Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems

The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital for the creation of a deployed task-oriented dialogue system. This paper describes our systems that are evaluated on all subtasks under this challenge. We study the problem of employing pre-trained attention-based network for multi-turn dialogue systems. Meanwhile, several adaptation methods are proposed to adapt the pre-trained language models for multi-turn dialogue systems, in order to keep the intrinsic property of dialogue systems. In the released evaluation results of Track 2 of DSTC 8, our proposed models ranked fourth in subtask 1, third in subtask 2, and first in subtask 3 and subtask 4 respectively.
Anharmonicity‐Driven Rashba Cohelical Excitons Break Quantum Efficiency Limitation

Closed‐shell light‐emitting diodes (LEDs) suffer from the internal quantum efficiency (IQE) limitation imposed by optically inactive triplet excitons. Here, an unrevealed emission mechanism of lead halide perovskites (LHPs) APbX3 (A = Cs/CN2H5; X = Cl/Br/I) that circumvents the efficiency limit of closed‐shell LEDs is explored. Though efficient emission is prohibited by optically inactive J = 0 in inversion symmetric LHPs, the anharmonicity arising from stereochemistry of Pb and resonant orbital‐bonding network along the imaginary A+…X−(T1u) transverse optical (TO) modes, breaks inversion symmetry, introducing disorder and Rashba–Dresselhaus spin‐orbit coupling (RD‐SOC). This results in bright cohelical and dark antihelical excitons. Many‐body theory and first‐principles calculations show that the optically active cohelical exciton is the lowest excited state in organic/inorganic LHPs. Thus, RD‐SOC can drive to achieve the ideal 50% IQE by utilizing anharmonicity, much over the 25% IQE limitation for closed‐shell LEDs.
MPC-Based Fast Frequency Control of Voltage Source Converters in Low-Inertia Power Systems

A rapid deployment of renewable generation has led to significant reduction in the rotational system inertia and damping, thus making frequency control in power systems more challenging. This paper proposes a novel control scheme based on Model Predictive Control (MPC) for converter-interfaced generators operating in a grid-forming mode, with the goal of exploiting their fast response capabilities to provide fast frequency control service to the system. The controller manipulates converter power injections to limit the frequency nadir and rate-of-change-of-frequency after a disturbance. Both centralized and decentralized MPC approaches are considered and compared in terms of performance and practical implementation. Special attention is given to the decentralized controller by generating an explicit MPC solution to enhance computational efficiency and reduce hardware requirements. Simulation results obtained from a detailed dynamic model of the IEEE 39-bus system demonstrate the effectiveness of the proposed control schemes.
Reinforcement Learning Architectures: SAC, TAC, and ESAC

The trend is to implement intelligent agents capable of analyzing available information and utilize it efficiently. This work presents a number of reinforcement learning (RL) architectures; one of them is designed for intelligent agents. The proposed architectures are called selector-actor-critic (SAC), tuner-actor-critic (TAC), and estimator-selector-actor-critic (ESAC). These architectures are improved models of a well known architecture in RL called actor-critic (AC). In AC, an actor optimizes the used policy, while a critic estimates a value function and evaluate the optimized policy by the actor. SAC is an architecture equipped with an actor, a critic, and a selector. The selector determines the most promising action at the current state based on the last estimate from the critic. TAC consists of a tuner, a model-learner, an actor, and a critic. After receiving the approximated value of the current state-action pair from the critic and the learned model from the model-learner, the tuner uses the Bellman equation to tune the value of the current state-action pair. ESAC is proposed to implement intelligent agents based on two ideas, which are lookahead and intuition. Lookahead appears in estimating the values of the available actions at the next state, while the intuition appears in maximizing the probability of selecting the most promising action. The newly added elements are an underlying model learner, an estimator, and a selector. The model learner is used to approximate the underlying model. The estimator uses the approximated value function, the learned underlying model, and the Bellman equation to estimate the values of all actions at the next state. The selector is used to determine the most promising action at the next state, which will be used by the actor to optimize the used policy. Finally, the results show the superiority of ESAC compared with the other architectures.
Measuring Social Biases of Crowd Workers using Counterfactual Queries

Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effective measure to gather labeled training datasets, is not immune to the inherent social biases of crowd workers. To ensure such social biases aren't passed onto the curated datasets, it's important to know how biased each crowd worker is. In this work, we propose a new method based on counterfactual fairness to quantify the degree of inherent social bias in each crowd worker. This extra information can be leveraged together with individual worker responses to curate a less biased dataset.
Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps

