Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Policy Mix on the Pollutant Emissions in the United Kingdom

Artykuł ukazuje skutki ekologiczne kombinacji polityki budżetowej i pieniężnej stosowanej do pobudzenia gospodarki w trakcie cykli koniunkturalnych. Jest to ważna kwestia zwłaszcza podczas kryzysu i w okresie pokryzysowym, kiedy rządy i banki centralne szukają nowej kombinacji polityki budżetowej i pieniężnej. Autorzy przeprowadzają analizę empiryczną opartą na dynamicznym modelu stochastycznym równowagi ogólnej uwzględniającym efekty środowiskowe (model E-DSGE), na danych kwartalnych dla Wielkiej Brytanii obejmujących okres od 1975 r. do 2016 r. Wyniki analizy wskazują, że stymulacja pieniężna gospodarki jest bardziej korzystna dla środowiska naturalnego aniżeli ekspansywna polityka fiskalna. Wynika to stąd, że ekspansja fiskalna wypiera na ogół inwestycje prywatne i powoduje przesunięcie produkcji do technik bardziej pracochłonnych, a mniej kapitałochłonnych, co wiąże się ze zwiększoną emisją zanieczyszczeń. Natomiast ekspansja monetarna silnie pobudza akumulację nowego kapitału fizycznego, co oznacza mniejsze zanieczyszczenie środowiska w kraju wysoko rozwiniętym, takim jak Wielka Brytania. (abstrakt oryginalny)
Dynamic Resource Allocation in UAV-Enabled mmWave Communication Networks

Unmanned aerial vehicle (UAV)-enabled cellular architecture over the millimeter-wave (mmWave) frequency band is likely to be the best solution for on-demand high data rate service provisioning in the next generation communication networks. The beam formed by the mmWave antenna array is highly directional and requires multiple-beam scans to cover the entire area. This work presents a novel sectoring approach to ensure coverage of the whole area. The side lobe gain of the antenna array is taken into consideration, which generates substantial interference in other sectors. The expression for the probability distribution of the signal-to-interference-plus-noise ratio due to simultaneous transmissions in different sectors is derived in the downlink communication scenario. To limit interference in the concurrent transmission strategy, a threshold on power spillage from adjacent sectors is placed. For this topology, a resource allocation problem is formulated aiming to maximize the sum rate while ensuring a minimum rate guarantee to each user. It is observed that sum-rate variation with height is unimodal. The sum power and backhaul capacity constraints are accounted. This optimization problem is mixed-integer nonconvex programming. Hence, it is solved using the Lagrangian dual decomposition method, which provides an asymptotic global optimal solution. Since this method is computationally intensive, a suboptimal solution is proposed. Simulation results demonstrate convergence to an optimal solution, and it is observed that backhaul link capacity restricts the sum rate. Numerical results are presented for multiple representative field environments consisting of different types of built-up areas. It is observed that the transmitter antenna array sidelobe has a strong impact on the performance as compared to the ideal scenario without sidelobe, which overestimates the total sum rate by a factor of 3.
Grouping of Aspects into Relevant Category Based on WordNet Definitions

Summary Machine Translation, Information Retrieval and Knowledge Acquisition are the three main applications of sentiment analysis based on aspect word. But in some cases, there may be two aspect words having different spellings, yet belonging to the same aspect category, e.g. the words ‘costly’ and ‘expensive’ belong to the same category ‘price. ’ Grouping such aspects into the same category is critical for sentiment analysis. Some have done this by using synonym, LDA (Latent Dirichlet Analysis) groups and Constrained-LDA. The WordNet dictionary is mostly used for such analysis. Although it contains a lot of synonyms, yet sometimes there are such aspects which are not synonymous, but still refer to the same broad category. For example, in the following two sentences: “this camera will not easily fit in a coat pocket” and “this camera is of large size;” aspects ‘fit’ and ‘size’ both are not synonyms yet belong to the same category. In this study, the proposed framework consists of four steps: DefString, Supporting-Words, SetWords and Intersection for Decision. The fourth stage will decide that the two aspects belong to the same category or not. After experimenting on a dataset comprising 252 pairs of same-meaning aspects and 62 pairs of different-meaning aspects, 91% accuracy and 86% F score were detected.
Phylogenetics of Freshwater Sculpin

Introduction. Reconciling observed genetic variation with evolutionary history is often a daunting task. Phylogenetics is a method of study that is particularly well-suited to addressing this task by combining the measureable genetic variability of individuals, populations, or species with proposed evolutionary relationships and processes (Barraclough & Nee 2001). Historically, phylogenetic trees have been generated using a single locus approach, where variation within a single gene is used to create a phylogenetic gene tree. However, this single gene method may fail to account for the full variation across a genome.
Hierarchically Attentive Graph Pooling with Subgraph Attention

