Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of seismic anisotropy on mining seismology

Summary Microseismics – the detection and characterisation of mining-induced fracture events in the rockmass – is widely used to improve geotechnical understanding of the rockmass response to mining, and for hazard assessment of rockbursts and roof falls. These uses of microseismics require that the seismic events be accurately located, which can only be done using an accurate velocity model. In sedimentary environments, seismic velocity is typically anisotropic, usually being somewhat faster in a horizontal direction than in the vertical direction. However, anisotropy is seldom taken into account when processing microseismic data, resulting in locations of seismic events which can be significantly in error. This paper presents a technique for inverting a set of calibration shots with known location, along with a set of mining-induced seismic events, for an anisotropic velocity model. An example of the differences in event location is shown, illustrating the potential geotechnical significance.
Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications

The pressure map at the interface of a prosthetic socket and a residual limb contains information that can be used in various prosthetic applications including prosthetic control and prosthetic fitting. The interface pressure is often obtained using force sensitive resistors (FSRs). However, as reported by multiple studies, accuracies of the FSR-based pressure sensing systems decrease when sensors are bent to be positioned on a limb. This study proposes the use of regression-based methods for sensor calibration to address this problem. A sensor matrix was placed in a pressure chamber as the pressure was increased and decreased in a cyclic manner. Sensors’ responses were assessed when the matrix was placed on a flat surface or on one of five curved surfaces with various curvatures. Three regression algorithms, namely linear regression (LR), general regression neural network (GRNN), and random forest (RF), were assessed. GRNN was selected due to its performance. Various error compensation methods using GRNN were investigated and compared to improve instability of sensors’ responses. All methods showed improvements in results compared to the baseline. Developing a different model for each of the curvatures yielded the best results. This study proved the feasibility of using regression-based error compensation methods to improve the accuracy of mapping sensor readings to pressure values. This can improve the overall accuracy of FSR-based sensory systems used in prosthetic applications.
Automated Fake News Detection Using Linguistic Analy- sis and Machine Learning

Recently, there have been multiple instances of unverified or false information spreading rapidly over online social networks. For example, there were recent reports about Russian hacking of an electrical grid in Vermont [1] and reports mentioning that Emmanuel Macron's presidential campaign is financed by Saudi Arabia [2]. Such unverified news have been spreading at a rapid pace in recent times and with the growth of “big data” in these fields it is impossible to manually filter such news. Hence, in this work we propose a novel text analysis based computational approach to automatically detect fake news. The results obtained for a test dataset show promise in this research direction.
Automatic data cleaning via tensor factorization for large urban environmental sensor networks

The US Environmental Protection Agency identifies that urban heat islands can negatively impact a community’s environment and quality of life. Using low cost urban sensing networks, it is possible to measure the impacts of mitigation strategies at a fine-grained scale, informing context-aware policies and infrastructure design. However, fine-grained city-scale data analysis is complicated by tedious data cleaning including removing outliers and imputing missing data. To address the challenge of data cleaning, this article introduces a robust low-rank tensor factorization method to automatically correct anomalies and impute missing entries for high-dimensional urban environmental datasets. We validate the method on a synthetically degraded National Oceanic and Atmospheric Administration temperature dataset, with a recovery error of 4%, and apply it to the Array of Things city-scale sensor network in Chicago, IL.
Comparison of Local vs. Global Optimization for Trajectory Planning in Automated Driving

Trajectory planning for automated driving has recently seen a shift from highly discrete models towards continuous models related to the calculus of variations. These allow intuitive, elegant and flexible formulations, however, a major challenge of these models lies in finding their optima. Current iterative solvers are prone to terminating in local optima, being sensitive to initial parameters, violating constraints and exceeding real-time restrictions. A possible solution is presented in [1], where such variational models are transformed into Hidden Markov Models (which provide global optimization) while retaining the original optimization goals. This paper evaluates the necessity of such global optimization by comparing local and global approaches on several realistic traffic scenarios.
Restorative Care With Cognitively Impaired Older Adults: Moving Beyond Behavior

More than half of individuals diagnosed with dementia experience significant functional limitations. A restorative philosophy of care focuses on the restoration and/or maintenance of physical function and helps older adults to compensate for functional impairments so that the highest level of function is obtained and complications of physical dependence are minimized. When working with cognitively impaired individuals who commonly exhibit behavioral and psychiatric symptoms, it is essential to move beyond the tendency to focus only on behavioral interventions, and also implement a restorative philosophy of care that maximizes physical function and quality of life. This article describes a 4-step approach that can be used to implement a restorative philosophy of care for older adults with cognitive impairment and their caregivers.
A Study on the Sustainable Development for Senior Learners

