Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlling Carbon Nanotube Type in Macroscopic Fibers Synthesized by the Direct Spinning Process

We report on the synthesis of kilometers of continuous macroscopic fibers made up of carbon nanotubes (CNT) of controlled number of layers, ranging from single-walled to multiwalled, tailored by the addition of sulfur as a catalyst promoter during chemical vapor deposition in the direct fiber spinning process. The progressive transition from single-walled through collapsed double-walled to multiwalled is clearly seen by an upshift in the 2D (G′) band and by other Raman spectra features. The increase in number of CNT layers and inner diameter results in a higher fiber macroscopic linear density and greater reaction yield (up to 9%). Through a combination of multiscale characterization techniques (X-ray photoelectron spectroscopy, organic elemental analysis, high-resolution transmission electron microscopy, thermogravimetric analysis, and synchrotron XRD) we establish the composition of the catalyst particles and position in the isothermal section of the C–Fe–S ternary diagram at 1400 °C. This helps explain.
COUNTY CSOUTH CGUIDE CUNEMP CBLPCT CCRIME CCONS IBLACK IMALE IACTCJS ITRIAL IREVOKE IDETAIN IPPRIS ICDRUG ICPROP

In a predictive model, what is the expected difference in the outcome associated with a unit difference in one of the inputs? In a linear regression model without interactions, this average predictive comparison is simply a regression coefficient (with associated uncertainty). In a model with nonlinearity or interactions, however, the average predictive comparison in general depends on the values of the predictors. We consider various definitions based on averages over a population distribution of the predictors, and we compute standard errors based on uncertainty in model parameters. We illustrate with a study of criminal justice data for urban counties in the United States. The outcome of interest measures whether a convicted felon received a prison sentence rather than a jail or non-custodial sentence, with predictors available at both individual and county levels. We fit three models: a hierarchical logistic regression with varying coefficients for the within-county intercepts as well as for each individual predictor; a hierarchical model with varying intercepts only; and a non-hierarchical model that ignores the multilevel nature of the data. The regression coefficients have different interpretations for the different models; in contrast, the models can be compared directly using predictive comparisons. Furthermore, predictive comparisons clarify the interplay between the individual and county predictors for the hierarchical models, as well as illustrating the relative size of varying county effects.
Use of the nematode, Caenorhabditis elegans, to evaluate bioavailability and toxicity of transition metals and manufactured zinc oxide nanoparticles

Metallothionein (MT), a protein involved in metal regulation and detoxification, has been used widely as a biomarker of metal exposure. In the present study, a transgenic strain of the free living soil nematode Caenorhabditis elegans was developed using the C. elegans MT-2 (mtl-2) promoter to control the transcription of green fluorescence protein (GFP) reporter. Response of this transgenic system to Cd, Hg, Cu, Zn, Ni, Pb, and As exposure in aquatic media was tested by quantifying GFP expression after 24 h of exposure. Response in Cd-spiked soil was tested in a similar manner. The mtl-2 transcription also was measured using real-time reverse transcription–polymerase chain reaction to gain a mechanistic understanding of the transgene expression. Green fluorescence protein is induced by Cd, Hg, Cu, and Zn in a timeand concentration dependent manner; mtl-2 transcription is consistent with the GFP response. The minimum concentrations of Cd, Hg, Cu, and Zn that induce GFP response are 2to 1,000fold lower than concentrations affecting traditional endpoints, such as lethality or behavioral change. The system responds to Cd in soil in a similar manner. Neither Ni nor Pb induces GFP, and neither induces mtl-2 transcription. Arsenic does not induce GFP, yet an increase in mtl-2 transcription was found, suggesting that As may interfere with GFP signaling. This mtl-2::GFP transgenic bioassay represents an alternative approach to quantify, both easily and quickly, a surrogate of MT in response to metal exposure (e.g., Cd, Hg, Cu, and Zn) in a variety of environments and potentially may be used for quantitative or semiquantitative biomonitoring of metal contamination in soils and aquatic systems.
Sentiment Analysis on COVID-19 Tweets

Initially this project was designed to explore possible relationships between sentiments among the general public’s tweets and Donald Trump’s tweets. However, after exploration determined no such relationship existed, the project pivoted to exploring sentiment analysis using several different models on Coronavirus after no exposure to Coronavirus tweets in training. These predictions are then compared to a ground truth, state-of-the-art sentiment analysis model from a Python library called NLTK to determine how well models adapted to Coronavirus as a negative concept.
Fine-tuning BERT On Fine Foods