Graph neural networks got signiﬁcant attention for graph representation and classiﬁcation in machine learning community. Different types of neighborhood aggregation and pooling strategies have been proposed in the literature. In this work, we introduce a higher order hierarchical GNN al-gorithm (SubGattPool) by employing (i) an attention mechanism which learns the importance and aggregates neighboring subgraphs of a node instead of ﬁrst-order neighbors, and (ii) a hierarchical pooling strategy which learns the importance of different hierarchies in a GNN. SubGattPool is able to achieve state-of-the-art graph classiﬁcation performance on multiple real-world datasets.
Developing a Structural Method for Eliciting Criteria in Project Manager Selection

As part of a comprehensive research, a structural model was conducted with experienced practitioners to identify the hierarchical structure for selecting project manager in MAPNA Company. Similar to many decision making problems, the choice of project managers in real life is very complicated. Although a good number of researches have examined these problems, no detailed and sound procedures have been stated to specify the requirement criteria. This paper presents a framework to help decision makers define and clarify the hierarchy of hiring criteria and sub criteria. The main objective of this paper it is to develop structural method to identify criteria for project manager selection by modifying Delphi method and as well draw out the hierarchical criteria. In this model, after investigating previous studies about project manager skills, the Delphi technique with some modification was used to seek best thinking from managers and experts for criteria selection. A case study was used to validate the models for the selection of project managers in a project based company. The results showed that the proposed model was satisfactory in choosing the criteria and are capable of improving the effectiveness in the decision making processes.
One size does not fit all: Single-subject analyses reveal substantial individual variation in electroencephalography (EEG) characteristics of antidepressant treatment response

Background: Electroencephalography (EEG) characteristics associated with treatment response show potential for informing treatment choices for major depressive disorder, but to date, no robust markers have been identified. Variable findings might be due to the use of group analyses on a relatively heterogeneous population, which neglect individual variation. However, the correspondence between group level findings and individual brain characteristics has not been extensively investigated. Using single-subject analyses, we explored the extent to which group-based EEG connectivity and complexity characteristics associated with treatment response could be identified in individual patients. Methods: Resting-state EEG data and Montgomery-Asberg Depression Rating Scale symptom scores were collected from 43 patients with depression (23 females) before, at 1 and 12 weeks of treatment with escitalopram, bupropion or both. The multivariate statistical technique partial least squares was used to: 1) identify differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between responders and non-responders to treatment (>=50% and <50% reduction in symptoms, respectively, by week 12), and 2) determine whether group patterns could be identified in individual patients. Results: The group analyses distinguished groups. Responders showed decreased alpha and increased beta connectivity and early, widespread decreases in coarse scale entropy over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in coarse scale entropy. These EEG characteristics were identified in ~40-60% of individual patients. Conclusion: Substantial individual variation highlighted by the single-subject analyses might explain why robust EEG markers of antidepressant treatment response have not been identified. As up to 60% of patients in our sample was not well represented by the group results, individual variation needs to be considered when investigating clinically useful characteristics of antidepressant treatment response.
A search for X-ray absorbed sources in the 3XMM catalogue using photometric redshifts and Bayesian spectral fits

Since its launch in 1999, the XMM-Newton mission has compiled the largest catalogue of serendipitous X-ray sources, with the 3XMM being the third version of this catalogue. This was possible thanks to the combination of a large effective area (5000 cm2 at 1 keV) and a wide field of view (30 arcmin). The 3XMM-DR6 catalogue contains about 470 000 unique X-ray sources over an area of 982 deg2. A significant fraction of these (100 178 sources) have reliable optical, near-(NIR), and mid-infrared (MIR) counterparts in the SDSS, PANSTARRS, VIDEO, UKIDSS, and WISE surveys. In a previous paper we presented photometric redshifts for these sources using the TPZ machine-learning algorithm. About a quarter of these (22 677) have adequate photon statistics, meaning that a reliable X-ray spectrum can be extracted. Owing to both the X-ray counts selection and the optical counterpart constraint, the sample above is biased towards the bright sources. Here, we present XMMFITCAT-Z: a spectral fit catalogue for these sources using the Bayesian X-ray Analysis technique. To demonstrate the potential of the present catalogue, we comment on the optical and MIR colours of the 765 X-ray absorbed sources with NH > 1022 cm−2. We show that a considerable fraction of X-ray-selected AGNs would not be classified as AGNs following the MIR W1–W2 versus W2 selection criterion. These are AGNs with lower luminosities, where the contribution of the host galaxy to the MIR emission is non-negligible. Only one-third of obscured AGNs in X-rays present red colours or r–W2 > 6. Also, it appears that the r–W2 criterion, often used in the literature for the selection of obscured AGNs, produces very different X-ray absorbed AGN samples compared to the standard X-ray selection criteria.
Comparing computational entropies below majority (or: When is the dense model theorem false?)