In an aging population with a changing demographic structure, the government aims to ensure that elderly people receive care. In the concept of lifelong learning, education opportunities are available to senior learners, not just children and young people. The sustainable development for senior learners becomes a very important issue because it promotes a variety of learning activities for senior learners. Many universities have started to offer education for senior learners in Taiwan. Positive experiences for senior learners in senior universities can be fostered by ensuring the sustainable development of senior education. In this paper, a study on sustainable development for senior learners is proposed. This study aims to explore potential tools or approaches in evaluating the sustainable development for senior learners for decision making. In this study, two approaches are applied to analyze the sustainable development for senior learners. The first is a statistical analysis, and the second is the random forest model. The methodology of statistical analysis focuses on three aspects such as social assistance, inspiration, and the learning fulfillment for senior learners in senior universities. The random forest model is used to generate decision rules to support decision making. The random forest in this study obtained 22 decision rules. The results suggest that the items in the questionnaire and the decision rules from random forest could provide useful information that allows decision-makers to analyze the sustainable development of senior learners.
Synthesis and Antimicrobial Screening of New Sulfonamides bearing Pharmacologically Active Components

In this work, several new sulfonamides linked to biologically active heterocycles namely were prepared. Preparation of the new slfonamides was performed via multistep synthesis. In the first step 2-amino-4-phenyl thiazole was introduced in reaction with sucsunic anhydride in glacial acetic acid under reflux condition producing compound[2] N-(4-phenyl thiazole-2-yl) succinic acid which in turn introduced in ring closer reaction by fusion in the second step producing compound N-(4-phenyl thiazole-2-yl) succinimide and this subssequently introduced in reacation with chlorosolfonel chlorid affording compound[3] of 4-[2-(N-succinimidyl) thiazole-4-yl] phenylsulfonyl chloride. Compound[4] reprsents the parent synthone from which all the target slfonamides drivitves were synthesized via following different synthetic paths. Antimicrobial activities of the prepared imides were evaluated and the results indicated that most of them possess high antimicrobial activity.
Resident microbes of lactation rooms and daycares

Dedicated lactation rooms are a modern development as mothers return to work while still providing breastmilk to their absent infants. This study describes the built environment microbiome of lactation rooms and daycares, and explores the influence of temperature and humidity on the microbiome of lactation rooms. Sterile swabs were used to collect samples from five different sites in lactation rooms at University of California, Davis and from five different sites in daycares located in Davis, California. DNA from the swabs was extracted and the V4 region of the 16S rRNA gene was sequenced using Illumina MiSeq. Temperature and relative humidity data were collected on a subset of the lactation rooms. Sampled lactation rooms could be either dedicated lactation rooms or could also serve other functions (e.g., combined lactation room and restroom lounge). The majority of sequence reads were identified as belonging to family Moraxellaceae, with 73% of all reads included in analysis identified as an unknown species of Acinetobacter. Alpha diversity was analyzed using the Shannon index, while beta diversity was analyzed using unweighted and weighted UniFrac distance. The Jaccard distance was used to measure amount of change at sampling locations between time points for analysis of the impact of temperature and humidity on the microbiome. There were significant differences in the beta diversity of the microbiome of lactation rooms by room type. There were also significant differences in the beta diversity of the microbiome by sample collection location. There were no significant differences in either alpha or beta diversity associated with room temperature or humidity. Additional studies are needed to understand if the differences in lactation room type may result in differences in the breastmilk microbiome of milk collected in those rooms, and to what extent any such differences may influence the infant microbiome.
IDENTIFICATION OF LARVAE OF INTERTIDAL BIVALVES

Despite the importance of the planktonic larval stage in intertidal bivalves, our understanding of this stage is not well developed. One of the important limitations in quantification of planktonic larval distributions is the identification of sampled larvae. Identification of larvae is difficult due to the uniform morphology of many larval species. Microscopic identification was mostly used for this purpose in the past. The problem with this technique is that direct identification requires specialist knowledge. Recently, considerable progress has been made in identification of bivalve larvae using immunological techniques or molecular biology. However, not every laboratory doing larval research is equipped for molecular or immunological identification of bivalve larvae and these techniques are time consuming and costly. The recent improvements in imagine analysis technology and the reduction in cost of such equipment make optical techniques for identification of bivalve larvae interesting again. We evaluate bivalve larvae reared in our laboratory (Crassostrea gigas, Cerastoderma edule, Macoma balthica, Mytilus edulis) and literature data of larvae from the 1960s using image analysis techniques. We use this dataset to compile species-specific dimensions (length – width of the larval shell) and shape parameters (contour of the larval shell). The first method yields different slopes when length and width are plotted against each other but regression lines overlap, which makes the technique impractical for field identification. Multidimensional scaling of larval shape within one species shows shape-development of the larvae during ontogeny. Linear discriminant analysis did not produce results when the whole data set was used. But discriminant analysis on larger individuals (length > 150 μm) was relatively successful for species for which sufficient individuals were available. The identity of up to 74 % of the larvae could be predicted correctly. Submitted to the Journal of Sea Research. Chapter 2 Identification of larvae of intertidal bivalves
S2045796019000179jra 1..10