In the field of Natural Language Processing (NLP), text classification is a classic problem. Previous work has shown that pretrained models are beneficial in classification tasks and other NLP problems. Especially Bidirectional Encoder Representations from Transformers (BERT), a multi-layer bidirectional Transformer which is trained on plain text or word prediction and next sentence prediction task. BERT have come to dominate the NLP landscape since their introduction by (Devlin et al. 2018). This largely replaced word embedding approaches such as word2vec (Mikolov et al. 2013) and Glove(Pennington, Socher, and Manning 2014). The transformer architecture that BERT uses was first introduced in (Vaswani et al. 2017) and has gained popularity on a multitude of tasks as it can train faster than previous RNN approaches such as (Liu, Qiu, and Huang 2016). Although BERT has achieved great performance in many NLP tasks, its potential in text classification has not been fully explored. "How to Fine-Tune BERT for Text Classification" (Sun et al. 2019) provides effective methods for fine-tuning BERT on the text classification task. Fine-tuning BERT for different NLP tasks is a valuable skill for deep-learning practitioners but can present a daunting challenge for novices in the field. This project will reproduce the approach used in the (Sun et al. 2019) paper and extend it to the Amazon Fine Food Reviews text classification dataset. This project will apply these fine-tuning methods on the Amazon Fine Food Review dataset, which was not included in the original paper. In this way, I will confirm whether the methods from the paper have broader applicability across different datasets, and particularly if they can work on a new, more challenging dataset.
GEOPHYSICAL STUDY AND 2D NON-LINEAR MODELING OF SITE EFFECTS IN VIÑA DEL MAR CITY, CHILE

The Maule earthquake of February 27, 2010 induced damage to several buildings throughout the central area of Chile. This was particularly the case in the downtown of Viña del Mar city, where an anomalous concentration of structural damage was found in several medium-rise buildings distributed along a narrow area of approximately 1 km in length. These observations suggest possible localized seismic amplification effects. A geophysical characterization using Surface-Wave based techniques and gravimetry were conducted to characterize the main dynamic properties of the soil and the depth of the basin. This data was complemented with several Standard Penetration Test (SPT) measurements to develop a geotechnical characterization of the area. Additionally, the cyclic behavior and mechanical properties of predominant materials were characterized experimentally based on remolded samples. Based on this whole description, a finite element model was developed to estimate possible singular site amplification effects explaining the concentration of structural damage in the area. The results suggest that the concentration of damaged buildings is probably most related to the basin depth (1D site amplification) than to the basin shape effects.
MULTI-AXIAL SUBASSEMBLAGE TESTING STAND FOR HYBRID SIMULATIONS UP TO SIX PHYSICAL DEGREES-OF-FREEDOM

Hybrid simulation has received increasing attention from the earthquake engineering community as a tool for simulating the nonlinear dynamic response of large structural systems in facilities with limited capacities. The quality of the simulation strongly depends of the correct application of the interface boundary conditions between the numerical and the physical subdomains. Nevertheless, in order to reduce experimental cost, often, coupling constraints are partially relaxed e.g., discarding interface rotational degrees-of-freedom. In this scenario, re-usable Multi-Axis Subassemblage Testing (MAST) system represent a convenient trade-off between fidelity of coupling, which preserves both rotational and translational Degrees-of-Freedom (DoF), and experiment cost. Along this line, this paper presents a new MAST system recently built at the laboratory of structural engineering of ETH Zürich. The ETHZ MAST system, adopts on a modular steel crosshead for loading a 6-DoF specimen. Two configurations are allowed: i) one of overall dimensions of 15.12 m x 11.52 m, which incorporates large specimens e.g., small buildings or long reinforced concrete walls; ii) one with overall dimensions of 5.36 m x 4.16 m, which incorporates specimens with narrow profiles e.g., columns or a bearings, but enables much larger rotations. In both cases, the vertical clearance under the crosshead is 4.39 m.
Effect of nitrogen application timing on protein constitutes and its dynamic change in wheat grain

