Miracle-dz/UniFAR-FTUs huggingface dataset:
Silicon Oxycarbide-Derived Carbon as Potential NO2 Gas Sensor: A First Principles’ Study

Silicon oxycarbide (SiCO)-derived porous carbon is a novel class of nano-porous material with unique properties including highly sensitive gas detection. In this letter, SiCO-derived porous carbon (porous SiCO) structures are successfully reproduced by simulating the etching process in experiments. Then, the gas sensing performance of SiCO-derived carbon with different porous morphologies is investigated. The calculated adsorption energy, Mulliken charge transfer, bandgap, and adsorption distance indicate that SiCO-derived porous carbon exhibits a higher sensitivity toward NO2 gas than CO, 2, and acetone in accordance with experimental conclusions. Moreover, the porous SiCO with the largest SSA and PV shows the most excellent NO2 sensing performance. The adsorption of NO2 leads to the appearance of a new strong peak at the edge of the conduction band, resulting in obvious changes of the conductivity of the systems, which is necessary for NO2 detection.
Neural Modular Control for Embodied Question Answering

We present a modular approach for learning policies for navigation over long planning horizons from language input. Our hierarchical policy operates at multiple timescales, where the higher-level master policy proposes subgoals to be executed by specialized sub-policies. Our choice of subgoals is compositional and semantic, i.e. they can be sequentially combined in arbitrary orderings, and assume human-interpretable descriptions (e.g. 'exit room', 'find kitchen', 'find refrigerator', etc.). We use imitation learning to warm-start policies at each level of the hierarchy, dramatically increasing sample efficiency, followed by reinforcement learning. Independent reinforcement learning at each level of hierarchy enables sub-policies to adapt to consequences of their actions and recover from errors. Subsequent joint hierarchical training enables the master policy to adapt to the sub-policies. On the challenging EQA (Das et al., 2018) benchmark in House3D (Wu et al., 2018), requiring navigating diverse realistic indoor environments, our approach outperforms prior work by a significant margin, both in terms of navigation and question answering.
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

We present a Few-Shot Relation Classification Dataset (dataset), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research.
Incorporating Background Knowledge into Video Description Generation

Most previous efforts toward video captioning focus on generating generic descriptions, such as, “A man is talking. ” We collect a news video dataset to generate enriched descriptions that include important background knowledge, such as named entities and related events, which allows the user to fully understand the video content. We develop an approach that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents. Then, given the video as well as the extracted events and entities, we generate a description using a Knowledge-aware Video Description network. The model learns to incorporate entities found in the topically related documents into the description via an entity pointer network and the generation procedure is guided by the event and entity types from the topically related documents through a knowledge gate, which is a gating mechanism added to the model’s decoder that takes a one-hot vector of these types. We evaluate our approach on the new dataset of news videos we have collected, establishing the first benchmark for this dataset as well as proposing a new metric to evaluate these descriptions.
Towards Transforming an Industrial Automation System from Monolithic to Microservices

Container technology enables designers to build (micro)service-oriented systems with on-demand scalability and availability easily, provided the original system has been well-modularized to begin with. Industry automation applications, built a long time ago, aim to adopt this technology to become more flexible and ready to be a part of the internet of thing based next-generation industrial system. In this paper, we share our work-in-progress experience of transforming a complex, distributed industrial automation system to a microservice based containerized architecture. We propose a containerized architecture of the “to-be” system and observe that despite being distributed, the “as-is” system tend to follow a monolithic architecture with strong coupling among the participating components. Consequently it becomes difficult to achieve the proposed microservice based architecture without a significant change. We also discuss the workload handling, resource utilization and reliability aspects of the “to-be” architecture using a prototype implementation.
CARER: Contextualized Affect Representations for Emotion Recognition

Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction

Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions. Previous studies address this problem by discovering a shared space across genres using manually crafted features, which requires great human effort. To effectively automate this process, we design a genre-separation network, which applies two encoders, one genre-independent and one genre-shared, to explicitly extract genre-specific and genre-agnostic features. Then we train a relation classifier using the genre-agnostic features on the source genre and directly apply to the target genre. Experiment results on three distinct genres of the ACE dataset show that our approach achieves up to 6.1% absolute F1-score gain compared to previous methods. By incorporating a set of external linguistic features, our approach outperforms the state-of-the-art by 1.7% absolute F1 gain. We make all programs of our model publicly available for research purpose
Deep Exhaustive Model for Nested Named Entity Recognition

We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of our model is to enumerate all possible regions or spans as potential entity mentions and classify them with deep neural networks. To reduce the computational costs and capture the information of the contexts around the regions, the model represents the regions using the outputs of shared underlying bidirectional long short-term memory. We evaluate our exhaustive model on the GENIA and JNLPBA corpora in biomedical domain, and the results show that our model outperforms state-of-the-art models on nested and flat NER, achieving 77.1% and 78.4% respectively in terms of F-score, without any external knowledge resources.
Synchronous Reluctance Machines as Drives for Rotary Anode X-Ray Tubes-A Feasibility Study

This paper investigates the possibility of replacing the rotor of an existing X-ray tube induction machine (IM) with that of a synchronous reluctance machine (SynRM), while keeping the existing IM stator. When compared with other applications, such machines operate with a very large air gap. Furthermore, their non-laminated rotors are placed in a vacuum and may heat up as high as 450°C. The primary design criteria for this special application is to reach a minimum speed of 10800 rpm in less than 3 sec, requiring an output torque of 1Nm. Three different types of SynRM rotors are considered. The capability of these alternative machines to provide the same output torque as the original 1M was assessed. In the next step, the losses of the different machines were identified and compared. While all machine designs were identified to be able to provide the desired output torque, the axially-layered rotor proved to be the best option, with more than 25 % reduction of the total losses when compared with the original 1M.
Joint Learning for Targeted Sentiment Analysis

Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes. Previous works only exploit word embeddings as features and do not explore more potentials of neural networks when jointly learning the two tasks. In this paper, we carefully design the hierarchical stack bidirectional gated recurrent units (HSBi-GRU) model to learn abstract features for both tasks, and we propose a HSBi-GRU based joint model which allows the target label to have influence on their sentiment label. Experimental results on two datasets show that our joint learning model can outperform other baselines and demonstrate the effectiveness of HSBi-GRU in learning abstract features.
Integrating Transformer and Paraphrase Rules for Sentence Simplification

Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we propose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple state-of-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available at https://github.com/Sanqiang/text_simplification.
Tracing Internet Path Transparency

Investigating Internet Path Transparency means measuring if a network path between two endhosts is impaired by in-network functions on the path. A path is considered transparent if it provides connectivity and the same performance independent of the protocol or protocol stack that is used for the transmission. Unfortunately this is not always the case. Simple firewalls that block e.g. UDP, are an example. Of course such in-network functions are often valuable, like firewalls. However, these middleboxes also, sometimes unintentionally, make assumptions about the traffic passing through them that restricts innovation in the Internet on the higher layers, e.g. the deployment of new UDP-based protocols such as QUIC, to stick with the previous example. PATHspider is an active measurement tool to test for Path Transparency. In this paper we present a new feature of PATH-spider that integrates tracebox-based functionality and analysis to not only detect in-transparency but also further locate the origin of the impairment observed. As an example study we show updated and extended measurements on ECN support and connectivity. By using our enhanced ECN PATHspider plugin to test network support of the ECN IP codepoint and additional path tracing that is correlated with DSCP testing, we show that most in-network ECN IP codepoint zeroing is due to use of the deprecated definition of the IP ToS field for domain-internal service differentiation, while pure resetting of the ECN IP field is more likely an active inference in border networks.
A Statistical Framework for Handling Network Anomalies

This paper proposes a statistical framework to automatically identify anomalous nodes in static networks. In our approach, we first associate to each node a neighborhood cohesiveness feature vector such that each element of this vector corresponds to a score quantifying the node's neighborhood connectivity, as estimated by a specific similarity measure. Next, based on the estimated node's feature vectors, we view the task of identifying anomalous nodes from a mixture modeling perspective, based on which we elaborate a statistical approach that exploits the Dirichlet distribution to automatically identify anomalies. The suitability of the proposed method is illustrated through experiments on both synthesized and real networks.
Individual-Level Social Capital in Weighted and Attributed Social Networks

