Miracle-dz/UniFAR-FTUs huggingface dataset:
"How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction

Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of urban traffic incidents. In this work, we aim to make use of the information of urban incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover urban traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features from the middle layer of the classifier. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate urban traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments on two real-world urban traffic datasets of San Francisco and New York City. The results demonstrate the superior performance of our model compare to the competing benchmarks.
Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift

We propose a new method that leverages contextual embeddings for the task of diachronic semantic shift detection by generating time specific word representations from BERT embeddings. The results of our experiments in the domain specific LiverpoolFC corpus suggest that the proposed method has performance comparable to the current state-of-the-art without requiring any time consuming domain adaptation on large corpora. The results on the newly created Brexit news corpus suggest that the method can be successfully used for the detection of a short-term yearly semantic shift. And lastly, the model also shows promising results in a multilingual settings, where the task was to detect differences and similarities between diachronic semantic shifts in different languages.
KELT-25 b and KELT-26 b: A Hot Jupiter and a Substellar Companion Transiting Young A Stars Observed by TESS

We present the discoveries of KELT-25 b (TIC 65412605, TOI-626.01) and KELT-26 b (TIC 160708862, TOI-1337.01), two transiting companions orbiting relatively bright, early A stars. The transit signals were initially detected by the KELT survey and subsequently confirmed by Transiting Exoplanet Survey Satellite (TESS) photometry. KELT-25 b is on a 4.40 day orbit around the V = 9.66 star CD-24 5016 ( K, M ⋆ = M ⊙), while KELT-26 b is on a 3.34 day orbit around the V = 9.95 star HD 134004 ( = K, M ⋆ = M ⊙), which is likely an Am star. We have confirmed the substellar nature of both companions through detailed characterization of each system using ground-based and TESS photometry, radial velocity measurements, Doppler tomography, and high-resolution imaging. For KELT-25, we determine a companion radius of R P = R J and a 3σ upper limit on the companion’s mass of ∼64 M J. For KELT-26 b, we infer a planetary mass and radius of M P = and R P = R J. From Doppler tomographic observations, we find KELT-26 b to reside in a highly misaligned orbit. This conclusion is weakly corroborated by a subtle asymmetry in the transit light curve from the TESS data. KELT-25 b appears to be in a well-aligned, prograde orbit, and the system is likely a member of the cluster Theia 449.
Controllability under positive constraints for quasilinear parabolic PDEs

This paper deals with the analysis of the internal control with constraint of positive kind of a parabolic PDE with nonlinear diffusion when the time horizon is large enough. The minimal controllability time will be strictly positive. We prove a global steady state constrained controllability result for a quasilinear parabolic with nonlinearity in the diffusion term. Then, under suitable dissipative assumption in the system and local controllability results, we conclude the result to any initial datum and any target trajectory.
Dream to Control: Learning Behaviors by Latent Imagination

To select effective actions in complex environments, intelligent agents need to generalize from past experience. World models can represent knowledge about the environment to facilitate such generalization. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks purely by latent imagination. We efficiently learn behaviors by backpropagating analytic gradients of learned state values through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.
Learning Word Ratings for Empathy and Distress from Document-Level User Responses

Despite the excellent performance of black box approaches to modeling sentiment and emotion, lexica (sets of informative words and associated weights) that characterize different emotions are indispensable to the NLP community because they allow for interpretable and robust predictions. Emotion analysis of text is increasing in popularity in NLP; however, manually creating lexica for psychological constructs such as empathy has proven difficult. This paper automatically creates empathy word ratings from document-level ratings. The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and has not used deep learning methods. We systematically compare a number of approaches to learning word ratings from higher-level supervision against a Mixed-Level Feed Forward Network (MLFFN), which we find performs best, and use the MLFFN to create the first-ever empathy lexicon. We then use Signed Spectral Clustering to gain insights into the resulting words. The empathy and distress lexica are publicly available at: http://www.wwbp. org/lexica.html.
Deep Learning for Symbolic Mathematics

Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing these mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models. We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica.
Decentralised Sparse Multi-Task Regression.

