Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Denoise Distantly-Labeled Data for Entity Typing

Distantly-labeled data can be used to scale up training of statistical models, but it is typically noisy and that noise can vary with the distant labeling technique. In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and denoised distant data with standard supervised training. Our denoising approach consists of two parts. First, a filtering function discards examples from the distantly labeled data that are wholly unusable. Second, a relabeling function repairs noisy labels for the retained examples. Each of these components is a model trained on synthetically-noised examples generated from a small manually-labeled set. We investigate this approach on the ultra-fine entity typing task of Choi et al. (2018). Our baseline model is an extension of their model with pre-trained ELMo representations, which already achieves state-of-the-art performance. Adding distant data that has been denoised with our learned models gives further performance gains over this base model, outperforming models trained on raw distant data or heuristically-denoised distant data.
What makes anti-vaccine websites persuasive? A content analysis of techniques used by anti-vaccine websites to engender anti-vaccine sentiment

Abstract Anti-vaccine sentiment can be extremely resistant to change, making it difficult to promote childhood vaccines. Thus, there is a need for effective strategies to communicate the benefits of vaccination to vaccine hesitant parents. Understanding how anti-vaccine advocates successfully persuade parents against vaccinating their children can provide insight into communication tactics that could be incorporated into vaccine promotion efforts. The internet is an important source of vaccine information for many parents, and plays a role informing vaccine hesitancy. To understand what might make anti-vaccine websites so convincing, we used persuasion theory as a lens to examine what information was being presented, and the persuasive tactics being used to communicate the information. We conducted a content analysis of 480 anti-vaccine websites. Four trained coders coded sites for the content of the vaccine information being presented, types of persuasive tactics used, and values and lifestyle norms associated with anti-vaccine advocacy. Anti-vaccine websites contain a considerable amount of misinformation, most commonly that vaccines are dangerous, cause autism and brain injury. Websites used both scientific evidence and anecdotes to support these claims. Values such as choice, freedom, and individuality were linked to anti-vaccine beliefs. The most commonly co-promoted behaviors included the use of alternative medicine and homeopathy, and eating a healthy or organic diet. Anti-vaccine websites use a battery of effective persuasive techniques to forward their agenda. The use of similar persuasive techniques and tapping into parents’ values and lifestyles are potentially useful strategies for vaccine promotion communication.
Joint Reconstruction and Calibration Using Regularization by Denoising with Application to Computed Tomography

Regularization by denoising (RED) is a broadly applicable framework for solving inverse problems by using priors specified as denoisers. While RED has been shown to provide state-of-the-art performance in a number of imaging applications, existing RED algorithms require exact knowledge of the measurement operator characterizing the imaging system, limiting their applicability in problems where the measurement operator has parametric uncertainties. We propose a new method, called Calibrated RED (Cal-RED), that enables joint calibration of the measurement operator along with reconstruction of the unknown image. Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator. We validate Cal-RED on the problem of image reconstruction in computerized tomography (CT) under perturbed projection angles. Our results corroborate the effectiveness of Cal-RED for joint calibration and reconstruction using pre-trained deep denoisers as image priors.
Accurate Spectral Collocation Solutions to some Bratu's Type Boundary Value Problems

We solve by Chebyshev spectral collocation some genuinely nonlinear Liouville-Bratu-Gelfand type, 1D and a 2D boundary value problems. The problems are formulated on the square domain $[-1, 1]\times[-1, 1]$ and the boundary condition attached is a homogeneous Dirichlet one. We pay a particular attention to the bifurcation branch on which a solution is searched and try to estimate empirically the attraction basin for each bifurcation variety. The first eigenvector approximating the corresponding the first eigenfunction of the linear problem is used as an initial guess in solving the non-linear algebraic system of Chebyshev collocation to find the "small" solution. For the same value of the bifurcation parameter we use another initial guess, namely lowest basis function (1 point approximation), to find the "big" solution. The Newton-Kantorovich method solves very fast the non-linear algebraic system in no more than eight iterations. Beyond being exact, the method is numerically stable, robust and easy to implement. Actually, the MATLAB code essentially contains three programming lines. It by far surpasses in simplicity and accuracy various methods used to solve some well-known problems. We end up by providing some numerical and graphical outcomes in order to underline the validity and the effectiveness of our method, i.e., norms of Newton updates in solving the algebraic systems and the decreasing rate of Chebyshev coeffcients of solution.
Reconstructing H i power spectrum with minimal parameters using the dark matter distribution beyond haloes

