Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusion of Multispectral Imagery and Spectrometer Data in UAV Remote Sensing

Abstract: High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unmanned aerial vehicles (UAVs) are emerging as lower cost and more flexible means to acquire very high resolution imagery. Miniaturized hyperspectral sensors have been developed for UAVs, but the sensors, associated hardware, and data processing software are still cost prohibitive for use by individual farmers or small remote sensing firms. This study simulated hyperspectral image data by fusing multispectral camera imagery and spectrometer data. We mounted a multispectral camera and spectrometer, both being low cost and low weight, on a standard UAV and developed procedures for their precise data alignment, followed by fusion of the spectrometer data with the image data to produce estimated spectra for all the multispectral camera image pixels. To align the data collected from the two sensors in both the time and space domains, a post-acquisition correlation-based global optimization method was used. Data fusion, to estimate hyperspectral reflectance, was implemented using several methods for comparison. Flight data from two crop sites, one being tomatoes, and the other corn and soybeans, were used to evaluate the alignment procedure and the data fusion results. The data alignment procedure resulted in a peak R2 between the spectrometer and camera data of 0.95 and 0.72, respectively, for the two test sites. The corresponding multispectral camera data for these space and time offsets were taken as the best match to a given spectrometer reading, and used in modelling to estimate hyperspectral imagery from the multispectral camera pixel data. Of the fusion approaches evaluated, principal component analysis (PCA) based models and Bayesian imputation reached a similar accuracy, and outperformed simple spline interpolation. Mean absolute error (MAE) between predicted and observed spectra was 17% relative to the mean of the observed spectra, and root mean squared error (RMSE) was 0.028. This approach to deriving estimated hyperspectral image data can be applied in a simple fashion at very low cost for crop assessment and monitoring within individual fields.
LittleTable: A Time-Series Database and Its Uses

We present LittleTable, a relational database that Cisco Meraki has used since 2008 to store usage statistics, event logs, and other time-series data from our customers' devices. LittleTable optimizes for time-series data by clustering tables in two dimensions. By partitioning rows by timestamp, it allows quick retrieval of recent measurements without imposing any penalty for retaining older history. By further sorting within each partition by a hierarchically-delineated key, LittleTable allows developers to optimize each table for the specific patterns with which they intend to access it. LittleTable further optimizes for time-series data by capitalizing on the reduced consistency and durability needs of our applications, three of which we present here. In particular, our applications are single-writer and append-only. At most one process inserts a given type of data collected from a given device, and applications never update rows written in the past, simplifying both lock management and crash recovery. Our most recently written data is also recoverable, as it can generally be re-read from the devices themselves, allowing LittleTable to safely lose some amount of recently-written data in the event of a crash. As a result of these optimizations, LittleTable is fast and efficient, even on a single processor and spinning disk. Querying an uncached table of 128-byte rows, it returns the first matching row in 31 ms, and it returns 500,000 rows/second thereafter, approximately 50% of the throughput of the disk itself. Today Meraki stores 320 TB of data across several hundred LittleTable servers system-wide.
Covert Attention and Eye Movements during Reading

Eye movements were monitored during the reading of spatially transformed text in order to examine covert attentional processes in reading. In some conditions, the sequence of letters within a word was congruent with (i.e. in the same direction as) the sequence of words in the sentence; in other conditions the direction of letters within words and the direction of words in the sentence were incongruent. In addition, the window of visible text was varied so that in some conditions only the fixated word (and all preceding words) were visible, whereas in other conditions the fixated word and the succeeding word were both visible. Readers were able to extract more parafoveal information from text when the words themselves were normal than when the letters within the words were transformed. However, with practice, readers were able to use some parafoveal information even when the words were transformed. The most important finding was that the congruity of the word and letter order had no reliable effect on the ability to extract parafoveal information and influenced reading performance only when the words themselves were normal. We conclude that covert attention in reading is not a letter-by-letter scan that sweeps across the page, but either an asymmetric spotlight held constant on each fixation or a shifting of an attentional spotlight extending across multiletter units (possibly words) with the direction of shifts of attention closely coupled to the direction of eye movements.
The use of dermatoscopy in diagnosis and therapy of nonmelanocytic skin cancer

