Miracle-dz/UniFAR-FTUs huggingface dataset:
On the use of multi‐algorithm, genetically adaptive multi‐objective method for multi‐site calibration of the SWAT model

With the availability of spatially distributed data, distributed hydrologic models are increasingly used for simulation of spatially varied hydrologic processes to understand and manage natural and human activities that affect watershed systems. Multi‐objective optimization methods have been applied to calibrate distributed hydrologic models using observed data from multiple sites. As the time consumed by running these complex models is increasing substantially, selecting efficient and effective multi‐objective optimization algorithms is becoming a nontrivial issue. In this study, we evaluated a multi‐algorithm, genetically adaptive multi‐objective method (AMALGAM) for multi‐site calibration of a distributed hydrologic model—Soil and Water Assessment Tool (SWAT), and compared its performance with two widely used evolutionary multi‐objective optimization (EMO) algorithms (i.e. Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non‐dominated Sorted Genetic Algorithm II (NSGA‐II)). In order to provide insights into each method's overall performance, these three methods were tested in four watersheds with various characteristics. The test results indicate that the AMALGAM can consistently provide competitive or superior results compared with the other two methods. The multi‐method search framework of AMALGAM, which can flexibly and adaptively utilize multiple optimization algorithms, makes it a promising tool for multi‐site calibration of the distributed SWAT. For practical use of AMALGAM, it is suggested to implement this method in multiple trials with relatively small number of model runs rather than run it once with long iterations. In addition, incorporating different multi‐objective optimization algorithms and multi‐mode search operators into AMALGAM deserves further research. Copyright © 2009 John Wiley & Sons, Ltd.
Ninety Plus Thirty Years of Nonlinear Dynamics: Less is More and More is Different

I review the early (1885–1975) and more recent history of dynamical systems theory, identifying key principles and themes, including those of dimension reduction, normal form transformation and unfolding of degenerate cases. I end by briefly noting recent extensions and applications in nonlinear fluid and solid mechanics, with a nod toward mathematical biology. I argue throughout that this essentially mathematical theory was largely motivated by nonlinear scientific problems, and that after a long gestation it is propagating throughout the sciences and technology.
Experience report: Anomaly detection of cloud application operations using log and cloud metric correlation analysis

Failure of application operations is one of the main causes of system-wide outages in cloud environments. This particularly applies to DevOps operations, such as backup, redeployment, upgrade, customized scaling, and migration that are exposed to frequent interference from other concurrent operations, configuration changes, and resources failure. However, current practices fail to provide a reliable assurance of correct execution of these kinds of operations. In this paper, we present an approach to address this problem that adopts a regression-based analysis technique to find the correlation between an operation's activity logs and the operation activity's effect on cloud resources. The correlation model is then used to derive assertion specifications, which can be used for runtime verification of running operations and their impact on resources. We evaluated our proposed approach on Amazon EC2 with 22 rounds of rolling upgrade operations while other types of operations were running and random faults were injected. Our experiment shows that our approach successfully managed to raise alarms for 115 random injected faults, with a precision of 92.3%.
Group norms, physical distance, and ecological efficiency in common pool resource management

Earlier research has repeatedly shown that people tend to follow group norms when using common pool resources. The present commons dilemma study seeks to extend these findings with two inherently relevant concepts: First, the ecological efficiency of the group norm, and second, the physical distance between the actors involved. Physical distance was manipulated by administering a web‐based commons dilemma task to participants in the laboratory versus participants in the Internet. Ecological efficiency was manipulated by giving participants feedback about an overusing or a conserving group norm while the pool was either big or small. Conformity effects were strongest when the perceived group norm was ecologically efficient and participants were physically closer. Moreover, the effect of physical distance was mediated by the importance a person attached to the group's behavior. When physically farther apart, individuals attached less importance to the group's behavior and, as a consequence, showed less conformity. The results are discussed in the light of previous commons dilemma research and social psychological theories, and consequences for natural resource management are reflected.
EVALUATION OF SOFTWARE USED IN AN INTERNET- BASED SIMULATION: ARE THERE ANY CULTURAL DIFFERENCES?

