Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation of High Refractive Index Composite Films Based on Titanium Oxide Nanoparticles Hybridized Hydrophilic Polymers

Optical materials with high refractive index (n) have been rapidly improved because of urgent demands imposed by the development of advanced photonic and electronic devices such as solar cells, light emitting diodes (LED and Organic LED), optical lenses and filters, anti-reflection films, and optical adhesives. One successful method to obtain high refractive index materials is the blending of metal oxide nanoparticles such as TiO2 and ZrO2 with high n values of 2.1–2.7 into conventional polymers. However, these nanoparticles have a tendency to agglomerate by themselves in a conventional polymer matrix, due to the strong attractive forces between them. Therefore, there is a limitation in the blending amount of inorganic nanoparticles. In this paper, various hydrophilic polymers such as poly(N-hydroxyl acrylamide) (pHEAAm), poly(vinyl alcohol), poly(ethylene glycol), and poly(acrylic acid) were examined for preparation of high refractive index film based on titanium oxide nanoparticle (TiNP) dispersed polymer composite. The hydrogen bonding sites in these hydrophilic polymers would improve the dispersibility of inorganic nanoparticles in the polymer matrix. As a result, pHEAAm exhibited higher compatibility with titanium oxide nanoparticles (TiNPs) than other water-soluble polymers. Transparent hybrid films were prepared by mixing pHEAAm with TiNPs and drop casting the mixture onto a glass plate. The refractive indices of the films were in good agreement with calculated values. The compatibility of TiNPs with pHEAAm was dependent on the surface characteristics of TiNPs. TiNPs with the highest observed compatibility could be hybridized with pHEAAm at concentrations of up to 90 wt%, and the refractive index of the corresponding film reached 1.90. The high compatibility of TiNPs with pHEAAm may be related to the hydrophilicity and amide and hydroxyl moieties of pHEAAm, which cause hydrogen bond formation on the TiO2 surface. The obtained thin film was slightly yellow due to the color of the original TiNP dispersion; however, the transmittance of the film was higher than 80% in the wavelength range from 480 to 900 nm.
Attack Detection : Generalization and Efficiency

We study the problem of fingerprint presentation attack detection (PAD) under unknown PA materials not seen during PAD training. A dataset of 5, 743 bonafide and 4, 912 PA images of 12 different materials is used to evaluate a state-of-the-art PAD, namely Fingerprint Spoof Buster. We utilize 3D t-SNE visualization and clustering of material characteristics to identify a representative set of PA materials that cover most of PA feature space. We observe that a set of six PA materials, namely Silicone, 2D Paper, Play Doh, Gelatin, Latex Body Paint and Monster Liquid Latex provide a good representative set that should be included in training to achieve generalization of PAD. We also implement an optimized Android app of Fingerprint Spoof Buster that can run on a commodity smartphone (Xiaomi Redmi Note 4) without a significant drop in PAD performance (from TDR = 95.7% to 95.3% @ FDR = 0.2%) which can make a PA prediction in less than 300ms.
Biomimetic jellyfish-inspired underwater vehicle actuated by ionic polymer metal composite actuators

This paper presents the design, fabrication, and characterization of a biomimetic jellyfish robot that uses ionic polymer metal composites (IPMCs) as flexible actuators for propulsion. The shape and swimming style of this underwater vehicle are based on the Aequorea victoria jellyfish, which has an average swimming speed of 20 mm s−1 and which is known for its high swimming efficiency. The Aequorea victoria is chosen as a model system because both its bell morphology and kinematic properties match the mechanical properties of IPMC actuators. This medusa is characterized by its low swimming frequency, small bell deformation during the contraction phase, and high Froude efficiency. The critical components of the robot include the flexible bell that provides the overall shape and dimensions of the jellyfish, a central hub and a stage used to provide electrical connections and mechanical support to the actuators, eight distinct spars meant to keep the upper part of the bell stationary, and flexible IPMC actuators that extend radially from the central stage. The bell is fabricated from a commercially available heat-shrinkable polymer film to provide increased shape-holding ability and reduced weight. The IPMC actuators constructed for this study demonstrated peak-to-peak strains of ∼0.7% in water across a frequency range of 0.1–1.0 Hz. By tailoring the applied voltage waveform and the flexibility of the bell, the completed robotic jellyfish with four actuators swam at an average speed 0.77 mm s−1 and consumed 0.7 W. When eight actuators were used the average speed increased to 1.5 mm s−1 with a power consumption of 1.14 W.
Protocol Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home ( KÄVELI ) : Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and JMIR Res Protoc 2019 | vol. 8 | iss. 3 | e12808 | p.1 http://www.researchprotocols. org/2019/3/e12808/ (page number not for citation purposes) Jauhiainen et al JMIR RESEARCH PROTOCOLS
Correlation of BMI , Oxygen Saturation and CRP Levels in COPD Patients

