Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate change and the mathematics of transport in sea ice

A s the boundary layer between the ocean and atmosphere in the polar regions, sea ice is a critical component of the global climate system. As temperatures on Earth have warmed, the Arctic sea ice pack in particular has exhibited a dramatic decline in its summer extent. Indeed, the polar sea ice packs are harbingers of climate change. Predicting what may happen over the next ten, fifty, or one hundred years requires extensive modeling of critical sea ice processes and the role that sea ice plays in global climate. Currently, large-scale climate models in general do not realistically treat a number of sea ice processes that can significantly affect predictions. Moreover, monitoring the state of Earth’s sea ice packs, in particular their thickness distributions, provides key data on the impact of global warming. Mathematics is currently playing an important role in addressing these fundamental issues and will likely play an even greater role in the future. Here we give a brief bullet-by-bullet summary of the contents of this article.
Large artery damage in hypertension

Arterial stiffness and wave reflections, which are now well accepted as the most important determinants of increasing systolic and pulse pressures in ageing societies, reflect large artery damage in hypertension. These parameters are increasingly used in the clinical assessment of patients with hypertension and various cardiovascular (CV) risk factors. This review addresses recent advances in our understanding of the role played by arterial stiffness and wave reflection in the pathophysiology and treatment of hypertension. We also report a large number of longitudinal epidemiological studies demonstrating the independent predictive value of arterial stiffness, carotid pulse pressure and augmentation index, for cardiovascular events, and show that these parameters are now well accepted as intermediate end-points for CV events. We also discuss the effects of pharmacological agents on aortic stiffness and wave reflections. A remaining major issue is to determine whether a reduction in PWV or wave reflection in hypertensive patients is associated with a concomitant reduction in CV events, independently of the normalization of classical CV risk factors.
Using Geographic Information Systems to Create and Analyze Statistical Surfaces of Population and Risk for Environmental Justice Analysis

Objective. Methodological issues associated with the conventional statistical approach to environmental justice research, such as scale of analysis, continue to make assessments of environmental injustice problematic. Geographic information systems (GIS) can be used to facilitate multiscale analysis through the generation of statistical surface representations of both socioeconomic character and environmental risk. Methods. As a case study, U.S. Bureau of the Census and U.S. Environmental Protection Agency data sets were used to generate statistical surfaces of socioeconomic character and environmental risk for the southeast Pennsylvania region. Results. Analysis of these statistical surfaces reveals that socioeconomic status decreases with proximity to, and density of, hazardous facilities. Conclusions. Further research calls for incorporating other relevant information, such as amount and toxicity of toxic release, into GIS‐based statistical surface representations of risk.
DATA MINING ANALYSIS OF IMPACT OF FAST FOOD AMONG CHILDREN: A DISTRICT WIDE CASE STUDY

Health is determined by human biology, health care systems, environment and lifestyle of human being in which lifestyle and environmental determinants have major on socioeconomic circumstances. Today, Fast food has become a prominent feature of the diet of childrenthroughout the world. Frequent consumption of fast food has adverse effects on nutrition because of excessive content of energy, fat and low nutritional value. Moreover, frequent consumption of fast food is associated with poor nutritional habits. Of note, the “empty calorie” content (low nutritional value) of children’s dietary intake from retail food stores and schools is similar to that of fast food restaurants. The source of added sugar and fat from retail food stores and schools, including sugar-sweetened beverages, grain desserts, pizza and high-fat milk was similar to that from fast food restaurants including high-fat milk beverages, dairy desserts, French fries and pizza. However, many studies have examined the effects of fast-food consumption on any nutrition or health-related outcome in our Indian scenario. More importantly, children under the age of 10 from middle and low socio-economic class backgrounds were more likely to report infrequent fruit and vegetable intake also result in low nutritious food habit. The aim of this study was to test the hypothesis that fastfood consumption adversely affects dietary factors linked to children health risk. We have collected the information from many children of Theni district, Tamil Nadu in India who is fond of eating junk foods. This process is done by using the classification algorithm in data mining and we select with the XL Miner tool.
THE APPLICATION OF DETREMDED FLUCTUATION ANALYSIS IN RUNNING AND ITS INTEGRATION INTO A REAL-TIME SYSTEM

Detrended fluctuation analysis (DFA) provides valuable information regarding both training and injury when applied to running time series. However, there is limited information when applied to recreational runners, or within a real-time environment. Firstly, DFA was applied to the stride time series of select training runs and competitive runs for recreational runners completing a half marathon and full marathon. Results indicate recreational runners maintain similar stride time dynamics in a half marathon, compared to training, however, stride time variability becomes increasingly deterministic during a marathon, compared to training. Secondly, we explore the implementation of DFA in a real-time system and provide evidence to support the use of DFA in running feedback.
Sheng-Influence of Transaction Trust in B 2 B e-Marketplaces : An Investigation of Tan and Thoen ' s Views

