A Wavelet Melt Detection Algorithm Applied to Enhanced Resolution Scatterometer Data over Antarctica (2000-2009)

Melting is mapped over Antarctica at a high spatial resolution using a novel melt detection algorithm based on wavelets and multiscale analysis. The method is applied to Ku-band (13.4 GHz) normalized backscattering measured by SeaWinds onboard the satellite QuikSCAT and spatially enhanced on a 5 km grid over the operational life of the sensor (1999–2009). Wavelet-based estimates of melt spatial extent and duration are compared with those obtained by means of threshold-based detection methods, where melting is detected when the measured backscattering is 3 dB below the preceding winter mean value. Results from both methods are assessed by means of automatic weather station (AWS) air surface temperature records. The yearly melting index, the product of melted area and melting duration, found using a fixed threshold and wavelet-based melt algorithm are found to have a relative difference within 7% for all years. Most of the difference between melting records determined from QuikSCAT is related to short-duration backscatter changes identified as melting using the threshold methodology but not the wavelet-based method. The ability to classify melting based on relative persistence is a critical aspect of the wavelet-based algorithm. Compared with AWS air-temperature records, both methods show a relative agreement to within 10% based on estimated melt conditions, although the fixed threshold generally finds a greater agreement with AWS. Melting maps obtained with the wavelet-based approach are also compared with those obtained from spaceborne brightness temperatures recorded by the Special Sensor Microwave/Image (SSM/I). With respect to passive microwave records, we find a higher degree of agreement (9% relative difference) for the melting index using the wavelet-based approach than threshold-based methods (11% relative difference)

In-situ multispectral and bathymetric measurements over a supraglacial lake in western Greenland using a remotely controlled watercraft

Supraglacial lakes form from meltwater on the Greenland ice sheet in topographic depressions on the surface, affecting both surface and sub-glacial processes. As the reflectance in the visible and near-infrared regions of a column of water is modulated by its height, retrieval techniques using spaceborne remote sensing data (e.g. Landsat, MODIS) have been proposed in the literature for the detection of lakes and estimation of their volume. These techniques require basic assumptions on the spectral properties of the water as well as the bottom of the lake, among other things. In this study, we report results obtained from the analysis of concurrent in-situ multi-spectral and depth measurements collected over a supraglacial lake during early July 2010 in West Greenland (Lake Olivia, 69°36'35" N, 49°29'40" W) and aim to assess some of the underlying hypotheses in remote sensing based bathymetric approaches. In particular, we focus our attention on the analysis of the lake bottom albedo and of the water attenuation coefficient. The analysis of in-situ data (collected by means of a remotely controlled boat equipped with a GPS, a sonar and a spectrometer) highlights the exponential trend of the water-leaving reflectance with lake depth. The values of the attenuation factor obtained from in-situ data are compared with those computed using approaches proposed in the literature. Also, the values of the lake bottom albedo from in-situ measurements are compared with those obtained from the analysis of reflectance of shallow waters. Finally, we quantify the error between in-situ measured and satellite-estimated lake depth values for the lake under study

A SIMPLE STOCHASTIC MODEL FOR THE SARS-COV-2 EPIDEMIC CURVE

An epidemic curve is a graphic depiction of the number of outbreak cases by date of illness onset, ordinarily constructed after the disease outbreak is over. However, a good estimate of the epidemic curve early in an outbreak would be invaluable to health care officials. On the other hand, from the end of February, the SARS-CoV-2 epidemic in Brazil seems to not following the Europe, or in particular, Italy or Spain. Even if less tests have been applied, there are less deaths occurring in Brazil than in both cited countries. However, due to the few applied tests, there is no certain planning on the real number of active cases. To estimate the number of future cases, epidemiologists make an educated guess as to how many people might become affected. We have proposed a simple fitting model using a simulated annealing technique, testing it with the South Korea data. We have tested and discussed the uncertainties of the model. We also have analyzed the trends in the confirmed cases using this model for the five most affected countries plus Brazil along several epidemic weeks

Human activity recognition for emergency first responders via body-worn inertial sensors

Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM

Dependence of nuclear magnetic moments on quark masses and limits on temporal variation of fundamental constants from atomic clock experiments

We calculate the dependence of the nuclear magnetic moments on the quark masses including the spin-spin interaction effects and obtain limits on the variation of the fine structure constant $\alpha$ and $(m_q/\Lambda_{QCD})$ using recent atomic clock experiments examining hyperfine transitions in H, Rb, Cs, Yb$^+$ and Hg$^+$ and the optical transition in H, Hg$^+$ and Yb$^+$

Semanticizing syntactic patterns in NLP processing using SPARQL-DL queries

Some recent works on natural language semantic parsing make use of syntax and semantics together using different combination models. In our work we attempt to use SPARQL-DL as an interface between syntactic information given by the Stanford statistical parser (namely part-of-speech tagged text and typed dependency representation) and semantic information obtained from the FrameNet database. We use SPARQL-DL queries to check the presence of syntactic patterns within a sentence and identify their role as frame elements. The choice of SPARQL-DL is due to its usage as a common reference language for semantic applications and its high expressivity, which let rules to be generalized exploiting the inference capabilities of the underlying reasoner

Sensor and feature selection for an emergency first responders activity recognition system

Human activity recognition (HAR) has a wide range of applications, such as monitoring ambulatory patients’ recovery, workers for harmful movement patterns, or elderly populations for falls. These systems often operate in an environment where battery lifespan, power consumption, and hence computational complexity, are of prime concern. This work explores three methods for reducing the dimensionality of a HAR problem in the context of an emergency first responders monitoring system. We empirically estimate the accuracy of k-Nearest Neighbours, Support Vector Machines, and Gradient Boosted Trees when using different combinations of (A)ccelerometer, (G)yroscope and (P)ressure sensors. We then apply Principal Component Analysis for dimensionality reduction, and the Kruskal-Wallis test for feature selection. Our results show that the best combination is that which includes all three sensors (MAE: 3.6%), followed by the A/G (MAE: 3.7%), and the A/P combination (MAE 4.3%): the same as that when using the accelerometer alone. Moreover, our results show that the Kruskal-Wallis test can be used to discard up to 50% of the features, and yet improve the performance of classification algorithms

Organic Weed Management of Primocane-Fruiting Raspberries for Iowa Growers

Weed accumulation in raspberry plantings is a primary concern of all producers in Iowa, especially in organic production. Tillage may be used to reduce weed growth as an alternative to herbicides in raspberry plantings. However, tillage leaves soil vulnerable to erosion and potentially depletes the nutrients and organic matter from the topsoil. Growing a living mulch on the soil surface reduces weed seed germination and growth, and reduces the need for tilling after planting between the rows of raspberry plants. Legume living mulches also can provide nitrogen compared to tilled areas and fit within the organic certification requirements. The overall objective of this research was to determine the best organically certified soil management techniques to be used in between rows in a perennial raspberry planting. Specific objectives are to determine soil management treatments’ contribution to the soil’s physical and chemical properties, weed growth, and raspberry growth and development

Evaluating surgical skills from kinematic data using convolutional neural networks

The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.Comment: Accepted at MICCAI 201