Miracle-dz/UniFAR-FTUs huggingface dataset:
OpenEL: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue

Entity linking in dialogue is the task of mapping entity mentions in utterances to a target knowledge base. Prior work on entity linking has mainly focused on well-written articles such as Wikipedia, annotated newswire, or domain-specific datasets. We extend the study of entity linking to open domain dialogue by presenting the OpenEL corpus: an annotated multi-domain corpus for linking entities in natural conversation to Wikidata. Each dialogic utterance in 179 dialogues over 12 topics from the EDINA dataset has been annotated for entities realized by definite referring expressions as well as anaphoric forms such as he, she, it and they. This dataset supports training and evaluation of entity linking in open-domain dialogue, as well as analysis of the effect of using dialogue context and anaphora resolution in model training. It could also be used for fine-tuning a coreference resolution algorithm. To the best of our knowledge, this is the first substantial entity linking corpus publicly available for open-domain dialogue. We also establish baselines for this task using several existing entity linking systems. We found that the Transformer-based system Flair + BLINK has the best performance with a 0.65 F1 score. Our results show that dialogue context is extremely beneficial for entity linking in conversations, with Flair + Blink achieving an F1 of 0.61 without discourse context. These results also demonstrate the remaining performance gap between the baselines and human performance, highlighting the challenges of entity linking in open-domain dialogue, and suggesting many avenues for future research using OpenEL.
Annotating Arguments in a Corpus of Opinion Articles

Interest in argument mining has resulted in an increasing number of argument annotated corpora. However, most focus on English texts with explicit argumentative discourse markers, such as persuasive essays or legal documents. Conversely, we report on the first extensive and consolidated Portuguese argument annotation project focused on opinion articles. We briefly describe the annotation guidelines based on a multi-layered process and analyze the manual annotations produced, highlighting the main challenges of this textual genre. We then conduct a comprehensive inter-annotator agreement analysis, including argumentative discourse units, their classes and relations, and resulting graphs. This analysis reveals that each of these aspects tackles very different kinds of challenges. We observe differences in annotator profiles, motivating our aim of producing a non-aggregated corpus containing the insights of every annotator. We note that the interpretation and identification of token-level arguments is challenging; nevertheless, tasks that focus on higher-level components of the argument structure can obtain considerable agreement. We lay down perspectives on corpus usage, exploiting its multi-faceted nature.
Bioenergy with or without carbon dioxide removal: influence of functional unit choice and parameter variability

PURPOSE. Bioenergy with carbon dioxide removal (CDR) is increasingly presented as an efficient way to mitigate climate change. This study set out to determine under which circumstances and methodological choices CDR bioenergy systems are preferable over a reference bioenergy system from a climate change mitigation perspective. In addition, the CDR systems investigated were compared to each other. METHODS. Three systems were modelled: two CDR systems (Biochar and Bioenergy with carbon capture and storage (BECCS)), with a combined heat and power (CHP) system as reference. A parametrised life cycle inventory model was developed and computed for all systems and four different functional units (FUs), resulting in distributions of climate impacts. Contribution analysis was performed, as well as a pairwise comparison of all scenarios to establish their ranking. First-order Sobol indices were computed to assess the contribution of each parameter to the variance. Whenever ranking of scenarios was largely dependent on parameter values, decision tree analysis was applied. and contribution analysis, allows for a deeper analysis than traditional sensitivity analyses.
Multidisciplinary design optimisation as strategy for building design

In this research project, an approach is proposed for applying multidisciplinary design optimisation on building design problems. The software used in the approach are Rhinoceros and Grasshopper for the parametric configuration of the geometry, the plug-in Octopus driving the optimisation process with its multi-objective genetic algorithm and a custom post-processing tool PPOR for comparing and filtering different results and investigating the trade-offs between different objectives. To test the functionality of the proposed approach, it was applied on a relevant case study. The large span steel roof of the Ukrainian Donbass Arena was chosen as test case. Focus was given on the structural and acoustic performance of the roof. The structural performance was described by the structural mass of the roof, whereas the acoustic deviation throughout the stadium was measured for the acoustic assessment. The plug-in Karamba was used for the structural analysis and a custom ray-tracing tool, SPL-Estimator, was developed for the acoustic analysis. A parametric model of the football stadium was set up, in which the geometry of the roof was described by eleven design variables.
A Simple Reachability-Based Complexity Measure for Case-Based Reasoners