Intensity mapping of 21-cm line by several radio telescope experiments will probe the large-scale structure of the Universe in the post-reionization epoch. It requires a theoretical framework of neutral hydrogen (H i) clustering, such as modelling of H i power spectrum for baryon acoustic oscillations (BAO) analysis. We propose a new method for reconstructing the H i map from dark matter distribution using N-body simulations. Several studies attempt to compute the H i power spectrum with N-body simulations by pasting H i gas at the dark matter halo centre, assuming the relation between the halo and H i masses. On the other hand, the method proposed in this paper reproduces the H i power spectrum from simulated dark matter distribution truncated at specific scales from the halo centre. With this method, the slope of H i power spectrum is reproduced well at the BAO scales, k < 1 h/Mpc. Furthermore, we find the fluctuation of spin temperature, which is often ignored at the post-reionization epoch, alters the power spectrum of brightness temperature by at most 8 per cent in the power spectrum. Finally, we discuss how our method works by comparing the density profiles of H i and dark matter around the dark matter haloes.
Literature review on Energy Efficiency of Base Stations and Improving Energy Efficiency of a network through Cognitive Radio

Context: The usage of cellular devices has been advanced in recent years and expected to increase rapidly in the coming years. Thanks to rapid development of technologies which is making us comfortable in the field of communications. The rapid development in technology is causing many serious hazards to the environment and also the mobile operators are facing problems with increasing energy costs and regulatory pressures to reduce carbon footprint to operate Green networks. Objectives: This research provides a study on the problems pertaining to energy efficiency and remedies to overcome issues in decreasing energy efficiency of networks, this research also concentrates on various approaches for energy efficiency in base stations, the goal is to identify the best technologies that best suits the base station platform in terms of energy efficiency. Methods: Two different methods are carried out throughout our research • Literature Review In this literature review problems pertaining to energy efficiency of base stations are studied and reported, there by different technologies which helps to increase energy efficiency of base stations are analysed and best technologies are reported. • Experimentation This section describes the concept of cognitive radio. It helps in energy efficiency in a cognitive network through analysis of two routing protocols AODV and OLSR using NS2 simulator. Results: • From the literature review we look at the energy consumptions issues of base stations (RQ1). • For RQ2, the remedies to reduce energy consumption are studied and the best suitable technologies for energy efficiency are suggested. • For answering RQ3, experimentation is carried out concentrating on achieving energy efficiency through cognitive radio network. Conclusions: Thus, Problems pertaining to energy efficiency of base stations are studied, and best technologies to reduce energy consumption of base stations are reported. Comparison of two routing protocols OLSR and AODV under cognitive radio is also studied proposing the best routing protocol.
Performance evaluation of microbial carbonate precipitation method compared with resin and fiber stabilization on the strength of compacted sandy soils

One of the methods that have recently linked the fields of civil engineering, geochemistry and microbiology, have a good compatibility with the environment, and have a lower administrative cost than other methods of soil improvement is Microbial Induced Carbonate Precipitation (MICP). This new idea is inspired by nature, involves using certain species of soil bacteria to bind soil particles together and stabilize the soil structure. In this study, the performance of MICP method on the strength of sandy soil compared with another two methods of mixing treatment for soil improvement (resin stabilization and fiber/resin reinforcement). Unconfined compression strength test (UCS) were performed in 3 graded for one type of sandy soil to investigate the optimum results for each methods and comparison between them. the results indicate that the highest increased of strength in resin/fiber reinforcement occurs in T90 soil with 1831 Kpa and resin stabilization raised the strength to1611 Kpa in T60 soil . In addition, the MICP method has improved the strength from 51 Kpa to 1109 Kpa and the results in this process indicate the importance of soil granular and percent of bacteria. Therefore, comparison of these three methods shows the MICP method as a new and environmental method of soil improvement can be considered in future.
Age, Growth, and Mortality of Lane Snapper from the Northern Gulf of Mexico