Abstract Cardiovascular diseases (CVD) and strokes produce immense health and economic burdens globally. Coronary Artery Disease (CAD) is the most common type of cardiovascular disease. Coronary Angiography, which is an invasive approach for detection and treatment, is also the standard procedure for diagnosing CAD. In this work, we illustrate a Medical Decision Support System for the prediction of Coronary Artery Disease (CAD) using Fuzzy Cognitive Maps (FCM). FCMs are a promising modeling methodology, based on human knowledge, capable of dealing with ambiguity and uncertainty and learning how to adapt to the unknown or changing environment. The newly proposed MDSS is developed using the basic notions of Fuzzy Cognitive Maps and is intended to diagnose CAD utilizing specific inputs related to the patient’s clinical conditions. We show that the proposed model, when tested on a dataset collected from the Laboratory of Nuclear Medicine of the University Hospital of Patras achieves accuracy of 78.2% outmatching several state-of-the-art classification algorithms.
Geometrically Principled Connections in Graph Neural Networks

Graph convolution operators bring the advantages of deep learning to a variety of graph and mesh processing tasks previously deemed out of reach. With their continued success comes the desire to design more powerful architectures, often by adapting existing deep learning techniques to non-Euclidean data. In this paper, we argue geometry should remain the primary driving force behind innovation in the emerging field of geometric deep learning. We relate graph neural networks to widely successful computer graphics and data approximation models: radial basis functions (RBFs). We conjecture that, like RBFs, graph convolution layers would benefit from the addition of simple functions to the powerful convolution kernels. We introduce affine skip connections, a novel building block formed by combining a fully connected layer with any graph convolution operator. We experimentally demonstrate the effectiveness of our technique, and show the improved performance is the consequence of more than the increased number of parameters. Operators equipped with the affine skip connection markedly outperform their base performance on every task we evaluated, i.e., shape reconstruction, dense shape correspondence, and graph classification. We hope our simple and effective approach will serve as a solid baseline and help ease future research in graph neural networks.
Optimal periodic dividend strategies for spectrally negative Lévy processes with fixed transaction costs

ABSTRACT Maximising dividends is one classical stability criterion in actuarial risk theory. Motivated by the fact that dividends are paid periodically in real life, periodic dividend strategies were recently introduced (Albrecher et al. 2011). In this paper, we incorporate fixed transaction costs into the model and study the optimal periodic dividend strategy with fixed transaction costs for spectrally negative Lévy processes. The value function of a periodic strategy is calculated by means of exiting identities and Itô's excusion when the surplus process is of unbounded variation. We show that a sufficient condition for optimality is that the Lévy measure admits a density which is completely monotonic. Under such assumptions, a periodic strategy is confirmed to be optimal. Results are illustrated.
Topology of spaces of regular sections and applications to automorphism groups

Let $G$ be a complex connected reductive algebraic group that acts on a smooth complex algebraic variety $X$, and let $E$ be a $G$-equivariant algebraic vector bundle over $X$. A section of $E$ is regular if it is transversal to the zero section. Let $U\subset\Gamma(X,E)$ be the subset of regular sections. We give a sufficient condition in terms of topological invariants of $E$ and $X$ that implies that every orbit map $O:G\to X$ induces a surjection in rational cohomology. If this condition is satisfied, then (1) the geometric quotient $U/G$ exists; (2) there is an isomorphism $H^*(U,\mathbb{Q})\cong H^*(G,\mathbb{Q})\otimes H^*(U/G,\mathbb{Q})$ of cohomology rings; (3) the order of the stabiliser $G_s,s\in U$ divides a certain expression that can be explicitly calculated if $X$ is a compact homogeneous space. In some cases we also prove similar statements for the space of the zero loci of elements of $U$. We apply these results to several explicit examples which include hypersurfaces in projective spaces, non-degenerate quadrics and complete flag varieties of the simple groups of rank 2, and also certain Fano varieties of dimension 3 and 4.
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction

Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional assumptions that have less discriminative power. In this work, we proposed a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. Experimental results on three datasets show the effectiveness and robustness of SelfORE on open-domain Relation Extraction when comparing with competitive baselines.
Generalized Newton Algorithms for Tilt-Stable Minimizers in Nonsmooth Optimization

