Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and application of quantitative methods for ecosystem services

Logsdon, Rebecca A. Ph.D., Purdue University, May 2015. Development and Application of Quantitative Methods for Ecosystem Services. Major Professor: Indrajeet Chaubey. Ecosystem services are benefits that people receive from the environment. Despite recent exponential increases in ecosystem service research, the ecosystem service framework has made little impact on policy and land management decisions, especially in the United States. Two of the main limitations for a lack of ecosystem service considerations in both policy and land management decisions are a need for more advanced quantification methods and the lack of engagement of key stakeholders who are responsible for making land management decisions. This research seeks to address these two limitations by testing and improving quantification methods of ecosystem services and by evaluating agricultural managers understanding and perceptions of ecosystem services. The main objectives of this research were to (1) test an existing ecosystem service evaluation method in the Upper Mississippi River Basin under current conditions and future climate change, (2) improve understanding of influences of aquatic genetic resource provisioning using the SWAT model, (3) improve quantification methods for climate regulation ecosystem services using the DayCent model; and (4) evaluate Indiana agricultural producers and
Size distribution and elemental composition of ultrafine and nanoparticles

A large body of epidemiological research has found an association between increased particulate air pollution and acute and chronic respiratory symptoms. One area of aerosol science that is currently of much interest, and is the focus of this article, is the formation of particles by combustion. Our major concern is with so-called “nanoparticles” (diameter less than 50 nm), which can be formed, for example, from diesel engine and can be associated with health effects. One of the objectives of this pilot study was to characterize the NanoMoudi Model 125B which a second generation micro-orifice uniform deposit impactor. This new equipment is designed for sampling atmospheric aerosols and also for diesel emission monitoring and analysis. As part of this study the concentrations of particles with different size including PM10, PM2.5, and PM1.0 were also measured at different locations away from busy road within Perth metropolitan area using TSI DustTrakTM 8520 Aerosol Particulate Monitor and TSI P-Trak 8525 Ultrafine Particle Counter. The results of the study showed that the NanoMoudi could be used to successfully collect and analyze fine and ultrafine particles including diesel particulates. Further to this, people who lived closer to busy roads were more likely to be exposed to higher concentrations of ultrafine particles which could be associated with adverse health effects.
Global characteristics of pluvial and dry multi‐year episodes, with emphasis on megadroughts

A multi‐millenial simulation with a coupled atmospheric–oceanic climatic model has been used to investigate the occurrence rate and characteristics of multi‐year dry and pluvial episodes. Such episodes are defined as having the same sign rainfall anomalies for the whole of their durations. Episodes of 5‐, 8‐ and 10‐year durations are shown to occur all over the globe. The occurrence rates are approximately in agreement with probabilistic expectations, although there are spatial variations associated with El Nino Southern Oscillation (ENSO) influences. The impacts of the dry and pluvial episodes are considerable, with 10‐year duration dry episodes resulting in a loss of at least one year's rainfall over the oceans and much larger losses over arid regions. Similar but positive outcomes are associated with pluvial episodes. Accumulated rainfall anomalies over various multi‐decadal periods indicate that regions with persistent features can occur, implying longer term impacts than might have been expected from a succession of dry and pluvial episodes. Time series of these episodes for individual model gridboxes highlight a disparity between the occurrence rates of dry and pluvial episodes and the lack of synchronicity of these episodes at the various gridboxes. These time series also clearly illustrate the rapid fall‐off in the occurrence rates of the episodes as their durations are increased. Despite claims in the literature that ‘megadroughts’ are typically associated with extended ENSO events, it is shown that for a very extended La Nina event the rainfall anomalies in a number of ENSO‐sensitive regions experienced interannual variability rather than a persistent anomaly. The role of a number of large‐scale oscillations in generating rainfall anomalies is examined, but it is concluded that stochastic processes are needed to account for the extended duration of dry and pluvial episodes, in addition to any forcing associated with such oscillations. This adds a random aspect to the determination of whether any given, say, ENSO event will become a dry or pluvial episode. Copyright © 2010 Royal Meteorological Society
Robust spatial memory maps encoded in networks with transient connections

