated their incredible versatility in applications such as in vitro and in vivo imaging, cancer therapy, and drug delivery.[1-3] These AuNPs come in many shapes including nanospheres, nanorods, nanoshells, and nanocages. Their versatility stems from the ability to construct or label a single AuNP with many functions. Many types of AuNPs are inherently flourescent, allowing for ex vivo utilization as well as small animal fluorescence imaging.[4] High atomic number and physical density allow for the possibility of using AuNPs as computed tomography (CT) contrast agents, especially in dual energy applications.[5] Some attempts have been made to bring AuNPs into the realm of nuclear medicine, mostly involving the extrinsic labeling of chelated radio-metals. Although these strategies have brought some success, an intrinsic labeling strategy could reduce concerns of in vivo instability, and changes in pharmacokinetic behavior.[6] Intrinsic radiolabeling strategies involve synthesizing the nanoparticles in the presence of a gold radioisotope, which is thereby structurally incorporated. The isotope of choice for this technique has typically been 198Au (t½ = 2.7 d, Eγ = 411.8 keV) as it is reactor produced and commercially available. However with such a high energy gamma ray, SPECT aquisition is far from optimal. Motivated by the shortcomings of previous intrinsic labeling techniques, we have sought to develop 194Au (t½ = 1.48 d, β+ = 1.73 %) as a potential PET isotope for labeling AuNPs. Although this nuclide has a weak positron branching ratio, it also has prominent gamma ray energies of 328 and 294 keV which are closer to the optimal SPECT energy window, allowing for the ability to image with both PET and SPECT. Material and Methods 194Au was produced by natPt(p,x) using 16 MeV protons. Target construction consisted of a water jet cooled platinum disc. Following irradiation, targets were etched by fresh concentrated aqua regia at 80 °C for four hours. The resulting solution was diluted by a factor of four and loaded onto a 50 mg UTEVA (Eichrom extraction resin) column equilibrated by 1 M HNO3. The column was rinsed with 10 mL 1 M HNO3, and the product was eluted using concentrated HNO3 in less than 1 mL. Results and Conclusion End of bombardment (EOB) yield for 194Au was measured to be 0.134 mCi/μAh by high purity germanium analysis. The half life was measured to be 38.5 ± 2.8 hours, which agrees well with the true half life of 37.92 hours. In addition to the production of 194Au, the production of 190–193Au and 196Au was observed. Most notably, the EOB yield for 193Au (t½ = 17.7 h) was 0.189 mCi/μAh. Target dissolution was slow and incomplete after four hours of etching. Alternative dissolution strategies i.e. electrolytic dissolution may be needed moving forward. The separation of 194Au from bulk Pt via the UTEVA extraction resin was robust and efficient, with an average separation efficiency of 96 %. An extensive literature review revealed no other Au/Pt separation from solutions containing aqua regia. Future goals include synthesis of ultrasmall 194Au incorporated AuNPs using a facile thermal reduction method.PET, CT and fluorescence imaging will also be carried out in vivo to establish the multimodal capabilities of the intrinsically radio-labeled nanoplatforms. To conclude, a novel separation technique has been developed to separate 194Au from Pt for use in intrinsically radiolabeled multi-modal AuNPs

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.Comment: To appear in ACM BuildSys'15, November 4--5, 2015, Seou

Observatory/data centre partnerships and the VO-centric archive: The JCMT Science Archive experience

We present, as a case study, a description of the partnership between an observatory (JCMT) and a data centre (CADC) that led to the development of the JCMT Science Archive (JSA). The JSA is a successful example of a service designed to use Virtual Observatory (VO) technologies from the start. We describe the motivation, process and lessons learned from this approach.Comment: Accepted for publication in the second Astronomy & Computing Special Issue on the Virtual Observatory; 10 pages, 5 figure