This paper aims at developing two versions of the generalized Newton method to compute not merely arbitrary local minimizers of nonsmooth optimization problems but just those, which possess an important stability property known as tilt stability. We start with unconstrained minimization of continuously differentiable cost functions having Lipschitzian gradients and suggest two second-order algorithms of the Newton type: one involving coderivatives of Lipschitzian gradient mappings, and the other based on graphical derivatives of the latter. Then we proceed with the propagation of these algorithms to minimization of extended-real-valued prox-regular functions, while covering in this way problems of constrained optimization, by using Moreau envelops. Employing advanced techniques of second-order variational analysis and characterizations of tilt stability allows us to establish the solvability of subproblems in both algorithms and to prove the Q-superlinear convergence of their iterations.
Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

In the massive machine-type communications (mMTCs) scenario for Internet-of-Things (IoTs) applications, though a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission simultaneously, which requires a novel design of protocols and receivers to enable efficient data transmission and accurate multiuser detection (MUD). Aiming at this problem, grant-free nonorthogonal multiple access (GF-NOMA) is proposed, which enables active devices to directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for nonorthogonal preambles (NOPs)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this article, from an aspect of network deployment, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG). Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. We optimize the energy efficiency and AP coverage efficiency for GF-NOMA via numerical methods to meet the low-energy-consumption and low-infrastructure-cost demands of IoT applications. Our analysis results are verified with Monte Carlo simulations, which show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and grant-free orthogonal multiple access.
Appraisal of dental X-ray requests and findings in a northern Nigeria healthcare facility

Background: Research in dental radiography has been a neglected area hence the aim of this study was to determine the diagnostic yield from dental X-rays in a northern Nigeria healthcare facility. Methods: This study was carried out at the dental department of Abubakar Tafawa Balewa University Teaching Hospital, Bauchi from June 2016 to February 2017. All radiographs were interpreted by certified dentists and radiologists and findings were documented. Results: A total of 335 dental X-rays were assessed. Chronic periapical periodontitis was the highest finding (n=72, 21%), followed by irreversible pulpitis (n=42, 12.5%), and dental caries (n=37, 11%). Conclusion: We observed that periapical X-rays was the commonest request. Chronic apical periodontitis, irreversible pulpitis, and dental caries, were found to be the most prevalent dental problems in the region.
Moving Metric Detection and Alerting System at eBay

At eBay, there are thousands of product health metrics for different domain teams to monitor. We built a two-phase alerting system to notify users with actionable alerts based on anomaly detection and alert retrieval. In the first phase, we developed an efficient anomaly detection algorithm, called Moving Metric Detector (MMD), to identify potential alerts among metrics with distribution agnostic criteria. In the second alert retrieval phase, we built additional logic with feedbacks to select valid actionable alerts with point-wise ranking model and business rules. Compared with other trend and seasonality decomposition methods, our decomposer is faster and better to detect anomalies in unsupervised cases. Our two-phase approach dramatically improves alert precision and avoids alert spamming in eBay production.
Generating Rationales in Visual Question Answering

Despite recent advances in Visual QuestionAnswering (VQA), it remains a challenge todetermine how much success can be attributedto sound reasoning and comprehension ability.We seek to investigate this question by propos-ing a new task ofrationale generation. Es-sentially, we task a VQA model with generat-ing rationales for the answers it predicts. Weuse data from the Visual Commonsense Rea-soning (VCR) task, as it contains ground-truthrationales along with visual questions and an-swers. We first investigate commonsense un-derstanding in one of the leading VCR mod-els, ViLBERT, by generating rationales frompretrained weights using a state-of-the-art lan-guage model, GPT-2. Next, we seek to jointlytrain ViLBERT with GPT-2 in an end-to-endfashion with the dual task of predicting the an-swer in VQA and generating rationales. Weshow that this kind of training injects com-monsense understanding in the VQA modelthrough quantitative and qualitative evaluationmetrics
Epidemiological study of patients with hepatitis E virus infection in Catalonia

