Miracle-dz/UniFAR-FTUs huggingface dataset:
Expanding Material Property Space Maps with Functionally Graded materials for Large Scale Additive Manufacturing

Big Area Additive Manufacturing (BAAM) is a large scale extrusion-based print system that exceeds the throughput of conventional printers by five hundred times. In addition, BAAM uses pelletized feedstocks, which allows for site-specific definition of material composition and provides an unprecedented variety of material options. This study applies Ashby’s concept of a material property space map to a variety of materials suitable for printing on BAAM. Ashby maps plot the performance of various materials across multiple parameters (such as strength, density, stiffness, etc) allowing for direct comparison of non-dimensional performance criteria. This study uses Ashby maps to identify opportunities for the use of functionally graded materials on BAAM to achieve structural performance not yet available with conventional printers and homogeneous materials. Introduction Additive manufacturing (AM), commonly called 3-D printing, is the process of creating a three-dimensional part with consecutive two-dimensional layers. The part is designed with computer aided design (CAD) software, and then divided into multiple layers through a virtual “slicing” software [1]. AM encompasses a variety of technologies, including selective laser sintering, stereo-lithography, and polymer extrusion [2]. One of the most common forms of 3-D printing is fused filament fabrication (FFF). In this process, filaments of thermoplastics, such as acrylonitrile butadiene styrene (ABS), are forced into a heating element, where it melts and flows through a die. A gantry system moves the deposition system in a plane, and deposits the material in a specified computer numerically controlled (CNC) path. This process is repeated for each 2D layer. These FFF systems are typically restricted to flows rates of 70 grams per hour and build volumes around 0.125 cubic meters [3]. Large format additive manufacturing, such as Cincinnati Incorporated’s Big Area Additive Manufacturing (BAAM), is an industrialized FFF system that creates parts 500 times faster than traditional FFF systems. BAAM has a build area of 2.5 (x-axis) meters by 6 meters 459 Solid Freeform Fabrication 2017: Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference
CTGA: Graph-based Biomedical Literature Search

Scientists are relying heavily on biomedical literature search (BLS) engines (e.g., PubMed) to acquire knowledge. Existing BLS systems adopt a “C-A” paradigm that is to design query-document similarity measurement based on words/phrases in the unstructured Content and to develop search Algorithms. In this work, we argue that structures should be extracted and utilized to bridge the gap between text content and knowledge-based search. And graph is one of the most effective structured forms of knowledge and more informative than words or phrases. So we carry out a paradigm shift from “C-A” to “CTGA”. Here “T” is for factual tuple of concepts and relations, and “G” is for knowledge graph. Our proposed graph-based BLS system has three parts: (1) it uses neural information extraction models to turn text into tuples; (2) it represents the tuples of a query or a document as a knowledge graph of linked concept nodes; (3) it has an efficient graph-based matching algorithm to return related documents at the level of structured knowledge. Experiments show that in both objective and subjective evaluation, our CTGA performs significantly better than traditional CA-based PubMed.
A Comparative Evaluation of Efficacy of 1.2% Atorvastatsin and 1.2% of Rosuvastatsin with Platelet Rich Fibrin in Intaosseous Alveolar Defects: A Randomized Clinical Trial

Aim: To determine the effectiveness of treating intra osseous defects with open flap debridement followed by placement of 1.2% Atorvastatin (ATV) gel and 1.2% Rosuvastatin (RSV) gel alone and mixed with autologous platelet rich fibrin (PRF). Materials and methods: 24 subjects with 82 periodontal intra osseous defects were selected and open flap debridement was carried out. Following 20 sites received 1.2% ATV gel (group I), 20 sites received 1.2% RSV gel (group II), 20 sites assigned to group III received 1.2% ATV gel mixed with PRF and group IV comprising of 20 sites received 1.2% RSV gel mixed with PRF. Site specific Plaque index, papillary bleeding index (PBI), probing depth reduction (PD) and clinical attachment level (CAL) gain and radiographic defect depth reduction was assessed at baseline 3, 6 and 9 months intervals. Statistical Analysis used: Kruskal Wallis test and Mann Whitney U test. Results: Comparing the results at 9 month interval all the groups has shown a significant reduction in PD reduction and CAL gain from baseline. The inter group analysis have shown no statistical significant difference in group I, II, III and IV in both clinical and radiographic parameters at a 9 months interval. Conclusion: The present study establishes a marked improvement in the clinical parameters and radiographic outcomes were noted with both 1.2% ATV gel and 1.2% RSV gel alone and Autologous PRF coated with 1.2% ATV gel and 1.2% RSV gel in the treatment of intra osseous defects.
SUPERVISING A HUMANOID ROBOT ACROSS TIME DELAY