Case-Based Reasoning relies on the underlying hypothesis that similar problems have similar solutions. The suitability of a domain to case-based reasoning is assessed by measuring the extent to which this hypothesis holds good in the case base. Several local and global alignment measures have been proposed for measuring the complexity of the case base. However, the existing measures only take similarity knowledge into account while computing complexity. We propose a new complexity measure called Reachability Based Complexity Measure (RBCM) that goes beyond similarity knowledge to include the eﬀects of all knowledge containers in the reasoner. The proposed measure is evaluated on several real-world datasets and results suggest that RBCM corroborates well with the generalization accuracy of the reasoner.
An improved method of the Globally Resolved Energy Balance Model by the Bayes network

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Optimal Rates of Convergence for Latent Generalized Correlation Matrix Estimation in Transelliptical Distribution

Correlation matrix plays a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson's sample correlation matrix. Although Pearson's sample correlation matrix enjoys various good properties under Gaussian models, its not an effective estimator when facing heavy-tail distributions with possible outliers. As a robust alternative, \cite{han2012transelliptical} advocated the use of a transformed version of the Kendall's tau sample correlation matrix in estimating high dimensional latent generalized correlation matrix under the transelliptical distribution family (or elliptical copula). The transelliptical family assumes that after unspecified marginal monotone transformations, the data follow an elliptical distribution. In this paper, we study the theoretical properties of the Kendall's tau sample correlation matrix and its transformed version proposed in \cite{han2012transelliptical} for estimating the population Kendall's tau correlation matrix and the latent Pearson's correlation matrix under both spectral and restricted spectral norms. With regard to the spectral norm, we highlight the role of "effective rank" in quantifying the rate of convergence. With regard to the restricted spectral norm, we for the first time present a "sign subgaussian condition" which is sufficient to guarantee that the rank-based correlation matrix estimator attains the optimal rate of convergence. In both cases, we do not need any moment condition.
Latent Stratification for Incrementality Experiments

Incrementality experiments compare customers exposed to a marketing action designed to increase sales to those randomly assigned to a control group. These experiments suffer from noisy responses which make precise estimation of the average treatment effect (ATE) and marketing ROI difficult. We develop a model that improves the precision by estimating separate treatment effects for three latent strata defined by potential outcomes in the experiment -- customers who would buy regardless of ad exposure, those who would buy only if exposed to ads and those who would not buy regardless. The overall ATE is estimated by averaging the strata-level effects, and this produces a more precise estimator of the ATE over a wide range of conditions typical of marketing experiments. Analytical results and simulations show that the method decreases the sampling variance of the ATE most when (1) there are large differences in the treatment effect between latent strata and (2) the model used to estimate the strata-level effects is well-identified. Applying the procedure to 5 catalog experiments shows a reduction of 30-60% in the variance of the overall ATE. This leads to a substantial decrease in decision errors when the estimator is used to determine whether ads should be continued or discontinued.
On the existence of value for a general stochastic differential game with ergodic payoff

In this paper we study a type of two-player zero-sum stochastic diﬀerential games with ergodic payoﬀ, in which the diﬀusion system does not need to be non-degenerate. We ﬁrst show the existence of a viscosity solution of the associated ergodic Hamilton-Jacobi-Bellman-Isaacs equation under the dissipativity condition. With the help of this viscosity solution, we then give the estimates for the upper and lower ergodic value functions by constructing a series of non-degenerate approximating processes and using the sup- and inf-convolution techniques. Finally, we obtain the existence of a value under the Isaacs condition and its representation formulae. In addition, we apply our results to study a type of pollution accumulation problems with the long-run average social welfare.
Training-free hyperparameter optimization of neural networks for electronic structures in matter

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations—this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of neural network models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn–Sham density functional theory, the most popular computational method in materials science and chemistry.
Effect of $D^0$-$\bar{D}{}^0$ Mixing upon Cabibbo-favored $D^0$ Decays

The parameter y CP is used to characterize mixing in the D 0 - D 0 meson system. To determine y CP , one measures the eﬀective decay width of a D 0 or D 0 decaying to a CP eigenstate, relative to the eﬀective width for a decay to a Cabibbo-favored, ﬂavor-speciﬁc ﬁnal state. When using y CP to extract information about D 0 - D 0 mixing and CP violation, the decay width of the Cabibbo-favored decay is usually assumed to equal 1 /τ , the reciprocal of the D 0 lifetime. However, there is a small correction to this that should be taken into account when y CP is measured with suﬃciently high precision. We calculate this correction in terms of charm mixing and CP violation parameters x , y , | q/p | , and φ .
Modelling the persistent low-state γ-ray emission of the PKS 1510−089 blazar with electromagnetic cascades initiated in hadronuclear interactions