We consider a sparse multi-task regression framework for fitting a collection of related sparse models. Representing models as nodes in a graph with edges between related models, a framework that fuses lasso regressions with the total variation penalty is investigated. Under a form of restricted eigenvalue assumption, bounds on prediction and squared error are given that depend upon the sparsity of each model and the differences between related models. This assumption relates to the smallest eigenvalue restricted to the intersection of two cone sets of the covariance matrix constructed from each of the agents' covariances. We show that this assumption can be satisfied if the constructed covariance matrix satisfies a restricted isometry property. In the case of a grid topology high-probability bounds are given that match, up to log factors, the no-communication setting of fitting a lasso on each model, divided by the number of agents. A decentralised dual method that exploits a convex-concave formulation of the penalised problem is proposed to fit the models and its effectiveness demonstrated on simulations against the group lasso and variants.
Habitability on local, Galactic and cosmological scales

The aim of this paper is to underline conditions necessary for the emergence and development of life. They are placed at local planetary scale, at Galactic scale and within the cosmological evolution, as pointed out by the Anthropic Cosmological Principle. We will consider the circumstellar habitable zone (CHZ) for planetary systems and a Galactic Habitable Zone (GHZ) including also a set of strong cosmological constraints to allow life (cosmological habitability (COSH)). Some requirements are specific of a single scale and its related physical phenomena, while others are due to the conspired effects occurring at more than one scale. The scenario emerging from this analysis is that all the habitability conditions here detailed must at least be met. Then life asks something which may appear as "a monstrous sequence of accidents" as Hoyle (1959) thought, or as providential collaborations leading to realize how finely tuned is the architecture within which Life, as precious stone, is embedded.
On the Validity of Bayesian Neural Networks for Uncertainty Estimation

Deep neural networks (DNN) are versatile parametric models utilised successfully in a diverse number of tasks and domains. However, they have limitations---particularly from their lack of robustness and over-sensitivity to out of distribution samples. Bayesian Neural Networks, due to their formulation under the Bayesian framework, provide a principled approach to building neural networks that address these limitations. This paper describes a study that empirically evaluates and compares Bayesian Neural Networks to their equivalent point estimate Deep Neural Networks to quantify the predictive uncertainty induced by their parameters, as well as their performance in view of this uncertainty. In this study, we evaluated and compared three point estimate deep neural networks against comparable Bayesian neural network alternatives using two well-known benchmark image classification datasets (CIFAR-10 and SVHN).
A Hidden Variables Approach to Multilabel Logistic Regression

Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Multilabel Logistic Regression with Hidden variables (MLRH), which extends the standard logistic regression by introducing hidden variables. Hidden variables make it possible to go beyond the conventional multiclass logistic regression by relaxing the one-hot-encoding constraint. We define a new joint distribution of labels and hidden variables which enables us to obtain one classifier for multilabel classification. Our experimental studies on a set of benchmark datasets demonstrate that the probabilistic model can achieve competitive performance compared with other multilabel learning algorithms.
Enumeration of weighted paths on a digraph and block hook determinant

Abstract In this article, we evaluate determinants of “block hook” matrices, which are block matrices consist of hook matrices. In particular, we deduce that the determinant of a block hook matrix factorizes nicely. In addition we give a combinatorial interpretation of the aforesaid factorization property by counting weighted paths in a suitable weighted digraph.
Simultaneous photometric and spectral analysis of a new outburst of V1686 Cyg

We present an analysis of the optical observations of Herbig AeBe star V1686 Cyg, which is associated with a small isolated star-forming region around HAeBe star BD+40$^{\circ}$4124. We observed this star as a part of our project of young eruptive stars investigation. Observations were held on 2.6m telescope of Byurakan Observatory from 2015 to 2017. For this period we obtained V1686 Cyg direct images and 14 medium- and low-resolution spectra. In the course of observations we noticed that this star underwent a not-typical brightness outburst. After data reduction we found that the full rise and decline of V1686 Cyg brightness had almost 3 magnitudes amplitude and lasted about 3 months. We were also able to trace the changes of the stellar spectrum during the outburst, which are correlated with the photometric variations.
Deep Bayesian Active Learning for Multiple Correct Outputs

Typical active learning strategies are designed for tasks, such as classification, with the assumption that the output space is mutually exclusive. The assumption that these tasks always have exactly one correct answer has resulted in the creation of numerous uncertainty-based measurements, such as entropy and least confidence, which operate over a model's outputs. Unfortunately, many real-world vision tasks, like visual question answering and image captioning, have multiple correct answers, causing these measurements to overestimate uncertainty and sometimes perform worse than a random sampling baseline. In this paper, we propose a new paradigm that estimates uncertainty in the model's internal hidden space instead of the model's output space. We specifically study a manifestation of this problem for visual question answer generation (VQA), where the aim is not to classify the correct answer but to produce a natural language answer, given an image and a question. Our method overcomes the paraphrastic nature of language. It requires a semantic space that structures the model's output concepts and that enables the usage of techniques like dropout-based Bayesian uncertainty. We build a visual-semantic space that embeds paraphrases close together for any existing VQA model. We empirically show state-of-art active learning results on the task of VQA on two datasets, being 5 times more cost-efficient on Visual Genome and 3 times more cost-efficient on VQA 2.0.
Comparison of caudal bupivacaine with and without tramadol for postoperative analgesia in paediatric inguino scrotal surgeries

