Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Clustering on Representation Expansion for Learning Multimodel MDPs

This paper addresses the problem of model learning in a Markov decision process (MDP) that exhibits an underlying multiple model structure. In particular, each observed episode from the MDP has a latent classification that determines from which of an unknown number of models it was generated, and the goal is to determine both the number and the parameterization of the underlying models. The main challenge in solving this problem arises from the coupling between the separation of observations into groupings and the selection of a low-dimensional representation for each group. Present approaches to multiple model learning involve computationally expensive probabilistic inference over Bayesian nonparametric models. We propose Simultaneous Clustering on Representation Expansion (SCORE), an iterative scheme based on classical clustering and adaptive linear representations, which addresses this codependence in an efficient manner and guarantees convergence to a local optimum in model error. Both a batch and an incremental version of SCORE are presented. Empirical results on simulated domains demonstrate the advantages of SCORE when compared to contemporary techniques with respect to both sample and time complexity.
Characterisation of different GLRaV-3 variant infections by determining virus concentration ratios and miRNA expression profiles

Grapevine leafroll disease (GLD) is present in all grape-growing regions of the world and is considered the most significant grapevine viral disease. Grapevine leafroll-associated virus 3 (GLRaV-3) is considered the primary cause of GLD and in South African vineyards five genetic variant groups (I, II, III, VI and VII) have been confirmed. Biological distinctions between GLRaV-3 variants have not been fully validated. By characterising virus concentration and stress-responsive microRNA expression in GLRaV-3 infected plants, this study aimed to glean a better understanding of the possible biological distinctions between GLRaV-3 variants. Quantitative reverse transcription PCR was utilised for virus concentration ratio (VCR) determination and miRNA quantitation in GLRaV-3 positive and negative grapevines grown under greenhouse and field conditions. This study found statistically significant differences in VCRs in plants singly infected with different GLRaV-3 variants. Interestingly, no difference in mean VCRs were observed between data sets, despite notable differences in plant age, duration of GLRaV-3 infection, scion/ rootstock combination and growing conditions. Several miRNAs showed statistically significant expression modulation between infected and healthy samples. miRNA expression between data sets varied substantially and a greater overall miRNA response was observed in plants with more established GLRaV-3 infections. The lack of significant differences in mean VCRs between data sets, coupled with the consistent modulation of certain miRNAs in plants that have likely been infected for longer is a promising result. This finding could indicate that successful inhibition of further virus replication by plant defence mechanisms occurred, and that these miRNAs are implicated in this response.
SC 18 : G : RACE-Recursive Algebraic Coloring Engine

1 Problem & Motivation Sparse linear algebra is a key component in many scientific simulations ranging from quantum physics to fluid and structural mechanics. However, iterative numerical methods and important building blocks of sparse linear algebra frequently feature strong data dependencies, making them difficult to parallelize. Typically, loop-carried dependencies occur in iterative solvers (e.g., Kaczmarz, Gauss-Seidel) or preconditioners and write conflicts show up in the parallelization of building blocks such as symmetric sparse matrix-vector multiplication. Scalable, hardware-efficient parallelization of such methods and kernels is known to be a challenge. Multicoloring is a widely used approach to enable parallelization of iterative solvers with distance-k dependency; e.g., the redblack Gauss-Seidel algorithm solves the distance-1 dependency problem. However, most of those standard solutions suffer from low performance onmodern hardware, are highly problem specific, or require tailored sparse matrix storage formats. RACE addresses these shortcomings by combining ideas from graph traversal and multi-coloring to ensure data locality, to generate appropriate levels of parallelism, and to enable hardware-efficient parallelization schemes. It is applicable to many problems (i.e., matrix structures) and general sparse data storage formats. Outline The paper is structured as follows. Section 2 describes the underlying dependency problems and conventional solutions. Section 3 demonstrates the major drawbacks of the existing approaches. We then introduce the RACE method in Section 4, its uniqueness and how its basic design addresses the existing problems. In Section 5 we compare RACE performance for thread-level parallelization of symmetric sparse matrix-vector multiplication (SymmSpMV) to available standard solutions including Intel MKL. Finally we use RACE to parallelize a sparse eigenvalue solver provided by Intel MKL and demonstrate RACE’s superiority in terms of performance and attainable problem sizes.
12 B . 2 OBSERVED CHANGES IN ORGANIZED TROPICAL DEEP CONVECTION AS IDENTIFIED BY CLOUD REGIME ANALYSIS