Computational pseudorandomness studies the extent to which a random variable $\bf{Z}$ looks like the uniform distribution according to a class of tests $\cal{F}$. Computational entropy generalizes computational pseudorandomness by studying the extent which a random variable looks like a \emph{high entropy} distribution. There are different formal definitions of computational entropy with different advantages for different applications. Because of this, it is of interest to understand when these definitions are equivalent. We consider three notions of computational entropy which are known to be equivalent when the test class $\cal{F}$ is closed under taking majorities. This equivalence constitutes (essentially) the so-called \emph{dense model theorem} of Green and Tao (and later made explicit by Tao-Zeigler, Reingold et al., and Gowers). The dense model theorem plays a key role in Green and Tao's proof that the primes contain arbitrarily long arithmetic progressions and has since been connected to a surprisingly wide range of topics in mathematics and computer science, including cryptography, computational complexity, combinatorics and machine learning. We show that, in different situations where $\cal{F}$ is \emph{not} closed under majority, this equivalence fails. This in turn provides examples where the dense model theorem is \emph{false}.
A Riemann Difference Scheme for Shock Capturing in Discontinuous Finite Element Methods

We present a novel structure-preserving numerical scheme for discontinuous finite element approximations of nonlinear hyperbolic systems. The method can be understood as a generalization of the Lax-Friedrichs flux to a high-order staggered grid and does not depend on any tunable parameters. Under a presented set of conditions, we show that the method is conservative and invariant domain preserving. Numerical experiments on the Euler equations show the ability of the scheme to resolve discontinuities without introducing excessive spurious oscillations or dissipation.
A gender comparison of psychological distress among medical students in Nigeria during the Coronavirus pandemic: a cross-sectional survey

Background: The Coronavirus disease (COVID-19) pandemic as a large scale stressor could have negative distress on the mental health on medical students. Since gender differences in mental health may exist between males and females, it would prove interesting to see if a large scale stressor such as the pandemic will cause variances in the psychological distress between both genders. This study assessed and compared the psychological distress of COVID-19 among male and female medical students in medical schools in South-Western Nigeria Materials and methods: A cross-sectional online survey using was carried out among 1010 medical students from three largest universities in south western Nigeria during the COVID-19 pandemic. The respondents were purposively selected, data was obtained on participants demographic and psychological distress was assessed using the General Health Questionnaire 12 (GHQ-12). Data was analyzed using the SPSS version 21 statistical software, chi square was used to assess gender differences, multivariate regression analysis assessed the predictors of psychological distress among both gendersand p values less than 0.05 were considered significant. Results: Female medical students are at a higher risk of psychological distress compared to their male counterparts (p<0.005). Females were almost twice at risk of psychological distress during the COVID-19 pandemic than males (OR=1.534, 95% p=0.003). Females with a positive history of mental illness were five times more at risk of psychological distress during the COVID-19 pandemic compared to females with no previous mental health history (OR=5.102, p=0.002) Conclusion: Females were at higher risk of psychological distress compared to male students. Gender specific interventions addressing psychological distress among medical students are recommended. Keywords: Psychological distress, psychological impact, gender, COVID-19, medical students.
Shift identification in time varying regression quantiles

This article investigates whether time-varying quantile regression curves are the same up to the horizontal shift or not. The errors and the covariates involved in the regression model are allowed to be locally stationary. We formalise this issue in a corresponding non-parametric hypothesis testing problem, and develop a integrated-squared-norm based test (SIT) as well as a simultaneous confidence band (SCB) approach. The asymptotic properties of SIT and SCB under null and local alternatives are derived. We then propose valid wild bootstrap algorithms to implement SIT and SCB. Furthermore, the usefulness of the proposed methodology is illustrated via analysing simulated and real data related to Covid-19 outbreak and climate science.
Deciding accuracy of differential privacy schemes

Differential privacy is a mathematical framework for developing statistical computations with provable guarantees of privacy and accuracy. In contrast to the privacy component of differential privacy, which has a clear mathematical and intuitive meaning, the accuracy component of differential privacy does not have a generally accepted definition; accuracy claims of differential privacy algorithms vary from algorithm to algorithm and are not instantiations of a general definition. We identify program discontinuity as a common theme in existing ad hoc definitions and introduce an alternative notion of accuracy parametrized by, what we call, — the of an input x w.r. t. a deterministic computation f and a distance d, is the minimal distance d(x,y) over all y such that f(y)≠ f(x). We show that our notion of accuracy subsumes the definition used in theoretical computer science, and captures known accuracy claims for differential privacy algorithms. In fact, our general notion of accuracy helps us prove better claims in some cases. Next, we study the decidability of accuracy. We first show that accuracy is in general undecidable. Then, we define a non-trivial class of probabilistic computations for which accuracy is decidable (unconditionally, or assuming Schanuel’s conjecture). We implement our decision procedure and experimentally evaluate the effectiveness of our approach for generating proofs or counterexamples of accuracy for common algorithms from the literature.
Augmenting BERT Carefully with Underrepresented Linguistic Features

