Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts ultimate human lifespan limit

We investigated the dynamic properties of the organism state fluctuations along individual aging trajectories in a large longitudinal database of CBC measurements from a consumer diagnostics laboratory. To simplify the analysis, we used a log-linear mortality estimate from the CBC variables as a single quantitative measure of aging process, henceforth referred to as dynamic organism state index (DOSI). We observed, that the age-dependent population DOSI distribution broadening could be explained by a progressive loss of physiological resilience measured by the DOSI auto-correlation time. Extrapolation of this trend suggested that DOSI recovery time and variance would simultaneously diverge at a critical point of 120 − 150 years of age corresponding to a complete loss of resilience. The observation was immediately confirmed by the independent analysis of correlation properties of intraday physical activity levels fluctuations collected by wearable devices. We conclude that the criticality resulting in the end of life is an intrinsic biological property of an organism that is independent of stress factors and signifies a fundamental or absolute limit of human lifespan.
Development of low sodium salted eggs and its antioxidant potential

Background: Salted egg is one of processed egg products which has a nutrient content that is comparable to the fresh egg, has a longer shelf life and can be consumed by all ages. The main process in the salted egg production is salting (NaCl) treatment that will give salty taste and acts as a preservative. Objectives: This study aimed to develop low-sodium salted eggs with an antioxidant potential through the innovations in the salting process. Methods: This was an experimental research. Salted eggs were produced with innovations in salting process using various concentration of teak leave extract and potassium chloride (KCl) substitution. The sensory qualities of the salted eggs were determined using differential tests. Salted eggs with the best sensory quality were determined their nutritional quality with proximate measurements and their antioxidative potency by measuring total phenolic content and antioxidant activity. Results: The research result showed that the KCl substitution did not affect the total phenolic content (TPC) of salted eggs, but it reduced the sensory qualities which include the quality of taste, color, fl avor and overall. The addition of teak leave extracts improved both the sensory quality and TPC of salted eggs produced with KCl substitutions. TPC increased along with increased of teak leave extract concentrations. KCl substitution or addition of teak leaves extract proved to increase the radical scavenging activity of salted eggs. The low-sodium salted egg with an antioxidant potential which was made using selected salting formulation had nutritional and sensory qualities which comparable to the salted eggs produced using sodium chloride (NaCl). Conclusion:The low-sodium salted eggs have antioxidant potential that can be produced by salting process innovation using teak leave extract and KCl substitution.
Robust Parameter Estimation for Biological Systems: A Study on the Dynamics of Microbial Communities

Interest in the study of in-host microbial communities has increased in recent years due to our improved understanding of the communities' significant role in host health. As a result, the ability to model these communities using differential equations, for example, and analyze the results has become increasingly relevant. The size of the models and limitations in data collection among many other considerations require that we develop new parameter estimation methods to address the challenges that arise when using traditional parameter estimation methods for models of these in-host microbial communities. In this work, we present the challenges that appear when applying traditional parameter estimation techniques to differential equation models of microbial communities, and we provide an original, alternative method to those techniques. We show the derivation of our method and how our method avoids the limitations of traditional techniques while including additional benefits. We also provide simulation studies to demonstrate our method's viability, the application of our method to a model of intestinal microbial communities to demonstrate the insights that can be gained from our method, and sample code to give readers the opportunity to apply our method to their own research.
Multi-View Dimensionality Reduction via Canonical Correlation Analysis