Tan and Thoen proposed a generic model of trust for e-commerce in which transaction trust combines party trust and control trust. The purpose of this study is to assess the influence of party trust and control trust on risk perception and exchange behavior in B2B e-marketplaces. The authors modeled party trust and control trust as higher-order formative constructs; they then developed a partially mediated research model to show how these two forms of trust influence perceived risk, information sharing, and transaction. The research model was tested on a sample of 82 manufacturing enterprises having experience in B2B e-marketplaces using Partial Least Squares. The results show that party trust negatively influences perceived risk, that control trust positively influences information sharing, that perceived risk negatively influences information sharing, and that information sharing positively influences transaction. Information sharing acts as the mediator between trust and transaction. E-marketplace operators should supply all participants with various tools and channels for sharing transaction-related information. 
Exponentially Weighted Imitation Learning for Batched Historical Data

We consider deep policy learning with only batched historical trajectories. The main challenge of this problem is that the learner no longer has a simulator or ``environment oracle'' as in most reinforcement learning settings. To solve this problem, we propose a monotonic advantage reweighted imitation learning strategy that is applicable to problems with complex nonlinear function approximation and works well with hybrid (discrete and continuous) action space. The method does not rely on the knowledge of the behavior policy, thus can be used to learn from data generated by an unknown policy. Under mild conditions, our algorithm, though surprisingly simple, has a policy improvement bound and outperforms most competing methods empirically. Thorough numerical results are also provided to demonstrate the efficacy of the proposed methodology.
Monocular self localization in an urban environment using a prior-based soft optimization robust estimation method

Autonomous vehicle driving in urban environments is a challenging task that requires localization accuracy exceeding that available from GPS-based inertial guidance systems. For map-based driving, a 3D laser scanner can be utilized to localize the vehicle within a previously recorded 3D map. Such scanners are however not feasible for mass production due to cost considerations. In this paper we present a localization algorithm that creates an off-line predefined map and then localizes with respect to this map. First, the map is constructed by a service vehicle equipped with a calibrated stereo camera rig and a high precision navigation system. Then, the global localization ego-pose can be obtained in any vehicle equipped with a standard GPS and a single forward looking camera for extracting and matching features to relevant map candidates. We use a recently proposed estimation method called SOREPP (Soft Optimization method for Robust Estimation based on Pose Priors) that utilizes relevant priors for achieving high performance, fast and reliable estimation, even with a small fraction of inliers. During the estimation it uses all the matched correspondences without need for random sampling to find the inliers. This method eventually obtains an outlier-free set of landmarks, used to estimate the ego-pose with high accuracy. We evaluate our algorithm on real world data comprised of a challenging 4.5km drive. Our algorithm achieves accurate localization results: a mean lateral absolute error of 14.35cm and a mean longitudinal absolute error of 18.63cm.
A Simple Theory of International Trade with Multinational Corporations

Using the idea that firm-specific assets associated with marketing, management, and product-specific R & D can be used to service production plants in countries other than the country in which these inputs are employed, I develop a simple general equilibrium model of international trade in which the location of plants in a differentiated product industry is a decision variable. The model is then used to derive predictions of trade pattern, volumes of trade, the share of intra-industry trade, and the share of intrafirm trade as functions of relative country size and differences in relative factor endowments.
Risky Bank Lending and Optimal Capital Adequacy Regulation

We study the welfare properties of a New Keynesian monetary economy with an essential role for risky bank lending. Banks lend funds deposited by households to a financial accelerator sector, and face penalties for maintaining insufficient net worth. The loan contract specifies an unconditional lending rate, which implies that banks can make loan losses. Their main response is to raise lending rates to rebuild net worth. Prudential rules that adjust minimum capital adequacy requirements in response to loan losses significantly increase welfare. But the gains from eliminating limited liability and moral hazard would be an order of magnitude larger.
Dark Higgs Bosons at FASER

FASER, ForwArd Search ExpeRiment at the LHC, has been proposed as a small, very far forward detector to discover new, light, weakly-coupled particles. Previous work showed that with a total volume of just ∼ 0.1 − 1 m3, FASER can discover dark photons in a large swath of currently unconstrained parameter space, extending the discovery reach of the LHC program. Here we explore FASER’s discovery prospects for dark Higgs bosons. These scalar particles are an interesting foil for dark photons, as they probe a different renormalizable portal interaction and are produced dominantly through B and K meson decays, rather than pion decays, leading to less collimated signals. Nevertheless, we find that FASER is also a highly sensitive probe of dark Higgs bosons with significant discovery prospects that are comparable to, and complementary to, much larger proposed experiments. ∗Electronic address: jlf@uci.edu †Electronic address: iftachg@uci.edu ‡Electronic address: fkling@uci.edu §Electronic address: strojano@uci.edu 1 ar X iv :1 71 0. 09 38 7v 1 [ he pph ] 2 5 O ct 2 01 7
Climate of the Neoproterozoic

