Miracle-dz/UniFAR-FTUs huggingface dataset:
Inter-Subject Differences of Head Related Transfer Functions

Examination of the characteristics of individual Head Related Transfer Functions (HRTF) requires the use of a specific measurement setup and procedure. This paper describes the design of a far field HRTF measurement setup in which the subject is continuously rotated towards a sound source placed at a certain elevation angle. Currently the system allows for the acquisition of HRTF pairs (left and right ear) at 370 source positions in the surrounding space. The test signal is recorded by two miniature microphones placed at the entrance to the blocked ear canal. Completing of a set of HRTFs at all spatial positions takes about 45 minutes for one subject. The present measurement setup was evaluated by acquiring HRTFs with the use of an acoustic manikin and with the participation of subjects. The paper discusses inter-subject differences among measured HRTFs, both in horizontal and vertical planes, with reference to the results obtained with the use of the acoustic manikin.
Incremental Graph Pattern Based Node Matching

Graph Pattern based Node Matching (GPNM) is to find all the matches of the nodes in a data graph GD based on a given pattern graph GP. GPNM has become increasingly important in many applications, e.g., group finding and expert recommendation. In real scenarios, both GP and GD are updated frequently. However, the existing GPNM methods need to perform a new GPNM procedure from scratch to deliver the node matching results based on the updated GP and updated GD, which consumes much time. Therefore, there is a pressing need for a novel method to efficiently deliver the node matching results. In this paper, we propose a novel INCremental GPNM method called INC-GPNM, where we first build up indices to incrementally maintain the shortest path length range between different label types in GD, and then identify the affected parts of GD in GPNM including nodes and edges w.r. t. the updates of GP and GD. Moreover, based on the index structure and our novel search strategies, INC-GPNM can efficiently deliver node matching results taking the updates of GP and GD as input, and can greatly save the query processing time with improved time complexity. The extensive experiments on five real-world social graphs demonstrate that our method greatly outperforms the state-of-the-art GPNM method in efficiency.
Multi-grained Attention Network for Aspect-Level Sentiment Classification

We propose a novel multi-grained attention network (MGAN) model for aspect level sentiment classification. Existing approaches mostly adopt coarse-grained attention mechanism, which may bring information loss if the aspect has multiple words or larger context. We propose a fine-grained attention mechanism, which can capture the word-level interaction between aspect and context. And then we leverage the fine-grained and coarse-grained attention mechanisms to compose the MGAN framework. Moreover, unlike previous works which train each aspect with its context separately, we design an aspect alignment loss to depict the aspect-level interactions among the aspects that have the same context. We evaluate the proposed approach on three datasets: laptop and restaurant are from SemEval 2014, and the last one is a twitter dataset. Experimental results show that the multi-grained attention network consistently outperforms the state-of-the-art methods on all three datasets. We also conduct experiments to evaluate the effectiveness of aspect alignment loss, which indicates the aspect-level interactions can bring extra useful information and further improve the performance.
3D Structure Line Start Synchronous Reluctance Motor Design Based on Selective Laser Melting of 3D Printing.

Due to the features of high efficiency, high torque, and without using permanent magnets, the synchronous reluctance motors (SynRMs) have become popular in industry. Such advantages are contributed by the design of the rotor barriers and ribs that the flux flow path are arranged as shown in Fig. 1(a). However, the requirements of motors usually are not just high efficiency but some other more operation capabilities such as low vibration and easy start. Unfortunately, as compared with the industrial most commonly used induction motors (IMs), position sensors are additionally required for initiating starting of SynRMs [1], [2]. Moreover, the barriers and ribs of SynRMs may increase the risk of structure deformation as rotation. Hence, this paper proposes a novel design of applying the 3D bionic structure in the SynRMs with new flux path design to solve the said problems. Further, the additive manufacturing (3D printing) is adopted to fabricate the complicated prototype of the rotor.
Neural Quality Estimation of Grammatical Error Correction