The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space---a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map remains transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? Here, we demonstrate, using novel Algebraic Topology techniques, that cognitive map's stability is a generic, emergent phenomenon. The model allows evaluating the effect produced by specific physiological parameters, e.g., the distribution of connections' decay times, on the properties of the cognitive map as a whole. It also points out that spatial memory deterioration caused by weakening or excessive loss of the synaptic connections may be compensated by simulating the neuronal activity. Lastly, the model explicates functional importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity, by establishing three complementary timescales at which spatial information unfolds. Thus, the model provides a principal insight into how can the brain develop a reliable representation of the world, learn and retain memories despite complex plasticity of the underlying networks and allows studying how instabilities and memory deterioration mechanisms may affect learning process.
Selective Sign-Determining Multiple Confidence Intervals with FCR Control

Given $m$ unknown parameters with corresponding independent estimators, the Benjamini-Hochberg (BH) procedure can be used to classify the sign of parameters such that the expected proportion of erroneous directional decisions (directional FDR) is controlled at a preset level $q$. More ambitiously, our goal is to construct sign-determining confidence intervals---instead of only classifying the sign---such that the expected proportion of non-covering constructed intervals (FCR) is controlled. We suggest a valid procedure which adjusts a marginal confidence interval in order to construct a maximum number of sign-determining confidence intervals. We propose a new marginal confidence interval, designed specifically for our procedure, which allows to balance a trade-off between power and length of the constructed intervals, and, in fact, often enjoy (almost) the best of both worlds. We apply our methods to detect the sign of correlations in a highly publicized social neuroscience study and, in a second example, to detect the direction of association for SNPs with Type-2 Diabetes in GWAS data. In both examples we compare our procedure to existing methods and obtain encouraging results.
Hydroid assemblages across the Atlantic–Mediterranean boundary: is the Strait of Gibraltar a marine ecotone?

Strong gradients in physico-chemical properties between abutting water masses create prominent transition zones in the marine environment. The Strait of Gibraltar forms the well defined boundary between the Mediterranean and Atlantic, and this paper examines spatial variation of hydroid assemblages in this transition zone. Although several studies highlighted the transitional character of the Strait and defined it as an ecotone, the benthic hydroid assemblages did not show differences between the Gulf of Cadiz and the Alboran Sea. However, there is an asymmetrical influence of the Atlantic waters on the coastal benthic ecosystems of the Alboran Sea, which maintains a more Mediterranean character in the hydroid assemblages of the northern coast, whereas a more Atlantic character was found in the rest of the studied sites. The transition zone between Atlantic and Mediterranean benthic communities could be associated with an Atlantic Influence Zone rather than with the Strait of Gibraltar itself.
Medium propagation effects in high-order harmonic generation of Ar and N{sub 2}

We report theoretical calculations of high-order harmonic generation (HHG) by intense infrared lasers in atomic and molecular targets taking into account the macroscopic propagation of both fundamental and harmonic fields. On the examples of Ar and N{sub 2}, we demonstrate that these ab initio calculations are capable of accurately reproducing available experimental results with isotropic and aligned target media. We further present detailed analysis of HHG intensity and phase under various experimental conditions, in particular, as the wavelength of the driving laser changes. Most importantly, our results strongly support the factorization of HHG at the macroscopic level into a product of a returning electron wave packet and the photorecombination transition dipole under typical experimental conditions. This implies that the single-atom or single-molecule structure information can be retrieved from experimentally measured HHG spectra.
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity

We develop a general duality between neural networks and compositional kernels, striving towards a better understanding of deep learning. We show that initial representations generated by common random initializations are sufficiently rich to express all functions in the dual kernel space. Hence, though the training objective is hard to optimize in the worst case, the initial weights form a good starting point for optimization. Our dual view also reveals a pragmatic and aesthetic perspective of neural networks and underscores their expressive power.
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text

The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expressed by a subject in a given short piece of text was fulfilled. We propose various unstructured and structured models that capture fulfillment cues such as the subject's emotional state and actions. Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task.
Subsidence Simulations for Depleting Reservoirs – Considerations on the Role of Hydromechanical Nonlinearities

