Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison on Microstructure and Properties of Stainless Steel Layer Formed by Extreme High-Speed and Conventional Laser Melting Deposition

A new technology named extreme high-speed laser melting deposition is presented in this paper. The deposition speed of this technology is as high as 30m/min, which can promote the deposition efficiency from 0.09m2/h to 0.54m2/h. In addition, surface of EHLMD layer is smoother which can save part of the machining process after deposition and promotes the production efficiency by 6 times. AISI 431 martensitic stainless steel layers were fabricated on 27SiMn substrate by conventional laser melting deposition and extreme high speed laser melting deposition. The macroscopic morphology, microstructure, microhardness and electrochemical corrosion resistance of the two kinds of layers were characterized. The results show that the microstructures of two kinds of layers comprise of martensite dendrite, retained austenite and continuous inter-dendritic eutectic phases. Although results of the XRD test indicate that there are more retained austenite in the EHLMD layer, the microhardnesses of the two kinds of layers are basically the same, in the range of 600HV to 720HV. Corrosion resistance of the EHLMD layer is better than that of the LMD layer because of the higher Cr content and more uniform alloying elements distribution. Therefore, EHLMD can be considered as an effective alternative to electroplating and applied for production and repair of shaft parts in the manufacturing field.
Detection of Abnormal Regions on Temporal Subtraction Images based on CNN

Recently, visual screening based on CT images become the useful tool in the medical diagnosis. However, due to the increasing data volumes and the computational complexity of the algorithms, image processing technique for the high quality visual screening is still required. To this end, some computer aided diagnosis (CAD) algorithms are proposed. Meanwhile, cancer is a leading cause of death in the world. Detection of cancer region in CT images is the most important task to early detection and early treatment. We design and develop a framework combining convolutional neural networks (CNN) with temporal subtraction techniques-based non-rigid image registration algorithm. However, conventional CNN has the issue that as the layers deeper, global information close to input images is lost. Therefore, we add a skip connection to conventional CNN. By adding a new skip connection, the proposed CNN network maintains the global information without loss of important features of input image. All in all, our proposed method can be built into three main steps; i) pre-processing for image segmentation, ii) image matching for registration, and iii) classification of abnormal regions based on machine learning algorithms. We perform our proposed technique to 25 thoracic MDCT sets and obtain the AUC score of 0.951.
The contrasting roles of shape in human vision and convolutional neural networks

Convolutional neural networks (CNNs) were inspired by human vision and, in some settings, achieve a performance comparable to human object recognition. This has lead to the speculation that both systems use similar mechanisms to perform recognition. In this study, we conducted a series of simulations that indicate that there is a fundamental difference between human vision and vanilla CNNs: while object recognition in humans relies on analysing shape, these CNNs do not have such a shape-bias . We teased apart the type of features selected by the model by modifying the CIFAR-10 dataset so that, in addition to containing objects with shape, the images concurrently contained non-shape features, such as a noise-like mask. When trained on these modiﬁed set of images, the model did not show any bias towards selecting shapes as features. Instead it relied on whichever feature allowed it to perform the best prediction – even when this feature was a noise-like mask or a single predictive pixel amongst 50176 pixels.
Reduced phase stability and faster formation/dissociation kinetics in confined methane hydrate

Significance Large amounts of methane hydrate—which consists of water forming molecular ice cages around methane molecules—are trapped in the porosity of rocks and seafloors. The impact of this so-called “burning ice” on our environment remains unclear (methane is a potent greenhouse gas), especially the parameters driving its phase stability and kinetics. Here, using molecular modeling, we identify the mechanisms involved in the formation/dissociation of confined methane hydrate. The phase stability of confined methane hydrate is shown to be restricted to a narrower temperature/pressure range with faster underlying formation/dissociation kinetics. Such facilitated freezing, which helps rationalize experimental observations, is described using a mesoscale thermodynamic approach of the nucleation mechanism. The mechanisms involved in the formation/dissociation of methane hydrate confined at the nanometer scale are unraveled using advanced molecular modeling techniques combined with a mesoscale thermodynamic approach. Using atom-scale simulations probing coexistence upon confinement and free energy calculations, phase stability of confined methane hydrate is shown to be restricted to a narrower temperature and pressure domain than its bulk counterpart. The melting point depression at a given pressure, which is consistent with available experimental data, is shown to be quantitatively described using the Gibbs–Thomson formalism if used with accurate estimates for the pore/liquid and pore/hydrate interfacial tensions. The metastability barrier upon hydrate formation and dissociation is found to decrease upon confinement, therefore providing a molecular-scale picture for the faster kinetics observed in experiments on confined gas hydrates. By considering different formation mechanisms—bulk homogeneous nucleation, external surface nucleation, and confined nucleation within the porosity—we identify a cross-over in the nucleation process; the critical nucleus formed in the pore corresponds either to a hemispherical cap or to a bridge nucleus depending on temperature, contact angle, and pore size. Using the classical nucleation theory, for both mechanisms, the typical induction time is shown to scale with the pore volume to surface ratio and hence the pore size. These findings for the critical nucleus and nucleation rate associated with such complex transitions provide a means to rationalize and predict methane hydrate formation in any porous media from simple thermodynamic data.
Multimodal Event Knowledge in Online Sentence Comprehension: the Influence of Visual Context on Anticipatory Eye Movements

