Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting Severe Weather with Random Forests

Using nine years of historical forecasts spanning April 2003–April 2012 from NOAA’s Second Generation Global Ensemble Forecast System Reforecast (GEFS/R) ensemble, random forest (RF) models are trained to make probabilistic predictions of severe weather across the contiguous United States (CONUS) at Days 1–3, with separate models for tornado, hail, and severe wind prediction at Day 1 in an analogous fashion to the Storm Prediction Center’s (SPC’s) convective outlooks. Separate models are also trained for the western, central, and eastern CONUS. Input predictors include fields associated with severe weather prediction, including CAPE, CIN, wind shear, and numerous other variables. Predictor inputs incorporate the simulated spatiotemporal evolution of these atmospheric fields throughout the forecast period in the vicinity of the forecast point. These trained RF models are applied to unseen inputs from April 2012 to December 2016, and their forecasts are evaluated alongside the equivalent SPC outlooks. The RFs objectively make statistical deductions about the relationships between various simulated atmospheric fields and observations of different severe weather phenomena that accord with the community’s physical understandings about severe weather forecasting. Using these quantified flow-dependent relationships, the RF outlooks are found to produce calibrated probabilistic forecasts that slightly underperform SPC outlooks at Day 1, but significantly outperform their outlooks at Days 2 and 3. In all cases, a blend of the SPC and RF outlooks significantly outperforms the SPC outlooks alone, suggesting that use of RFs can improve operational severe weather forecasting throughout the Day 1–3 period.
Does gamification matter?: a systematic mapping about the evaluation of gamification in educational environments

Gamification is a technique that reuses game design and elements in others contexts, such as in e-commerces systems and virtual learning environments. When applied in the educational area, the gamification purpose is to promote a better user experience by improving students' motivation and engagement. There are already several studies applying gamification in the educational systems, and many ways to evaluate its effects on students. Thus, the aim of this work is to investigate how these studies evaluate gamification and compare its usage in educational environments through a systematic mapping. From the 832 studies returned by the search engines, only 20 of them met the defined selection criteria. As a result, these studies compared and evaluated gamification through students' interaction, performance, and user experience, based on their activities and answers in satisfaction surveys and tests.
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision

State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings. We observe that when they fail, they often make entity predictions that are incompatible with the type required by the relation. In response, we enhance each base factorization with two type-compatibility terms between entity-relation pairs, and combine the signals in a novel manner. Without explicit supervision from a type catalog, our proposed modification obtains up to 7% MRR gains over base models, and new state-of-the-art results on several datasets. Further analysis reveals that our models better represent the latent types of entities and their embeddings also predict supervised types better than the embeddings fitted by baseline models.
Downward Wage Rigidity in the United States: New Evidence from Administrative Data∗

This paper examines the extent and consequences of downward nominal wage rigidity using administrative worker-firm linked data for a large U.S. state. The distribution of nominal hourly wages changes of job stayers exhibits substantially less asymmetry and a smaller spike at zero than has been previously documented based on survey data. During the Great Recession, the proportion of job stayers experiencing a wage cut rose markedly, followed by a sustained increase in the proportion of wage freezes as the economy recovered. We also document a fairly symmetric distribution of hours changes in the data, with cyclical cuts in hours for job stayers. As a result, the distribution of nominal earnings changes of job stayers is more symmetric, meaning workers face higher annual earnings risk than is reflected in the wage change distribution. We rationalize these findings with a model of downward wage rigidity that features selection effects from both hiring and separations. We then exploit the worker-firm link of the data and find that during the Great Recession, firms with indicators of downward nominal wage rigidity had systematically higher job destruction and separation rates and lower job creation and hiring rates. ∗Kurmann: LeBow College of Business, Drexel University (email: kurmann.andre@gmail.com); Erika McEntarfer: Center for Economic Studies, U.S. Census Bureau (email: erika.mcentarfer@census.gov). We are grateful to Cynthia Forland and the Washington Employment Security Department for permission to use the Washington data for this project and feedback on our work. We also thank Jim Spletzer for earlier involvement in the project, and Gary Solon for insightful comments. The views expressed in this paper do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed.
Cervical lymph node metastases in salivary gland adenoid cystic carcinoma: a systematic review and meta-analysis

