Miracle-dz/UniFAR-FTUs huggingface dataset:
A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements

High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area.
An adaptive hyperplane approach for multiple objective optimization problems with complex constraints

Many real-world decision problems involve multiple and conflicting objectives, which need to be optimized simultaneously while respecting various complex constraints. In this paper, we investigated how to solve these kinds of problems by using genetic algorithms. A new fitness assignment method for multiple objective optimization problems - the adaptive hyperplane-based fitness assignment method - was proposed in order to give a genetic algorithm the search pressure toward the positive ideal point in the objective space. An adaptive penalty function was used together with the adaptive hyperplane method in order to let genetic search explore the optima through both feasible and infeasible areas in the solution space. A Pareto solution reserving method was incorporated into normal genetic algorithm loop in order to maintain a set of Pareto solutions during the evolutionary process.
Privacy Odometers and Filters: Pay-as-you-Go Composition

In this paper we initiate the study of adaptive composition in differential privacy when the length of the composition, and the privacy parameters themselves can be chosen adaptively, as a function of the outcome of previously run analyses. This case is much more delicate than the setting covered by existing composition theorems, in which the algorithms themselves can be chosen adaptively, but the privacy parameters must be fixed up front. Indeed, it isn't even clear how to define differential privacy in the adaptive parameter setting. We proceed by defining two objects which cover the two main use cases of composition theorems. A privacy filter is a stopping time rule that allows an analyst to halt a computation before his pre-specified privacy budget is exceeded. A privacy odometer allows the analyst to track realized privacy loss as he goes, without needing to pre-specify a privacy budget. We show that unlike the case in which privacy parameters are fixed, in the adaptive parameter setting, these two use cases are distinct. We show that there exist privacy filters with bounds comparable (up to constants) with existing privacy composition theorems. We also give a privacy odometer that nearly matches non-adaptive private composition theorems, but is sometimes worse by a small asymptotic factor. Moreover, we show that this is inherent, and that any valid privacy odometer in the adaptive parameter setting must lose this factor, which shows a formal separation between the filter and odometer use-cases.
Human Cytomegalovirus TR Strain Glycoprotein O Acts as a Chaperone Promoting gH/gL Incorporation into Virions but Is Not Present in Virions

ABSTRACT Human cytomegalovirus (HCMV) produces the following two gH/gL complexes: gH/gL/gO and gH/gL/UL128-131. Entry into epithelial and endothelial cells requires gH/gL/UL128-131, and we have provided evidence that gH/gL/UL128-131 binds saturable epithelial cell receptors to mediate entry. HCMV does not require gH/gL/UL128-131 to enter fibroblasts, and laboratory adaptation to fibroblasts results in mutations in the UL128-131 genes, abolishing infection of epithelial and endothelial cells. HCMV gO-null mutants produce very small plaques on fibroblasts yet can spread on endothelial cells. Thus, one prevailing model suggests that gH/gL/gO mediates infection of fibroblasts, while gH/gL/UL128-131 mediates entry into epithelial/endothelial cells. Most biochemical studies of gO have involved the HCMV lab strain AD169, which does not assemble gH/gL/UL128-131 complexes. We examined gO produced by the low-passage clinical HCMV strain TR. Surprisingly, TR gO was not detected in purified extracellular virus particles. In TR-infected cells, gO remained sensitive to endoglycosidase H, suggesting that the protein was not exported from the endoplasmic reticulum (ER). However, TR gO interacted with gH/gL in the ER and promoted export of gH/gL from the ER to the Golgi apparatus. Pulse-chase experiments showed that a fraction of gO remained bound to gH/gL for relatively long periods, but gO eventually dissociated or was degraded and was not found in extracellular virions or secreted from cells. The accompanying report by P. T. Wille et al. (J. Virol., 84:2585-2596, 2010) showed that a TR gO-null mutant failed to incorporate gH/gL into virions and that the mutant was unable to enter fibroblasts and epithelial and endothelial cells. We concluded that gO acts as a molecular chaperone, increasing gH/gL ER export and incorporation into virions. It appears that gO competes with UL128-131 for binding onto gH/gL but is released from gH/gL, so that gH/gL (lacking UL128-131) is incorporated into virions. Thus, our revised model suggests that both gH/gL and gH/gL/UL128-131 are required for entry into epithelial and endothelial cells.
Neural Network Music Composition by Prediction: Exploring the Benefits of Psychoacoustic Constraints and Multi-scale Processing