We analyze the multi-view regression problem where we have two views X = ( X (1) , X (2) ) of the input data and a target variable Y of interest. We provide sufﬁcient conditions under which we can reduce the dimensionality of X (via a projection) without loosing predictive power of Y . Crucially, this projection can be computed via a Canonical Correlation Analysis only on the unlabeled data. The algorithmic template is as follows: with unlabeled data, perform CCA and construct a certain projection; with the labeled data, do least squares regression in this lower dimensional space. We show how, under certain natural assumptions, the number of labeled samples could be signiﬁcantly reduced (in comparison to the single view setting) — in particular, we show how this dimensionality reduction does not loose predictive power of Y (thus it only introduces little bias but could drastically reduce the variance). We explore two separate assumptions under which this is possible and show how, under either assumption alone, dimensionality reduction could reduce the labeled sample complexity. The two assumptions we consider are a conditional independence assumption and a redundancy assumption. The typical conditional independence assumption is that conditioned on Y the views X (1) and X (2) are independent — we relax this assumption to: conditioned on some hidden state H the views X (1) and X (2) are independent. Under the redundancy assumption, we show that the best predictor from each view is roughly as good as the best predictor using both views.
To meet or to compete?: The effect of ethnic and gender workforce diversity on ingroup preferences in the workplace

This dissertation investigates how so-called contact theory and threat theory can be used to quantify the effect of gender and ethnic diversity at work on ingroup preferences. Contact and threat theory have been tested against each other many times in the context of someone's social circles, but until now neither at the workplace nor with respect to gender. This dissertation thus fills a gap by combining two novel empirical aims: first, to verify whether and to what extent these theories hold in a context that is different but of considerable importance to many people: the work place; and second, to verify whether these theories are applicable to explain gender ingroup preferences in addition to ethnicity. The ingroup preferences are operationalised using the two inherently workplace-related processes of dismissal and recruitment. To be explicit, it is assessed how the composition of the workforce, in terms of (1) ethnicity and (2) gender, influences: (A) someone's preference to dismiss an outgroup member rather than an ingroup member; (B) someone's support for tiebreak preferential treatment in the recruitment process. This results in four combinations and empirical studies: 1A, 1B, 2A, 2B. The dataset underlying this dissertation is derived from a survey conducted among more than 1000 employees from various organisations. Results indicated that the degree of diversity at the workplace is a relevant factor, but not always confirm the hypotheses.
An object-oriented expert system for coal-fired MHD power plant fault monitoring and diagnosis

Abnormal process behaviors observed through sensor data values are symptomatic of process faults. It has been demonstrated that pattern recognition techniques that associate faults with the symptoms they produce can be successfully applied in performing on-line automated fault detection and diagnosis [1-3]. This paper describes an object-oriented implementation of a knowledge-based expert system designed to aid power plant operators by performing automated sensor based fault detection and diagnosis. This object-oriented implementation replaces a rule-based backward chaining implementation of a Plant Intelligent Supervisory Control Expert System (PISCES) developed earlier at the University of Tennessee Space Institute [4-9]. All references to PISCES in this paper refer to the new object-oriented implementation. The earlier version will be referred to as a rule-based system.
Upsetting of Tri-Metallic St-Cu-Al and St-Cu60Zn-Al Cylindrical Billets

This work investigates upsetting of the tri-metallic cylindrical billets both experimentally and analytically with a reduction ratio 30%. Steel, brass, and copper are used for the outer and outmost rings and aluminum for the inner core. Two different models have been designed to show material flow and the cavity took place over the two interfaces during forming after this reduction ratio. Each model has an outmost ring material as steel. Model 1 has an outer ring between the outmost ring and the solid core material as copper and Model 2 has a material as brass. Solid core is aluminum for each model. Billets were upset in press machine by using parallel flat dies. Upsetting load was recorded and compared for models and single billets. To extend the tests and compare with experimental procedure to a wider range of inner core and outer ring geometries, finite element model was performed. ABAQUS software was used for the simulations. The aim is to show how contact between outmost ring, outer ring and the inner core are carried on throughout the upsetting process. Results have shown that, with changing in height, between outmost ring, outer ring and inner core, the Model 1 and Model 2 had very good interaction, and the contact surfaces of models had various interface behaviour. It is also observed that trimetallic materials have lower weight but better mechanical properties than single materials. This can give an idea for using and producing these new materials for different purposes. Keywords—Tri-metallic, upsetting, copper, brass, steel, aluminum.
Evaluating the Psychometric Properties of a Measure of Ethnic Identity Among Black South African Youth