Methods and compositions useful for inducing a cytotoxic T lymphocyte response (CTL) in a human or domesticated or agriculturally important animal. The method includes the steps of providing the antigen to which the CTL response is desired and providing an antigen formulation which comprises, consists, or consists essentially of two or more of a stabilizing detergent, a micelle-forming agent, and an oil. This antigen formulation is preferably lacking in an immunostimulating peptide component, or has sufficiently low levels of such a component that the desired CTL response is not diminished. This formulation is provided as a stable oil-in-water emulsion.
Small Town, Great Expectations: Urbanization and Beautification in Leh

Urban modifications have considerably altered the appearance of Leh, a district capital in the Indian Transhimalaya, over the last few years. The beautification project in Leh is concentrated at and around the main bazaar and currently is the most visible and powerful instrument shaping the urban space. It is a subproject under the national “Urban Infrastructure Development Scheme for Small & Medium Towns” (UIDSSMT, Ministry of Urban Development 2009) and started in December 2013. Constructio.
A Peptide of Glutamic Acid Decarboxylase 65 Can Recruit and Expand a Diabetogenic T Cell Clone, BDC2.5, in the Pancreas1

Self peptide-MHC ligands create and maintain the mature T cell repertoire by positive selection in the thymus and by homeostatic proliferation in the periphery. A low affinity/avidity interaction among T cells, self peptides, and MHC molecules has been suggested for these events, but it remains unknown whether or how this self-interaction is involved in tolerance and/or autoimmunity. Several lines of evidence implicate the glutamic acid decarboxylase 65 (GAD-65) peptide, p524–543, as a specific, possibly low affinity, stimulus for the spontaneously arising, diabetogenic T cell clone BDC2.5. Interestingly, BDC2.5 T cells, which normally are unresponsive to p524–543 stimulation, react to the peptide when provided with splenic APC obtained from mice immunized with the same peptide, p524–543, but not, for example, with hen egg white lysozyme. Immunization with p524–543 increases the susceptibility of the NOD mice to type 1 diabetes induced by the adoptive transfer of BDC2.5 T cells. In addition, very few CFSE-labeled BDC2.5 T cells divide in the recipient’s pancreas after transfer into a transgenic mouse that overexpresses GAD-65 in B cells, whereas they divide vigorously in the pancreas of normal NOD recipients. A special relationship between the BDC2.5 clone and the GAD-65 molecule is further demonstrated by generation of a double-transgenic mouse line carrying both the BDC2.5 TCR and GAD-65 transgenes, in which a significant reduction of BDC2.5 cells in the pancreas has been observed, presumably due to tolerance induction. These data suggest that unique and/or altered processing of self Ags may play an essential role in the development and expansion of autoreactive T cells.
Optical spectroscopy of IRAS 02091+6333

We present a detailed spectroscopic investigation, spanning four winters, of the asymptotic giant branch (AGB) star IRAS 02091+6333. Zijlstra & Weinberger (2002) found a giant wall of dust around this star and modelled this unique phenomenon. However their work suered from the quality of the optical investigations of the central object. Our spectroscopic investigation allowed us to define the spectral type and the interstellar foreground extinction more precisely. Accurate multi band photometry was carried out. This provides us with the possibility to derive the physical parameters of the system. The measurements presented here suggest a weak irregular photometric variability of the target, while there is no evidence of a spectroscopic variability over the last four years.
Violating Work-Family Boundaries: Reactions to Interruptions at Work and Home

Our study builds on recent trends to understand the work-family interface through daily experiences of boundary management. In particular, we investigated boundary violations, or events in which family life breaches the boundary of work and vice versa. Our purpose was to enlighten the process between violations and relevant outcomes, building on the foundations of affective events theory and boundary theory. Specifically, we aim to (1) tease apart boundary violations at work and at home from the established construct of work-family conflict, (2) explore the affective events theory process through which cognitive and affective reactions to boundary violation events contribute to work-family conflict and satisfaction, and (3) examine positive and negative reactions to boundary violations. Findings from a 2-week daily diary study of 121 employed participants partially supported our predictions. Boundary violations contributed to general perceptions of work-family conflict both directly and indirectly through cognitive appraisals of thwarted goals and, in the work domain, negative affective reactions. Violations were also related to satisfaction through goal appraisal. Finally, benefits in the form of positive affect were found from boundary violations due to facilitated goals in the interrupting domain.
Horizontally Aligned Carbon Nanotube Based Biosensors for Protein Detection

A novel horizontally aligned single-walled carbon nanotube (CNT) Field Effect Transistor (FET)-based biosensing platform for real-time and sensitive protein detections is proposed. Aligned nanotubes were synthesized on quartz substrate using catalyst contact stamping, surface-guided morphological growth and chemical vapor deposition gas-guided growth methods. Real-time detection of prostate-specific antigen (PSA) using as-prepared FET biosensors was demonstrated. The kinetic measurements of the biosensor revealed that the drain current (Id) decreased exponentially as the concentration of PSA increased, indicating that the proposed FET sensor is capable of quantitative protein detection within a detection window of up to 1 µM. The limit of detection (LOD) achieved by the proposed platform was demonstrated to be 84 pM, which is lower than the clinically relevant level (133 pM) of PSA in blood. Additionally, the reported aligned CNT biosensor is a uniform sensing platform that could be extended to real-time detections of various biomarkers.
Depression and Brain-derived Neurotrophic Factor Levels in Alzheimer's Disease