The Predictive Interactive Graphical Interface (PIGI) was developed at NASA/JSC to provide supervisors with better situational awareness when commanding mobile robots across time delay. By simulating the expected outcome of queues of discrete commands already sent to the robot, the supervisor is able to explore, plan, and submit future commands, thus reducing robot idle time. In the work presented here, PIGI is extended in several ways: to work with a dual-armed robot, to model internal state variables of the robot and the state of the environment, and to provide the ability to browse historical data. These features enable the supervisor to remain an active participant in the planning and execution of tasks, despite a five-to-ten-second time delay. A comparison is made between PIGI for commanding NASA’s R2 humanoid and the Prophesy system for commanding Boston Dynamic’s Atlas humanoid.
Topic Extraction of Crawled Documents Collection using Correlated Topic Model in MapReduce Framework

The tremendous increase in the amount of available research documents impels researchers to propose topic models to extract the latent semantic themes of a documents collection. However, how to extract the hidden topics of the documents collection has become a crucial task for many topic model applications. Moreover, conventional topic modeling approaches suffer from the scalability problem when the size of documents collection increases. In this paper, the Correlated Topic Model with variational Expectation-Maximization algorithm is implemented in MapReduce framework to solve the scalability problem. The proposed approach utilizes the dataset crawled from the public digital library. In addition, the full-texts of the crawled documents are analysed to enhance the accuracy of MapReduce CTM. The experiments are conducted to demonstrate the performance of the proposed algorithm. From the evaluation, the proposed approach has a comparable performance in terms of topic coherences with LDA implemented in MapReduce framework.
Proceedings of Meetings on Acoustics

The Fourier pseudospectral time-domain method is an efficient domain-discretization wave-based method to model sound propagation in inhomogeneous bounded media. The method was successfully applied to model atmospheric sound propagation and acoustics in urban environments. One of the limitations of the method is its restriction to a Cartesian grid, confining it to staircase-like geometries. When applying a transform from the Cartesian coordinate system to the curvilinear coordinate system, more arbitrary geometries may be solved by the method. In free field, the frequency dependent accuracy of the curvilinear Fourier pseudospectral time-domain method is investigated as a function of the deformation angle of the grid. Further, the performance of the curvilinear pseudospectral method is investigated for sound propagation in a box and for scattering from a rigid body. Finally, the sound field in a concert hall configuration with realistic boundary impedance values is computed. All computed results are in 2D and show good agreement against the boundary element method for low deformation angles.
PA Design for Future Wireless Systems

This paper presents an overview of efficiency enhancement techniques for microwave power amplifiers (PAs) through supply modulation. The focus is on next-generation wireless systems, which are predicted to operate at higher carrier frequencies, with significantly larger signal bandwidths and under conditions that require more functionality and flexibility at the RF front end. Several PAs with supply modulation are presented to illustrate the challenges and propose some solutions. Starting from continuous envelope tracking of a 10-W X-band MMIC GaN PAs with up to 40-MHz signal bandwidth with >40% composite efficiency (CPAE), the approach is extended to signals with bandwidths >100MHz and with PAPR>11dB using slew-rate reduction methods. Further extensions to supply modulation for multiple simultaneous signals, each with a bandwidth of a few tens of MHz, randomly positioned over a 1GHz band are shown. Supply modulation is also demonstrated to improve efficiency for more complex PA architectures, such as a 10-GHz Chireix outphasing MMIC PA and a 2-GHz linear and efficient harmonically-injected PA. The flexibility of supply modulation is discussed for PAs that can efficiently amplify both communications and radar signals with improved performance. Keywords—power amplifier, high efficiency, broadband signals, supply modulation, envelope tracking
Spiral Particle Distribution for Template based Tracking

This paper presents a new approach for tracking template-based objects based on spiral particle distribution algorithm. Proposed algorithm uses points on Archimedean spiral as possible location of object in next frame. Before applying algorithm, the system is provided with off-line learning of training data. After that, start point is initialized and algorithm for tracking is applied. Tracking starts from initialization location and searches for the best matching point on spiral as a new starting point in the next frame. In this work our algorithm is explained and compared with basic particle filter tracking algorithm. Experiment is demonstrated with real data on Croatian popular amateur game. Key-Words: Particle filter, template-based tracking, spiral particle distribution
Using Reinforcement Learning to Optimize the Rules of a Board Game

Reinforcement learning using deep convolutional neural networks has recently been shown to be exceptionally powerful in teaching artificial agents how to play complex board games. We have applied the learning framework used by Google Deepmind’s AlphaZero to train an agent that plays “Organism”, a board game that is undergoing active development. The hexagonal geometry of the game board and the possibility of multiple moves per turn required major modifications from the existing framework. The resulting agent was capable of playing the game at a competitive level, and provided massive amounts of valuable play data that could be used to optimize the rules of the board game before its release.
Three-year-olds infer polite stance from intonation and facial cues