ABSTRACT The authors examined the dimensionality and psychometric properties of the Multigroup Ethnic Identity Measure (MEIM) among Black South African adolescents (ages = 13–14; 52% female) representing several ethnic groups (Ndebele, Pedi, Sotho, Swati, Tsonga, Tswana, Venda, Xhosa, Zulu) and evaluated the measure for differential item functioning primarily among four ethnic groups (Sotho, Tswana, Xhosa, and Zulu). Results indicated that a two-dimensional model best represented the data, reflecting ethnic search/clarity and ethnic affirmations. Subsequently, we evaluated the equivalence of the MEIM among the four South African ethnic groups and a sample of African American adolescents (Mage = 15.57 years; SD = 1.22; 51% female). Further analyses revealed that configural and metric models were excellent across the four South African ethnic groups and the African American group. However, scalar invariance (i.e., intercept) was not found; the item intercepts were different for the South African ethnic groups and African Americans. Findings are discussed with consideration for conducting research on ethnic identity among youth in South Africa.
A General-purpose Parameterized Shortest Diagonal Algorithm for Surface Reconstruction from Planar Contours

In the development of a mining CAD program, the authors of this paper first attempted to use the widely accepted shortest diagonal algorithm to reconstruct 3D iron ore body from a set of planar contours, so as to aid in the comprehension of the ore body structure and facilitate its manipulation and analysis. However, the algorithm failed under certain circumstances, to be specific, the routine got “suspended” and “cones” were formed, due to its limitations. To address this problem, we first made an analysis of possible causes for these failures and then experimented on various approaches to intervene the advance speed of either contour in a contour pair so as to break the “dead loop”. Finally, we arrived at a cost effective, general-purpose parameterized shortest diagonal algorithm for triangulation and 3D surface reconstruction from planar contours. For easier apprehension, we present in sequence in this paper an explanatory description of the problem, a detailed analysis of the problem, an informative elucidation of the synchronization mechanism introduced into the new algorithm, and the new algorithm itself as well as the function of the differential parameter.
APAM: Adaptive Eager-Lazy Hybrid Evaluation of Event Patterns for Low Latency

Event pattern detection refers to identifying combinations of events matched to a user-specified query event pattern from a real-time event stream. Latency is an important measure of the performance of an event pattern detection system. Existing methods can be classified into the eager evaluation method and the lazy evaluation method depending on when each event arrival is evaluated. These methods have advantages and disadvantages in terms of latency depending on the event arrival rate. In this paper, we propose a hybrid eager-lazy evaluation method that combines the advantages of both methods. For each event type, the hybrid method, which we call APAM ( Adaptive Partitioning-And-Merging), determines which method to use: eager or lazy. We also propose a formal cost model to estimate the latency and propose a method of finding the optimal partition based on the cost model. Finally, we show through experiments that our method can improve the latency by up to 361.48 times over the eager evaluation method and 27.94 times over the lazy evaluation method using a synthetic data set.
Genetically Encoded Förster Resonance Energy Transfer Sensors for the Conformation of the Src Family Kinase Lck1

The current model for regulation of the Src family kinase member Lck postulates a strict correlation between structural condensation of the kinase backbone and catalytic activity. The key regulatory tyrosine 505, when phosphorylated, interacts with the Src homology 2 domain on the same molecule, effectively suppressing tyrosine kinase activity. Dephosphorylation of Tyr505 upon TCR engagement is supposed to lead to unfolding of the kinase structure and enhanced kinase activity. Studies on the conformation-activity relationship of Lck in living cells have not been possible to date because of the lack of tools providing spatiotemporal resolution of conformational changes. We designed a biochemically active, conformation-sensitive Förster resonance energy transfer biosensor of human Lck using the complete kinase backbone. Live cell imaging in Jurkat cells demonstrated that our biosensor performed according to Src family kinase literature. A Tyr505 to Phe mutation opened the structure of the Lck sensor, while changing the autophosphorylation site Tyr394 to Phe condensed the molecule. The tightly packed structure of a high-affinity YEEI tail mutant showed that under steady-state conditions the bulk of Lck molecules exist in a mean conformational configuration. Although T cell activation commenced normally, we could not detect a change in the conformational status of our Lck biosensor during T cell activation. Together with biochemical data we conclude that during T cell activation, Lck is accessible to very subtle regulatory mechanisms without the need for acute changes in Tyr505 and Tyr394 phosphorylation and conformational alterations.
Modeling Languages Study and Evaluation Techniques