The importance of organized deep convection is underscored by its association with high levels of rainfall. Despite occurring only about 5% of the time, it contributes to about half the amount of precipitation in the tropics (Tan et al., 2013) and is associated with extreme precipitation (Rossow et al., 2013). Aside from rainfall, organized deep convection has a critical impact on weather and climate such as through cloud feedbacks (Stephens, 2005), mesoscale convective systems (Houze, 2004), and tropical phenomena such as the Madden-Julian Oscillation (Zhang, 2005). How organized deep convection will change in a warming climate has been the subject of some studies. For example, Chen et al. (2002) reported a change in organized convection during the 1990s by utilizing a variety of satellite and reanalysis product. Del Genio et al. (2007) used estimates of convective updraft speeds from a global climate model to associate an intensification of convection with global warming. Berg et al. (2013) employed high quality regional gauge data to quantify the increase in convective precipitation due to higher temperatures. However, purely observational studies on organized deep convection have been scant because of a lack of long-term and global measurement of variables that can reliably represent not just deep convection but its degree of organization. One study of this nature is Tselioudis et al. (2010). By using a categorization of tropical cloud fields from satellite records that began in 1983, they discovered an increase in the frequency of organized deep convection by about 20%. However, their analysis is limited due to potential satellite artifacts in observing clouds from less organized deep convection. Here, we extend their analysis in two ways. First, we minimize artifacts by implementing a satellite zenith angle filter and thus broaden the scope to less organized modes of deep convection. Second, we divide their representation of organized deep convection into two components: one that is primarily deep convection and one that is primarily stratiform. The goals of our study are twofold. One, we wish to investigate the details of this change in organized deep convection, not just in itself but also in relation to other
IS FREE TRADE GOOD FOR GROWTH ? RECONCILING MACRO-AND MICROECONOMIC EVIDENCE AND DATA

By exploiting the intertemporal, intersectorial and international variation in trade policy, this paper uses macroand microeconomic data from six Latin American countries between 2006 and 2010 to investigate the extent and channels by which artificial trade barriers impact firm growth. Using a structural procedure, the effect of tariff barriers on firm growth is mediated by its impact on import flows. Counterfactual changes in imports are estimated within the gravity framework, and then used on representative survey data to estimate the differentiated effects of import-competition and imported inputs on firm growth. Main findings suggest generalized static and dynamic gains in productivity from the free inflow of imported inputs. Gains are higher for firms where policies and institutions are supportive. The elusive effect of import-competition, on the contrary, is very heterogeneous across firms, industries and countries, kicking in depending on firms’ technology intensity, comparative advantage and relative productivity. JEL classification: F13, F14, O12, O24, O47, O54
Cubic stylization

We present a 3D stylization algorithm that can turn an input shape into the style of a cube while maintaining the content of the original shape. The key insight is that cubic style sculptures can be captured by the as-rigid-as-possible energy with an ℓ1-regularization on rotated surface normals. Minimizing this energy naturally leads to a detail-preserving, cubic geometry. Our optimization can be solved efficiently without any mesh surgery. Our method serves as a non-realistic modeling tool where one can incorporate many artistic controls to create stylized geometries.
Title Soluble Epoxide Hydrolase-Derived Linoleic Acid Oxylipins in Serum Are Associated with Periventricular White Matter Hyperintensities and Vascular Cognitive Impairment Permalink

White matter hyperintensities (WMH) are presumed to indicate subcortical ischemic vascular disease but their underlying pathobiology remains incompletely understood. The soluble epoxide hydrolase (sEH) enzyme converts anti-inflammatory and vasoactive cytochrome p450-derived polyunsaturated fatty acid epoxides into their less active corresponding diol species. Under the hypothesis that the activity of sEHmight be associated with subcortical ischemic vascular disease and vascular cognitive impairment, this study aimed to compare the relative abundance of sEH substrates and products in peripheral blood between patients with extensive WMH (discovered due to transient ischemic attack; n= 29) and healthy elderly with minimal WMH (n= 25). The concentration of 12,13-DiHOME (a sEHderived linoleic acid metabolite), and the ratio of 12,13-DiHOME to its sEH substrate, 12,13-EpOME, were elevated in the extensive WMH group (F1,53 = 5.9, p= 0.019), as was the 9,10-DiHOME/9,10-EpOME ratio (F1,53 = 5.4, p= 0.024). The 12,13-DiHOME/ 12,13-EpOME ratio was associated with poorer performance on a composite score derived from tests of psychomotor processing speed, attention, and executive function (β=− 0.473, p= 0.001, adjusted r = 0.213), but not with a composite verbal memory score. In a mediation model, periventricular WMH (but not deepWMH), explained 37% of the effect of the 12,13-DiHOME/12,13-EpOME ratio on the speed/attention/executive function composite score (indirect effect = − 0.50, 95%bootstrap confidence interval [− 0.99,− 0.17] Zscore units). Serum oxylipin changes consistent with higher sEH activity were markers of vascular cognitive impairment, and this association was partly explained by injury to the periventricular subcortical white matter.
Realistic face rendering for 3-D mixed reality experience