Objective: Th e aim of the study was to determine the postoperative analgesic eff ect of tramadol when given with caudally administered bupivacaine in children undergoing inguinoscrotal surgeries. Study Design: Quasi experimental Place and Duration: Th e study was conducted in Department of Anaesthesia, Sindh Govt Lyari General Hospital over a period of 6 months from January 2009 to July 2009. Materials & Methods: In this double blind comparative study we included total hundred children undergoing inguino-scrotal surgeries. Th ey were aged from 2 to 10 years. Our inclusion criteria were the children ASA I and II group. Th e patients were allocated into two equal groups. Th e patients in Group A (n=50) received 0.25% bupivacaine 0.75 ml/kg and the children in group B (n=50) were given 0.25 % bupivacaine 0.75 ml/kg and tramadol 2 mg/ kg through caudal route aft er induction of general anaesthesia. Th e postoperative pain was evaluated by using visual analogue pain scale in children over 6-7 years and observation of behavior in preverbal children. Sedation was assessed by 4 points sedation score, heart rate, mean arterial pressure, arterial oxygen saturation, respiratory rate. Pain and sedation scores were recorded immediately aft er awakening from anaesthesia till 24 hours postoperatively at periodic intervals. Acetaminophen was administered rectally (20 mg/kg) if the pain score was more than 4. Results: Addition of tramadol with caudally administered bupivacaine resulted in signifi cantly longer postoperative analgesic period (10.4 ± 2.2 hours) in group B while in group A in which only bupivacaine was used, mean duration of analgesia was (2.85 ± 0.84 hours). Th ere were no signifi cant changes in heart rate, arterial pressure and oxygen saturation between groups. Except nausea and vomiting, no other side eff ects like respiratory depression, pruritis, urinary retention were found. Conclusion: Caudal tramadol with bupivacaine provides prolonged and good quality postoperative analgesia compared to plain bupivacaine in children undergoing inguino-scrotal surgeries.
Impactos dos métodos de colheita da cana-de-açúcar sobre a macrofauna do solo em área de produção no Espírito Santo – Brasil

The objective of this study was to evaluate the effects of harvesting methods on the macrofauna, a known indicator of soil quality that detects changes in the system and indicates management alternatives. The experimental design was randomized blocks with six replications, with each block consisting of four parcels that corresponded to treatments green cane (CC), burnt cane (CQ), green cane – burnt cane (CC-Q) and burnt cane – green cane (CQ-C). Samples were collected in February and July 2010. The animals were divided into major taxonomic groups and accounted. Were evaluated ecological indexes (Shannon, Pielou and richness) and average total density of individuals and groups. Data were analyzed by the nonparametric statistical tools by Friedman or Signal test at 5%. To check relationships between 1 Parte da dissertacao de mestrado da primeira autora apresentada ao Curso de Pos-Graduacao em Agronomia, Ciencia do Solo CPGA-CS, Universidade Federal Rural do Rio de Janeiro, UFRRJ, RJ. Apoio Capes. 2
Label-free quantitative proteomic analysis of the YAP/TAZ

1 interactome 2 Priyanka Kohli , Malte P. Bartram , Sandra Habbig , Caroline Pahmeyer , 3 Tobias Lamkemeyer , Thomas Benzing , Bernhard Schermer#* , Markus M. 4 Rinschen* 1,2,4 5 6 7 (*) shared senior authorship 8 (#) corresponding author 9 10 11 Department II of Internal Medicine and Center for Molecular Medicine, University of 12 Cologne; Cologne Excellence Cluster on Cellular Stress Responses in Aging13 Associated Diseases, University of Cologne; Department of Pediatrics, University of 14 Cologne; Systems Biology of Ageing Cologne, University of Cologne, Cologne, 15 Germany 16
In Vitro Generation of “Long-Lived” Human Plasma Cells

Abstract 2187 Plasma cells, the terminal effectors of the B-cell lineage include both short- and long-lived cells. The latter persist for extended periods in the absence of cell division, supported in niche environments. No model system has successfully recapitulated the function of the niche to allow the in vitro generation of long-lived plasma cells. This limits investigation into the factors controlling and targeting plasma cell populations. Here we describe the generation of mature human plasma cells with extended lifespan from peripheral blood B-cells. Cell division accompanies phenotypic maturation between plasmablasts and plasma cells. These cells then persist in the absence of cell division, remaining functional and viable in extended culture, currently limited solely by elective termination. Extended survival is accompanied by maturation to a phenotype consistent with human bone marrow plasma cells. By establishing a set of conditions sufficient to allow the development and persistence of mature human plasma cells in vitro, we recapitulate the essential function of the plasma cell niche. We definitively link phenotypic maturation to lifespan and provide the first platform with which to explore and manipulate the full trajectory of human plasma cell differentiation. Disclosures: No relevant conflicts of interest to declare.
The Indoleamine 2,3-Dioxygenase Pathway Is Essential for Human Plasmacytoid Dendritic Cell-Induced Adaptive T Regulatory Cell Generation.