People predict incoming words during online sentence comprehension based on their knowledge of real-world events that is cued by preceding linguistic contexts. We used the visual world paradigm to investigate how event knowledge activated by an agent-verb pair is integrated with perceptual information about the referent that fits the patient role. During the verb time window participants looked significantly more at the referents that are expected given the agent-verb pair. Results are consistent with the assumption that event-based knowledge involves perceptual properties of typical participants. The knowledge activated by the agent is compositionally integrated with knowledge cued by the verb to drive anticipatory eye movements during sentence comprehension based on the expectations associated not only with the incoming word, but also with the visual features of its referent.
An ACT-R approach to investigating mechanisms of performance-related changes in an interrupted learning task

Learning constitutes an essential part of human experience over the life course. Independent of the domain, it is characterized by changes in performance. But what cognitive mechanisms are responsible for these changes and how do situational features affect the dynamics? To inspect that in more detail, this paper introduces a cognitive modeling approach that investigates performance-related changes in learning situations. It leverages the cognitive architecture ACT-R to model learner behavior in an interrupted learning task in two conditions of task complexity. Comparisons with the original human dataset indicate a good fit in terms of both accuracy and reaction times. Although interruption effects are more obvious in the human data, they are prevalent as well in the model. Furthermore, the model can map the learning effects, particularly in the easy task condition. Based on the existing mapping of ACT-R module activity with fMRI data, simulated neural activity is computed to investigate underlying cognitive mechanisms in more detail. The resulting evidence connects learning and interruption effects in both task conditions with activation-related patterns to explain changes in performance.
Graph-KD: Exploring Relational Information for Knowledge Discovery

This paper presents Graph-KD, a tool to navigate through large relational knowledge sources. Graph-KD provides methods to understand relationships between concepts using open discovery, closed discovery and knowledge inference. The purpose of the tool is the support of biomedical knowledge discovery and exploration. It is primarily intended to be used by medical researchers and presents a use case involving millions of relations from UMLS. Graph-KD is able to process even large graphs efficiently and can be accessed via a webinterface (http://biomedical.dfki.de/graph-kd).
EARSHOT: A minimal network model of human speech recognition that operates on real speech

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), we experience phonetic constancy and typically perceive what a speaker intends. Models of human speech recognition have sidestepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, automatic speech recognition powered by deep learning networks have allowed robust, real-world speech recognition. However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support human speech recognition. We developed a simple network that borrows one element from automatic speech recognition (long short-term memory nodes, which provide dynamic memory for short and long spans). This allows the network to learn to map real speech from multiple talkers to semantic targets with high accuracy. Internal representations emerge that resemble phonetically-organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of human speech recognition.
Multi-Query Optimization in RDF Q/A System

In this paper, we present an optimization to answer a question with multiple queries by detecting all common subqueries of that question. Moreover, we apply mutual information in reducing ambiguity of queries of a question to improve the quality of common subqueries. Finally, to improve the accuracy of SPARQL query generated, we evaluate the semantic importance of each word in a question via TF-IDF during the generating queries process. Experiments show that our proposal ourperforms those single query execution
Modeling individual performance in cross-situational word learning

What mechanisms underlie people’s ability to use cross- situational statistics to learn the meanings of words? Here we present a large-scale evaluation of two major models of cross-situational learning: associative (Kachergis, Yu, & Shiffrin, 2012a) and hypothesis testing (Trueswell, Medina, Hafri, & Gleitman, 2013). We fit each model individually to over 1500 participants across seven experiments with a wide range of conditions. We find that the associative model better captures the full range of individual differences and conditions when learning is cross-situational, although the hypothesis testing approach outperforms it when there is no referential ambiguity during training.
How to debate a border: supporting infrastructure publics through communication system design