Today, dermatoscopy is an integral part of every clinical skin examination, as it markedly enhances the early detection of melanocytic and nonmelanocytic skin cancer (NMSC) compared to naked‐eye inspection. Besides its diagnostic use, this noninvasive method is increasingly important in the selection of as well as the response assessment to various therapies used for NMSC, including basal cell carcinoma, actinic keratoses, squamous cell carcinoma, and also rare tumors such as Merkel cell carcinoma, angiosarcoma, or dermatofibrosarcoma protuberans. Thus, dermatoscopy is a valid tool for the preoperative assessment of tumor margins in basal cell carcinoma, but also for follow‐up of actinic keratoses after topical treatment. The present article presents an overview on the use of dermatoscopy in the diagnosis and therapy of various types of NMSC.
Point Estimation of the Central Orientation of Random Rotations

Data as three-dimensional rotations have application in computer science, kinematics, and materials sciences, among other areas. Estimating the central orientation from a sample of such data is an important problem, which is complicated by the fact that several different approaches exist for this, motivated by various geometrical and decision-theoretical considerations. However, little is known about how such estimators compare, especially on common distributions for location models with random rotations. We examine four location estimators, three of which are commonly found in different literatures and the fourth estimator (a projected median) is newly introduced. Our study unifies existing literature and provides a detailed numerical investigation of location estimators for three commonly used rotation distributions in statistics and materials science. While the data-generating model influences the best choice of an estimator, the proposed projected median emerges as an overall good performer, which can be suggested without particular distributional assumptions. We illustrate the estimators and our findings with data from a materials science study by approximating the central orientation of cubic crystals on the microsurface of a metal. Accompanying supplementary materials are available online.
Profiles of gonadal development, sex steroids, aromatase activity, and gonadotropin II in the controlled sex change of protandrous black porgy, Acanthopagrus schlegeli Bleeker.

The objectives of the present study were to investigate the effects of oral administration of estradiol-17 beta (E(2)) on the sex steroids and gonadotropin II (GTH II) in plasma, aromatase activity in gonad and brain, and sex change in protandrous black porgy, Acanthopagus schlegeli Bleeker. Two-year-old black porgy were divided into two groups, one fed a control diet and the other a diet mixed with E(2) (4.0 mg/kg feed) for 7 months. Significantly higher GSI was observed in the E(2) group. Fish treated with E(2) showed complete suppression of spermatogenesis and spermiation and induced sex change with vitellogenic oocytes and large primary oocytes. Lower levels of plasma 11-ketotestosterone and higher levels of plasma vitellogenin were shown in the E(2) group. Higher gonadal aromatase activity in concordance with elevated plasma levels of GTH II was observed in the E(2) group. The highest aromatase activity in the forebrain in the control group was observed in January. Higher aromatase activity in forebrain, midbrain, and hindbrain was also detected in the E(2) group than the control group. The change of aromatase activity in brain was more susceptible than that in gonad. The data showed that the increase of plasma GTH II levels and gonadal aromatase activity may be important to the controlled sex change by administration in black porgy.
Conformal densities , boundary representations , ergodic theorems , irreducibility

We prove an equidistribution theorem à la Bader-Muchnik ([3]) for operator-valued measures associated with boundary representations in the context of discrete groups of isometries of CAT(-1) spaces thanks to an equidistribution theorem of T. Roblin ([22]). This result can be viewed as a von Neumann’s mean ergodic theorem for quasi-invariant measures. In particular, this approach gives a dynamical proof of the fact that boundary representations are irreducible. Moreover, we prove some equidistribution results for conformal densities using elementary techniques from harmonic analysis. AMS subject classifications: Primary 37A25, 37A30; Secondary 43A65, 43A90. AMS keywords: Conformal densities, boundary representations, ergodic theorems, irreducibility, equidistribution. Adrien Boyer, Technion, Haifa, Israel. E-mail address: adrienboyer@technion.ac. il.
Perturbation Density Functional Theory for Inhomogeneous Fluids

A recently developed third order+second order perturbation density functional approximation (DFA) is briefly described. The applicability of this theory is demonstrated in the study of the density profiles of Lennard-Jones (LJ) fluid next to a large hard sphere (mimicking a colloidal particle) of various sizes. The accuracy of DFA predictions is tested against the results of a grand canonical ensemble Monte Carlo simulation. The chosen density and potential parameters for the equilibrium bulk LJ fluid correspond to the conditions situated at ‘dangerous’ regions of the phase diagram, i.e. near the critical temperature or close to the gas-liquid coexistence curve. It is found that the DFA theory performs successfully for both supercritical and subcritical temperatures. It is also shown that the ‘universality’ of the adjustable parameter associated with this theory holds also in the present case of a large spherical particle as a source of external potential. Here the term universality means independence of this parameter on the particular external field responsible for the generation of a non-uniform density profile of the fluid. This DFA results can be used as a useful starting point for further investigation of solvent-induced excess potential of mean force in the similar systems.
Segmentation of 3D Lidar Points Using Extruded Surface of Cross Section