Virtual Reality (VR) has advanced significantly in the recent years. VR allows users to explore novel environments (both real and imaginary), play games, and engage with media in a way that is unprecedentedly immersive. However, compared to physical reality, sharing these experiences is difficult because the user's face is not easily observable from the outside and is partly occluded by the VR headset. Mixed Reality (MR) is a medium that alleviates some of this disconnect by sharing the virtual context of a VR user in a flat video format that can be consumed by an audience to get a feel for the user's experience. Our project aims to present a solution to address this problem by virtually removing the headset and revealing the face underneath it using a combination of 3D vision, machine learning and graphics techniques for images. We are now able to "remove" headsets and show a person's identity, focus and full face in virtual reality. The solution to this problem helps in enhancing communication through videos and video conferencing and in health care treatments for mental disorders.
A ug 2 01 9 Using fuel cells to power electric propulsion systems

The origin of fuel cell technology has a notable connection to the history of spaceflight, having been used in remarkable programs such as Gemini, Apollo and the Space Shuttle. With the constant growth of the electric propulsion technology in the last years, one natural application for fuel cells to be considered would be the electrical feeding of those thrusters for different mission profiles. In this article we explore in details this possibility, showing what would the necessary characteristics of such a device be in order to improve mission parameters, as payload ratio and thrust, among others. In the first section, a brief review of the applications of fuel cells in the space industry is shown, and the classical analytical modeling of these devices is presented. In the second part, two case studies illustrating most of the possible ways to use fuel cells in conjunction with electric propulsion systems are shown , and the possible advantages and limitations of the applications are demonstrated analytically. The result of the analysis shows that, in the case where the fuel cell reaction products are disposed of and the propulsion has its own feed system, the application of fuel cell technology would bring no advantages for most kinds of missions. On the other hand, when the case where the fuel cell exhaust is used as a propellant is considered, it is shown that it is possible to improve mission parameters, such as thrust and specific impulse, if certain conditions are met.
Incorporating the Structure of the Belief State in End-to-End Task-Oriented Dialogue Systems

End-to-end trainable networks try to overcome error propagation, lack of generalization and overall brittleness of traditional modularized task-oriented dialoguesystem architectures. Most proposed models expand on the sequence-to-sequence architecture. Some of them don’t track belief state, which makes it difficult to interact with ever-changing knowledge bases, while the ones that explicitly track the belief state do it with classifiers. The use of classifiers suffers from the outof-vocabulary words problem, making these models hard to use in real-world applications with ever-changing knowledge bases. We propose Structured Belief Copy Network (SBCN), a novel end-to-end trainable architecture that allows for interaction with external symbolic knowledge bases and solves the out-of-vocabulary problem at the same time. It explicitly uses the structure of the belief state in its architecture by using different sequential decoders with a copy mechanism for the different informable slots and a multi-label decoder for the requestable slots, and this provides better inductive bias. Through empirical results we show that SBCN achieves state-of-the-art results on dialogue datasets while providing a practical architecture that can be used in real-world applications.
The Real Effects of Checks and Balances: Policy Uncertainty and Corporate Investment

This article explores the economic effects of checks and balances on corporate investment and employment. I use U.S. gubernatorial election results from 1978 to 2010 as a source of exogenous variation in whether the party controls both the executive and the legislative branch (unified government) or not (divided government), which determines its ability to implement its political agenda. I find that both public and private firms respond to the political cycle by reducing investment and hiring when government becomes unified. Investment drops by three to five percent in the year following an election resulting in unified government, while stock returns volatility is three percent higher. The findings support the hypothesis that moving from divided to unified government raises policy uncertainty by increasing the probability of future policy changes. Consistent with a real option channel, the effect is stronger for capital intensive firms with lower asset redeployability.
Time-consistent strategy for a multi-period mean-variance asset-liability management problem with stochastic interest rate

In this paper, we investigate a multi-period mean-variance asset-liability management problem with stochastic interest rate and seek its time-consistent strategy. The financial market is assumed to be composed of one risk-free asset and multiple risky assets, and the stochastic interest rate is characterized by the discrete-time Vasicek model proposed by Yao et al. (2016a)[ 38 ]. We regard this problem as a non-cooperative game whose equilibrium strategy is the desired time-consistent strategy. We derive the analytical expressions of the equilibrium strategy, the equilibrium value function and the equilibrium efficient frontier by the extended Bellman equation. Some special cases of our model are discussed, and some properties of our equilibrium strategy, including a two-fund separation theorem, are proposed. Finally, a numerical example with real data is given to illustrate our theoretical results.
Joint model of accelerated failure time and mechanistic nonlinear model for censored covariates, with application in HIV/AIDS