DOI: 10.21276/sjams.2017. 5.10. 88 Abstract: Chronic Obstructive Pulmonary Disease (COPD), the fourth leading cause of death in the world, is a common preventable and treatable disease. Weight loss being the most common extra pulmonary manifestation in COPD, with increased inflammatory markers like CRP with disease progression. We aimed at correlating these with severity of COPD. To study the BMI (Body mass index), Oxygen Saturation and CRP(C reactive protein) levels in COPD (Chronic obstructive pulmonary disease) patients and correlate these with regards to severity of COPD. This will be useful in the prognosis and the management of COPD. A total of 339 adult patients of COPD diagnosed on the basis of history, clinical examination and investigations attending pulmonary medicine department at GGCH/GMC, Hyderabad went through investigations like CBP, CXR PA, sputum for AFB, ECG, CRP, O2 saturation and spirometry. In these patients TB, HIV, MALIGNANCY was excluded. These patients were graded according to GOLD 2016 guidelines into mild, moderate, severe and very severe and were correlated with their BMI, O2 saturation and CRP levels respectively. Of these 339 patients 12.09% were mild, 32.74% moderate, 43.36% severe and 11.79% very severe COPD. In the present study, the overall prevalence of under nutrition among COPD patients was around 55%. And, with increasing COPD grade, the BMI and mean SpO2 values decreased in progressive manner. Also the increase in CRP value correlates with the increase in severity of the COPD, suggesting the fact that COPD is a systemic inflammatory disease which primarily affects the lungs.
Explorer Semantic Domains for Combining Probability and NonDeterminism

We present domain-theoretic models that support both probabilistic and nondeterministic choice. In [36], Morgan and McIver developed an ad hoc semantics for a simple imperative language with both probabilistic and nondeterministic choice operators over a discrete state space, using domain-theoretic tools. We present a model also using domain theory in the sense of D.S. Scott (see e.g. [15]), but built over quite general continuous domains instead of discrete state spaces. Our construction combines the well-known domains modelling nondeterminism – the lower, upper and convex powerdomains, with the probabilistic powerdomain of Jones and Plotkin [24] modelling probabilistic choice. The results are variants of the upper, lower and convex powerdomains over the probabilistic powerdomain (see Chapter 4). In order to prove the desired universal equational properties of these combined powerdomains, we develop sandwich and separation theorems of Hahn-Banach type for Scott-continuous linear, suband superlinear functionals on continuous directed complete partially ordered cones, endowed with their Scott topologies, in analogy to the corresponding theorems for topological vector spaces in functional analysis (see Chapter 3). In the end, we show that our semantic domains work well for the language used by Morgan and McIver.
Aalborg Universitet Speech Intelligibility Prediction Based on Mutual Information

This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ) in predicting the intelligibility of speech signals contaminated by additive noise and potentially non-linearly processed using time-frequency weighting.
Representing Personality Traits as Conditionals

This paper compares two approaches to representing personality traits in synthetic agents. It proposes a set of goals that any computational implementation of personality should satisfy. It describes the personality trait system used in The Sims 3.Then an alternative system is described, in which traits are represented as conditionals relating world state to emotional state. It is shown that the conditionals model does a better job of satisfying the desiderata.
Effects of sample dispersion on ultrafast laser focusing