While crucial infrastructures in the United States and similar countries are increasingly in conditions of crisis and decay, people living in these countries often lack a shared understanding of these systems from which to imagine the kind of radical change that theorists such as Lauren Berlant have argued is needed. As recent political controversies make clear, this lack of shared understanding extends to national infrastructures such as borders and points of entry. However, like all infrastructures, these are complex sociotechnical objects that challenge simple understanding. Collectively debating future infrastructure is complex and feels, in the present moment, insurmountable. At the same time, reimagining these systems for more equitable futures is crucial. Drawing on theories from a range of fields including anthropology and design, this paper considers the communication systems that might support broad groups with the shared understandings needed to debate the futures of infrastructures. It first describes a series of events in early 2017 related to the increased search and seizure of personal electronic devices at United States airports. It then analyzes the online comments left in response to articles about these events, arguing that the comments demonstrate both the tracing of existing infrastructures and the imagining of new infrastructures. Implications from this analysis include the potential of including speculation as a value that could be formally recognized by evaluation systems and the need to carefully consider how expert knowledge about present infrastructures is communicated alongside more future-oriented commentary.
Three-dimensional Reconstruction of Optical Coherence Tomography Images of Esophagus

The combination of optical coherence tomography (OCT) and endoscope can take images of the body tissues for clinical diagnosis. OCT images are difficult to photograph with regular imaging devices, such as the esophagus and gastrointestinal tract. Three-dimensional reconstruction of the two-dimensional sequence images can help the doctor understand the clinical situation of the body tissue, therefore improve the accuracy of diagnosis. In this paper, Ray Casting method is used to reconstruct three-dimensional image of OCT cross-section images of guinea pig esophagus. Preprocessing including image segmentation, coordinate transformation, angle correction is used to achieve a better result in three-dimensional reconstruction. The performance of the algorithm is discussed and can achieve the same effect as what of commercial software.
A Geometric Interpretation of Feedback Alignment

Feedback alignment has been proposed as a biologically plausible alternative to error backpropagation in multi-layer per-ceptrons. However, feedback alignment currently has not been demonstrated to scale beyond relatively shallow network topologies, or to solve cognitively interesting tasks such as high-resolution image classiﬁcation. In this paper, we provide an overview of feedback alignment and review suggested mappings of feedback alignment onto biological neural networks. We then discuss a novel geometric interpretation of the feedback alignment algorithm that can be used to analyze its limitations. Finally, we discuss a series of experiments in which we compare the performance of backpropagation and feedback alignment. We hope that these insights can be used to systematically improve feedback alignment under biological constraints, which may allow us to build better models of learning in cognitive systems.
Predicting Relations between RDF Entities by Multi-Order Interaction Neural Network

In this paper, we present a multi-order interaction neural network (MINN) for relation prediction, which can calculate the 2nd-order and 3rd-order feature interactions and automatically combine them within linear complexity. The proposed MINN contains three layers: embedding, multi-order interaction pooling (MI-Pooling), and Deep & Wide layer. In embedding layer, we convert sparse input features into dense representations to narrow the size of features. In MI-Pooling layer, the 2nd-order and 3rd-order feature interactions are calculated and combined in linear time. The results of MI-Pooling will be input into a deep neural network (DNN) for learning nonlinear feature in the last Deep & Wide layer, which will output the ﬁnal prediction value. The experiments evaluating on two well-known datasets, WN18 and FB15k, show that MINN eﬃciently performs better than most of the state-of-the-art sparse models in relation prediction.
Querying Enterprise Knowledge Graph With Natural Language

Conversational interface to enterprise knowledge graph with large amount of data has become popular recently for AI powered applications. Natural language understanding (NLU) techniques not only provide user-friendly interaction, but also greatly boost productivity by eliminating the need of learning structured query languages (such as SPARQL) required to access knowledge databases. We present Yugen as a first step to conversational AI that answers user queries using knowledge graph in the enterprise domain. Yugen is a deep learning based NLU & QA enginecurrently and serving the product of Microsoft Enterprise Graph on Azure cloud. The development of Yugen has tackled many enterprise domain challenges, such as data cold start problem, detecting domain-specific query intents, key entities and their relationships, as well as generating correct graph queries and restating the query results in natural language back to users.
Spam elimination and bias correction : ensuring label quality in crowdsourced tasks.