The paper aims at investigating some issues related to the theoretical and numerical modelization of subsidence phenomena induced by hydrocarbon withdrawal. First a brief review of the implications of facing the problem as coupled / uncoupled is provided accordingly to the theory of mechanics of porous media as formulated by Biot. On the basis of these considerations, the classical nucleus of strain solution given by Geertsma is coupled to the hydrodinamic problem of a single phase fluid draining towards a well. The approach implemented has the advantage of providing a series of subsidence profiles evolving with time, together with the well production. The problem is handled both introducing or not the possibility of hydromechanical nonlinearities in the behaviour of the reservoir rock. When accounted, the mechanical nonlinearity is accomplished by assuming that the rock deforms accordingly to classical logarithmic relationships for oedometric stress paths, as foreseen by critical state models such as Cam Clay. On its turn the hydraulic nonlinearity is accounted for by implementing a literature relationship valid for Mexico Gulf sands, postulating that an exponential relationship exists between porosity and permeability.
Ensemble Characteristics of the ZZ Ceti Stars

We present the observed pulsation spectra of all known noninteracting ZZ Ceti stars (hydrogen atmosphere white dwarf variables [DAVs]) and examine changes in their pulsation properties across the instability strip. We confirm the well-established trend of increasing pulsation period with decreasing effective temperature across the ZZ Ceti instability strip. We do not find a dramatic order-of-magnitude increase in the number of observed independent modes in ZZ Ceti stars, traversing from the hot to the cool edge of the instability strip; we find that the cool DAVs have one more mode on average than the hot DAVs. We confirm the initial increase in pulsation amplitude at the blue edge and find strong evidence of a decline in amplitude prior to the red edge. We present the first observational evidence that ZZ Ceti stars lose pulsation energy just before pulsations shut down at the empirical red edge of the instability strip.
Exploratory study of possible resonances in heavy meson - heavy baryon coupled-channel interactions

We use a unitary coupled-channel model to study the D ¯ Λ c − D ¯ Σ c interactions. In our calculation, SU(3) flavor symmetry is applied to determine the coupling constants. Several resonant and bound states with different spin and parity are dynamically generated in the mass range of the recently observed pentaquarks. The approach is also extended to the hidden beauty sector to study the B Λ b − B Σ b interactions. As the b-quark mass is heavier than the c-quark mass, there are more resonances observed for the B Λ b − B Σ b interactions and they are more tightly bound.
Towards Better Summarizing Bug Reports with Crowdsourcing Elicited Attributes

Recent years have witnessed the growing demands for resolving numerous bug reports in software maintenance. Aiming to reduce the time testers/developers take in perusing bug reports, the task of bug report summarization has attracted a lot of research efforts in the literature. However, no systematic analysis has been conducted on attribute construction which heavily impacts the performance of supervised algorithms for bug report summarization. In this study, we first conduct a survey to reveal the existing methods for attribute construction in mining software repositories. Then, we propose a new method named Crowd-Attribute to infer new effective attributes from the crowdgenerated data in crowdsourcing and develop a new tool named Crowdsourcing Software Engineering Platform to facilitate this method. With Crowd-Attribute, we successfully construct 11 new attributes and propose a new supervised algorithm named Logistic Regression with Crowdsourced Attributes (LRCA). To evaluate the effectiveness of LRCA, we build a series of large scale data sets with 105,177 bug reports. Experiments over both the public data set SDS with 36 manually annotated bug reports and new large-scale data sets demonstrate that LRCA can consistently outperform the state-of-the-art algorithms for bug report summarization.
An Evaluation of the Human-Interpretability of Explanation

Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable under three specific tasks that users may perform with machine learning systems: simulation of the response, verification of a suggested response, and determining whether the correctness of a suggested response changes under a change to the inputs. Through carefully controlled human-subject experiments, we identify regularizers that can be used to optimize for the interpretability of machine learning systems. Our results show that the type of complexity matters: cognitive chunks (newly defined concepts) affect performance more than variable repetitions, and these trends are consistent across tasks and domains. This suggests that there may exist some common design principles for explanation systems.
Least Squares After Model Selection in High-Dimensional Sparse Models