Aims. The second Singapore Mental Health Study (SMHS) – a nationwide, cross-sectional, epidemiological survey was initiated in 2016 with the intent of tracking the state of mental health of the general population in Singapore. The study employed the same methodology as the first survey initiated in 2010. The SMHS 2016 aimed to (i) establish the 12-month and lifetime prevalence and correlates of major depressive disorder (MDD), dysthymia, bipolar disorder, generalised anxiety disorder (GAD), obsessive compulsive disorder (OCD) and alcohol use disorder (AUD) (which included alcohol abuse and dependence) and (ii) compare the prevalence of these disorders with reference to data from the SMHS 2010. Methods. Door-to-door household surveys were conducted with adult Singapore residents aged 18 years and above from 2016 to 2018 (n = 6126) which yielded a response rate of 69.0%. The subjects were randomly selected using a disproportionate stratified sampling method and assessed using World Health Organization Composite International Diagnostic Interview version 3.0 (WHO-CIDI 3.0). The diagnoses of lifetime and 12-month selected mental disorders including MDD, dysthymia, bipolar disorder, GAD, OCD, and AUD (alcohol abuse and alcohol dependence), were based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria. Results. The lifetime prevalence of at least one mood, anxiety or alcohol use disorder was 13.9% in the adult population. MDD had the highest lifetime prevalence (6.3%) followed by alcohol abuse (4.1%). The 12-month prevalence of any DSM-IV mental disorders was 6.5%. OCD had the highest 12-month prevalence (2.9%) followed by MDD (2.3%). Lifetime and 12-month prevalence of mental disorders assessed in SMHS 2016 (13.8% and 6.4%) was significantly higher than that in SMHS 2010 (12.0% and 4.4%). A significant increase was observed in the prevalence of lifetime GAD (0.9% to 1.6%) and alcohol abuse (3.1% to 4.1%). The 12-month prevalence of GAD (0.8% vs. 0.4%) and OCD (2.9% vs. 1.1%) was significantly higher in SMHS 2016 as compared to SMHS 2010. Conclusions. The high prevalence of OCD and the increase across the two surveys needs to be tackled at a population level both in terms of creating awareness of the disorder and the need for early treatment. Youth emerge as a vulnerable group who are more likely to be associated with mental disorders and thus targeted interventions in this group with a focus on youth friendly and accessible care centres may lead to earlier detection and treatment of mental disorders.
Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering

Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding attention and a multi-step decoding strategy to steer our model to focus on relevant features for response generation. Experiments on two benchmark datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues. Our code is available at https://github.com/siat-nlp/TransDG.
Dynamics of small grains in transitional discs

Transitional discs have central regions characterised by significant depletion of both dust and gas compared to younger, optically-thick discs. However, gas and dust are not depleted by equal amounts: gas surface densities are typically reduced by factors of $\sim 100$, but small dust grains are sometimes depleted by far larger factors, to the point of being undetectable. While this extreme dust depletion is often attributed to planet formation, in this paper we show that another physical mechanism is possible: expulsion of grains from the disc by radiation pressure. We explore this mechanism using 2D simulations of dust dynamics, simultaneously solving the equation of radiative transfer with the evolution equations for dust diffusion and advection under the combined effects of stellar radiation and hydrodynamic interaction with a turbulent, accreting background gas disc. We show that, in transition discs that are depleted in both gas and dust fraction by factors of $\sim 100-1000$ compared to minimum mass Solar nebular values, and where the ratio of accretion rate to stellar luminosity is low ($\dot{M}/L \lesssim 10^{-10}$ $M_\odot$ yr$^{-1}$ $L_\odot^{-1}$), radiative clearing of any remaining $\sim 0.5$ $\mu$m and larger grains is both rapid and inevitable. The process is size-dependent, with smaller grains removed fastest and larger ones persisting for longer times. Our proposed mechanism thus naturally explains the extreme depletion of small grains commonly-found in transition discs. We further suggest that the dependence of this mechanism on grain size and optical properties may explain some of the unusual grain properties recently discovered in a number of transition discs. The simulation code we develop is freely available.
Conquering the Extensional Scalability Problem for Value-Flow Analysis Frameworks