There is no agreed definition of social capital in the literature. However, one interpretation is that it refers to those resources embedded in an individual's social network offering benefits to that individual in relation to achieving goals and facilitating actions. This can be viewed as a resource-based interpretation of social capital aimed at the level of individuals. In this paper, we propose a family of social capital measures in line with this interpretation. Our measures are designed for a model of social networks based on weighted and attributed graphs, and cover four dimensions of social capital: (i) access to resources, (ii) access to superiors, (iii) homogeneity of ties, and (iv) heterogeneity of ties. We demonstrate the real-world application of our measures by exploring an illustrative use case in the form of a workplace social network.
A Low Frequency of IL-17-Producing CD8+ T-Cells Is Associated With Persistent Immune Activation in People Living With HIV Despite HAART-Induced Viral Suppression

Immune activation is the hallmark of HIV infection, even in patients with highly active anti-retroviral therapy (HAART)-induced viral suppression. A major cause of immune activation during HIV infection is the intestinal microbial translocation as a consequence, among other factors, of the decrease and/or dysfunction of interleukin (IL)-17-producing T-cells, due to their role promoting the integrity of the intestinal barrier. A population of IL-17-producing CD8+ T-cells (Tc17 cells), characterized by the expression of CD161, has been described, but its relation with the persistent immune activation in non-viremic people living with HIV (PLWH) on HAART is unclear. By flow cytometry, we characterized the activation phenotype (evaluated by the expression of HLA-DR and CD38) of circulating CD161-expressing CD8+ T-cells; in addition, we explored the functionality of polyclonally-stimulated Tc17 cells in PLWH under HAART-induced viral suppression, and in healthy individuals. Finally, we determined the association of Tc17 cells with the expression of cellular and soluble activation markers. Circulating CD161-expressing CD8+ T-cells were decreased in PLWH compared with healthy individuals, despite their similar basal activation state. After polyclonal stimulation, IL-17 production was higher in CD8+ T-cells co-expressing HLA-DR and CD38 in healthy individuals. In contrast, although PLWH had a higher frequency of HLA-DR+ CD38+ CD8+ T-cells after stimulation, they had a lower production of IL-17. Interferon (IFN)-γ-producing CD8+ T-cells (Tc1 cells) were increased in PLWH. The low Tc17 cells response was associated with a high expression of CD38 and programmed death 1 protein, high levels of soluble CD14 and the treatment duration. Finally, to explore potential immunomodulatory strategies, the in vitro effect of the anti-inflammatory agent sulfasalazine was assessed on Tc17 cells. Interestingly, a decreased inflammatory environment, death of activated CD8+ T-cells, and an increased frequency of Tc17 cells were observed with sulfasalazine treatment. Thus, our findings suggest that activated CD8+ T-cells have a marked capacity to produce IL-17 in healthy individuals, but not in PLWH, despite HAART. This dysfunction of Tc17 cells is associated with the persistent immune activation observed in these patients, and can be partially restored by anti-inflammatory agents.
Analysis on the Lightning Flash Activities Over Beijing Area Based on Beijing Lightning Network

The temporal and spatial distribution characteristics of lightning flashes in Beijing area during 146 thunderstorms in 2015-2017 were statistically analyzed on the basis of lightning location data from the Beijing Lightning Location Network (BLNET). The results showed that 588,520 lightning flashes in total were observed, most of which were intra-cloud lightning flashes (IC) accounting for about 93.8%. Cloud-to-ground lightning flashes (CG) accounting for only 16.2% of the total flashes, and 11.7% of CG lightning flashes were positive CG lightning flashes. The daily change in lightning activity appears a double-peak, with frequent lightning flash occurrences at 14:00-22:00 (Beijing Local Time) and 23:00-the next day at 03:00. The total lightning activity density shows interannual differences from 2015-2107. The temporally averaged flash distribution shows that there are several regions with higher flash density that mainly located in the urban areas and might be closely related to dense population, heavy traffic and aerosol density.
A Review on Magnetic Gears: Topologies, Computational Models and Design Aspects