Abstract Despite the evidence that infants are sensitive to facial cues and prosody for the detection of emotion, we have contradictory evidence regarding the use of these cues by older preschool and school children when inferring both emotional and politeness stance. This study assessed preschool aged children’s sensitivity to intonational and facial cues signalling a speaker’s polite stance in requestive speech acts with controlled lexical and contextual materials. Thirty-six 3-year-old American English-speaking children performed a forced-choice decision task which investigated whether children at this age use pitch and/or facial cues to infer a speaker’s affective stance in either audio-only, visual-only or audio-visual presentation modalities, when lexical cues are controlled for. Results showed that (a) children at three years can infer a speaker’s polite stance equally well in all three conditions (audio-only, visual-only and audio-visual) and thereby (b) unlike previous research, in the present task both intonation and facial cues are equally strong cues in children’s understanding of a speaker’s polite stance in requestive speech acts. The authors discuss especially the implications of this early use of intonation to detect politeness, relating it to other previous research on children’s ability to infer meaning from pitch.
Audio Classification of Accelerating Vehicles

Many emerging technologies depend on effective vehicle recognition to understand the evolution of traffic patterns, predict individual vehicle behaviors, and monitor roadway usage. In vehicle identification and classification, computer vision and lidar-based technologies dominate, although these approaches involve extensive computing power or expensive detection schemes. Audio identification of vehicles is promising because it requires simple and cheap recording devices and much lower quantities of data than other technologies. Importantly, audio-based technologies don’t suffer from low visibility and are equally effective in low-light conditions. In this work, using careful feature extraction and various deep learning architectures, we demonstrate that vehicles can be effectively classified by recording vehicle acceleration with a cellphone.
Austenitic Stainless Steel Powders with Increased Nitrogen Content for Laser Additive Manufacturing

Nitrogen is used as an alloying element, substituting the expensive and allergenic element nickel, in austenitic stainless steels to improve their mechanical properties and corrosion resistance. The development of austenitic stainless steel powders with increased nitrogen content for laser additive manufacturing has recently received great interest. To increase nitrogen content in the austenitic steel powders (for example AISI 316L), two measures are taken in this study: (1) melting the steel under a nitrogen atmosphere, and (2) adding manganese to increase the solubility of nitrogen in the steel. The steel melt is then atomized by means of gas atomization (with either nitrogen or argon). The resulting powders are examined and characterized with regard to nitrogen content, particle size distribution, particle shape, microstructure, and flowability. It shows that about 0.2–0.3 mass % nitrogen can be added to the austenitic stainless steel 316L by adding manganese and melting the steel under nitrogen atmosphere. The particles are spherical in shape and very few satellite particles are observed. The steel powders show good flowability and packing density, therefore they can be successfully processed by means of laser powder bed fusion (L-PBF).
Political Economics versus Public Choice Two views of political economy in competition *

Political economics, like public choice, is defined as the economic analysis of politics. But its exponents claim that political economics is not a complement, but the successor of public choice, a new paradigm replacing the public-choice approach. We evaluate this claim of political economics in three fields: political business cycles, integration and secession, and constitutional political economy. We find that political economics has contributed substantially to the first, but little to the second and the third, where it sticks to the world of planning and benevolent dictators. Hence the public-choice paradigm emerges strengthened from its dispute with political economics.
Theme classification for texts using Naive Bayes and Neural Networks

The motivation behind this project is to find a way of classifying and summarizing the theme of a text. We do so by building a model that learns from labelled quotes to detect the theme of a sentence or group of sentences. In this paper, we focus on the classification of a few number of sentences at a time. It could then be extended to full text classification. Three models are tested and compared: Multinomial Naive Bayes, Dense Neural Network and Convolutional Neural Network. The Convolutional Neural Network outperforms the other two models and achieve good accuracy on both the training and test set, providing encouraging results in the matter of theme classification.
Active Learning to Solve Class Imbalance in Bird Species Classification

Bird species serve as an important indicator for habitat health in ecology. With the advance in computer vision through deep learning, this paper explores the application of convolution neural networks to bird species classification. This paper also explores the effect of active learning on unbalanced datasets to improve model performance through selective sampling of which images to label for training. Both maximum entropy and least confidence active learning improved the model performance to create a more robust network.
Final Report: combining signal transformation to learning models for human activity recognition