We present a new unsupervised technique to segment 3D Lidar points in outdoor environments. The main idea of this work is to identify artificial objects according to the existence of extruded shapes. Many artificial objects are composed of extruded shapes such as cylinders, planes, cubes, and lines. Therefore, we detect these arbitrarily extruded shapes on the basis of an indicator for repetitive crosssection shapes, and connect the components according to the strength between the overlapping areas in the extruded surfaces. Conventional segmentation methods that use local geometry information may sometimes produce erroneous results in scenes where there are many objects that are very near to and partially in contact with each other. In contrast, our method is more robust against these complex scenes using large scale surface overlapping strength. Experiments show it provides good results in urban environments and expressway scenes.
The Study of Auto-Focus System for Biomedical Digital Microscope

The bioengineering is a popular and fast-developing researching topic in recent years. Among them, the observation of microorganism is a very important experimental approach of bioengineering. As the biological sensing chip technology becomes mature, it is even beneficial to the research. Most of the biological sensing chips for measuring and observation adopt different type of precise microscopes and the corresponding software. However, the size is very big and the cost is expensive. Besides, the manufacturing processes and requirements of different biological sensing chips are different, different software are required for analysis. This limits the applying scope. Therefore, we focus on manufacturing low-cost, small size and portable auto-focus digital microscope in this study. The auto- focus technology is combined with microscope so that it is more convenient for the users to observe the biological sensing chip. Different objects for observation have been considered in the design process. Therefore, the microscope has been designed for both standing inverted or upright. Observers can use the standing inverted microscope to observe the behavior and track of microorganism on biological sensing chip, such as cell separation chip. Besides, the standing upright microscope can be used to observe cell slides, such as animal or plant slides. In addition to the low cost and portability, the free focus technology also makes the focus concentrating on the target and it can link up with computer so that the observed data can be processed with different types of operation and analysis. This includes counting of cells and analysis of motion tracks so that the application of microscope would be wider.
Human-Driven Spatial Language for Human-Robot Interaction

This extended abstract outlines a new study that investigates spatial language for use in human-robot communication. The scenario studied is a home setting in which the elderly resident has misplaced an object, such as eyeglasses, and the robot will help the resident find the object, We present preliminary results from the initial study in which we investigate spatial language generated to a human addressee or a robot addressee in a virtual environment.
Cingulotomy for intractable obsessive-compulsive disorder : prospective long-term follow-up of 18 patients

Background: The purpose of this study was to assess prospectively long-term change in obsessive-compulsive disorder (OCD) symptoms in patients with an OCD diagnosis that was confirmed by structured interview and with documented unsuccessful trials of multiple medications and attempts at behavior therapy. Methods: We conducted an unblinded preoperative and follow-up assessment of comorbid diagnosis; OCD, depressive, and anxiety symptoms; and functional status in 18 patients who underwent cingulotomy. Results: At a mean follow-up of 26.8 months, five patients (28%) met conservative criteria for treatment responders, and three others (17%) were partial responders. The group improved significantly in mean functional status, and few serious adverse events were found. Improvement in OCD symptoms was strongly correlated with improvement in depressive and anxiety symptoms. Conclusions: The rate of clinical improvement was consistent with a previous retrospective study in the same setting, indicating that 25% to 30% of the patients who previously were unresponsive to medication and behavioral treatments are significantly improved after cingulotomy. Cingulotomy remains a last resort treatment for severely incapacitated patients who have not responded to all other state-of-the-art pharmacological and behavioral treatments for OCD and is not to be taken lightly.
Static and dynamic knowledge for the embodied conversational agent Max - A first approach

The online encyclopedia Wikipedia is currently one of the best known collaboratively edited knowledge sources. While humans are able to inform themselves by reading the according articles in Wikipedia, machines lack the ability to understand natural language. Thus, a central task of bridging this deficit is the text technological reconstruction of the information held in Wikipedia to make this knowledge available to artificial agents. Our paper presents an approach for a connection between the embodied conversational agent Max and an ontology-based representation of encyclopedic knowledge held in Wikipedia. Thereby we strive for an automatic distinction between static and dynamic knowledge to enable a more human-like knowledge handling by the agent. The approach to infer stable knowledge proposed in this paper grounds on the so-called history pages provided by Wikipedia.
Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation

