Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sequential Ultrasound and Electrical Capacitance Process Tomography System

Abstract A novel concept for the fusion of Ultrasound Reflection Tomography (URT) and Electrical Capacitance Tomography (ECT) for industrial process applications is presented. The method is intended to combine the strengths of both principles while reducing their respective disadvantages. ECT is sensitive to bulk rather than to phase boundaries and suffers from blurred images. URT is able to detect phase boundaries with good resolution but is not able to reconstruct absolute material parameters. In our approach URT and nonlinear Gauss-Newton based ECT are sequentially coupled by using the URT results as a natural regularization for the ECT reconstruction algorithm. Two different methods are investigated that can also be simultaneously used. Adaptive mesh grouping uses data from the ultrasonic measurements to merge regions without phase boundaries before performing the ECT inversion, thus reducing the number of unknowns. For the second method a weighted regularization matrix is composed based on the URT data, leading to a physically meaningful regularization. The methods are tested with simulated gas-liquid two-phase flow profiles. The achieved results show a significant improvement compared to stand-alone ECT.
Meta-Learning for Hierarchical Classification and Applications in Bioinformatics

Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.
Urbanization , Fast-food Restaurants , and Individual Agency : An Ecological and Life Course Analysis of Body Weight Changes in Chinese Youth *

The conventional trickle-down model of community effects on body weight status is theoretically flawed. A better understanding of the association between community contextual effects and youth‟s body weight status is needed to improve the existing theory and inform public health policy. We propose to integrate an ecological approach with a life course perspective and introduce human agency and its dynamic interactions with contextual factors into our model. Drawing upon longitudinal and multilevel data, we study body weight changes captured by both general and central obesity measures in Chinese youth. We employ a difference-in-difference model to adjust for “pretreatment heterogeneity bias. ” We examine three dimensions of individual agency, including family resources, health knowledge, and selfperceived body shape, and their interactions with two important community factors, urbanization and fast-food restaurants, thereby adjusting for “treatment effect heterogeneity bias. ” Our preliminary results highlight complex patterns of body weight change as Chinese youth transitioned from early childhood to late adolescence.
Computers and Information in Engineering Conference IDETC / CIE 2014 August 17 – August 20 , 2014 , Buffalo , NY , USA DETC 2014-34383 Efficient Microstructural Design for Additive Manufacturing

A salient feature of additive manufacturing is that the cost of fabrication, to a large extent, is independent of geometric complexity. This opens new opportunities for custom-designing parts both at a macro and micro-level. An elegant and powerful method of designing custom-parts is through topology optimization. While the theory of topology optimization is well understood, current methods can be extraordinarily expensive. The focus of this paper is on efficient microstructural topology optimization for 3d-printing. In particular, the computational bottle-necks in microstructural topology optimization are identified. Then, a framework that not only eliminates these bottle-necks, but incorporates other significant improvements, is developed. The framework is demonstrated through numerical experiments involving microstructures with millions of degrees of freedom, using multi-core CPUs and NVidia GPU.
Language Recognition for Telephone and Video Speech: The JHU HLTCOE Submission for NIST LRE17

This paper presents our newest language recognition systems developed for NIST LRE17. For this challenging limited data multidomain task, we were able to get very good performance with our state-of-the-art DNN senone and bottleneck joint i-vector systems by effective utilization of all of the available training and development data. Data augmentation techniques were very valuable for this task, and our discriminative Gaussian classiﬁer combined with naive fusion used all of the development data for system design rather than holding some out for separate back-end training. Finally, our newest research with discriminatively-trained DNN embeddings allowed us to replace i-vectors with more powerful x-vectors to further improve language recognition accuracy, resulting in very good LRE17 performance for this single system, our JHU HLTCOE site fusion primary submission, and the JHU MIT team submission.
Ammonia and Carbon Dioxide Emissions of Three Laying-Hen Housing Systems as Affected by Manure Accumulation Time

