Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemical determination of the ionization potential and electron affinity of poly(9,9-dioctylfluorene)

We report cyclic voltammetry measurements for the blue electroluminescent conjugated polymer poly(9,9-dioctylfluorene). Both oxidation and reduction potentials are determined and thus estimates of both the ionization potential Ip and electron affinity Ea of the polymer are obtained for the same sample under the same experimental conditions. We estimate Ip=5.80 eV and Ea=2.12 eV. These results disagree with the common assumption that Ea is, to good approximation, given by the difference between Ip and the optical gap. Measurements on indium tin oxide/polyfluorene/calcium light emitting diode structures are consistent with the deductions from the electrochemical data.
Charakterisierung von Membranen

Generell sind bei der Anwendung von Membranen zwei Aspekte von besonderer Bedeutung. Zum einen werden Transporteigenschaften und Trennleistung fur Einsatz und Anlagenauslegung benotigt. Zum anderen sind die Parameter wichtig, die die Transporteigenschaften und andere Grundlagen zum Transportverhalten bei der Trennung mittels Membranen beschreiben. Um Trennprozesse und deren Effizienz genauer zu definieren, sind weitere Parameter wichtig, z. B. die Ruckhaltung gegenuber bestimmten Stoffen bzw. bei Gasen die Selektivitat. Dieser Beitrag geht auf verschiedene Methoden zur Bestimmung von Membraneigenschaften mit dem Schwerpunkt auf porosen Systemen ein. Dabei werden Bild gebende Verfahren der Mikroskopie ebenso vorgestellt wie Methoden, die die Wechselwirkung eines Mediums mit der Membran betrachten und bewerten.
The Improvement of Ibuprofen Dissolution Rate Through Microparticles Spray Drying Processed in an Aqueous System

A study to enhance the dissolution rate of ibuprofen, a poorly water-soluble drug, was carried out through combining specific formulations and processes with the addition of a hydrophilic carrier for the preparation of microparticles. Microparticle production was performed by spray drying ibuprofen microsuspensions formulated in an aqueous system with the addition of ethanol containing Aerosil 200® and Tween 80®. We were able to consistently produce microparticles as much as 40% of the dry weight of the input microsuspension. Spray-dried microparticles were characterized by scanning electron microscopy, X-ray diffraction, differential scanning calorimetry, laser diffractometer mastersizer, and infrared spectroscopy. No modification to the crystalinity and chemical structure of ibuprofen was observed. Dissolution of ibuprofen microparticles reached 100% in 3 minutes compared with less than 10% for unmodified ibuprofen. We concluded that both by the modification of formulation and the spray drying process it is possible to increase the dissolution rate of the tested model drug.
Strain tuning of nematicity and superconductivity in single crystals of FeSe

Michele Ghini,1, 2, ∗ Matthew Bristow,1 Joseph C. A. Prentice,3 Samuel Sutherland,1 Samuele Sanna,2 A. A. Haghighirad,1, 4 and A. I. Coldea1, † Clarendon Laboratory, Department of Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK Department of Physics and Astronomy ”A. Righi”, University of Bologna, via Berti Pichat 6-2, I-40127 Bologna, Italy Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, United Kingdom Institute for Quantum Materials and Technologies (IQMT), Karlsruhe Institute of Technology, 76021 Karlsruhe, Germany (Dated: February 25, 2021)
Dynamical model of an obscuring clumpy torus in AGNs – I. Velocity and velocity dispersion maps for interpretation of ALMA observations

We have developed the dynamical model of a clumpy torus in an active galactic nucleus (AGN) and compared to recent ALMA observations. We present N -body simulations of a torus in the field of a supermassive black hole (SMBH), made of up to N = 10 gravitationally interacting clouds. As initial conditions, we choose random distributions of the orbital elements of the clouds with a cut-off in the inclination to mimic the presence of wind cones produced at the early AGN stage. When the torus reaches an equilibrium, it has a doughnut shape. We discuss the presence of box orbits. We have then constructed the velocity and velocity dispersion maps using the resulting distributions of the clouds at equilibrium. The effects of torus inclination and cloud sizes are duly analyzed. We discuss the obscuration effects of the clouds using a ray tracing simulation matching the model maps to ALMA resolution. By comparing the model with the observational maps of NGC 1068 we find that the SMBH mass is Msmbh = 5 × 10M for the range of the torus inclination angles 45◦ − 60◦. We also construct the velocity dispersion maps for NGC 1326 and NGC 1672. They show that the peaks in the ALMA dispersion maps are related to the emission of the torus throat. Finally, we obtain the temperature distribution maps with parameters that correspond to our model velocity maps for NGC 1068. They show stratification in temperature distribution with the shape of the high temperature region as in the VLTI/MIDI map.
Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving

Explainability is essential for autonomous vehicles and other robotics systems interacting with humans and other objects during operation. Humans need to understand and anticipate the actions taken by the machines for trustful and safe cooperation. In this work, we aim to develop an explainable model that generates explanations consistent with both human domain knowledge and the model's inherent causal relation. In particular, we focus on an essential building block of autonomous driving, multi-agent interaction modeling. We propose Grounded Relational Inference (GRI). It models an interactive system's underlying dynamics by inferring an interaction graph representing the agents' relations. We ensure a semantically meaningful interaction graph by grounding the relational latent space into semantic interactive behaviors defined with expert domain knowledge. We demonstrate that it can model interactive traffic scenarios under both simulation and real-world settings, and generate semantic graphs explaining the vehicle's behavior by their interactions.
OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach

Large-scale question-answer (QA) pairs are critical for advancing research areas like machine reading comprehension and question answering. To construct QA pairs from documents requires determining how to ask a question and what is the corresponding answer. Existing methods for QA pair generation usually follow a pipeline approach. Namely, they first choose the most likely candidate answer span and then generate the answer-specific question. This pipeline approach, however, is undesired in mining the most appropriate QA pairs from documents since it ignores the connection between question generation and answer extraction, which may lead to incompatible QA pair generation, i.e., the selected answer span is inappropriate for question generation. However, for human annotators, we take the whole QA pair into account and consider the compatibility between question and answer. Inspired by such motivation, instead of the conventional pipeline approach, we propose a model named OneStop generate QA pairs from documents in a one-stop approach. Specifically, questions and their corresponding answer span is extracted simultaneously and the process of question generation and answer extraction mutually affect each other. Additionally, OneStop is much more efficient to be trained and deployed in industrial scenarios since it involves only one model to solve the complex QA generation task. We conduct comprehensive experiments on three large-scale machine reading comprehension datasets: SQuAD, NewsQA, and DuReader. The experimental results demonstrate that our OneStop model outperforms the baselines significantly regarding the quality of generated questions, quality of generated question-answer pairs, and model efficiency.
MGait: Model-Based Gait Analysis Using Wearable Bend and Inertial Sensors

Movement disorders, such as Parkinson’s disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step and stride lengths provide valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, this article presents a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. Experimental results show that the proposed model estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.
Applications of nanomagnets as dynamical systems: I

When magnets are fashioned into nanoscale elements, they exhibit a wide variety of phenomena replete with rich physics and the lure of tantalizing applications. In this topical review, we discuss some of these phenomena, especially those that have come to light recently, and highlight their potential applications. We emphasize what drives a phenomenon, what undergirds the dynamics of the system that exhibits the phenomenon, how the dynamics can be manipulated, and what specific features can be harnessed for technological advances. For the sake of balance, we point out both advantages and shortcomings of nanomagnet based devices and systems predicated on the phenomena we discuss. Where possible, we chart out paths for future investigations that can shed new light on an intriguing phenomenon and/or facilitate both traditional and non-traditional applications.
ELISA Determination of the Sulfonylurea Herbicide Metsulfuron-Methyl in Different Water Types

Sulfonylurea herbicides efficiently control broad-leafed weeds and some grasses in cereals. They are typically applied at rates of <100 g/ha. The application of a recently developed competitive enzyme-linked immunosorbent assay (ELISA) for metsulfuron-methyl to spiked real-world water samples such as tap water, surface water from lakes and rivers, municipal wastewater, and landfill leachate is described. With pure water samples the IC50 value for the target analyte is 1.4 μg/L with the LOD being 40 ng/L. Determination of tap water and surface water was possible without any sample preparation such as filtration, extraction, pH adjustment, or dilution. On the contrary, with wastewater and leachate strong matrix interferences were observed, and, therefore, filtration and dilution of these samples prior to the immunochemical determination was necessary. Whereas with wastewater a 100-fold dilution was sufficient to ensure accurate herbicide quantification, even with a 500-fold dilution of leachate a positive b.
Detecting Compliance of Privacy Policies with Data Protection Laws

Privacy Policies are the legal documents that describe the practices that an organization or company has adopted in the handling of the personal data of its users. But as policies are a legal document, they are often written in extensive legal jargon that is difficult to understand. Though work has been done on privacy policies but none that caters to the problem of verifying if a given privacy policy adheres to the data protection laws of a given country or state. We aim to bridge that gap by providing a framework that analyzes privacy policies in light of various data protection laws, such as the General Data Protection Regulation (GDPR). To achieve that, firstly we labeled both the privacy policies and laws. Then a correlation scheme is developed to map the contents of a privacy policy to the appropriate segments of law that a policy must conform to. Then we check the compliance of privacy policy's text with the corresponding text of the law using NLP techniques. By using such a tool, users would be better equipped to understand how their personal data is managed. For now, we have provided a mapping for the GDPR and PDPA, but other laws can easily be incorporated in the already built pipeline.
Modern Koopman Theory for Dynamical Systems