Grammatical error correction (GEC) systems deployed in language learning environments are expected to accurately correct errors in learners’ writing. However, in practice, they often produce spurious corrections and fail to correct many errors, thereby misleading learners. This necessitates the estimation of the quality of output sentences produced by GEC systems so that instructors can selectively intervene and re-correct the sentences which are poorly corrected by the system and ensure that learners get accurate feedback. We propose the first neural approach to automatic quality estimation of GEC output sentences that does not employ any hand-crafted features. Our system is trained in a supervised manner on learner sentences and corresponding GEC system outputs with quality score labels computed using human-annotated references. Our neural quality estimation models for GEC show significant improvements over a strong feature-based baseline. We also show that a state-of-the-art GEC system can be improved when quality scores are used as features for re-ranking the N-best candidates.
Translating a Math Word Problem to an Expression Tree

Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimation. In this paper, by considering the uniqueness of expression tree, we propose an equation normalization method to normalize the duplicated equations. Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving. We find that each model has its own specialty in solving problems, consequently an ensemble model is then proposed to combine their advantages. Experiments on dataset Math23K show that the ensemble model with equation normalization significantly outperforms the previous state-of-the-art methods.
Sparse Poisson Latent Block Model for Document Clustering (Extended Abstract)

We present a novel generative mixture model for co-clustering text data. This model, the Sparse Poisson Latent Block Model (SPLBM), is based on the Poisson distribution, which arises naturally for contingency tables, such as document-term matrices. The advantages of SPLBM are two-fold. First, it is a rigorous statistical model which is also very parsimonious. Second, it has been designed from the ground up to deal with data sparsity problems. Extensive experiments on various real-world datasets of different size and structure provide strong evidence for the effectiveness of the proposed approach.
Miniaturization and Quality-Factor in Substrate Integrated Waveguide Cavities

This paper presents the study of the quality-factors of cavity resonators based on the substrate integrated waveguide (SIW) technology. Compact resonator topologies can be obtained by removing part of the metallic structure of the cavities. The study shows a detailed comparison between the performance of various compact resonator topologies at the frequencies of 4 GHz and 10 GHz, using two different substrates of varying thickness. Then, a technique to improve the performance of the compact resonators is presented. The results of the study are then validated by measuring some physical prototypes.
Don't Sweat the Small Stuff: The Effect of Challenge-Skill Manipulation on Electrodermal Activity

Challenge plays a critical role in enabling an enjoyable and successful player experience, but not all dimensions of challenge are well understood. A more nuanced understanding of challenge and its role in the player experience is possible through assessing player psychophysiology. The psychophysiology of challenge (i.e. what occurs physiologically during experiences of video game challenge) has been the focus of some player experience research, but consensus as to the physiological markers of challenge has not been reached. To further explore the psychophysiological impact of challenge, three video game conditions -- varying by degree of challenge -- were developed and deployed within a large-scale psychophysiological study (n = 90). Results show decreased electrodermal activity (EDA) in the low-challenge (Boredom) video game condition compared to the medium- (Balance) and high-challenge (Overload) conditions, with a statistically non-significant but consistent pattern found between the medium- and high-challenge conditions. Overall, these results suggest electrodermal response increases with challenge. Despite the intuitiveness of some of these conclusions, the results do not align with extant literature. Possible explanations for the incongruence with the literature are discussed. Ultimately, with this work we hope to both enable a more complete understanding of challenge in the player experience, and contribute to a more granular understanding of the psychophysiological experience of play.
On Characterizing the Twitter Elite Network

The most-followed Twitter users and their pairwise relationships form a sub-graph of all Twitter users that we call the Twitter elite network. The connectivity patterns and influence (in terms of reply and retweet activity) among these elite users illustrate how the “important” users connect and interact with one another on Twitter. Such an elite-focused view also provides valuable information about the structure of the Twitter network as a whole. This paper presents the first detailed characterization of the top-10K Twitter elite network. We describe a new technique to efficiently and accurately capture the Twitter elite network along with social attributes of individual elite accounts. We show that a sufficiently large elite network is typically composed of 15–20 resilient and socially cohesive communities representing “socially meaningful” components of the elite network. We then characterize the community-level structure of the elite network in terms of bias in directed pairwise connectivity and relative reachability. We demonstrate that both the retweet and reply activity between elite users are effectively contained within individual elite communities. Finally, we illustrate that a majority of the elite friends of regular Twitter users tend to belong to a single elite community. This finding offers a promising criterion to group regular users into “shadow partitions” based on their association with elite communities. We show that the level of overall inter-connectivity between shadow partitions mirrors the inter-connectivity of the elite communities. This suggests that these shadow partitions can be viewed as extensions of their corresponding elite communities.
Differentiating Concepts and Instances for Knowledge Graph Embedding

Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e.,instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https://github.com/davidlvxin/TransC.
Non-circulating Bearing Currents Mitigation Approach Based on Machine Stator Design Options

Different mechanisms of bearing currents spreading inside the electrical machine are well-studied. In contrast, investigation and development of different techniques for bearing currents' practical mitigation remains a field for improvements. The final common and practically acceptable solution on the problem is not presented yet. Current paper deals with non-circulating (or capacitive) bearing currents. After a short explanation of the capacitive currents' circuiting mechanism, an approach for their mitigation will be introduced. The paper deals with stator winding positioning in a slot and presents several different geometries of the suggested motor modification to understand the role of machine geometry on the problem. The effectiveness of the suggested mitigation technique is described with the help of finite element-based research. During the evaluations, a number of geometric parameters have been varied. The technique presented can be, especially, utilized for the non-circulating currents' mitigation but it offers also potential for further research.
Informing a BDI Player Model for an Interactive Narrative

This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge about the environment, the difference in each player's style and preferences represents a challenge when trying to make an intelligent system mimic their actions. Based on observations from players interactions with an extract from the interactive fiction Anchorhead, we created a player profile to guide the behaviour of a generic player model based on the BDI (Belief-Desire-Intention) model of agency. We evaluated our approach using qualitative and quantitative methods and found that the player profile can improve the performance of the BDI player model. However, we found that players self-assessment did not yield accurate data to populate their player profile under our current approach.
Preparation for Endurance Competitions at Altitude: Physiological, Psychological, Dietary and Coaching Aspects. A Narrative Review

It was the Summer Olympic Games 1968 held in Mexico City (2,300 m) that required scientists and coaches to cope with the expected decline of performance in endurance athletes and to establish optimal preparation programs for competing at altitude. From that period until now many different recommendations for altitude acclimatization in advance of an altitude competition were proposed, ranging from several hours to several weeks. Those recommendations are mostly based on the separate consideration of the physiology of acclimatization, psychological issues, performance changes, logistical or individual aspects, but there is no review considering all these aspects in their entirety. Therefore, the present work primarily focusses on the period of altitude sojourn prior to the competition at altitude based on physiological and psychological aspects complemented by nutritional and sports practical considerations.
Analytical and Numerical Calculations of Overvoltages in Underground Cable of Intrusion Detection System Directly Hit by Lightning

The paper presents an analysis of exposure of an underground cable sensor of an intrusion detection system to direct lightning strike. Results of calculations using the analytical transmission-line model and the numerical model implemented in the SES CDEGS computer code are compared. The analytical model can be used for validation of numerical calculations of more complex underground systems.
The Effect of Captivity on the Dynamics of Active Bacterial Communities Differs Between Two Deep-Sea Coral Species

Microbes play a crucial role in sustaining the coral holobiont’s functions and in particular under the pressure of environmental stressors. The effect of a changing environment on coral health is now a major branch of research that relies heavily on aquarium experiments. However, the effect of captivity on the coral microbiome remains poorly known. Here we show that different cold-water corals species have different microbiome responses to captivity. For both the DNA and the RNA fraction, Madrepora oculata bacterial communities were maintained for at least 6 months of aquarium rearing, while Lophelia pertusa bacteria changed within a day. Interestingly, bacteria from the genus Endozoicomonas, a ubiquitous symbiont of numerous marine hosts, were resilient and remained active in M. oculata for several months. Our results demonstrate that a good knowledge of the coral microbiome and an understanding of the ecological strategy of the holobiont is needed before designing aquarium experiments.
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis

Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. We show that our method outperforms the state of the art by 1.6% on average in two distinct domains: restaurant and laptop.
The Microbiome and Preterm Birth: A Change in Paradigm with Profound Implications for Pathophysiologic Concepts and Novel Therapeutic Strategies