Modern static analyzers often need to simultaneously check a few dozen or even hundreds of value-flow properties, causing serious scalability issues when high precision is required. A major factor to this deficiency, as we observe, is that the core static analysis engine is oblivious of the mutual synergy among the properties being checked, thus inevitably losing many optimization opportunities. Our work is to leverage the inter-property awareness and to capture redundancies and inconsistencies when many properties are considered at the same time. We have evaluated our approach by checking twenty value-flow properties in standard benchmark programs and ten real-world software systems. The results demonstrate that our approach is more than 8× faster than existing ones but consumes only 1/7 of the memory. Such substantial improvement in analysis efficiency is not achieved by sacrificing the effectiveness: at the time of writing, thirty-nine bugs found by our approach have been fixed by developers and four of them have been assigned CVE IDs due to their security impact.
Analysis and Control of Fiber-Reinforced Elastomeric Enclosures (FREEs)

While rigid robots are extensively used in various applications, they are limited in the tasks they can perform and can be unsafe in close human-robot interactions. Soft robots on the other hand surpass the capabilities of rigid robots in several ways, such as compatibility with the work environments, degrees of freedom, manufacturing costs, and safe interactions with the environment. This thesis studies the behavior of Fiber Reinforced Elastomeric Enclosures (FREEs) as a particular type of soft pneumatic actuator that can be used in soft manipulators. A dynamic lumped-parameter model is created to simulate the motion of a single FREE under various operating conditions and to inform the design of a controller. The proposed PID controller determines the response of the FREE to a defined step input or a trajectory following polynomial function, using rotation angle to control the orientation of the end-effector. Additionally, Finite Element Analysis method is employed, incorporating the inherently nonlinear material properties of FREEs, to precisely evaluate various parameters and configurations of FREEs. This tool is also used to determine the workspace of multiple FREEs in a module, which is essentially a building block of a soft robotic arm.
The column density structure of Orion A depicted by N-PDF

We have conducted a large-field simultaneous survey of 12CO, 13CO and C18O J = 1 – 0 emission toward the Orion A giant molecular cloud (GMC) with a sky coverage of ∼4.4 deg2 using the Purple Mountain Observatory (PMO)-13.7 m millimeter-wavelength telescope. We use the probability distribution function of the column density (N-PDF) to investigate the distribution of molecular hydrogen in the Orion A GMC. The H2 column density, derived from the 13CO emission, of the GMC is dominated by a log-normal distribution in the range from ∼ 4 × 1021 to ∼ 1.5 × 1023 cm−2 with excesses both at the low-density and high-density ends. The excess of the low-density end is possibly caused by an extended and low-temperature (∼10 K) component with velocities in the range of 5 – 8 km s−1. Compared with the northern sub-regions, the southern sub-regions of the Orion A GMC contain less gas with column density in NH2 > 1.25 × 1022 cm−2. The dispersions of the N-PDFs of the sub-regions are found to correlate with the evolutionary stages of the clouds across the Orion A GMC. The structure hierarchy of Orion A GMC is explored with the DENDROGRAM algorithm, and it is found that the GMC is composed of two branches. All structures except one in the tree have virial parameters less than 2, indicating self-gravity is important on the spatial scales from ∼0.3 to ∼4 pc. Although power-laws and departures from lognormal distributions are found at the high-density end of N-PDFs for active star-forming regions, the N-PDFs of structures in the Orion A GMC are predominantly lognormal on scales from R∼0.4 to 4 pc.
Probing the early phases of high-mass star formation with 6.7 GHz methanol masers

Methanol masers at 6.7 GHz are the brightest of class II methanol masers and have been found exclusively towards massive star forming regions. These masers can thus be used as a unique tool to probe the early phases of massive star formation. We present here a study of the spectral energy distributions of 320 6.7 GHz methanol masers chosen from the MMB catalogue, which fall in the Hi-GAL range ($|l| \le 60^{\circ}$, $|b| \le 1^{\circ}$). The spectral energy distributions are constructed from $870 - 70~\mu$m using data from the ATLASGAL and Hi-GAL surveys. The emission from cold dust is modelled by a single grey body component fit. We estimate the clump properties such as mass, FIR luminosity and column density using the best fit parameters of the SED fits. Considering the Kauffman criteria for massive star formation, we find that all but a few maser hosts have the potential to harbour at least one high mass star. The physical properties of the methanol maser hosts are also discussed. The evolutionary stages of 6.7 GHz maser sources, explored using the mass luminosity diagram, suggests that they are predominantly associated with high mass stars with the majority being in the accretion phase. However, we observe a small number of sources that could possibly be related to intermediate or low-mass stars.