Hepatitis E virus (HEV) is a non-enveloped single stranded RNA virus member of the Hepeviridae family and genus Orthohepevirus, which is a common cause of acute hepatitis worldwide and, in high-risk groups, can develop into a chronic infection. An epidemiological study of the from Hospital Universitari Vall d’Hebron (HUVH) HEV Data Base was carried out to study which is the most probable source of infections, risk factors, and diagnosis confidence test based on clinical data. Our results suggest that there HEV infection is associated with old age and/or immunocompromised status. A deeper analysis should be performed. Interestingly, our results show that HEV subtyping diagnosis classification should be improved, especially to classify HEV genotypes 3 infected patients. Sliding windows analysis was used to identify a short region of 400 nucleotides able to confidently classify genotype 3 genomes into subtypes. From the clinical analysis, we identified an HEV food-borne infected patient who got infected in 2012 and developed a long-lasting chronic infection having still HEV-RNA positive detection in 2017 samples. Surprisingly, this patient had HEV-RNA serum negativization periods (between 2014-2015). To study whether this patient had got multiple infections or whether it was the result of reactivation of the original infection, a sample from 2012 before starting Ribavirin treatment (pre-treatment) and a sample obtained in 2016 (post-treatment) were compared using Next Generation Sequencing (NGS). Reactivation of the original infection was demonstrated by phylogenetic and genetic distance studies. This result has important clinical implications, since any patient that develop a chronic infection, despite negativization of circulating HEV-RNA in serum sample, should be followed up for several months or years with highly sensitive test before assuring that HEV infection has been completely resolved. Also, one described resistance-variant was observed and a list of variants were identificated in pre-ttm and post-ttm samples. This study is a translational study which has a clinical relevance and which opens new lines of research for new future projects.
Snapshot Interferometric 3D Imaging by Compressive Sensing and Deep Learning

We demonstrate single-shot compressive three-dimensional (3D) $(x, y, z)$ imaging based on interference coding. The depth dimension of the object is encoded into the interferometric spectra of the light field, resulting a $(x, y, \lambda)$ datacube which is subsequently measured by a single-shot spectrometer. By implementing a compression ratio up to $400$, we are able to reconstruct $1G$ voxels from a 2D measurement. Both an optimization based compressive sensing algorithm and a deep learning network are developed for 3D reconstruction from a single 2D coded measurement. Due to the fast acquisition speed, our approach is able to capture volumetric activities at native camera frame rates, enabling 4D (volumetric-temporal) visualization of dynamic scenes.
Simulations and Design of a Single-Photon CMOS Imaging Pixel Using Multiple Non-Destructive Signal Sampling

A single-photon CMOS image sensor (CIS) design based on pinned photodiode (PPD) with multiple charge transfers and sampling is described. In the proposed pixel architecture, the photogenerated signal is sampled non-destructively multiple times and the results are averaged. Each signal measurement is statistically independent and by averaging, the electronic readout noise is reduced to a level where single photons can be distinguished reliably. A pixel design using this method was simulated in TCAD and several layouts were generated for a 180-nm CMOS image sensor process. Using simulations, the noise performance of the pixel was determined as a function of the number of samples, sense node capacitance, sampling rate and transistor characteristics. The strengths and limitations of the proposed design are discussed in detail, including the trade-off between noise performance and readout rate and the impact of charge transfer inefficiency (CTI). The projected performance of our first prototype device indicates that single-photon imaging is within reach and could enable ground-breaking performances in many scientific and industrial imaging applications.
Towards a Science of Resilient Robotic Autonomy

This discussion paper aims to support the argument process for the need to develop a comprehensive science of resilient robotic autonomy. Resilience and its key characteristics relating to robustness, redundancy, and resourcefulness are discussed, followed by a selected - but not exhaustive - list of research themes and domains that are crucial to facilitate resilient autonomy. Last but not least, an outline of possible directions of a new and enhanced design paradigm in robotics is presented. This manuscript is intentionally short and abstract. It serves to open the discussion and raise questions. The answers will necessarily be found in the actual process of conducting research by the community and in the framework of introducing robotics in an ever increasing set of real-life use cases. Its current form is based on thoughts identified within the ongoing experience of conducting research for robotic systems to gain autonomy in certain types of extreme environments such as subterranean settings, nuclear facilities, agriculture areas, and long-term off-road deployments. The very context of this document will be subject to change and it will be iteratively revisited.
Locality Sensitive Hashing-based Sequence Alignment Using Deep Bidirectional LSTM Models

Bidirectional Long Short-Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN) architecture which is designed to model sequences and their long-range dependencies more precisely than RNNs. This paper proposes to use deep bidirectional LSTM for sequence modeling as an approach to perform locality-sensitive hashing (LSH)-based sequence alignment. In particular, we use the deep bidirectional LSTM to learn features of LSH. The obtained LSH is then can be utilized to perform sequence alignment. We demonstrate the feasibility of the modeling sequences using the proposed LSTM-based model by aligning the short read queries over the reference genome. We use the human reference genome as our training dataset, in addition to a set of short reads generated using Illumina sequencing technology. The ultimate goal is to align query sequences into a reference genome. We first decompose the reference genome into multiple sequences. These sequences are then fed into the bidirectional LSTM model and then mapped into fixed-length vectors. These vectors are what we call the trained LSH, which can then be used for sequence alignment. The case study shows that using the introduced LSTM-based model, we achieve higher accuracy with the number of epochs.