A field experiment was conducted in Urumqi and Changji to investigate the effect of nitrogen application timing on protein constitute and its dynamic change in wheat grain during 2002-2003. Split plot design was adapted in this (experiment.) Two cultivars, GC8901 and XD23, as main plots and N application timing, top-dressing at re-greening (RG), jointing(JT) and booting(BT) stage, respectively, as subplots. The results showed that under the condition of same N application rate, wheat yield in the treatment of RG was significant higher than that of JT and BT, which indicated that top-dressing N at jointing stage was of great advantage to get a higher grain yield. The albumin and globulin content of grain decreased and gliadin and glutenin increased due to the lag of N application timing. It was observed that the application of nitrogen fertilizer at booting stage could increase glutenin content markedly, which was favorable for the improvement of wheat processing quality. Whereas, we could conclude that the responses of wheat quality to nitrogen application timing was not in agreement with that of grain yield of wheat. The ratio of glutenin to gliadin was enhanced due to the lagging of N application, but the response of different cultivars on the lagging of N application varied with the change of plantingloci.
2 State of the Art at the Time of the Proposal and Original Research Issues

Our goal in the DIALOG project was to develop a flexible dialog system for tutoring mathematical problem solving, in particular, theorem proving. We set out to investigate what requirements the flexible dialog paradigm poses on the components of a tutorial dialog system. Empirical findings in the area of intelligent tutoring show that flexible natural language dialog supports active learning (Moore, 1993). In the DIALOG project, therefore, the focus has been on the development of solutions allowing flexible dialog. However, little is known about the use of natural language in dialog settings in formal domains, such as mathematics, due to the lack of empirical data. To fill this gap, we designed and performed an experiment with a simulated tutorial dialog system for teaching proofs in naive set theory. An investigation of the collected corpus revealed challenging phenomena at all levels of system design: from input analysis, through mathematical domain reasoning, to tutorial dialog strategies. Input analysis in dialog systems is for most domains commonly performed using shallow syntactic analysis combined with keyword spotting; slot-filling templates, however, are not suitable in our case. Moreover, tight interleaving of natural and symbolic language makes key-phrase spotting difficult because of the variety of possible verbalizations. Statistical methods are employed in tutorial systems to compare student responses with a domain-model built from pre-constructed gold-standard answers Harter, & Person, 2000). In our context, such a static domain-modeling solution is impossible because of the wide quantitative and qualitative range of acceptable proofs, i.e., generally, our set of gold-standard answers is even infinite. The language in our dialogs is more informal: natural language and symbolic mathematical expressions are mixed more freely, there is a higher degree and more variety of verbalization, and mathematical objects are not properly introduced. Moreover, both above approaches rely on typesetting and additional information that identifies mathematical symbols, formulae, and proof steps, whereas our input does not contain any such information. Forcing the user to delimit formulae would reduce the flexibility of the system, make the interface harder to use, and would not guarantee a clean separation of the natural language and the non-linguistic content anyway. Recent research into dialog modeling has delivered a variety of approaches more or less suitable for the tutorial dialog setting. For instance, scripting is employed in Autotutor (Person, Graesser, Harter, Mathews, & the Tutoring Research Group, 2000) and knowledge construction dialogs are implemented in Geometry Tutor (Matsuda & VanLehn, …
Dicing up Newton: Exploring Deep Learning Solutions to the Chaotic Three-Body Problem with Nonlinear Data Transformations

Many important problems in physics, chemistry, biology, and economics involve N -body dynamics, where large number of identical units mutually interact to present larger-scale behaviors. E ectively simulating these dynamics can elucidate complex mysteries ranging from income inequality in ride sharing [1] to the possibility of fusion reactors to solve the energy crisis [2]. Unfortunately, these systems invariably exhibit nonlinear and chaotic behaviors that obscure analytical solutions to problems. Fully simulating N -body dynamics allows researchers to better explore their properties, but becomes unfeasible for small time steps, large time scales, many particles, and various iterations.
Trash Classiﬁcation Using Convolutional Neural Networks Project Category: Computer Vision

As the waste problem becomes increasingly eminent across the globe, we aim to provide an automated waste sorting tool to make it easier for residents to classify trash. Our project used TrashNet [2] as our dataset, and classiﬁed recyclables or trash into six categories. To achieve our objective, we focused on Convolutional Neural Networks (CNN), and explored several well-known architectures at early stages. We ended up with modiﬁed AlexNet by taking two layers out, and experimented different techniques based on this model architecture, including dropout, data augmentation and learning rate decay. We also experimented two classiﬁers, Softmax and Support Vector Machine (SVM), as the last layer of our model structure. The highest test accuracy we achieved was 79.94% with the model using partial data augmentation and SVM classiﬁer.
Determining the Degree of Characteristics for Internet of Healthcare Devices Using Fuzzy ANP