Laying-hen housing design and management are the most significant factors affecting the generation and release of gaseous ammonia to the atmosphere. Transitioning the hen housing type from traditional high-rise (where manure is stored within the house for about one year) to modern manure-belt style (where manure is removed every 1 to 4 d and placed into long-term storage) has significantly improved in-barn air quality and reduced farm-level ammonia emissions. As a direct result of the advantages, 100% of new construction for U.S. egg production incorporates manure-belt systems that regularly remove manure from the houses. However, manure-belt system designs (e.g., active vs. passive drying of manure on the belt) and management practices (e.g., frequency of manure removal) vary considerably across the industry, leading to large variations in system performance and efficiency. Thus, questions remain about the optimal design and management of manurebelt facilities to achieve the desired reductions in ammonia emissions. As part of the Coalition for a Sustainable Egg Supply (CSES) project, 27 months of continually monitored environmental data (including ammonia and greenhouse gas emissions) were collected from three hen-housing systems: a conventional cage house (CC) with a 200,000-hen capacity, an enriched colony house (EC) with a 50,000-hen capacity, and an aviary house (AV) with a 50,000-hen capacity. All three hen houses were located on the same farm and were populated with Lohmann white hens of the same age. All houses were equipped with manure-drying air ducts above the manure belts using recirculated indoor air (flow rate ranging from 0.46 to 1.49 m3 h-1 hen-1). Manure on the belts was completely removed every 3 to 4 d. Average daily house-level ammonia (NH3) and carbon dioxide (CO2) emissions as affected by manure accumulation time (MAT, from 1 to 4 d) on the manure belts were analyzed. Results indicate that for all three types of houses, NH3 emission rates (g hen-1 d-1) were significantly lower for MAT of 1 and 2 d (mean Â±SE of 0.061 Â±0.005 and 0.064 Â±0.004, respectively) than for MAT of 3 and 4 d (0.085 Â±0.005 and 0.115 Â±0.007, respectively) (p < 0.001). Emissions of CO2 (g hen-1 d-1) were not significantly affected by MAT, averaging 67.8 Â±5.7 for CC, 74.7 Â±10.2 for EC, and 75.9 Â±10.5 for AV. Estimating annual NH3 emissions from each type of house revealed that shortening the manure removal interval from every 4 d to every 2 d has the potential of reducing NH3 emissions by 27% for the CC and EC houses and by 19% for the AV house. However, verification of the potential reductions is needed.
Computational Models of Higher Cognition

Models in cognitive science span all three of Marr’s (1982) levels (see Holyoak & Morrison, Chapter 1). Normative systems (see Chater & Oaksford, Chapter 2), including the Bayesian framework (see Griffi ths et al., Chapter 3), address the level of computational theory; neural models (see Morrison & Knowlton, Chapter 6) are specifi ed at the implementation level; and informationprocessing models, or process models (perhaps the most common type of model in cognitive science), are specifi ed at the level of representation and algorithm. Process models of higher cognition come in three basic varieties: traditional symbolic models, traditional connectionist models, and symbolicconnectionist models. Th is chapter reviews the basic representational and processing assumptions embodied in each of these approaches and considers the strengths and limitations of each.
SEMANTIC TAGGING FOR DOCUMENTS USING 'S HORT TEXT ' INFORMATION

Tagging documents with relevant and comprehensive keywords offer invaluable assistance to the readers to quickly overview any document. With the ever increasing volume and variety of the documents published on the internet, the interest in developing newer and successful techniques for annotating (tagging) documents is also increasing. However, an interesting challenge in document tagging occurs when the full content of the document is not readily accessible. In such a scenario, techniques which use “short text”, e.g., a document title, a news article headline, to annotate the entire article are particularly useful. In this paper, we propose a novel approach to automatically tag documents with relevant tags or key-phrases using only “short text” information from the documents. We employ crowd-sourced knowledge from Wikipedia, Dbpedia, Freebase, Yago and similar open source knowledge bases to generate semantically relevant tags for the document. Using the intelligence from the open web, we prune out tags that create ambiguity in or “topic drift” from the main topic of our query document. We have used real world dataset from a corpus of research articles to annotate 50 research articles. As a baseline, we used the full text information from the document to generate tags. The proposed and the baseline approach were compared using the author assigned keywords for the documents as the ground truth information. We found that the tags generated using proposed approach are better than using the baseline in terms of overlap with the ground truth tags measured via Jaccard index (0.058 vs. 0.044). In terms of computational efficiency, the proposed approach is at least 3 times faster than the baseline approach. Finally, we qualitatively analyse the quality of the predicted tags for a few samples in the test corpus. The evaluation shows the effectiveness of the proposed approach both in terms of quality of tags generated and the computational time.
On the time decay of solutions for non‐simple elasticity with voids