Abstract In algorithmic music composition, a simple technique involves selecting notes sequentially according to a transition table that specifies the probability of the next note as a function of the previous context. An extension of this transition-table approach is described, using a recurrent autopredictive connectionist network called CONCERT. CONCERT is trained on a set of pieces with the aim of extracting stylistic regularities. CONCERT can then be used to compose new pieces. A central ingredient of CONCERT is the incorporation of psychologically grounded representations of pitch, duration and harmonic structure. CONCERT was tested on sets of examples artificially generated according to simple rules and was shown to learn the underlying structure, even where other approaches failed. In larger experiments, CONCERT was trained on sets of J. S. Bach pieces and traditional European folk melodies and was then allowed to compose novel melodies. Although the compositions are occasionally pleasant, and are.
Maternal postpartum depression and infant social withdrawal among human immunodeficiency virus (HIV) positive mother–infant dyads

Maternal postpartum depression poses significant risks for mother–child interaction and long-term infant outcomes. Human immunodeficiency virus (HIV) status has also been implicated in the development of postpartum depression, but the association between maternal depression and infant social behavior in the context of HIV infection has not been fully investigated. First, we examined the relationship between maternal postpartum depression and infant social withdrawal at 10–12 months of age in HIV-infected mothers and infants. Second, we ascertained whether infant social withdrawal could be significantly predicted by maternal postpartum depression. The sample consisted of 83 HIV-infected mother–infant dyads. Mothers were assessed for postpartum depression with the Edinburgh Postnatal Depression Scale (EPDS), and infant social withdrawal behavior was rated using the Modified Alarm Distress Baby Scale (m-ADBB). 42.2% of the mothers scored above the cut-off point for depression on the EPDS, and a third of infants (31%) were socially withdrawn. Notably, maternal depression did not predict infant social withdrawal as measured by the m-ADBB. Infant social withdrawal was also not significantly associated with failure to thrive or gender. These preliminary findings need further investigation with respect to the impact on long-term neurodevelopmental and behavioral outcomes.
A Boosting Framework on Grounds of Online Learning

By exploiting the duality between boosting and online learning, we present a boosting framework which proves to be extremely powerful thanks to employing the vast knowledge available in the online learning area. Using this framework, we develop various algorithms to address multiple practically and theoretically interesting questions including sparse boosting, smooth-distribution boosting, agnostic learning and, as a by-product, some generalization to double-projection online learning algorithms.
Predator prey optimization for snake-based contour detection

Purpose – The purpose of this paper is to describe a work that aims to solve contour detection problem using a planar deformable model and a swarm‐based optimization technique. Contour detection is an important task in image processing as it allows depicting boundaries of objects in an image. The proposed approach uses snakes as active contour model and adapts predator prey optimization (PPO) metaheuristic so that to define a new dynamic for evolving snakes in a way to reduce time complexity while providing good quality results.Design/methodology/approach – In the proposed approach, contour detection has been cast as an optimization problem requiring function minimization. PPO has been used to develop a search strategy to handle the optimization process. PPO is a population‐based method inspired by the phenomenon of predators attack and preys evasion. It has been proposed as an improvement of particle swarm optimization (PSO) where additional particles are introduced to repel the other particles into the .
Detecting Selection on Temporal and Spatial Scales: A Genomic Time-Series Assessment of Selective Responses to Devil Facial Tumor Disease

Detecting loci under selection is an important task in evolutionary biology. In conservation genetics detecting selection is key to investigating adaptation to the spread of infectious disease. Loci under selection can be detected on a spatial scale, accounting for differences in demographic history among populations, or on a temporal scale, tracing changes in allele frequencies over time. Here we use these two approaches to investigate selective responses to the spread of an infectious cancer—devil facial tumor disease (DFTD)—that since 1996 has ravaged the Tasmanian devil (Sarcophilus harrisii). Using time-series ‘restriction site associated DNA’ (RAD) markers from populations pre- and post DFTD arrival, and DFTD free populations, we infer loci under selection due to DFTD and investigate signatures of selection that are incongruent among methods, populations, and times. The lack of congruence among populations influenced by DFTD with respect to inferred loci under selection, and the direction of that selection, fail to implicate a consistent selective role for DFTD. Instead genetic drift is more likely driving the observed allele frequency changes over time. Our study illustrates the importance of applying methods with different performance optima e.g. accounting for population structure and background selection, and assessing congruence of the results.
Evaluation of early age mechanical properties of concrete in real structure