Age, growth and mortality of lane snapper {Lutjanus synagris) from the northern Gulf of Mexico (Port Aransas, Texas, to Panama City, Fla.) were examined. Otolith sections from 694 fish collected from January 1991 to December 1994 were used to develop growth information using 2 methods (direct proportion and regression) of back-calculation. Fish ranged in age from 2 to 17 years and their size ranged from 210 to 673 mm total length. Males grew slightly faster and were larger at age than females; however, the oldest (age 17 at 500 mm) and largest (673 mm at age 11) were females. Full recruitment to the recreational fishery was at age 4-5. Estimated instantaneous total mortality (Z) ranged from 0.3750 to 0.5767. Natural mortality (M) ranged from 0.1125 to 0.2388. Proc. Annu. Conf. Southeast. Assoc. Fish and Wildl. Agencies 49:178-186 Lane snapper is a member of the reef fish complex that supports important recreational and commercial fisheries in the northern Gulf of Mexico. Little information is available from the northern Gulf of Mexico; Shipp (1991) reported on observed size at age of 107 fish using otolith sections. There are several reports on age and growth of this species from outside this area (Alegria C. and Menezes 1970; Reshetnikov and Claro 1976a, b; Cruz 1978; Manooch and Mason 1984; Rubio et al. 1985; Torres and Chavez 1987; Acosta and Appel1995 Proc. Annu. Conf. SEAFWA Lane Snapper Age, Growth 179 doom 1992). The present report provides information on age, growth, and mortality of lane snapper from the northern Gulf of Mexico.
Polyhedral Friction Cone Estimator for Object Manipulation

A polyhedral friction cone is a set of reaction wrenches that an object can experience whilst in contact with its environment. This polyhedron is a powerful tool to control an object’s motion and interaction with the environment. It can be derived analytically, upon knowledge of object and environment geometries, contact point locations and friction coefficients. We propose to estimate the polyhedral friction cone so that a priori knowledge of these quantities is no longer required. Additionally, we introduce a solution to transform the estimated friction cone to avoid re-estimation while the object moves. We present an analysis of the estimated polyhedral friction cone and demonstrate its application for manipulating an object in simulation and with a real robot.
Transformation Driven Visual Reasoning

This paper defines a new visual reasoning paradigm by introducing an important factor, i.e. transformation. The motivation comes from the fact that most existing visual reasoning tasks, such as CLEVR in VQA, are solely defined to test how well the machine understands the concepts and relations within static settings, like one image. We argue that this kind of state driven visual reasoning approach has limitations in reflecting whether the machine has the ability to infer the dynamics between different states, which has been shown as important as state-level reasoning for human cognition in Piaget’s theory. To tackle this problem, we propose a novel transformation driven visual reasoning task. Given both the initial and final states, the target is to infer the corresponding single-step or multi-step transformation, represented as a triplet (object, attribute, value) or a sequence of triplets, respectively. Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i.e. Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event and View. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and real data need to be investigated in this direction. The resource of TVR is available at https://hongxin2019.github. io/TVR.
The price of liquidity in the reinsurance of fund returns

This paper aims to extend downside protection to a hedge fund investment portfolio based on shared loss fee structures that have become increasing popular in the market. In particular, we consider a second tranche and suggest the purchase of an upfront reinsurance contract for any losses on the fund beyond the threshold covered by the first tranche, i.e. gaining full portfolio protection. We identify a fund's underlying liquidity as a key parameter and study the pricing of this additional reinsurance using two approaches: First, an analytic closed-form solution based on the Black-Scholes framework and second, a numerical simulation using a Markov-switching model. In addition, a simplified backtesting method is implemented to evaluate the practical application of the concept.
Vehicle Speed Estimation using Image Processing

Article history: Received 5 October 2018 Received in revised form 4 December 2018 Accepted 8 December 2018 Available online 5 January 2019 This paper aims at determining vehicle speed which is necessary for traffic surveillance systems. These systems are very much useful to monitor and manage various traffic conditions such as traffic management, prevention of accident, also secure transportation. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. In speed determination vehicle detection and vehicle tracking are the key steps. To overcome the disadvantages of traditional methods, here, vehicle speed determination using image processing is done with Python language and OpenCV library. In this paper, the proposed method consists of few basic steps-— background subtractions, feature extraction, vehicle tracking etc. The speed is determined using distance travelled by vehicle over number of frames and frame rate.
4D Human Body Capture from Egocentric Video via 3D Scene Grounding