Plasmacytoid dendritic cells (PDCs) are a unique DC subset that plays a critical role in regulating innate and adaptive immune responses. Recently, we have shown that human PDCs activated by CpG oligodeoxynucleotide (CpG ODN) can drive naive, allogeneic CD4+CD25− T cells to differentiate into CD4+CD25+Foxp3+ regulatory T cells (Tregs). However, the intracellular mechanism(s) underlying PDC-induced Treg generation is unknown. Here we show human PDCs express high levels of indoleamine 2,3-dioxygenase (IDO), an intracellular enzyme that catabolizes tryptophan degradation. Triggering of Toll-like receptor 9 (TLR9) with CpG oligodeoxynucleotides activates PDCs to upregulate surface expression of B7 ligands and HLA-DR antigen, significantly increases the expression of IDO, and results in the generation of inducible Tregs from CD4+25− T cells with potent suppressor cell function. Blocking IDO activity with a pharmacologic inhibitor 1-methyl-D-tryptophan (1MT) significantly abrogates PDC-driven inducible Treg generation and suppressor cell function. Adding kynurenine (KYN), the immediate downstream metabolite of tryptophan, bypasses the 1MT effect, and restores PDC-driven Treg generation. Our results demonstrate that IDO pathway is essential for PDC-driven Treg generation from CD4+25− T cells, and implicates the generation of KYN-pathway metabolites as the critical mediator of this process.
PREVALENCE OF MRSA IN A PERIPHERAL HOSPITAL OF LAHORE

The rate of MRSA in increasing in hospitalized patients all over the world. The present study is done to find out the pravalence of MRSA in a peripheral hospital of Lahore over a period of one year from January to December 2009. Study was done by finding out percentage of MRSA among all the Staphylococcus Aureus isolated from admitted patients of ghurki trust teaching hospital. Results show that 50% of all the S. Aureus were MRSA.
Folate and choline metabolism gene variants in relation to ovarian cancer risk in the Polish population

Data indicates that genetic factors alone do not account for ovarian tumorigenesis, suggesting that epige- netic status additionally affects this process. Therefore, we assessed the possible contribution of polymorphic variants of genes that may affect DNA methylation to the risk of ovarian cancer incidence in the Polish population. Using PCR-RFLP and HRM analyses, we studied the distribution of BHMT (rs3733890), MTHFD1 (rs2236225), MTHFR (rs1801133), MTR (rs1805087), MTRR (rs1801394) and TCN2 (rs1801198) genotypes and alleles in patients with ovarian cancer (n = 136) and controls (n = 160). More- over, using DNA and methylation-specific PCR (MSP) we also determined the methylation of the Cadherin 13 (CDH13) promoter in cancerous tissue from these patients. We did not observe a significant association between all studied gene variants and the incidence of ovarian cancer. The lowest Ptrend = 0.1226 was observed for the MTHFR Ala222Val polymorphism. Moreover, the lowest P = 0.0772 was found in the comparison of MTHFR Ala/Ala versus Val/Val and Val/Ala genotypes in patients and control groups. The multifactor dimensionality reduction analysis also did not indicate a significant interactive genetic effect on ovarian cancer incidence for all analyzed SNPs. However, we observed frequent methylation of the CDH13 promoter in approximately 21% (29/136) patients with ovarian carcinomas. Our results might suggest that the selected polymorphic gene variants may not contribute to ovarian cancer incidence.
Two-dimensional multicomponent Abelian-Higgs lattice models

We study the two-dimensional lattice multicomponent Abelian-Higgs model, which is a lattice compact U(1) gauge theory coupled with an N-component complex scalar field, characterized by a global SU(N) symmetry. In agreement with the Mermin-Wagner theorem, the model has only a disordered phase at finite temperature and a critical behavior is only observed in the zero-temperature limit. The universal features are investigated by numerical analyses of the finite-size scaling behavior in the zero-temperature limit. The results show that the renormalization-group flow of the 2D lattice N-component Abelian-Higgs model is asymptotically controlled by the infinite gauge-coupling fixed point, associated with the universality class of the 2D CP(N-1) field theory.