The curing temperature is known to influence the rate of mechanical properties development of early age concrete. In realistic sites the temperature of concrete is not isothermal 20 0C, so the paper measured adiabatic temperature increases of four different concretes to understand heat emission during hydration at early age. The temperature-matching curing schedule in accordance with adiabatic temperature increase is adopted to simulate the situation in real massive concrete. The specimens under temperature-matching curing are subjected to realistic temperature for first few days as well as adiabatic condition. The mechanical properties including compressive strength, splitting strength and modulus of elasticity of concretes cured under both temperature-matching curing and isothermal 20 0C curing are investigated. The results denote that comparing temperature-matching curing with isothermal 20 0C curing, the early age concretes mechanical properties are obviously improved, but the later mechanical properties of concretes with pure Portland and containing silica fume are decreased a little and still increased for concretes containing fly ash and slag. On this basement using an equivalent age approach evaluates mechanical properties of early age concrete in real structures, the model parameters are defined by the compressive strength test, and can predict the compressive strength, splitting strength and elasticity modulus through measuring or calculating by finite element method the concreted temperature at early age, and the method is valid, which is applied in a concrete wall for evaluation of crack risking.
Channel allocation in wireless data center networks

Unbalanced traffic demands of different data center applications are an important issue in designing Data center networks (DCNs). In this paper, we present our exploratory investigation of utilizing wireless transmissions in DCNs. Our work aims to solve the congestion problem caused by a few hot nodes to improve the global performance. We model the wireless transmissions in a DCN by considering both the wireless interference and the adaptive transmission rate. Moreover, both throughput and job completion time are taken into account to evaluate the impact of wireless transmissions on the global performance. Based on this model, we formulate the channel allocation in wireless DCNs as an optimization problem and design a genetic algorithm (GA) based approach to address it. To demonstrate the effectiveness of wireless transmissions as well as our GA-based algorithm in a wireless DCN, extensive simulation study is carried out and the results validate our design.
Thermodynamic Prediction of Spinodal Decomposition in Multi-component Silicate Glass for Design of Functional Porous Glass Materials

Abstract The authors have investigated metastable phase separation in multi-component silicate glass for the fabrication of porous glass from multi-component slag. Spinodal decomposition forms an interconnected microstructure in glass spontaneously, and porous glass is obtained by leaching one of the decomposed phases with an acid solution. This porous glass can be used for a filter to remove impurities in polluted water or air. In this study, the metastable miscibility gap was predicted in multi-component silicate glass using thermodynamic analyses where glass was regarded as a super-cooled liquid phase. Occurrence of spinodal decomposition was observed in annealed glass, and it corresponded to the predicted miscibility gap. Then, we fabricated porous glass using spinodal decomposition in multi-component borosilicate glass and by removing one of the decomposed phases. Furthermore, for the creation of functional porous glass applicable for environmental purification, the spinodal decomposition was prepared in multi-component borosilicate glass containing titanium oxide based on the predicted metastable miscibility gap in multi-component silicate glass.
Private group communication : two perspectives and a unifying solution