For a time-to-event outcome with censored time-varying covariates, a joint Cox model with a linear mixed effects model is the standard modeling approach. In some applications such as AIDS studies, mechanistic nonlinear models are available for some covariate process such as viral load during anti-HIV treatments, derived from the underlying data-generation mechanisms and disease progression. Such a mechanistic nonlinear covariate model may provide better-predicted values when the covariates are left censored or mismeasured. When the focus is on the impact of the time-varying covariate process on the survival outcome, an accelerated failure time (AFT) model provides an excellent alternative to the Cox proportional hazard model since an AFT model is formulated to allow the influence of the outcome by the entire covariate process. In this article, we consider a nonlinear mixed effects model for the censored covariates in an AFT model, implemented using a Monte Carlo EM algorithm, under the framework of a joint model for simultaneous inference. We apply the joint model to an HIV/AIDS data to gain insights for assessing the association between viral load and immunological restoration during antiretroviral therapy. Simulation is conducted to compare model performance when the covariate model and the survival model are mis-specified.
Global stability in the 2 D Ricker equation

The scalar equation has been studied for a long time. W. E. Ricker proposed the equation in 1954 to model fishery stocks. One approach is to fit the Ricker model to data gathered over a long period of time and then use this model to gain insight into future stocks. For example the best Ricker’s model for the Skeena sockeye salmon population from 1908 to 1952 is done in the book by Mahaffy and Chavez-Ross [9] based on data collected by Shepard and Withler [15], Pnþ1 1⁄4 1:535Pnen :
GPS-free Localisation and Navigation of an Unmanned Ground Vehicle for Yield Forecasting in a Vineyard

Yield forecasting is an important practice for vineyards to ensure that seasonal yield targets are met. Currently this is performed by manually collecting samples from around the vineyard that are measured and weighed. There has been increased research into automating this process, particularly through computer vision techniques. This paper presents a localisation and navigation system for an autonomous ground vehicle that is capable of traversing a vineyard and acquiring geo-referenced images for use in yield forecasting, without relying on high- precision GPS. Localisation and navigation is achieved using laser scanners to detect the rows in the vineyard, which are used as features in line-based SLAM. Successful autonomous navigation of a block, including travelling along the length of the rows and transitioning between rows, in combination with geo-referencing images acquired during this time, has been demonstrated using this approach.
Introducing Environmental Taxes in Russia : Relevance of Tax-Interaction Effects

The theoretical literature on the double dividend concept is mainly focused in pre-existing distortionary taxes in the labour and capital market; however, the relevance of interactions with other taxes and tariffs is often neglected. Using an analytical model and a numerical general equilibrium model, we analyze the incidence of carbon taxes as well as tax interactions in Russia. The main findings are the following: substituting carbon taxes for labour taxes can lead to increases in revenues from export taxes, import tariffs, value added taxes, and some excise taxes because of the expansion of tax bases. Increases in revenues from taxes and tariffs decrease the cost of environmental tax reform. On the other hand, revenues from taxes on labour and capital income, mineral resource extraction taxes, excise taxes on petroleum products, and social security contribution decrease. JEL classification code: Q50, H20, C30, C68
haracterising fire spatial pattern interactions with climate and vegetation in olombia

Vegetation burning in tropical countries is a threat to the environment, causing not only local ecological, economic and social impacts, but also large-scale implications for global change. The burning is usually a result of interacting factors, such as climate, land-use and vegetation type. Satellite-derived monthly time series datasets of rainfall, burned area and active fire detections between December 2000 and 2009 were used in this study. A map of vegetation types was also used to determine these factors’ spatial and temporal variability and interactions with the total amount of burned area and active fires detected in Colombia. Grasslands represented the vegetation most affected by fires every year in terms of burned area (standardised by their total area), followed by secondary vegetation, pasture and forests. Grasslands were also most affected by active fires, but followed closely by pasture, agricultural areas, secondary vegetation and forests. The results indicated strong climate and fire seasonality and marked regional difference, partly explained by climatic differences amongst regions and vegetation types, especially in the Orinoco and Caribbean regions. The incidence of fire in the Amazon and Andes was less influenced by climate in terms of burned area impacted, but the strength of the ENSO phenomenon affected the Orinoco and the Andes more in terms of burned area. Many of the active fires detected occurred in areas of transition between the submontane and lowland Andes and the Amazon, where extensive conversion to pasture is occurring. The possible high impact of small fires on the tropical rainforest present in this transition area and the Amazonian rainforest deserves more attention in Colombia due to its previous ntribu lack of attention to its co