We theoretically investigate the effects of sample dispersion on the focusing of ultrashort laser pulses. Both the conventional point focusing and temporal focusing are studied. The influence of laser parameters, objective lenses, and sample materials is discussed in detail. The sample dispersion introduces spatial and temporal distortions to the laser focus, and reduces the focal intensity. These effects are more significant when a laser with a shorter pulse duration is focused into the sample at a larger depth. The presented results show the significance of sample dispersion effects in different scenarios, and look to inform on a proper choice of specific focusing method in practical cases.
Pedestrian Detection for Autonomous Vehicle Using Multi-Spectral Cameras

Pedestrian detection is a critical feature of autonomous vehicle or advanced driver assistance system. This paper presents a novel instrument for pedestrian detection by combining stereo vision cameras with a thermal camera. A new dataset for vehicle applications is built from the test vehicle recorded data when driving on city roads. Data received from multiple cameras are aligned using trifocal tensor with pre-calibrated parameters. Candidates are generated from each image frame using sliding windows across multiple scales. A reconfigurable detector framework is proposed, in which feature extraction and classification are two separate stages. The input to the detector can be the color image, disparity map, thermal data, or any of their combinations. When applying to convolutional channel features, feature extraction utilizes the first three convolutional layers of a pre-trained convolutional neural network cascaded with an AdaBoost classifier. The evaluation results show that it significantly outperforms the traditional histogram of oriented gradients features. The proposed pedestrian detector with multi-spectral cameras can achieve 9% log-average miss rate. The experimental dataset is made available at http://computing.wpi. edu/dataset.html.
A decentralized proportional-integral sliding mode tracking controller for a 2 D.O.F robot arm

Trajectory tracking with high accuracy is a very challenging topic in direct drive robot control. This is due to the nonlinearities and input couplings present in the dynamics of the arm. This paper deals with the tracking control of a class of direct-drive robot manipulators. A robust Proportional-Integral (PI) sliding mode control law is derived so that the robot trajectory tracks a desired trajectory as closely as possible despite the highly non-linear and coupled dynamics. The controller is designed using the decentralized approaches. Application to a two degree of freedom direct drive robot arm is considered.
Dupuytren’s Disease

Dupuytren’s disease is a fibroproliferative disease affecting the palmar fascia of the hand and leading to flexion contractures of the digits. It was first described in Northern European populations and derived its namesake from Dr. Baron Dupuytren, a French sur - geon, who was one of the first to lecture on the disease. The etiology of Dupuytren’s dis ease is unclear but is likely influenced by both genetic and environmental factors. Older individuals and men are most at risk of developing the disease. Dupuytren’s disease is a clinical diagnosis and patients often present with gradually worsening flexion contrac - tures. Mild disease is usually observed, but surgical treatment is preferred for debilitat ing contractures. A variety of surgical techniques have been described involving either incising or excising diseased fascia. Overall, surgery is effective in correcting contractures and improving function, but despite successful treatment some patients still experience recurrence. More recently, collagenase injections and percutaneous procedures have been utilized to treat Dupuytren’s disease and have yielded promising results in select patients.
Spherical-Model-Based SLAM on Full-View Images for Indoor Environments

As we know, SLAM (Simultaneous Localization and Mapping) relies on surroundings. A full-view image provides more benefits to SLAM than a limited-view image. In this paper, we present a spherical-model-based SLAM on full-view images for indoor environments. Unlike traditional limited-view images, the full-view image has its own specific imaging principle (which is nonlinear), and is accompanied by distortions. Thus, specific techniques are needed for processing a full-view image. In the proposed method, we first use a spherical model to express the full-view image. Then, the algorithms are implemented based on the spherical model, including feature points extraction, feature points matching, 2D-3D connection, and projection and back-projection of scene points. Thanks to the full field of view, the experiments show that the proposed method effectively handles sparse-feature or partially non-feature environments, and also achieves high accuracy in localization and mapping. An experiment is conducted to prove that the accuracy is affected by the view field.
Deep Learning YouTube Video Tags