Private communication in groups of users is a security problem that is relevant in a host of real-world applications like pay-per-view, secure online conferencing and the protection of content on digital media. Despite a long history of research on the topic, there exists a fundamental dichotomy in the literature in the manner in which the problem is modeled and security analysis of protocols conducted. Most of the existing protocols for the problem have been analyzed using a simple "symbolic'' model of computation, one in which cryptographic primitives are treated as ideal objects and adversarial behavior defined using a fixed set of inference rules. Some others have been approached using a more detailed computational model, wherein security is based on complexity assumptions (like the existence of one-way functions) and proven using very careful probabilistic analysis. The two approaches have their individual merits but are in conflict with each other: while the method of the computational approach is more realistic, security proofs in the symbolic approach are far easier to produce and to verify. This thesis reconciles the two approaches and proposes a methodology for analyzing protocols that is symbolic in nature, and still powerful enough to guarantee security against arbitrary computationally-bounded entities. We define a large class of protocols for private group communication, and provide syntactic condon protocols in this class such that any protocol that satisfies these conditions, and meets the symbolic definition of security, is guaranteed to be secure in the computational model as well. Such an implication enables us to conduct security analysis of protocols (falling within our class) symbolically, and simultaneously reap the benefits of computational security analysis. As an illustration, we apply our methodology to the security analysis of four existing protocols for group privacy, two of which were not known to be computationally secure prior to our work. An important contribution of this thesis is a new technique to prove security of group privacy protocols in the presence of computational adversaries who can corrupt protocol participants in an adaptive manner. We show that if one suitably restricts the length of encryption chains (sequences of ciphertexts of the form EK1 (K2), EK2 (K3), EK3 (K4),…) generated in protocol executions, then security of a protocol against adaptive corruptions follows almost immediately from its security in the symbolic model. Prior to our work, all techniques to proving adaptive security of privacy protocols involved either restricting the security model severely (for example, by requiring all users to be stateless) or using non-standard, and inefficient, ways to implement the encryption operation.
Stochastic Control of Network Systems II: NETCAD Optimal Control & the HJB Equation

The centralized optimal stochastic control of call admission (CAC) and routing (RC) problems is analysed for NETCAD systems as formulated in (Z. Ma et al., 2006). In the principal result an HJB equation is derived for the optimal stochastic control of NETCAD systems; this equation is equivalent to a hierarchy of HJB PDEs linked by integral coefficients. In the main example this system of equations is solved for a very simple network with a Poisson call request process and an exponential connection holding time process
Housing and Monetary Policy

Since the mid-1980s, monetary policy has contributed to a great moderation of the housing cycle by responding more proactively to inflation and thereby reducing the boom bust cycle. However, during the period from 2002 to 2005, the short term interest rate path deviated significantly from what this two decade experience would suggest is appropriate. A counterfactual simulation with a simple model of the housing market shows that this deviation may have been a cause of the boom and bust in housing starts and inflation in the last two years. Moreover, a significant time series correlation between housing price inflation and delinquency rates suggests that the poor credit assessments on subprime mortgages may also have been caused by this deviation.
Learning and adaptivity in interactive recommender systems

Recommender systems are intelligent E-commerce applications that assist users in a decision-making process by offering personalized product recommendations during an interaction session. Quite recently, conversational approaches have been introduced in order to support more interactive recommendation sessions. Notwithstanding the increased interactivity offered by these approaches, the system employs an interaction strategy that is specified apriori (at design time) and followed quite rigidly during the interaction. In this paper, we present a new type of recommender system which is capable of learning autonomously an adaptive interaction strategy for assisting the users in acquiring their interaction goals. We view the recommendation process as a sequential decision problem and we model it as a Markov Decision Process (MDP). We learn a model of the user behavior, and use it to acquire the adaptive strategy using Reinforcement Learning (RL) techniques. In this context, the system learns the optimal strategy by observing the consequences of its actions on the users and also on the final outcome of the recommendation session. We apply our approach within an existing travel recommender system which uses a rigid, non-adaptive support strategy for advising a user in refining a query to a travel product catalogue. The initial results demonstrate the value of our approach and show that our system is able to improve the non-adaptive strategy in order to learn an optimal (adaptive) recommendation strategy.
Ontological Document Filtering

Recent development in business has led to new demands for information. On the other hand the spread of Internet based information services has grown to such extent that it is often impossible to find relevant information in short time. Term based information filtering systems often are not efficient enough. Therefore a need for more efficient mechanisms has arisen. This article discusses the requirements for ontological document filtering for the purposes of Information
Arbeitsgemeinschaft mit aktuellem Thema: LIMITS OF DISCRETE STRUCTURES Mathematisches Forschungsinstitut Oberwolfach March 31-April 5

A recently emerging and fast growing topic in mathematics considers very large finite structures as approximations of infinite analytic objects. This approach enables one to use a variety of tools from analysis to study graphs, hypergraphs, permutations, subsets of groups and many other fundamental structures. There are several applications in extremal combinatorics, Fourier analysis (also in a higher order version of it), property testing, group theory, topology and many other areas. The subject is very rich and many of its aspects are discussed in the recent book [29] by L. Lovász. The goal of the workshop is to present a landscape of beautiful ideas developed by researchers from various fields.