In this work we consider the non‐simple theory of elastic material with voids and we investigate how the coupling of these two aspects of the material affects the behavior of the solutions. We analyze only two kind of different behavior, slow or exponential decay. We introduce four different dissipation mechanisms in the system and we study, in each case, the effect of this mechanism in the behavior of the solutions.
Frequency Upconversion in Rare Earth Ions

A large number of solid host materials have been considered to study the consequence of host materials on the lasing properties of the active ions since the development of the solid state lasers. With increasing interest in the photonic devices the appeal for employing photonic glasses has grown much interest because they may be chemically and mechanically stable hence being ideal for substituting the crystalline systems in many conditions. Rare earth doped solid materials are of great importance due to their wide range of applications in lasers, temperature sensors, optical amplifiers, etc. among other devices [Savage, 1987; Digonnet, 1993; Jackson, 2003; Rai, 2007]. Study of the spectroscopic properties of rare earth doped systems is very important from several points of view. The effect of environment on the energy levels, variations in their emission and absorption characteristics, upconversions observed in near infrared (NIR), visible pumping etc. can easily be studied from these. The longer lifetime of the energy levels is one of the prime requirements for lasing. Lasing in these rare earth ions occur due to the transition between the spin-orbit splitted components of the same state or between the states arising from the unfilled (4f) electronic configuration, which are usually forbidden as per electric dipole selection rules. The electric dipole transitions between states of the 4f configuration for free ions are strictly forbidden by the parity rule. The transitions can take only due to the mixing of states of opposite parity in the wave functions. The optical properties of rare earth ions doped solid host materials depend on the chemical composition of the host materials [Reisfield, 1973; Babu et al., 2000; Dumbaugh, 1992; Rai, 2006; Dieke et al., 1968; Ebendroff, 1996]. Rare earth ions are found to be very much sensitive to small changes in chemical surroundings. On the other hand network modifiers affect the local environment around the fluorescent ions and thereby its optical properties. Therefore, it becomes essential to get the information about the symmetry and bonding of the probe ion and how they change their optical properties with chemical composition of the solid host materials and also from site to site variations. In the solid host materials with higher phonon frequencies, it is not easy to get efficient infrared and visible emission even in the Er3+, Nd3+, Ho3+, Tm3+, etc. The effect of host materials on the optical properties of rare earth ions introduced as doping along with compositional changes of modifiers have been studied by different workers [Babu et al., 2000; Rai et al., 2006; Nageno et al., 1994; Tripathi et al., 2006; Rai, 2010; Rai et al., 2008; Sai Sudha et al., 1996; Lin et al., 2002; Hussain et al., 2000; Nachimuthu et al., 1997; Tripathi et al., 2006]. They concluded that
Wavelet methods to eliminate resonances in the Galerkin-truncated Burgers and Euler equations

It is well known that solutions to the Fourier-Galerkin truncation of the inviscid Burgers equation (and other hyperbolic conservation laws) do not converge to the physically relevant entropy solution after the formation of the first shock. This loss of convergence was recently studied in detail in [S. S. Ray et al., Phys. Rev. E 84, 016301 (2011)], and traced back to the appearance of a spatially localized resonance phenomenon perturbing the solution. In this work, we propose a way to remove this resonance by filtering a wavelet representation of the Galerkin-truncated equations. A method previously developed with a complex-valued wavelet frame is applied and expanded to embrace the use of real-valued orthogonal wavelet basis, which we show to yield satisfactory results only under the condition of adding a safety zone in wavelet space. We also apply the complex-valued wavelet based method to the 2D Euler equation problem, showing that it is able to filter the resonances in this case as well.
Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data