Crowdsourcing is proposed as a powerful mechanism for accomplishing large scale tasks via anonymous workers online. It has been demonstrated as an effective and important approach for collecting labeled data in application domains which require human intelligence, such as image labeling, video annotation, natural language processing, etc. Despite the promises, one big challenge still exists in crowdsourcing systems: the difficulty of controlling the quality of crowds. The workers usually have diverse education levels, personal preferences, and motivations, leading to unknown work performance while completing a crowdsourced task. Among them, some are reliable, and some might provide noisy feedback. It is intrinsic to apply worker filtering approach to crowdsourcing applications, which recognizes and tackles noisy workers, in order to obtain high-quality labels. The presented work in this dissertation provides discussions in this area of research, and proposes efficient probabilistic based worker filtering models to distinguish varied types of poor quality workers. Most of the existing work in literature in the field of worker filtering either only concentrates on binary labeling tasks, or fails to separate the low quality workers whose label errors can be corrected from the other spam workers (with label errors which cannot be corrected). As such, we first propose a Spam Removing and De-biasing Framework (SRDF), to deal with the worker filtering procedure in labeling tasks with numerical label scales. The developed framework can detect spam workers and biased workers separately. The biased workers are defined as those who show tendencies of providing higher (or lower) labels than truths, and their errors are able to be corrected. To tackle the biasing problem, an iterative bias detection approach is introduced to recognize the biased workers. The spam filtering algorithm proposes to eliminate three types of spam workers, including random spammers who provide random labels, uniform spammers who give same labels for most of the items, and sloppy workers who offer low accuracy labels. Integrating the spam filtering and bias detection approaches into aggregating algorithms, which infer truths from labels obtained from crowds, can lead to high quality consensus results. The common characteristic of random spammers and uniform spammers is that they provide useless feedback without making efforts for a labeling task. Thus, it is not necessary to distinguish them separately. In addition, the removal of sloppy workers has great impact on the detection of biased workers, with the SRDF framework. To combat these problems, a different way of worker classification is presented in this dissertation. In particular, the biased workers are classified as a subcategory of sloppy workers. Finally, an ITerative Self Correcting - Truth Discovery (ITSC-TD) framework is then proposed, which can reliably recognize biased workers in ordinal labeling tasks, based on a probabilistic based bias detection model. ITSC-TD estimates true labels…
Comparing Gated and Simple Recurrent Neural Network Architectures as Models of Human Sentence Processing

The Simple Recurrent Network (SRN) has a long tradition in cognitive models of language processing. More recently, gated recurrent networks have been proposed that often outperform the SRN on natural language processing tasks. Here, we investigate whether two types of gated networks perform better as cognitive models of sentence reading than SRNs, beyond their advantage as language models.This will reveal whether the filtering mechanism implemented in gated networks corresponds to an aspect of human sentence processing.We train a series of language models differing only in the cell types of their recurrent layers. We then compute word surprisal values for stimuli used in self-paced reading, eye-tracking, and electroencephalography experiments, and quantify the surprisal values' fit to experimental measures that indicate human sentence reading effort.While the gated networks provide better language models, they do not outperform their SRN counterpart as cognitive models when language model quality is equal across network types. Our results suggest that the different architectures are equally valid as models of human sentence processing.
Expanding Learners as Designers (LasD) in IT

The trend to actively include learners as designers in higher education continues to positively improve learning outcomes. This paper expands this application in a Learners as Designers (LasD) process in an upper level undergraduate course in organizational networking and communication tools. Students participated in selection of tools, as well as associated readings and assessment rubrics. Nineteen students participated in the study. A survey comprising open-ended questions was distributed, and a follow-up interview was conducted with select participants. A qualitative content analysis was performed to understand how students think about being an active part of the course development. Initial results indicate that students appreciated having a voice in the design process but found certain parts of the implementation needed more structure.
Control of Upper Limb Motions by Combinations of Basic Muscle Synergies

Motor system relies on the recruitment of motor modules to perform various movements. Muscle synergies are the modules used by the central nervous system to simplify the control of complex motor tasks. In this paper, we verified the hypothesis that the control of upper limb motions can be realized by combinations of basic muscle synergies extracted from electromyography signals of simple upper limb motions. Muscle synergies and corresponding activation coefficients were extracted via non-negative matrix factorization from three basic and five complex motions. Similarities across basic synergies and complex synergies were compared. We found that the structure of muscle synergies from complex tasks were similar to the corresponding basic synergies and muscle synergies from basic tasks can be used to reconstruct muscle patterns of the complex tasks. This study demonstrates that different kinds of upper limb motions can be accomplished by the combinations of basic muscle synergies modulated by different activation coefficients.