Blazars may accelerate protons and/or nuclei as well as electrons. The hadronic component of accelerated particles in blazars may constitute the bulk of their high-energy budget; nevertheless, this component is elusive because of the high value of the energy threshold of proton interactions with photon fields inside the source. However, the broad line regions (BLRs) of some flat spectrum radio quasars (FSRQs) may contain a sufficient amount of matter to render primary protons ‘visible’ in γ-rays via hadronuclear interactions. In this paper, we study the persistent γ-ray emission of the FSRQ PKS 1510−089 in its low state, utilizing the publicly available Fermi-LAT data, as well as using the spectrum measured with the Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes. We find an indication that there is an excess of γ-rays at the energy range ≳ 20 GeV with respect to a simple baseline log-parabolic intrinsic spectral model. This excess could be explained in a scenario invoking hadronuclear interactions of primary protons on the BLR material with the subsequent development of electromagnetic cascades in photon fields. We present a Monte Carlo calculation of the spectrum of this cascade component, taking as input the BLR photon field spectrum calculated with the cloudy code. To our knowledge, this is the first calculation of an electromagnetic cascade spectrum inside a blazar based on a direct calculation of the photon field spectrum with a spectral synthesis code.
Style Transfer of Black and White Silhouette Images using CycleGAN and a Randomly Generated Dataset

— CycleGAN can be used to transfer an artistic style to an image. It does not require pairs of source and stylized images to train a model. Taking this advantage, we propose using randomly generated data to train a machine learning model that can transfer traditional art style to a black and white silhouette image. The result is noticeably better than the previous neural style transfer methods. However, there are some areas for improvement, such as removing artifacts and spikes from the transformed image.
DeepGen: Diverse Search Ad Generation and Real-Time Customization

We present DeepGen, a system deployed at web scale for automatically creating sponsored search advertisements (ads) for BingAds customers. We leverage state-of-the-art natural language generation (NLG) models to generate fluent ads from advertiser's web pages in an abstractive fashion and solve practical issues such as factuality and inference speed. In addition, our system creates a customized ad in real-time in response to the user's search query, therefore highlighting different aspects of the same product based on what the user is looking for. To achieve this, our system generates a diverse choice of smaller pieces of the ad ahead of time and, at query time, selects the most relevant ones to be stitched into a complete ad. We improve generation diversity by training a controllable NLG model to generate multiple ads for the same web page highlighting different selling points. Our system design further improves diversity horizontally by first running an ensemble of generation models trained with different objectives and then using a diversity sampling algorithm to pick a diverse subset of generation results for online selection. Experimental results show the effectiveness of our proposed system design. Our system is currently deployed in production, serving ${\sim}4\%$ of global ads served in Bing.
NeuCASL: From Logic Design to System Simulation of Neuromorphic Engines

— With Moore’s law saturating and Dennard scaling hitting its wall, traditional Von Neuman systems cannot offer the GFlops/watt for compute-intensive algorithms such as CNN. Recent trends in unconventional computing approaches give us a hope to design highly energy-efficient computing systems for such algorithms. Neuromorphic computing is a promising such approach with its brain-inspired circuitry, use of emerging technologies, and low power nature. Researchers use a variety of novel technologies such as memristors, silicon photonics, FinFET, and carbon nanotubes to demonstrate a neuromorphic computer. However, a flexible CAD tool to start from neuromorphic logic design and go up to architectural simulation is yet to be demonstrated to support the rise of this promising paradigm. In this project, we aim to build NeuCASL , an opensource python-based full system CAD framework for neuromorphic logic design, circuit simulation, and system performance and reliability estimation. This is a first-of-its-kind to the best of our knowledge. device that under suitable operating conditions can amplify photonic signals. In a typical high bandwidth photonic link, an off-chip laser source (either on the board or on a 2.5D interposer) generates multiple wavelengths, which are coupled by an optical grating coupler to an on-chip photonic waveguide. Further, we plan to deploy novel memory technologies such as memristor crossbar and photonic phase change memory.
Effects of Stubbornness on Opinion Dynamics