The field of dynamical systems is being transformed by the mathematical tools and algorithms emerging from modern computing and data science. First-principles derivations and asymptotic reductions are giving way to data-driven approaches that formulate models in operator theoretic or probabilistic frameworks. Koopman spectral theory has emerged as a dominant perspective over the past decade, in which nonlinear dynamics are represented in terms of an infinite-dimensional linear operator acting on the space of all possible measurement functions of the system. This linear representation of nonlinear dynamics has tremendous potential to enable the prediction, estimation, and control of nonlinear systems with standard textbook methods developed for linear systems. However, obtaining finite-dimensional coordinate systems and embeddings in which the dynamics appear approximately linear remains a central open challenge. The success of Koopman analysis is due primarily to three key factors: 1) there exists rigorous theory connecting it to classical geometric approaches for dynamical systems, 2) the approach is formulated in terms of measurements, making it ideal for leveraging big-data and machine learning techniques, and 3) simple, yet powerful numerical algorithms, such as the dynamic mode decomposition (DMD), have been developed and extended to reduce Koopman theory to practice in real-world applications. In this review, we provide an overview of modern Koopman operator theory, describing recent theoretical and algorithmic developments and highlighting these methods with a diverse range of applications. We also discuss key advances and challenges in the rapidly growing field of machine learning that are likely to drive future developments and significantly transform the theoretical landscape of dynamical systems.
Investigation on thermal conductivity and viscosity of nanofluids using analytical and machine learning models

Knowledge of thermal properties is essential to design and evaluate thermal systems and processes using nanofluids. This paper presents different analytical models to predict thermal conductivity and viscosity. The efforts have been made to develop machine learning models to predict these properties. An extensive literature survey was carried out to collect thermal properties data of different nanofluids to train and test these machine learning models. The most influential properties like thermal conductivity, diameter, volume concentration of nanoparticles, base fluid thermal conductivity and nanofluid temperature are used as input variables to the thermal conductivity models and molecular weight, diameter and volume fraction nanoparticles, base fluid viscosity and nanofluid temperature are taken as an input variable to the viscosity model. Data is divided into two-part, one part is used to train the models and remaining part is used to test it. Result shows linear regression and ANN model do predict thermal conductivity more closely and ANN predict viscosity more accurately compared to analytical models.
Non-Singular Adversarial Robustness of Neural Networks

Adversarial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. While being critical, we argue that solving this singular issue alone fails to provide a comprehensive robustness assessment. Even worse, the conclusions drawn from singular robustness may give a false sense of overall model robustness. Specifically, our findings show that adversarially trained models that are robust to input perturbations are still (or even more) vulnerable to weight perturbations when compared to standard models. In this paper, we formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights. To our best knowledge, this study is the first work considering simultaneous input-weight adversarial perturbations. Based on a multi-layer feed-forward neural network model with ReLU activation functions and standard classification loss, we establish error analysis for quantifying the loss sensitivity subject to ℓ∞-norm bounded perturbations on data inputs and model weights. Based on the error analysis, we propose novel regularization functions for robust training and demonstrate improved non-singular robustness against joint input-weight adversarial perturbations.
On Relative Pose Recovery for Multi-Camera Systems

The point correspondence (PC) and affine correspondence (AC) are widely used for relative pose estimation. An AC consists of a PC across two views and an affine transformation between the small patches around this PC. Previous work demonstrates that one AC generally provides three independent constraints for relative pose estimation. For multi-camera systems, there is still not any AC-based minimal solver for general relative pose estimation. To deal with this problem, we propose a complete solution to relative pose estimation from two ACs for multi-camera systems, consisting of a series of minimal solvers. The solver generation in our solution is based on Cayley or quaternion parameterization for rotation and hidden variable technique to eliminate translation. This solver generation method is also naturally applied to relative pose estimation from PCs, resulting in a new six-point method for multi-camera systems. A few extensions are made, including relative pose estimation with known rotation angle and/or with unknown focal lengths. Extensive experiments demonstrate that the proposed AC-based solvers and PC-based solvers are effective and efficient on synthetic and real-world datasets.
Deep Learning Order Parameter for Polymer Phase Transition