We introduce a novel task of reconstructing a time series of second-person1 3D human body meshes from monocular egocentric videos. The unique viewpoint and rapid embodied camera motion of egocentric videos raise additional technical barriers for human body capture. To address those challenges, we propose a simple yet effective optimization-based approach that leverages 2D observations of the entire video sequence and human-scene interaction constraint to estimate second-person human poses, shapes, and global motion that are grounded on the 3D environment captured from the egocentric view. We conduct detailed ablation studies to validate our design choice. Moreover, we compare our method with the previous state-of-the-art method on human motion capture from monocular video, and show that our method estimates more accurate human-body poses and shapes under the challenging egocentric setting. In addition, we demonstrate that our approach produces more realistic human-scene interaction.
Learning from Simulation, Racing in Reality

We present a reinforcement learning-based solution to autonomously race on a miniature race car platform. We show that a policy that is trained purely in simulation using a relatively simple vehicle model, including model randomization, can be successfully transferred to the real robotic setup. We achieve this by using a novel policy output regularization approach and a lifted action space which enables smooth actions but still aggressive race car driving. We show that this regularized policy does outperform the Soft Actor Critic (SAC) baseline method, both in simulation and on the real car, but it is still outperformed by a Model Predictive Controller (MPC) state-of-the-art method. The refinement of the policy with three hours of real-world interaction data allows the reinforcement learning policy to achieve lap times similar to the MPC controller while reducing track constraint violations by 50%.
REFERENCE-SET AUGMENTED BOOTSTRAPPING

We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature. First, we train a neural NER (NNER) model over a small seed of fully-labeled examples. Second, we use a reference set of entity names (e.g., proteins in UniProt) to identify entity mentions with high precision, but low recall, on an unlabeled corpus. Third, we use the NNER model to assign weak labels to the corpus. Finally, we retrain our NNER model iteratively over the augmented training set, including the seed, the reference-set examples, and the weakly-labeled examples, which results in refined labels. We show empirically that this augmented bootstrapping process significantly improves NER performance, and discuss the factors impacting the efficacy of the approach.
The Vertical and Horizontal Distributive Effects of Energy Taxes: A Case Study of a French Policy

This paper proposes a micro-simulation assessment of the distributional impacts of the French carbon tax. It shows that the policy is regressive, but could be made progressive by redistributing the revenue through flat-recycling. However, it would still generate large horizontal distributive effects and harm a significant share of low-income households. The determinants of the tax incidence are characterized precisely, and alternative targeted transfers are simulated on this basis. The paper shows that given the importance of unobserved heterogeneity in the determinants of energy consumption, horizontal distributive effects are much more difficult to tackle than vertical ones.
An Equilibrium Model for the Cross-Section of Liquidity Premia

We study a risk-sharing economy where an arbitrary number of heterogeneous agents trades an arbitrary number of risky assets subject to quadratic transaction costs. For linear state dynamics, the forward–backward stochastic differential equations characterizing equilibrium asset prices and trading strategies in this context reduce to a coupled system of matrix-valued Riccati equations. We prove the existence of a unique global solution and provide explicit asymptotic expansions that allow us to approximate the corresponding equilibrium for small transaction costs. These tractable approximation formulas make it feasible to calibrate the model to time series of prices and trading volume, and to study the cross section of liquidity premia earned by assets with higher and lower trading costs. This is illustrated by an empirical case study.
The Einstein Toolkit: A Student's Guide.

The Einstein Toolkit represents a unique opportunity for students to explore the world of numerical relativity, without the need for high-level computing power or knowledge of the mathematics behind the simulations themselves. This document aims to act as a resource for students aiming to learn about and get involved with Numerical Relativity, focusing specifically on the modelling of Binary Black Hole Mergers in order to produce sample Gravitational Waveforms as would be measured by Gravitational Wave detectors such as LIGO. The feasibility of using the Einstein Toolkit on Amazon's Web Services (AWS) has also been explored for the first time, confirming a potential - yet expensive - method for students or citizen scientists to gain access to high-level computing power sufficient to play with and run their own numerical relativity simulations.