Background: Depression is often viewed as a risk factor for the development of Alzheimer's disease (AD), however little is known regarding the underlying biological mechanisms linking these two diseases. Brain-derived neurotrophic factor (BDNF) has been linked to both cognitive impairment and depression in past research; however few studies have ex-amined this relation in a sample of Alzheimer's patients. The present study sought to address this gap in the literature by examining the relation between serum BDNF levels and depression assessed by the Geriatric Depression Scale (GDS) in a group of patients diagnosed with probable Alzheimer's disease. Methods: Participants included 169 individuals diagnosed with Probable AD enrolled in the TARC Longitudinal Research Cohort with available BDNF levels and GDS scores. The participants were divided into Depressed (N = 20) and Not Depressed (N = 149) based on GDS scores. Re-sults: BDNF levels significantly predicted level (High vs. Low) of depression (β = 0.066, SE = 0.031, p = 0.034). BDNF levels for the Depressed group were significantly higher than those observed in the Not Depressed group (p. > 0.036). Conclusions: These findings suggest that an upregulation of BDNF possibly exists among depressed AD patients as a response to the chronic inflammatory processes that occur in depression. This upregulation of BDNF appears to persist at least into early stages of Alzheimer's.
No . 2007-05 Education , unemployment and migration

This paper studies a two-region model in which unemployment, education decisions and interregional migration are endogenous. The poorer region exhibits both lower wages and higher unemployment rates, and migrants to the richer region are disproportionally skilled. The brain drain from the poor to the rich region is accompanied by stronger incentives to acquire skills even for immobile workers. Regional shocks tend to affect both regions in a symmetric fashion, and skilled-biased technological change reduces wages of the unskilled. Both education and migration decisions are distorted by a uniform unemployment compensation, which justifies a corrective subsidization. JEL classification: H23; I20; J61; J64; R10.
Hiding Traces of Resampling in Digital Images

Resampling detection has become a standard tool for forensic analyses of digital images. This paper presents new variants of image transformation operations which are undetectable by resampling detectors based on periodic variations in the residual signal of local linear predictors in the spatial domain. The effectiveness of the proposed method is supported with evidence from experiments on a large image database for various parameter settings. We benchmark detectability as well as the resulting image quality against conventional linear and bicubic interpolation and interpolation with a sinc kernel. These early findings on ldquocounter-forensicrdquo techniques put into question the reliability of known forensic tools against smart counterfeiters in general, and might serve as benchmarks and motivation for the development of much improved forensic techniques.
RUM: A Layered Architecture for Reasoning with Uncertainty

New reasoning techniques for dealing with uncertainty in expert systems have been embedded in RUM, a Reasoning with Uncertainty Module. RUM is an integrated software tool based on a frame system (KEE) that is implemented in an object oriented language. RUM's capabilities are subdivided into three layers: representation, inference, and control. The representation layer is based on frame-like data structures that capture the uncertainty information used in the inference layer and the uncertainty mete-information used in the control layer. Linguistic probabilities are used to describe the lower and upper bounds of the certainty measure attached to a well formed formula. The source and the conditions under which the information was obtained represent the non-numerical meta-information. The inference layer provides the uncertainty calculi with which to perform the intersection, detachment, union, and pooling of information. Five uncertainty calculi, based on their underlying Triangular norms, are used in this layer. The control layer uses the meta-information to select the appropriate calculus for each context and to resolve eventual ignorance or conflict in the information. This layer enables the programmer to declaratively express the local (context dependent) meta-knowledge that wil l substitute for the global assumptions traditionally used in uncertain reasoning. R U M has been tested in a sequence of experiments in both naval and aerial situation assessment, consisting of correlating reports and tracks, locating and classifying platforms, and identifying intents and threats.
Data-Centric Computing Frontiers: A Survey On Processing-In-Memory