We report a deep learning (DL) framework viz. deep autoencoder that autonomously discovers an appropriate order parameter from molecular dynamics (MD) simulation data to characterize the coil to globule phase transition of a polymer. The deep autoencoder encodes the 3N dimensional MD trajectory of a polymer in a one-dimensional feature space and, subsequently, decodes the one-dimensional feature to its original 3N dimensional polymer trajectory. The feature space representation of a polymer provides a new order parameter that accurately describes the coil to globule phase transition as a function of temperature. This method is very generic and extensible to identify flexible order parameters to characterize wide range of phase transitions that take place in polymers and other soft materials. Moreover, this MD-DL approach is computational very efficient than a pure MD based characterization of phase transition, and has potential implications in accelerating phase prediction.
"Train one, Classify one, Teach one" - Cross-surgery transfer learning for surgical step recognition

Prior work demonstrated the ability of machine learning to automatically recognize surgical workflow steps from videos. However, these studies focused on only a single type of procedure. In this work, we analyze, for the first time, surgical step recognition on four different laparoscopic surgeries: Cholecystectomy, Right Hemicolectomy, Sleeve Gastrectomy, and Appendectomy. Inspired by the traditional apprenticeship model, in which surgical training is based on the Halstedian method, we paraphrase the"see one, do one, teach one"approach for the surgical intelligence domain as"train one, classify one, teach one". In machine learning, this approach is often referred to as transfer learning. To analyze the impact of transfer learning across different laparoscopic procedures, we explore various time-series architectures and examine their performance on each target domain. We introduce a new architecture, the Time-Series Adaptation Network (TSAN), an architecture optimized for transfer learning of surgical step recognition, and we show how TSAN can be pre-trained using self-supervised learning on a Sequence Sorting task. Such pre-training enables TSAN to learn workflow steps of a new laparoscopic procedure type from only a small number of labeled samples from the target procedure. Our proposed architecture leads to better performance compared to other possible architectures, reaching over 90% accuracy when transferring from laparoscopic Cholecystectomy to the other three procedure types.
Automatic Feature Extraction for Heartbeat Anomaly Detection

We focus on automatic feature extraction for raw audio heartbeat sounds, aimed at anomaly detection applications in healthcare. We learn features with the help of an autoencoder composed by a 1D non-causal convolutional encoder and a WaveNet decoder trained with a modified objective based on variational inference, employing the Maximum Mean Discrepancy (MMD). Moreover we model the latent distribution using a Gaussian chain graphical model to capture temporal correlations which characterize the encoded signals. After training the autoencoder on the reconstruction task in a unsupervised manner, we test the significance of the learned latent representations by training an SVM to predict anomalies. We evaluate the methods on a problem proposed by the PASCAL Classifying Heart Sounds Challenge and we compare with results in the literature.
Targeted False Data Injection Attack against DC State Estimation without Line Parameters

A novel false data injection attack (FDIA) model against DC state estimation is proposed, which requires no network parameters and exploits only limited phasor measurement unit (PMU) data. The proposed FDIA model can target specific states and launch large deviation attacks using estimated line parameters. Sufficient conditions for the proposed method are also presented. Different attack vectors are studied in the IEEE 39-bus system, showing that the proposed FDIA method can successfully bypass the bad data detection (BDD) with high success rates of up to 95.3%.
Formation of colloidal threads in geometrically varying flow-focusing channels

When two miscible fluids are brought into contact with each other, the concentration gradients induce stresses. These are referred to as Korteweg stresses and are analogous to interfacial tension between two immiscible fluids, thereby acting as an effective interfacial tension (EIT) in inhomogeneous miscible systems. EIT governs the formation of a viscous thread in flow-focusing of two miscible fluids. To further investigate its significance, we have studied thread formation of a colloidal dispersion focused by its own solvent. Experiments are combined with three-dimensional numerical models to systematically expand previous knowledge utilising different flow-focusing channel setups. In the reference setup, the sheath flows impinge the core flow orthogonally while in four other channel setups, the sheath flows impinge the core flow at an oblique angle that is both positive and negative with respect to the reference sheath direction. As an initial estimate of the EIT, we fit the experimentally determined thread shape in the reference setup to a master curve that depends on EIT through an effective capillary number. By numerically reproducing these experimental results, it is concluded that the estimated EIT is within 25% of the optimal EIT value that can be deduced by iteratively fitting the numerical results to the experimental measurements. Regardless of channel setups, further numerical calculations performed using the optimal EIT evaluated from the reference setup show good agreement with the experimental findings in terms of thread shapes, wetted region morphologies, and velocity flow fields. The one-to-one comparison of numerical and experimental findings unveil the crucial role of EIT on the thread detachment from the top and bottom walls of the channel, bringing useful insights to understand the physical phenomenons involved in miscible systems with a high-viscosity contrast.