The Neoproterozoic is a time of transition between the ancient microbial world and the Phanerozoic, marked by a resumption of extreme carbon isotope fluctuations and glaciation after a billion-year absence. The carbon cycle disruptions are probably accompanied by changes in the stock of oxidants and connect to glaciations via changes in the atmospheric greenhouse gas content. Two of the glaciations reach low latitudes and may have been Snowball events with near-global ice cover. This review deals primarily with the Cryogenian portion of the Neoproterozoic, during which these glaciations occurred. The initiation and deglaciation of Snowball states are discussed in light of a suite of general circulation model simulations designed to facilitate intercomparison between different models. Snow cover and the nature of the frozen surface emerge as key factors governing initiation and deglaciation. The most comprehensive model discussed confirms the possibility of initiating a Snowball event with a plausible reduction of CO2. Deglaciation requires a combination of elevated CO2 and tropical dust accumulation, aided by some cloud warming. The cause of Neoproterozoic biogeochemical turbulence, and its precise connection with Snowball glaciations, remains obscure.
Distributed Power Control in Single-Stream MIMO Wiretap Interference Networks With Full-Duplex Jamming Receivers

We consider a multi-link interference network that is tapped by an external eavesdropper. To conceal information from the eavesdropper, legitimate links are equipped with transmitter-based friendly jamming (TxFJ) and receiver-based friendly jamming (RxFJ). Each link seeks to maximize its secrecy rate by determining the best power assignment (PA) for the information, TxFJ, and RxFJ signals. Joint optimization of these parameters is a non-convex problem. Hence, we seek sub-optimal solutions. Specifically, we find a lower bound on the allocated power to TxFJ above which positive secrecy is achievable for a given link. Once positive secrecy is achieved, the secrecy rate becomes monotonically increasing in the power at the transmitter (Alice). Therefore, the rest of Alice's power is allocated to the information signal. Despite its sub-optimality, such an approach precludes the possibility of employing successive interference cancellation by the eavesdropper. The RxFJ PA of a link is adjusted using an on-off PA that depends only on the link's local channel state information (CSI). With every link following such a strategy, we model this interaction as a non-cooperative game. We derive sufficient conditions for the uniqueness of the resulting Nash equilibrium. We then propose an algorithm to implement the PA game. Lastly, we relax knowledge of eavesdropper's CSI (E-CSI) and propose a framework that is robust to unknown E-CSI. Our results indicate that this robust framework performs close to when E-CSI is fully known to legitimate links. Moreover, empirically it is shown that the secrecy sum-rate scales with the power budget of transmitters.
QSDPNAL : A two-phase Newton-CG proximal augmented Lagrangian method for convex quadratic semidefinite programming problems

In this paper, we present a two-phase Newton-CG proximal augmented Lagrangian method, called QSDPNAL, for convex quadratic semidefinite programming (QSDP) problems with constraints consisting of a large number of linear equality, inequality constraints, a simple convex polyhedral set constraint, and a positive semidefinite constraint. QSDPNAL is in fact an extension of SDPNAL+ [Mathematical Programming Computation, DOI 10.1007/s12532-015-00826] and SDPNAL [SIAM Journal on Optimization, 20 (2010), pp. 1737–1765] for solving linear SDPs. As the cornerstone of QSDPNAL, we first introduce the powerful and elegant inexact symmetric Gauss-Seidel (sGS) decomposition technique for solving a convex minimization problem whose objective is the sum of a multi-block quadratic function and a non-smooth function involving only the first block. A first order algorithm which takes advantage of our inexact sGS decomposition technique is adopted in our QSDPNAL-Phase I to generate a reasonably good initial point. Then, in QSDPNAL-Phase II, we design a proximal augmented Lagrangian method (ALM) where the inner sub-problem in each iteration is solved via the inexact accelerated block coordinate descent (ABCD) method [arXiv preprint arXiv:1505.04278, (2015)], which again relies on our inexact sGS decomposition technique, together with the intelligent incorporation of the semismooth Newton-CG algorithm. Moreover, under suitable conditions, we are able to analyze the rate of convergence of the proposed algorithm. We further discuss the important numerical issues in the implementation of QSDPNAL. Extensive numerical results for various large scale QSDPs show that our two-phase framework is not only fast but also robust in obtaining accurate solutions.
Energy flux, body size and density in relation to bird species richness along an elevational gradient in Taiwan