Many research questions in visual perception involve determining whether stimulus properties are represented and processed independently. In visual neuroscience, there is great interest in determining whether important object dimensions are represented independently in the brain. For example, theories of face recognition have proposed either completely or partially independent processing of identity and emotional expression. Unfortunately, most previous research has only vaguely defined what is meant by “independence,” which hinders its precise quantification and testing. This article develops a new quantitative framework that links signal detection theory from psychophysics and encoding models from computational neuroscience, focusing on a special form of independence defined in the psychophysics literature: perceptual separability. The new theory allowed us, for the first time, to precisely define separability of neural representations and to theoretically link behavioral and brain measures of separability. The framework formally specifies the relation between these different levels of perceptual and brain representation, providing the tools for a truly integrative research approach. In particular, the theory identifies exactly what valid inferences can be made about independent encoding of stimulus dimensions from the results of multivariate analyses of neuroimaging data and psychophysical studies. In addition, commonly used operational tests of independence are re-interpreted within this new theoretical framework, providing insights on their correct use and interpretation. Finally, we apply this new framework to the study of separability of brain representations of face identity and emotional expression (neutral/sad) in a human fMRI study with male and female participants. Author Summary A common question in vision research is whether certain stimulus properties, like face identity and expression, are represented and processed independently. We develop a theoretical framework that allowed us, for the first time, to link behavioral and brain measures of independence. Unlike previous approaches, our framework formally specifies the relation between these different levels of perceptual and brain representation, providing the tools for a truly integrative research approach in the study of independence. This allows to identify what kind of inferences can be made about brain representations from multivariate analyses of neuroimaging data or psychophysical studies. We apply this framework to the study of independent processing of face identity and expression.
Visual Comparative Case Analytics

Criminal Intelligence Analysis (CIA) faces a challenging task in handling high-dimensional data that needs to be investigated with complex analytical processes. State-of-the-art crime analysis tools do not fully support interactive data exploration and fall short of computational transparency in terms of revealing alternative results. In this paper we report our ongoing research into providing the analysts with such a transparent and interactive system for exploring similarities between crime cases. The system implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed Visual Analytics (VA) workflow iteratively supports the interpretation of obtained clustering results, the development of alternative models, as well as cluster verification. The visualizations offer a usable way for the analyst to provide feedback to the system and to observe the impact of their interactions
Modeling and Dynamics of HTS Motors for Aircraft Electric Propulsion

In this paper, the methodology of how a dynamic model of a conventional permanent magnet synchronous motor (PMSM) may be modified to model the dynamics of a high-temperature superconductor (HTS) machine is illustrated. Simulations of a typical PMSM operating under room temperature conditions and also at temperatures when the stator windings are superconducting are compared. Given a matching set of values for the stator resistance at superconducting temperature and flux-trapped rotor field, it is shown that the performance of the HTS PMSM is quite comparable to a PMSM under normal room temperature operating conditions, provided the parameters of the motor are appropriately related to each other. From these simulations, a number of strategies for operating the motor so as to get the propeller to deliver thrust with maximum propulsive efficiency are discussed. It is concluded that the motor–propeller system must be operated so as to deliver thrust at the maximum propulsive efficiency point. This, in turn, necessitates continuous tracking of the maximum propulsive efficiency point and consequently it is essential that the controller requires a maximum propulsive efficiency point tracking (MPEPT) outer loop.
Motivation-oriented scenario-based gamification design method using the user requirements notation modeling

Gamification, defined as the use of game design elements in non-game contexts, has been a trending in both academic and in a variety of industrial domains for around half a decade. A number of empirical studies have showed the usefulness and effectiveness of applying gamification to enhance people’s motivation and engagement in different activities. On the other hand, multiple design frameworks and methods have been proposed to guide the practice of gamification design and development. However, one of the key issues for gamification, which is being only focusing on using less essential game elements on motivating instead of providing genuinely meaningful gameful experiences, has not yet been addressed fully. Furthermore, the existing gamification design frameworks contribute limitedly in providing constructive and easily applicable design methods or tools that to facilitate the motivation and behavior analysis and ideation of gamification projects. Therefore, in this study, the author proposes the Motivationoriented Scenario-based Gamification Design method in order to address the issues mentioned above. The method is an adaption of the User Requirement Notation to the modeling of system goals, gamification motivations, system user behaviors, existing gameplay, and the mapping between them. The new method aims to provide not only an innovative way of specifying gamification designs on the mechanic and dynamic levels, but also a possibly insightful perspective towards improving gamification experiences and effectiveness with meaningful plays. In addition, a case study is conducted by applying the proposed method to a real-life language learning system, WordDive, with the method validated and evaluated via an interview with an expert from the WordDive company.