Preterm birth poses a global challenge with a continuously increasing disease burden during the last decades. Advances in understanding the etiopathogenesis did not lead to a reduction of prematurely born infants so far. A balanced development of the host microbiome in early life is key for the maturation of the immune system and many other physiological functions. With the tremendous progress in new diagnostic possibilities, the contribution of microbiota changes to preterm birth and the acute and long-term sequelae of prematurity have come into the research focus. This review summarizes the latest advances in the understanding of microbiomes in the amniotic cavity and the female lower genital tract and how changes in microbiota structures contribute to preterm delivery. The exhibition of these highly vulnerable infants to the hostile environment in the neonatal intensive care unit necessarily entails the rapid colonization with a nonbalanced microbiome in a situation where the organism is still very prone and at an early stage of development. The global research efforts to decipher pathologic changes will pave the way to new pre- and postnatal therapeutic concepts.
Better Transition-Based AMR Parsing with a Refined Search Space

This paper introduces a simple yet effective transition-based system for Abstract Meaning Representation (AMR) parsing. We argue that a well-defined search space involved in a transition system is crucial for building an effective parser. We propose to conduct the search in a refined search space based on a new compact AMR graph and an improved oracle. Our end-to-end parser achieves the state-of-the-art performance on various datasets with minimal additional information.
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension

Sequence encoders are crucial components in many neural architectures for learning to read and comprehend. This paper presents a new compositional encoder for reading comprehension (RC). Our proposed encoder is not only aimed at being fast but also expressive. Specifically, the key novelty behind our encoder is that it explicitly models across multiple granularities using a new dilated composition mechanism. In our approach, gating functions are learned by modeling relationships and reasoning over multi-granular sequence information, enabling compositional learning that is aware of both long and short term information. We conduct experiments on three RC datasets, showing that our proposed encoder demonstrates very promising results both as a standalone encoder as well as a complementary building block. Empirical results show that simple Bi-Attentive architectures augmented with our proposed encoder not only achieves state-of-the-art / highly competitive results but is also considerably faster than other published works.
Full-Wave Interior Penalty Discontinuous Galerkin Method for Waveguide Analysis

A novel full-wave method to perform mode analysis on waveguides is introduced in this paper. The core of the method is based on an Interior Penalty Discontinuous Galerkin discretization of the vector wave equation. With an appropriate penalty function a spurious-free and high accuracy method is achieved. We prove the efficiency of the proposed method in several waveguides, including intricate optical waveguides with leaky modes and also on a plasmonic waveguide. We compare our results with the state-of-the-art mode solvers described in the literature. We also discuss the importance of this new approach.
Efficient Probabilistic K-Core Computation on Uncertain Graphs

As uncertainty is inherent in a wide spectrum of graph applications such as social network and brain network, it is highly demanded to re-visit classical graph problems in the context of uncertain graphs. Driven by real-applications, in this paper, we study the problem of k-core computation on uncertain graphs and propose a new model, namely (k,θ )-core, which consists of nodes with probability at least θ to be kcore member in the uncertain graph. We show the computation of (k,θ )-core is NP-hard, and hence resort to sampling based methods. Effective and efficient pruning techniques are proposed to significantly reduce the candidate size. To further reduce the cost of k-core computation on multiple sampled graphs, we design a k-core membership check algorithm following a novel expansion-based search paradigm. Extensive experiments on reallife graphs demonstrate the effectiveness and efficiency of our proposed techniques.
Thread Popularity Prediction and Tracking with a Permutation-invariant Model

The task of thread popularity prediction and tracking aims to recommend a few popular comments to subscribed users when a batch of new comments arrive in a discussion thread. This task has been formulated as a reinforcement learning problem, in which the reward of the agent is the sum of positive responses received by the recommended comments. In this work, we propose a novel approach to tackle this problem. First, we propose a deep neural network architecture to model the expected cumulative reward (Q-value) of a recommendation (action). Unlike the state-of-the-art approach, which treats an action as a sequence, our model uses an attention mechanism to integrate information from a set of comments. Thus, the prediction of Q-value is invariant to the permutation of the comments, which leads to a more consistent agent behavior. Second, we employ a greedy procedure to approximate the action that maximizes the predicted Q-value from a combinatorial action space. Different from the state-of-the-art approach, this procedure does not require an additional pre-trained model to generate candidate actions. Experiments on five real-world datasets show that our approach outperforms the state-of-the-art.