Aim To e xamine the species richness of breeding birds along a local elevational gradient and to test the following assumptions of the energy limitation hypothesis: (1) the energy flux through birds is positively correlated with above-ground net primary productivity, (2) bird density is positively correlated with total energy flux, and (3) bird species richness is positively correlated with bird density. Location An elevational gradient from 1400 to 3700 m on Mt. Yushan, the highest mountain in Taiwan (23 ° 28 ′ 30 ″ N, 120 ° 54 ′ 00 ″ E), with a peak of 3952 m a.s. l. Methods We established 50 sampling stations along the elevational gradient. From March to July 1992, we estimated the density of each bird species using the variable circular-plot method. Above-ground net primary productivity was modelled using monthly averages from weather data for the years 1961‐90. Results Bird species richness had a hump-shaped relationship with elevation and with net primary productivity. Bird energy flux was positively correlated with net primary productivity and bird species richness was positively correlated with bird density. The relationship between bird density and energy flux was hump-shaped, which does not support one assumption of the energy limitation hypothesis. Main conclusions The results supported two essential assumptions of the energy limitation hypothesis. However, when energy availability exceeded a certain level, it could decrease species richness by increasing individual energy consumption, which reduced bird density. Thus, energy availability is a primary factor influencing bird species richness at this scale, but other factors, such as body size, could also play important roles.
Quantifying the Effects of Solar Panel Orientation on the Electrical Grid

As the prices of solar panels continue to decline, energy production from solar farms is skyrocketing, leading to a situation where, at certain times, solar farms produce more energy than can be consumed. Today, the only control over this excess electricity generation is curtailment, meaning excess power is wasted. A source of control that is often overlooked is the choice of orientation of fixed (non-tracking) solar panels: panels oriented towards the east, for example, produce more electricity earlier in the day and less later in the day, compared to similar panels oriented south. While this results in an overall reduction in generation, it matches solar generation with electrical grid load better, thus reducing curtailment. We use optimization to study the degree to which non-tracking panel orientation can be used to meet each of four possible grid requirements: load following, peak reduction, reduction in operation cost, and ramp reduction. Assuming complete control over the orientation of all panels, we find that, in the three jurisdictions we studied, i.e., Ontario, British Columbia, and Texas, orientation can be very useful in reducing ramps (reducing them up to 30%) but does not appear to be useful for reducing net load peaks.
Chemical mass balance estimation of arsenic in atmospheric dust fall out in an urban residential area, Raipur, Central India

The manuscripts shows a good attempt at measuring the flux of dust depositing in an industrial city with the main focus on Arsenic. The manuscript can be improved with some editing for grammar and language. However, several key things were missing: detail meteorological information, explanationof wind channels, data for other chemical species measured, particle size distribution information, error bugdet and details about accuracy and method detection values for the chemical analyses.
Tree Edit Distance for Recognizing Textual Entailment : Estimating the Cost of Insertion

The focus of our participation in PASCAL RTE2 was estimating the cost of the information of the hypothesis which is missing in the text and can not be matched with entailment rules. We have tested different system settings for calculating the importance of the words of the hypothesis and investigated the possibility of combining them with machine learning algorithm.
Constructing a Story-Based Interactive Learning Approach with the Anchor Strategy and Modularity Concept for Foreign Spouses (Case of Taiwan)

In Taiwan, because of the change of social structure, the number of transnational marriage immigrants, mostly females, coming from Southeast Asia has increased dramatically in recent years. This oneyexchanged mates and cultural circumstances differ will cause them to face the acculturation pressures. The aims of this study are to construct a story-based interactive for foreign spouses accommodation courses. Considering the varied cultural background and education divide, we integrated the anchor strategy, modularity concept and web navigation to construct a vivid, flexible and effective learning environment. An exploratory test was carried out with 32 foreign spouses. Research data were collected through the questionnaire. The results revealed that the interactive PowerPoint could effectively attract and maintain the participants attention; they felt that the course content and activities were related to their personal goals. Also, they were confident to achieve the expected outcomes of the course and satisfied with the instruction.
Deep Asynchronous Reinforcement Knowledge ( DARK ) for Embodied Intelligent Agent

Advanced Embodied Artificial Intelligent Agents with high degree of freedom and complex articulation such as anthropomorphic humanoid robots present an exciting training opportunity using Deep Reinforcement Learning paradigm. Deep Reinforcement Learning methods facilitate creation of complex action policy for such advanced physiology structures that may result in superior learning, reward generation and post learning performance. However, training of such complex policy can take a long time usually amounting to years of experience. We propose transferring knowledge from a learned agent to an untrained agent to improve the performance in early training time.