Video tagging is a complex problem that combines single-image feature extraction with arbitrarily long sequence understanding. By improving at the task of tagging videos with useful metadata labels, we necessarily improve our ability to understand the content and context of video data. Until recently, however, there hasn’t been a large corpus of labelled video data for researchers to study. With Google’s release of the YouTube-8M [1] 1 dataset, academic researchers now have access to 7 million video URLs, 450 000 hours of video, 3.2 billion features, and 4716 label classes. In conjunction with Kaggle’s announcement of their Video Understanding Challenge , this project seeks to combine state-of-the-art 2 deep learning methods to the problem of automatically labelling video frame data. We propose a hybrid CNN-RNN architecture that takes the image features generated for each video, and combines them with an LSTM model run over the word embeddings of the label set, to generate label predictions that take label correlation and dependency into account. We show that this model outperforms baseline models that operate only on raw image features without accounting for structural label similarity.
Dose expression for plant protection products Specific scope

This standard defines how the dose of plant protection products is expressed and describes the parameters, specific to the site and type of application, which should be recorded so that the dose can be determined in a standard way whatever the crop or product, or the equipment used for treatment. Included in the standard is a description of dose expression methods in three-dimensional crops (aerial parts of plants in commercial orchards (e.g. pome fruit, stone fruit), hop gardens and vineyards, small fruit and cane fruit crops (e.g. blueberries, gooseberries, raspberries), taller, isolated fruit trees and other high-growing crops such as fruiting vegetables both in the open field and under protected cultivation) and methods for converting between different dose expression terms. Specific approval and amendment
UvA-DARE (Digital Academic Repository) Porosity governs normal stresses in polymer gels

When sheared, most elastic solids including metals, rubbers, and polymer gels dilate perpendicularly to the shear plane. This behavior, known as the Poynting effect, is characterized by a positive normal stress. Surprisingly, fibrous biopolymer gels exhibit a negative normal stress under shear. Here we show that this anomalous behavior originates from the open-network structure of biopolymer gels. Using fibrin networks with a controllable pore size as a model system, we show that the normal-stress response to an applied shear is positive at short times, but decreases to negative values with a characteristic time scale set by pore size. Using a two-fluid model, we develop a quantitative theory that unifies the opposite behaviors encountered in synthetic and biopolymer gels.
Understanding liquidity: a closer look at the limit order book

This paper estimates a new measure of liquidity costs in a market driven by orders. It represents the cost of simultaneously buying and selling a given amount of shares, and it is given by a single measure of liquidity which is just an increasing function relating bid-ask spreads with size. This measure completely characterizes the cost of liquidity of any given asset. It does not suffer from the usual ambiguities related to either the bid-ask spread or depth when they are considered separately. With a single measure, we are able to capture all dimensions associated with liquidity costs. The seasonality behavior of the liquidity cost is also analyzed.
Microwave Properties of Ice-Phase Hydrometeors for Radar and Radiometers: Sensitivity to Model Assumptions

A simplified framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase hydrometeors. Properties considered included the shape parameterm of a gamma size distribution and the melted-equivalent mass median diameter D0, the particle density, the dielectric mixing formula, and the choice of complex index of refraction for ice. These properties are examined for selected radiometer frequencies of 18.7, 36.5, 89.0, and 150.0 GHz and radar frequencies at 2.8, 13.4, 35.6, and 94.0 GHz—consistent with existing and planned remote sensing instruments. Passive and active microwave observables of ice particles are found to be extremely sensitive to the D0 of the size distribution. Similar large sensitivities are found for variations in the ice volumefractionwheneverthe geometric massmediandiameter exceeds approximately 1 /8th of the wavelength. At 94 GHz the two-way path-integrated attenuation is potentially large for dense/compact particles. The distribution parameter m has a comparatively weak effect on any observable: less than 1‐2 K in brightness temperature and a maximum of 2.7 dB (S band only) in the effective radar reflectivity. Reversal of the roles of ice and air in the Maxwell Garnett dielectric mixing formula leads to a substantial change in both microwave brightness temperature (;10 K) and radar reflectivity (approximately 2 dB across all frequencies). The choice of the complex indexof refraction of ice can produce a 3%‐4% change in the brightness temperature depression.