With the revolution in Internet and digital technology, every organization is adopting digital things to carry out their day-to-day activities. Internet of Things (IoT) is a concept that is used to connect various devices over the Internet to increase the production and quality of service, deliver a huge amount of data in seconds, and automate the processes. IoT implementation in the health sector has changed the typical setup into smart and intelligent setup. With the “smart” and “Intelligent” abilities of IoT devices such as sensors and with the collaboration of humans and computers, physical processes can be monitored and, based on the received data, optimal decisions can ultimately be taken. IoT applications in healthcare will increase flexibility, patient’s care, quality of health, and control of diseases. As the IoT combines various heterogeneous devices and the inappropriate determination of the degree of characteristics for Internet of healthcare (IoH) devices may affect the efficiency of services, this research is carried out with the use of decision support system and application of fuzzy analytic network process (fuzzy ANP) was used for optimal determination of the degree of characteristics for IoH devices based on unique properties which would greatly increase the efficiency of industry. The experimental results are efficient and show the usefulness of the approach.
Influence of Luddism on innovation diffusion

We generalize the classical Bass model of innovation diffusion to include a new class of agents— Luddites—that oppose the spread of innovation. Our model also incorporates ignorants, susceptibles, and adopters. When an ignorant and a susceptible meet, the former is converted to a susceptible at a given rate, while a susceptible spontaneously adopts the innovation at a constant rate. In response to the rate of adoption, an ignorant may become a Luddite and permanently reject the innovation. Instead of reaching complete adoption, the final state generally consists of a population of Luddites, ignorants, and adopters. The evolution of this system is investigated analytically and by stochastic simulations. We determine the stationary distribution of adopters, the time needed to reach the final state, and the influence of the network topology on the innovation spread. Our model exhibits an important dichotomy: when the rate of adoption is low, an innovation spreads slowly but widely; in contrast, when the adoption rate is high, the innovation spreads rapidly but the extent of the adoption is severely limited by Luddites.
Grouping-Based Channel Estimation and Tracking for Millimeter Wave Massive MIMO Systems

Although the millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping-based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low-complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple-signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed-form solution for updating the step-size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.
SEMI3D: Deep Learning Based Defect Finder using Design Data

— Wafer Inspection systems help to identify the defects on the wafer which could eventually lead to failure of the chip. These systems are used in the production cycles of chip manufacturing. Traditionally defects are found by doing Die-Die comparison of adjacent dies to find the abnormalities within a die. But this approach is getting challenged with new advancements in Chip manufacturing. The underlined assumption of all dies in a wafer are identical is no more valid for many of the new use cases. However, design information of each Die that is used during manufacturing is available. We propose a deep supervised model to find the defects on Die based on that design information. This has several steps in it. We propose a Deep network that can convert Binary design image that has polygons into a rendered optical image. Using that rendered optical image and real optical image of dies which is collected using KLA High resolution Inspection tool, we propose deep supervised model that can detect the defects on the optical image of the die.
Reinforcement Learning as an Approach for Language Generation

Natural language generation is a process in which an algorithm tries to generate human-understandable text. It’s a difﬁcult task for artiﬁcial intelligence to solve and has recently become a highly explored space. In this paper, we try two approaches to language generation the (1) LSTM model and a (2) GAN network model. We achieved a result of about 45% accuracy with the LSTM model and were able to score 22/28 with the reinforcement learning approach in an environment of our own creation.
A Deep Learning Approach for Tracheostomy Risk Prediction during Trauma Admission Healthcare

Prediction of tracheostomy is an ongoing challenge as clinicians are currently unable to accurately predict whether patients will require prolonged mechanical ventilation. Because early tracheostomy is associated with decreased medical costs and improved patient outcomes, the ability to predict which patients will require the procedure at an earlier timepoint in hospitalization is a crucial area of study. However, there is limited existing research on building these necessary, accurate models for the hospital setting. Here, we develop a binary classiﬁcation neural network that analyzes a patient’s medical history, using data from the National Trauma Data Bank which includes basic demographics as well as much more detailed metrics of injury characteristics and prior medical conditions. Our model performed better than prior models on tracheostomy in the literature, with an AUC of 0.841 and F1 of approximately 0.81. Further work is needed to identify the top factors of importance and to improve upon the deep learning approach we used. Overall, the development of such predictive models and incorporation into the hospital and ICU setting will signiﬁcantly impact cost improvements and quality of care.