As an important factor governing opinion dynamics, stubbornness strongly affects various aspects of opinion formation. However, a systematically theoretical study about the influences of heterogeneous stubbornness on opinion dynamics is still lacking. In this paper, we study a popular opinion model in the presence of inhomogeneous stubbornness. We show analytically that heterogeneous stubbornness has a great impact on convergence time, expressed opinion of every node, and the overall expressed opinion. We provide an explanation of the expressed opinion in terms of stubbornness-dependent spanning diverging forests. We propose quantitative indicators to quantify some social concepts, including conflict, disagreement, and polarization by incorporating heterogeneous stubbornness, and develop a nearly linear time algorithm to approximate these quantities, which has a proved theoretical guarantee for the error of each quantity. To demonstrate the performance of our algorithm, we perform extensive experiments on a large set of real networks, which indicate that our algorithm is both efficient and effective, scalable to large networks with millions of nodes.
DialogSum Challenge: Results of the Dialogue Summarization Shared Task

We report the results of DialogSum Challenge, the shared task on summarizing real-life scenario dialogues at INLG 2022. Four teams participate in this shared task and three submit their system reports, exploring different methods to improve the performance of dialogue summarization. Although there is a great improvement over the baseline models regarding automatic evaluation metrics, such as Rouge scores, we find that there is a salient gap between model generated outputs and human annotated summaries by human evaluation from multiple aspects. These findings demonstrate the difficulty of dialogue summarization and suggest that more fine-grained evaluatuion metrics are in need.
Video-based Human Action Recognition using Deep Learning: A Review

Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously unseen data sequence acquired by sensors. The ability to recognize, understand, and predict complex human actions enables the construction of many important applications such as intelligent surveillance systems, human-computer interfaces, health care, security, and military applications. In recent years, deep learning has been given particular attention by the computer vision community. This paper presents an overview of the current state-of-the-art in action recognition using video analysis with deep learning techniques. We present the most important deep learning models for recognizing human actions, and analyze them to provide the current progress of deep learning algorithms applied to solve human action recognition problems in realistic videos highlighting their advantages and disadvantages. Based on the quantitative analysis using recognition accuracies reported in the literature, our study identifies state-of-the-art deep architectures in action recognition and then provides current trends and open problems for future works in this field.
Geometric Approach For Majorizing Measures

Gaussian processes can be considered as subsets of a standard Hilbert space, but the geometric understanding that would relate the size of a set with the size of its convex hull is still lacking. In this work, we adopt a geometric approach to the majorizing measure problem by identifying the covering number relationships between a given space $T$ and its convex hull, represented by $T_h$. If the space $T$ is a closed bounded polyhedra in $\mathbb{R}^n$, we can evaluate the volume ratio between the space $T$ and its convex hull obtained by the Quickhull algorithm. If the space $T$ is a general compact object in $\mathbb{R}^n$ with non-empty interior, we first establish a more general reverse Brunn-Minkowski inequality for nonconvex spaces which will assist us to bound the volume of $T_h$ in terms of the volume of $T$ if $T_h$ can be acquired by the finite average of the space $T$ with respect to the Minkowski sum. If the volume ratio between the space $T$ and the space $T_h$ is obtained, the covering number ratio between the space $T$ and the space $T_h$ can also be obtained which will be used to build majorizing measure inequality. For infinite dimensional space, we show that the constant $L$ at majorizing measure inequality may not always exist and the existence condition will depend on geometric properties of $T$.
DeepTLS: comprehensive and high-performance feature extraction for encrypted traffic

Feature extraction is critical for TLS trafﬁc analysis using machine learning techniques, which it is also very difﬁcult and time-consuming requiring huge engineering efforts. We designed and implemented DeepTLS, a system which extracts full spectrum of features from pcaps across meta, statistical, SPLT, byte distribution, TLS header and certiﬁcates. The backend is written in C++ to achieve high performance, which can analyze a GB-size pcap in a few minutes. DeepTLS was thoroughly evaluated against two state-of-the-art tools Joy and Zeek with four well-known malicious trafﬁc datasets consisted of 160 pcaps. Evaluation results show DeepTLS has advantage of analyzing large pcaps with half analysis time, and identiﬁed more certiﬁcates with acceptable performance loss compared with Joy. DeepTLS can signiﬁcantly accelerate machine learning pipeline by reducing feature extraction time from hours even days to minutes. The system is online at https://deeptls.com , where test artifacts can be viewed and validated. In addition, two open source tools Pysharkfeat and Tlsfeatmark are also released.