Background The purpose of this research was to determine whether neck dissection is necessary for the adenoid cystic carcinoma (ACC) of head and neck. Materials and methods This article screened the abstract and full-text papers that investigated salivary gland primary ACC of head and neck. Two independent reviewers searched for articles published before October 2017 in three databases (Web of Science, PubMed, and Ovid), having no limits in date and language. Statistical data were analyzed statistically by Review Manager 5.3. Results In total, 18 studies involving 2993 patients were included in the analysis. Of the 2993 patients, 473 patients had cervical lymph node metastasis, with a merge frequency of 16% (95% CI: 13–19). Among included articles, only 4 involved cervical lymph node occult metastases, with a merge frequency of 14% (95% CI: 9–20). There were 5 articles containing minor salivary glands (MiSGs) involving 370 patients of which 92 patients had cervical lymph node metastases and the merge frequency was 25% (95% CI: 11–38). Moreover, there were 4 studies on major salivary glands involving 904 patients of which 158 patients had cervical lymph node metastases and the merge frequency was 17% (95% CI: 15–20). Conclusion Elective neck dissection is unnecessary for all patients with salivary gland ACC of head and neck. Moreover, compared with major salivary glands, MiSGs have a higher cervical lymph node metastases rate in ACC. The overall cervical lymph node metastases rate of MiSGs is 25%, which is enough to attract our attention. Therefore, we suggest that neck dissection might be applied to ACC of MiSGs.
Illusion of a Density-Dependent Effect in Biology

Population changes are believed to be controlled by multiple factors, including an important density-dependent effect. This paper reviews the literature on this topic and shows that this density-dependent effect does not exist. This paper also gives a typical example in which no density-dependent effect was detected in the stock-recruitment relationship in Japanese sardines. The recruitment was found to be determined in proportion to the spawning stock biomass and to be affected by environmental factors. This simple mechanism is applicable not only in fish species but also in insects such as Thrips imaginis in Australia. The reason that many biologists have not become aware that the density-dependent effect does not exist is discussed using a metaphor. This paper proposes a new concept in the study of population change. The new concept proposed here will replace the currently used basic concept that has been assumed to be correct for more than 50 years.
FaceVR

We propose FaceVR, a novel image-based method that enables video teleconferencing in VR based on self-reenactment. State-of-the-art face tracking methods in the VR context are focused on the animation of rigged 3D avatars (Li et al. 2015; Olszewski et al. 2016). Although they achieve good tracking performance, the results look cartoonish and not real. In contrast to these model-based approaches, FaceVR enables VR teleconferencing using an image-based technique that results in nearly photo-realistic outputs. The key component of FaceVR is a robust algorithm to perform real-time facial motion capture of an actor who is wearing a head-mounted display (HMD), as well as a new data-driven approach for eye tracking from monocular videos. Based on reenactment of a prerecorded stereo video of the person without the HMD, FaceVR incorporates photo-realistic re-rendering in real time, thus allowing artificial modifications of face and eye appearances. For instance, we can alter facial expressions or change gaze directions in the prerecorded target video. In a live setup, we apply these newly introduced algorithmic components.
Three-Dimensional Accuracy of Digital Impression versus Conventional Method: Effect of Implant Angulation and Connection Type

Purpose The aim of this in vitro study was to compare the accuracy of different implant impression techniques of the maxillary full arch with tilted implants of two connection types. Materials and Methods Two maxillary edentulous acrylic resin models with two different implant connections (internal or external) served as a reference model. Each model had two anterior straight and two posterior angulated implants. Ninety impressions were made using an intraoral scanner (Trios 3Shape) with scan bodies for digital impression (groups DII and DIE), a custom open tray with additional silicone for the conventional direct group (groups CDI and CDE), and a custom closed tray with additional silicone for the conventional indirect group (groups CII and CIE) from both internal and external models, respectively. A coordinate-measuring machine (CMM) was used to measure linear and angular displacement for conventional specimens. For digital groups, an optical CMM was used to scan the reference model. STL data sets from the digital specimen were superimposed on STL reference data sets to assess angular and linear deviations. Data were analyzed with three-way ANOVA and t-test at α=0.05. Results There were significant angular and linear distortion differences among three impression groups (P < 0.001), angular distortion differences between internal and external connections (P < 0.001), and between straight and tilted implants for either linear (P < 0.001) or angular (P=0.002) distortion. The type of the connection and implant angle did not have any effect on linear and angular distortion of the digital technique (p > 0.05). Minimum angular and linear distortion was seen for tilted implants in DII and DIE groups (0.36° ± 0.37 and 0.16 ± 0.1 mm). Conclusion Impression techniques (digital versus conventional) affected the transfer accuracy. Digital techniques demonstrated superior outcome in comparison with conventional methods, and the direct technique was better than the indirect conventional technique. Connection type and implant angulation were other factors that influenced accuracy. However, when digital impression was applied, accuracy was not affected by the type of connection and angulation.
Stochastic Layer-Wise Precision in Deep Neural Networks

Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks. Recently, low precision networks have even shown to be more robust to adversarial attacks. However, typical implementations of low precision DNNs use uniform precision across all layers of the network. In this work, we explore whether a heterogeneous allocation of precision across a network leads to improved performance, and introduce a learning scheme where a DNN stochastically explores multiple precision configurations through learning. This permits a network to learn an optimal precision configuration. We show on convolutional neural networks trained on MNIST and ILSVRC12 that even though these nets learn a uniform or near-uniform allocation strategy respectively, stochastic precision leads to a favourable regularization effect improving generalization.
Connecting Concepts to Problem-solving

Traditional quantitative problems of the type commonly found at the end of chapters in physics textbooks are assigned to students most introductory physics courses. Many students use a formula-driven approach to solve these problems that does not rely on understanding underlying physics concepts and that does little to encourage the problem-solving skills employed by experts. In another paper presented at this conference, we gave an example from electric circuits to illustrate the use of bridging exercises as part of students homework to encourage students to solve problems by starting with developed physics concepts and models. In this paper, we describe our attempts to use the same approach in the context of electrostatics.
Performance, growth and carcass characteristics of alternatives lineages of broiler chickens created with access to paddock

Objetivou-se com este estudo avaliar o desempenho de linhagens alternativas de frangos de corte, criados em sistema com acesso a piquete. Foram utilizados 312 pintos de corte de um dia de idade, alojados com uma densidade de 6,4 aves/m2 na area interna e 2 m2/animal na area de pastejo. Os tratamentos adotados corresponderam a cada linhagem: Redbro Cou Nu, Gris Barre Cou Nu, Master Gris Cou Plume e Gris Barre Cou Plume. O delineamento estatistico adotado foi inteiramente casualizado, com quatro tratamentos e seis repeticoes. Para o estudo do crescimento a equacao de Gompertz apresentou melhor ajuste, quando comparadas as demais. A linhagem Master Gris Cou Plume obteve maior peso assintotico, taxa de crescimento absoluto maximo e menor viabilidade, maiores valores de peso medio e ganho de peso, quando comparada a linhagem Gris Barre Cou Nu. Mesmo com as diferencas de desempenho zootecnico entre as linhagens estudadas, nao a diferencas significativas entre medias de peso do corpo apos o jejum e peso das carcacas evisceradas. Foram observados maiores pesos de carcaca, peito, coxa+sobrecoxa, dorso e asa para a linhagem Master Gris Cou Plume, quando comparado a Gris Barre Cou Nu e Gris Barre Cou Plume. As linhagens Redbro Cou Nu e Master Gris Cou Plume apresentaram desempenho similar, no entanto a aves da linhagem Redbro Cou Nu demonstraram caracteristicas de crescimento mais adequadas a criacoes alternativas, com acesso a area de piquete.
When Gaussian Process Meets Big Data: A Review of Scalable GPs

The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process regression (GPR), a well-known nonparametric, and interpretable Bayesian model, which suffers from cubic complexity to data size. To improve the scalability while retaining desirable prediction quality, a variety of scalable GPs have been presented. However, they have not yet been comprehensively reviewed and analyzed to be well understood by both academia and industry. The review of scalable GPs in the GP community is timely and important due to the explosion of data size. To this end, this article is devoted to reviewing state-of-the-art scalable GPs involving two main categories: global approximations that distillate the entire data and local approximations that divide the data for subspace learning. Particularly, for global approximations, we mainly focus on sparse approximations comprising prior approximations that modify the prior but perform exact inference, posterior approximations that retain exact prior but perform approximate inference, and structured sparse approximations that exploit specific structures in kernel matrix; for local approximations, we highlight the mixture/product of experts that conducts model averaging from multiple local experts to boost predictions. To present a complete review, recent advances for improving the scalability and capability of scalable GPs are reviewed. Finally, the extensions and open issues of scalable GPs in various scenarios are reviewed and discussed to inspire novel ideas for future research avenues.
Microscopic Viscoelasticity: Shear Moduli of Soft Materials Determined from Thermal Fluctuations

We describe a high-resolution, high-bandwidth technique for determining the local viscoelasticity of soft materials such as polymer gels. Loss and storage shear moduli are determined from the power spectra of thermal fluctuations of embedded micron-sized probe particles, observed with an interferometric microscope. This provides a passive, small-amplitude measurement of rheological properties over a much broader frequency range than previously accessible to microrheology. We study both F-actin biopolymer solutions and polyacrylamide (PAAm) gels, as model semiflexible and flexible systems, respectively. We observe high-frequency ${\ensuremath{\omega}}^{3/4}$ scaling of the shear modulus in F-actin solutions, in contrast to ${\ensuremath{\omega}}^{1/2}$ scaling for PAAm.
Viewpoint Estimation-Insights & Model