In Multimodal Neural Machine Translation (MNMT), a neural model generates a translated sentence that describes an image, given the image itself and one source descriptions in English. This is considered as the multimodal image caption translation task. The images are processed with Convolutional Neural Network (CNN) to extract visual features exploitable by the translation model. So far, the CNNs used are pre-trained on object detection and localization task. We hypothesize that richer architecture, such as dense captioning models, may be more suitable for MNMT and could lead to improved translations. We extend this intuition to the word-embeddings, where we compute both linguistic and visual representation for our corpus vocabulary. We combine and compare different confi
Geomagnetic Field Based Indoor Localization Using Recurrent Neural Networks

The time-varying, unstable nature of RF signals has limited the accuracy of RF-based indoor positioning techniques such as Wi-Fi fingerprinting. Positioning errors of over 10 meters are reported in large-scale indoor environment such as airport and department stores. Compared to RF or ultrasound signals, the geomagnetic field signal exhibits stable signal strength in time domain. However, the existing geomagnetic field based indoor localization still relies on the fingerprinting technique, which is borrowed from the RF-based indoor positioning. This cannot resolve the distribution of the same geomagnetic field values and thus became a major reason for diminished performance of geomagnetic- based indoor localization. In this paper, we propose a novel indoor localization technique that uses magnetometer sensor readings as input to the artificial neural network models. The idea is that although there can be multiple locations having the same magnetic field value, as a pedestrian moves the sequence of magnetic field values will lead to a unique pattern of the sensor readings over time. We use a recurrent neural network (RNN) since it can characterize a particular location based on the current input as well as the past sequence of inputs. We first build a geomagnetic field map on our campus test-bed. Then, we generate a million traces of various pedestrian walking patterns from the map. We use Google Tensorflow with NVIDIA cuDNN library as a Deep Learning framework. 95% of the traces are used for training and 5% of the traces are used for localization evaluation. In this preliminary evaluation, we show an average positioning error of 1.062 meters compared to the average error of 3.14 meters of our BLE fingerprinting results. We cannot only improve the localization accuracy but we are also able to address the problem of continuous route tracking, which was not possible with the RF-based fingerprinting.
Smart Cities - System for Monitoring Microclimate Conditions based on Crowdsensing

The aim of this paper is to provide an overview of technologies, application domains and services for smart city development. We developed, implemented and evaluated a system for monitoring microclimate conditions based on crowdsensing. The developed system aims to demonstrate one of the ways of integration of Internet of Things, web and big data technologies. This system measures microclimate conditions in smart city such as: temperature, humidity, and air pressure. In this way citizens can receive quick information about microclimate conditions in their environment, such as smart buildings and homes, and to share information via web application and social media services. As a system support, web application that provides services of microclimate conditions is developed.
Strong Growth Polarity of Yeast Prion Fiber Revealed by Single Fiber Imaging*

Using the yeast prion as a model, we have developed a novel system to observe the growth of individual prion fibers directly. NM fragments, the prion-determining region of the yeast protein Sup35p, were labeled by either red or green fluorescent dyes, and the fiber growth was observed under a fluorescence microscope. When green-Sup35NM was added to the preformed fibers made of red-Sup35NM, 70–97% of green fibers grew unidirectionally, from only one end of individual red fibers, whereas the remainder grew from both ends. Similarly, the majority of red fibers grew from only one end of green fibers when the order of addition was reversed. Sonication of preformed fibers to expose fresh ends did not change the results, excluding a possibility of occasional deformation of one end as the reason of the apparent unidirectional growth. These results indicate the polarity of Sup35 prion fibers and impose constraints on the models of fiber growth.
THE DYNAMICS OF STELLAR CORONAE HARBORING HOT JUPITERS. I. A TIME-DEPENDENT MAGNETOHYDRODYNAMIC SIMULATION OF THE INTERPLANETARY ENVIRONMENT IN THE HD 189733 PLANETARY SYSTEM

We carry out the first time-dependent numerical magnetohydrodynamic modeling of an extrasolar planetary system to study the interaction of the stellar magnetic field and wind with the planetary magnetosphere and outflow. We base our model on the parameters of the HD 189733 system, which harbors a close-in giant planet. Our simulation reveals a highly structured stellar corona characterized by sectors with different plasma properties. The star–planet interaction (SPI) varies in magnitude and complexity, depending on the planetary phase, planetary magnetic field strength, and the relative orientation of the stellar and planetary fields. It also reveals a long, comet-like tail which is a result of the wrapping of the planetary magnetospheric tail by its fast orbital motion. A reconnection event occurs at a specific orbital phase, causing mass loss from the planetary magnetosphere that can generate a hot spot on the stellar surface. The simulation also shows that the system has sufficient energy to produce hot spots observed in Ca ii lines in giant planet hosting stars. However, the short duration of the reconnection event suggests that such SPI cannot be observed persistently.