Fine-tuned Bidirectional Encoder Representations from Transformers (BERT)-based sequence classification models have proven to be effective for detecting Alzheimer's Disease (AD) from transcripts of human speech. However, previous research shows it is possible to improve BERT's performance on various tasks by augmenting the model with additional information. In this work, we use probing tasks as introspection techniques to identify linguistic information not well-represented in various layers of BERT, but important for the AD detection task. We supplement these linguistic features in which representations from BERT are found to be insufficient with hand-crafted features externally, and show that jointly fine-tuning BERT in combination with these features improves the performance of AD classification by upto 5\% over fine-tuned BERT alone.
A Two-Stage Radar Sensing Approach based on MIMO-OFDM Technology

Recently, integrating the communication and sensing functions into a common network has attracted a great amount of attention. This paper considers the advanced signal processing techniques for enabling the radar to sense the environment via the communication signals. Since the technologies of orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) are widely used in the legacy cellular systems, this paper proposes a two-stage signal processing approach for radar sensing in an MIMO-OFDM system, where the scattered channels caused by various targets are estimated in the first stage, and the location information of the targets is then extracted from their scattered channels in the second stage. Specifically, based on the observations that radar sensing is similar to multi-path communication in the sense that different targets scatter the signal sent by the radar transmitter to the radar receiver with various delay, and that the number of scatters is limited, we show that the OFDM-based channel training approach together with the compressed sensing technique can be utilized to estimate the scattered channels efficiently in Stage I. Moreover, to tackle the challenge arising from range resolution for sensing the location of closely spaced targets, we show that the MIMO radar technique can be leveraged in Stage II such that the radar has sufficient spatial samples to even detect the targets in close proximity based on their scattered channels. Last, numerical examples are provided to show the effectiveness of our proposed sensing approach which merely relies on the existing MIMO-OFDM communication techniques.
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving

For realizing safe autonomous driving, the end-to-end delays of real-time object detection systems should be thoroughly analyzed and minimized. However, despite recent development of neural networks with minimized inference delays, surprisingly little attention has been paid to their end-to-end delays from an object’s appearance until its detection is reported. With this motivation, this paper aims to provide more comprehensive understanding of the end-to-end delay, through which precise best- and worst-case delay predictions are formulated, and three optimization methods are implemented: (i) on-demand capture, (ii) zero-slack pipeline, and (iii) contention-free pipeline. Our experimental results show a 76% reduction in the end-to-end delay of Darknet YOLO (You Only Look Once) v3 (from 1070 ms to 261 ms), thereby demonstrating the great potential of exploiting the end-to-end delay analysis for autonomous driving. Furthermore, as we only modify the system architecture and do not change the neural network architecture itself, our approach incurs no penalty on the detection accuracy.
Quench Dynamics of the Ideal Bose Polaron at Zero and Nonzero Temperatures

We give a detailed account of a stationary impurity in an ideal Bose-Einstein condensate, which we call the ideal Bose polaron, at both zero and non-zero temperatures and arbitrary strength of the impurity-boson coupling. The time evolution is solved exactly and it is found that, surprisingly, many of the features that have been predicted for the real BEC are already present in this simpler setting and can be understood analytically therein. We obtain explicit formulae for the time evolution of the condensate wave function at $T=0$ and of the one-particle density matrix at $T>0$. For negative scattering length, the system is found to thermalize even though the dynamics are perfectly coherent. The time evolution and thermal values of the Tan contact are derived and compared to a recent experiment. We find that contrary to the Fermi polaron, the contact is not bounded at unitarity as long as a condensate exists. An explicit formula for the dynamical overlap at $T=0$ allows us to compute the rf spectrum which can be understood in detail by relating it to the two-body problem of one boson and the impurity.
Rediscovering alignment relations with Graph Convolutional Networks

Knowledge graphs are concurrently published and edited in the Web of data. Hence they may overlap, which makes key the task that consists in matching their content. This task encompasses the identification, within and across knowledge graphs, of nodes that are equivalent, more specific, or weakly related. In this article, we propose to match nodes of a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings. We experimented this approach on a biomedical knowledge graph and particularly investigated the interplay between formal semantics and GCN models with the two following main focuses. Firstly, we applied various inference rules associated with domain knowledge, independently or combined, before learning node embeddings, and we measured the improvements in matching results. Secondly, while our GCN model is agnostic to the exact alignment relations (e.g., equivalence, weak similarity), we observed that distances in the embedding space are coherent with the "strength" of these different relations (e.g., smaller distances for equivalences), somehow corresponding to their rediscovery by the model.