This paper addresses the problem of viewpoint estimation of an object in a given image. It presents five key insights that should be taken into consideration when designing a CNN that solves the problem. Based on these insights, the paper proposes a network in which (i) The architecture jointly solves detection, classification, and viewpoint estimation. (ii) New types of data are added and trained on. (iii) A novel loss function, which takes into account both the geometry of the problem and the new types of data, is propose. Our network improves the state-of-the-art results for this problem by 9.8%.
Screen watermarking for data theft investigation and attribution

Organizations not only need to defend their IT systems against external cyber attackers, but also from malicious insiders, that is, agents who have infiltrated an organization or malicious members stealing information for their own profit. In particular, malicious insiders can leak a document by simply opening it and taking pictures of the document displayed on the computer screen with a digital camera. Using a digital camera allows a perpetrator to easily avoid a log trail that results from using traditional communication channels, such as sending the document via email. This makes it difficult to identify and prove the identity of the perpetrator. Even a policy prohibiting the use of any device containing a camera cannot eliminate this threat since tiny cameras can be hidden almost everywhere. To address this leakage vector, we propose a novel screen watermarking technique that embeds hidden information on computer screens displaying text documents. The watermark is imperceptible during regular use, but can be extracted from pictures of documents shown on the screen, which allows an organization to reconstruct the place and time of the data leak from recovered leaked pictures. Our approach takes advantage of the fact that the human eye is less sensitive to small luminance changes than digital cameras. We devise a symbol shape that is invisible to the human eye, but still robust to the image artifacts introduced when taking pictures. We complement this symbol shape with an error correction coding scheme that can handle very high bit error rates and retrieve watermarks from cropped and compressed pictures. We show in an experimental user study that our screen watermarks are not perceivable by humans and analyze the robustness of our watermarks against image modifications.
Qualitative and quantitative analysis with scheduling policies in model checking

An execution order of the processes in a system depends on the scheduler. Therefore, the scheduling strategy needs to be taken into account during the analysis of the system behaviors. The goal of this research is to improve the support for analyzing concurrent systems executed under different scheduling policies. A domain-specific language (DSL) is proposed to provide an effective specification of various schedulers. Necessary artifacts for the analysis are generated automatically. Based on the specification of the scheduler, an algorithm to explore the state space is realized. We use discrete time to consider a transition from a state to another state taking one time unit during the search. A graph is determined from the state space. To analyze the behaviors of the system, this graph is then labeled following an input formula which represents a qualitative/quantitative property. The results of applying our approach to several scheduling policies demonstrated that the system with scheduling policies could be analyzed accurately and easily.
Search for squarks and gluinos in final states with jets and missing transverse momentum using 36fb1 of s=13TeV pp collision data with the ATLAS detector

A search for the supersymmetric partners of quarks and gluons (squarks and gluinos) in final states containing hadronic jets and missing transverse momentum, but no electrons or muons, is presented. The data used in this search were recorded in 2015 and 2016 by the ATLAS experiment in ﬃﬃﬃ s p ¼ TeV proton-proton collisions at the Large Hadron Collider, corresponding to an integrated luminosity of 36 . 1 fb − 1 . The results are interpreted in the context of various models where squarks and gluinos are pair produced and the neutralino is the lightest supersymmetric particle. An exclusion limit at the 95% confidence level on the mass of the gluino is set at 2.03 TeV for a simplified model incorporating only a gluino and the lightest neutralino, assuming the lightest neutralino is massless. For a simplified model involving the strong production of mass-degenerate first- and second-generation squarks, squark masses below 1.55 TeVare excluded if the lightest neutralino is massless. These limits substantially extend the region of supersymmetric parameter space previously excluded by searches with the ATLAS detector. and the dotted lines are obtained by varying the signal cross section by the renormalization and factorization scale and PDF uncertainties. Results are compared with the observed limits obtained by the previous ATLAS searches with jets, missing transverse momentum, and no leptons [11].
A tf-idf based topic model for identifying lncRNAs from genomic background

The developments in high throughput technologies identified a large number of long non-coding RNAs (lncRNAs) whose functional characterization remains an open problem. The available research confirmed that lncRNA plays a major role in genetic and epigenetic regulation, and its expression level has a significant association with some complex diseases like cancers. The identification of lncRNA and their functional characterization is an important task in RNA Bioinformatics. In spite of their abundance in the cell, lncRNAs are less conserved at their sequence level which makes the analysis challenging. Many machine learning based models are developed in the literature for the identification and analysis of lncRNAs. This paper proposes a topic model based method for the identification of lncRNAs. To investigate the applicability of topic model in lncRNA analysis, this work develops an LDA based topic model to group lncRNAs from a collection of transcriptome sequences. The features derived from transformed k-mer patterns and secondary structure of lncRNA sequences are used for the topic model. The results are promising compared to the classic algorithms and prove that the topic models are reasonable for lncRNA analysis.