In this paper we study post-model selection estimators which apply ordinary least squares (ols) to the model selected by first-step penalized estimators, typically lasso. It is well known that lasso can estimate the non-parametric regression function at nearly the oracle rate, and is thus hard to improve upon. We show that ols post lasso estimator performs at least as well as lasso in terms of the rate of convergence, and has the advantage of a smaller bias. Remarkably, this performance occurs even if the lasso-based model selection “fails” in the sense of missing some components of the “true” regression model. By the “true” model we mean here the best s-dimensional approximation to the nonparametric regression function chosen by the oracle. Furthermore, ols post lasso estimator can perform strictly better than lasso, in the sense of a strictly faster rate of convergence, if the lasso-based model selection correctly includes all components of the “true” model as a subset and also achieves sufficient sparsity. In the extreme case, when lasso perfectly selects the “true” model, the ols post lasso estimator becomes the oracle estimator. An important ingredient in our analysis is a new sparsity bound on the dimension of the model selected by lasso which guarantees that this dimension is at most of the same order as the dimension of the “true” model. Our rate results are non-asymptotic and hold in both parametric and nonparametric models. Moreover, our analysis is not limited to the lasso estimator acting as selector in the first step, but also applies to any other estimator, for example various forms of thresholded lasso, with good rates and good sparsity properties. Our analysis covers both traditional thresholding and a new practical, data-driven thresholding scheme that induces maximal sparsity subject to maintaining a certain goodness-of-fit. The latter scheme has theoretical guarantees similar to those of lasso or ols post lasso, but it dominates these procedures as well as traditional thresholding in a wide variety of experiments. First arXiv version: December 2009.
Hst/nicmos Imaging of Disks and Envelopes around Very Young Stars

We present HST/NICMOS observations with ∼ 0. ′′ 1 ≈ 15 AU resolution of six young stellar objects in the Taurus star-formation region. The targets of our survey are three Class I IRAS sources (IRAS 04016+2610, IRAS 04248+2612, and IRAS 04302+2247) and three low-luminosity stars (DG Tau B, Haro 6-5B, and CoKu Tau/1) associated with Herbig Haro jets. The broad-band images show that the near-infrared radiation from these sources is dominated by light scattered from dusty circumstellar material distributed in a region 10-15 times the size of our solar system. Although the detailed morphologies of the individual objects are unique, the observed young stellar objects share common features. All of the circumstellar reflection nebulae are crossed by dark lanes from 500-900 AU in extent and from less than 50 to 350 AU in apparent thickness. The absorption lanes extend perpendicular to known optical and millimeter outflows in these sources. We interpret the dark lanes as optically thick circumstellar disks seen in silhouette against bright reflection nebulosity. The bipolar reflection nebulae extending perpendicular to the dust lanes appear to be produced by scattering from the upper and lower surfaces of the disks and from dusty material within or on the walls of the outflow cavities. Out of five objects in which the central source is directly detected, two are found to be subarcsecond binaries. This mini-survey is the highest resolution near-infrared study to date of circumstellar environments around solar-type stars with age ≤ 1 Myr.
Distributed Multi-resource Allocation with Little Communication Overhead

We propose a distributed algorithm to solve a special distributed multi-resource allocation problem with no direct inter-agent communication. We do so by extending a recently introduced additive-increase multiplicative-decrease (AIMD) algorithm, which only uses very little communication between the system and agents. Namely, a control unit broadcasts a one-bit signal to agents whenever one of the allocated resources exceeds capacity. Agents then respond to this signal in a probabilistic manner. In the proposed algorithm, each agent is unaware of the resource allocation of other agents. We also propose a version of the AIMD algorithm for multiple binary resources (e.g., parking spaces). Binary resources are indivisible unit-demand resources, and each agent either allocated one unit of the resource or none. In empirical results, we observe that in both cases, the average allocations converge over time to optimal allocations.
Analysis of Multipath Channel Delay Estimation Using Subspace Fitting

The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.