The task we worked on, human activity recognition, is a multi-class classification problem. The inputs are 3-axis accelerometer signals recorded when humans are doing one of six different activities (walking, walking upstairs, walking downstairs, sitting, standing, and laying). The output would be one of the six given activities. We first apply signal processing techniques including discrete Fourier transform (DFT), discrete cosine transform (DCT), wavelet transform (WT), high-pass filtering to the signals. Then feed the data into k-NN, SVM, gradient boosting models to output a predicted activity.
Contextual Program Token Prediction

We consider the problem of predicting a Python source code token given its context. Predictive software development tools can improve programmer productivity and help implement correct code. Although a given programming language has a known syntactic structure, developer tools can be difficult to implement, usually don’t formally model higher-level programming idioms, and may fail in the presence of syntactically invalid programs. Our goal is ultimately to explore the effectiveness of autocompletion suggestions and error detection without relying on a formal model of the programming language. We use a transformer [6] based architecture trained with the masked language model (MLM) objective over a dataset of publicly available open-source Python programs and evaluate it on the token prediction problem.
Effect of relative failure consequences in reliability based dual performance design

During its design life, a structure has to satisfy several performance requirements. It is now common to express all safety requirements, as well as some functionality requirements, in reliability based formats. Failure consequences (hence target reliabilities), structural behavior, capacity, loads and modeling uncertainties and thus the limit states themselves are different at these various performance levels, and the design must take these aspects into account. This paper develops optimal partial safety factors over a range of structural configurations for the design of partially prestressed sections in flexure corresponding to two performance levels — cracking and collapse — satisfying respective target reliabilities. Detailed numerical examples and derivations are presented. The role of relative failure consequences at the two performance levels in determining the governing limit state, the “balance point” where both limit states are active, and the possible implication on maintenance strategies, measured in terms of average rating factors for a prestressed section satisfying both performance requirements, is highlighted.
Methylphenidate substitution for methamphetamine addiction and implications for future randomized clinical trials: a unique case series

Abstract Background: Methamphetamine Use Disorders (MUD) pharmacotherapy research yields contradictory results. Objective: Our objective was to identify treatment outcome features of intravenous users of methamphetamine (IUM) treated by one healthcare provider using stable methylphenidate (MPH) substitution protocols over an extended interval. Method: We retrospectively examined a database of all intravenous MUD cases (24 patients) treated with short acting MPH and psychotherapy between 2007 and 2013 in a single outpatient addiction treatment facility. Results: Mean duration of treatment was 8 months (<1–31 months). Treatment resulted in full abstinence in 10 cases – maintained until the end of the observation period. Patients who stayed in treatment for more than a month and remained abstinent were more likely to be regular pattern users of methamphetamine (MA), female and characterized as having co-occurring psychiatric disorders that responded to treatment. These patients discontinued intravenous MA use and demonstrated stabilization of their health conditions. One adverse effect was reported by one patient who required emergency department treatment for accidental alcohol poisoning. Conclusion: Our clinical series indicated favorable outcomes and low dropout rates in patients with MUD who were treated several months with short acting MPH with individualized dosing. Adverse effects were rare. Keeping patients engaged in treatment in the first four weeks was critical for good clinical outcomes. Our findings indicated the need for the possible application of pre-selective criteria regarding patterns of use prior to initiation of (MPH) agonist pharmacotherapy of MA dependence. Prospective studies are needed to confirm or reject these clinical observations.
The Unreasonable Effectiveness of Adam on Cycles

Generative adversarial networks (GANs) are state of the art generative models for images and other domains. Training GANs is difficult, although not nearly as difficult as expected given theoretical results on finding a Nash (PPAD complete) and our understanding of dynamical systems. Several new algorithms and techniques have been proposed to stabilize GAN training, but nearly all employ Adam or RMSProp. In fact, training a GAN with SGD instead of Adam often fails. Here, we aim to understand how Adam circumvents some of the difficulties associated with GAN training. To this end, we study Adam in the context of a cycle problem. The cycle problem is a canonical equilibrium problem for which naive optimization approaches, e.g., simultaneous SGD, fail. Understanding how Adam works in this context helps reveal reasons for its unexpected success.
An Efficient Combined Active Noise Control and Noise Reduction Method for Hearing Aids

Recently, a reduced complexity integrated noise reduction and active noise control approach has been proposed for improving speech intelligibility of digital hearing aids. The complexity reduction over the filtered-x multichannel Wiener filter MWF (FxMWF) has been obtained using the dichotomous coordinate descent approach, but a slight performance reduction has been reported as well. In this paper, a new method based on an approximation of the autocorrelation matrix is proposed. It is shown that the proposed method has a much-reduced numerical complexity, better performance/cost and memory requirements than the competing MWF based algorithms. The performance for various parameters is confirmed by different measurement metrics for a wide range of SNR values of the noisy speech.