Magnetic gears are becoming promising devices for high-torque applications. Recently several new topologies have been proposed to the aim of achieving better performances. In this paper, a general review of magnetic gears is presented, including a discussion of the most common and upcoming topologies and a description of the working principle. In particular, a comparison of different topologies is carried out, in terms of gear ratio and torque density. Furthermore, the main computational techniques generally adopted for coaxial magnetic gears are listed and described. Finally, some key aspects concerning the design are reported.
Social-EOC: Serviceability Model to Rank Social Media Requests for Emergency Operation Centers

The public expects a prompt response from emergency services to address requests for help posted on social media. However, the information overload of social media experienced by these organizations, coupled with their limited human resources, challenges them to timely identify and prioritize critical requests. This is particularly acute in crisis situations where any delay may have a severe impact on the effectiveness of the response. While social media has been extensively studied during crises, there is limited work on formally characterizing serviceable help requests and automatically prioritizing them for a timely response. In this paper, we present a formal model of serviceability called Social-EOC (Social Emergency Operations Center), which describes the elements of a serviceable message posted in social media that can be expressed as a request. We also describe a system for the discovery and ranking of highly serviceable requests, based on the proposed serviceability model. We validate the model for emergency services, by performing an evaluation based on real-world data from six crises, with ground truth provided by emergency management practitioners. Our experiments demonstrate that features based on the serviceability model improve the performance of discovering and ranking (nDCG up to 25%) service requests over different baselines. In the light of these experiments, the application of the serviceability model could reduce the cognitive load on emergency operation center personnel, in filtering and ranking public requests at scale.
Commonsense Justification for Action Explanation

To enable collaboration and communication between humans and agents, this paper investigates learning to acquire commonsense evidence for action justification. In particular, we have developed an approach based on the generative Conditional Variational Autoencoder(CVAE) that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. Our empirical results have shown that, compared to a typical attention-based model, CVAE achieves significantly higher performance in both action prediction and justification. A human subject study further shows that the commonsense evidence gathered by CVAE can be communicated to humans to achieve a significantly higher common ground between humans and agents.
Using tethered drones to investigate ESD in wind turbine blades during fair and thunderstorm weather

Elevated structures are submitted to the atmospheric potential which in fair weather can reach few tens of kilovolts for structures as high as modern wind turbines. In thunderstorm conditions, the potential of the lowest atmosphere below thunderstorms can be much higher submitting tall structures to very high potential differences. The effects of the atmospheric potential to tall structures will be the induction of electrostatic charge and, in case of ground-isolated structures such as some wind turbine blades, the acquiring of a potential. In this paper, we focus on the ESD produced by the fair and thunderstorm weather in vertical isolated wires and we apply the experiments and models to evaluate the ESD in wind turbines. We have found that a vertical isolated wire of 200 m can acquire potentials of about 10 kV and produce ESD of $\sim5$ A with time to peak of the order of 24 ns and electric charge of $1.5 \mu$ C. In the case of thunderstorms, we estimate that peak currents can reach several hundred of amperes being able to induce intense electromotive forces to electrical systems in wind turbine blades and nacelles.
Model Predictive Control for Multiphase Motor Drives – a Technology Status Review

The application of model predictive algorithms for three-phase electric drives has gained an impressive attention in the last decade. Nowadays, the model predictive controls (MPC) represent one of the most competitive solutions in alternative to the conventional feedback control schemes. At the same time, multiphase motor drives have become a feasible solution for high power/high current applications and most likely they will be employed in the applications like transportation electrification and energy production. Although the technical literature contains many references regarding the application of MPC for three-phase drives, there are only few attempts about the application of predictive algorithms for multiphase machines. The paper provides a comprehensive analysis and survey on the main MPC schemes applied to multiphase drives. In addition, experimental results on the application of a MPC scheme for a six-phase asymmetrical induction machine are provided.