A major shift from compute-centric to data-centric computing systems can be perceived, as novel big data workloads like cognitive computing and machine learning strongly enforce embarrassingly parallel and highly efficient processor architectures. With Moore's law having surrendered, innovative architectural concepts as well as technologies are urgently required, to enable a path for tackling exascale and beyond -- even though current computing systems face the inevitable instruction-level parallelism, power, memory, and bandwidth walls. As part of any computing system, the general perception of memories depicts unreliability, power hungriness and slowness, resulting in a future prospective bottleneck. The latter being an outcome of a pin limitation derived by packaging constraints, an unexploited tremendous row bandwidth is determinable, which off-chip diminishes to a bare minimum. Building upon a shift towards data-centric computing systems, the near-memory processing concept seems to be most promising, since power efficiency and computing performance increase by co-locating tasks on bandwidth-rich in-memory processing units, whereas data motion mitigates by the avoidance of entire memory hierarchies. By considering the umbrella of near-data processing as the urgent required breakthrough for future computing systems, this survey presents its derivations with a special emphasis on Processing-In-Memory (PIM), highlighting historical achievements in technology as well as architecture while depicting its advantages and obstacles.
Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations

With the emergence of Web 2.0, tag recommenders have become important tools, which aim to support users in finding descriptive tags for their bookmarked resources. Although current algorithms provide good results in terms of tag prediction accuracy, they are often designed in a data-driven way and thus, lack a thorough understanding of the cognitive processes that play a role when people assign tags to resources. This thesis aims at modeling these cognitive dynamics in social tagging in order to improve tag recommendations and to better understand the underlying processes. As a first attempt in this direction, we have implemented an interplay between individual micro-level (e.g., categorizing resources or temporal dynamics) and collective macro-level (e.g., imitating other users' tags) processes in the form of a novel tag recommender algorithm. The preliminary results for datasets gathered from BibSonomy, CiteULike and Delicious show that our proposed approach can outperform current state-of-the-art algorithms, such as Collaborative Filtering, FolkRank or Pairwise Interaction Tensor Factorization. We conclude that recommender systems can be improved by incorporating related principles of human cognition.
The Energy Box: Locally Automated Optimal Control of Residential Electricity Usage

The Energy Box is proposed as a 24/7 background processor operating on a local computer or in a remote location, silently managing one's home or small business electrical energy usage hour-by-hour and even minute-by-minute. It operates best in an environment of demand-sensitive real-time pricing, now made feasible via `smart grid' technology. We assume that, in time, virtually every electrical device in a home or small business will be controllable from the Energy Box. There are multiple motivations for an Energy Box: (1) By delaying or pushing forward various uses of electricity (e.g., space conditioning), widespread use of the Energy Box could `shave the peaks and fill in the valleys of demand,' thereby reducing the need for capacity expansion in electrical power generation and distribution; (2) The system should result in reduced electrical energy costs to the consumer; (3) The system supports local generation, storage and sale of electricity back to the grid; (4) The system supports graceful reductions in power consumption by allowing voluntary partial load shedding as requested by the electric utility during times of extreme high demand; (5) Requiring numerous minute-by-minute decisions over the course of a day, the system alleviates the home owner or small business manager from making such decisions, each only involving pennies but in the aggregate involving significant dollars. The primary integrating method of optimization and control is stochastic dynamic programming. [ Service Science , ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3. ]
ASSESSMENT OF LOCAL HYDRAULIC PARAMETERS BY ENKF DATA ASSIMILATION IN REAL AQUIFERS : A CASE STUDY IN DOWNTOWN PADOVA ( ITALY )

The calibration of natural aquifer model parameters in real world cases is a challenging goal that requires a careful tuning work, whose complexity increases with the number of available data. Paradoxically, instead of making easier the subsurface model numerical assessment, a high spatial density of data highlights the local scale heterogeneity effects, complicating the calibration procedure when an accurate reproduction of the local water table variations is requested. This is the case of the subsurface in the “Eremitani” area in downtown Padova (Italy), where an extensive monitoring was recently carried out to assess the water table alteration due to the possible realization of important underground works close to the “Scrovegni Chapel”, a renowned historical monument. Due to the implications for the foundation stability of the monumental building, the delicate structural equilibrium of the famous Giotto’s frescoes may be altered by the variations of the water table. For this reason, a relatively large number of piezometers and wells (16) were drilled in an area of approximately 8 ha, within two aquifers characterizing the subsurface medium down to a depth of 30 m. Measurements were collected for a relatively long period and some pumping tests, as well as a field experiment involving the controlled variation of the Piovego Canal – the watercourse crossing the study area –, were realized for monitoring the corresponding response of the water table in each observation well. To overcome the difficulties related to the calibration of a fully 3D finite element model solving the Richards’ equation, a data assimilation procedure was developed by integrating the groundwater model with the ensemble Kalman filter (EnKF) and the augmented state technique. The objective of this study is to retrieve the most relevant subsurface parameters (e.g., hydraulic conductivity and specific storage coefficient) by assimilating the piezometric data collected by the monitoring network, thus assessing the local scale heterogeneity due not only to the vertical stratification, but also to the horizontal spatial variability characterizing the area under investigation.