Modeling languages are needed to describe the conceptual construct underlying software. Several modeling languages have been proposed during the last decades. In order to properly understand and assess software modeling languages, we believe that a set of techniques is needed. In this paper, we describe several techniques that can be applied for studying and evaluating cognitive complexity (comprehensibility) of software modeling languages. These techniques have been applied to human-computer interaction, human languages, programming languages and cognitive science before.
Feature selection for Chinese online reviews sentiment classification

Considering that traditional feature selection methods (DF, MI and IG) usually lost useful information, we propose the Feature Selection for Chinese Online Reviews Sentiment Classification (FSCSC), FSCSC takes empirical analysis into account and focus on how to effectively select different types of features based on statistical approaches to improve sentiment classification performance. FSCSC was tested on a Chinese online reviews corpus with a size of 4000 documents. The experiment indicates that FSCSC can improve the classification effectiveness.
Matched-filter bound for binary signaling over dispersive fading channels with receive diversity

The matched-filter bound (MFB) for continuously dispersive Rayleigh fading channels with receive diversity is derived. The treatment is quite general, as the case of discrete multipath channels with correlated fading is included in our model as a special case, and we impose no constraints with regard to the selectivity of the channel. It is assumed that a statistical characterization of the channel is available through its direction-delay-Doppler power spectral density. The Karhunen-Loe/spl grave/ve expansion for vector processes is used to describe the received signal. The MFB is analyzed with respect to several parameters characterizing the mobile radio channel, the waveform, and the antenna configuration.
BDD decomposition for efficient logic synthesis

A unified logic optimization method efficient at handling both AND/OR-intensive and XOR-intensive functions is proposed. The method is based on iterative BDD decomposition using various dominators. Detail analysis of decomposable BDD structures leading to AND/OR, XOR and MUX decompositions are presented. Experiment shows that our synthesis results for AND/OR-intensive functions are comparable to those of SIS, and results for XOR-intensive functions are comparable to those of techniques targetting specifically XOR decomposition.
On the condition number distribution of complex wishart matrices

This paper investigates the distribution of the condition number of complex Wishart matrices. Two closely related measures are considered: the standard condition number (SCN) and the Demmel condition number (DCN), both of which have important applications in the context of multiple-input multiple-output (MIMO) communication systems, as well as in various branches of mathematics. We first present a novel generic framework for the SCN distribution which accounts for both central and non-central Wishart matrices of arbitrary dimension. This result is a simple unified expression which involves only a single scalar integral, and therefore allows for fast and efficient computation. For the case of dual Wishart matrices, we derive new exact polynomial expressions for both the SCN and DCN distributions. We also formulate a new closed-form expression for the tail SCN distribution which applies for correlated central Wishart matrices of arbitrary dimension and demonstrates an interesting connection to the maximum eigenvalue moments of Wishart matrices of smaller dimension. Based on our analytical results, we gain valuable insights into the statistical behavior of the channel conditioning for various MIMO fading scenarios, such as uncorrelated/semi-correlated Rayleigh fading and Ricean fading.
Approximately Efficient Iterative Mechanisms for Combinatorial Exchanges

Combinatorial exchanges are double sided marketplaceswith multiple sellers and multiple buyers trading with thehelp of combinatorial bids. The allocation and other associated problems in such exchanges are known to be among the hardest to solve among all economic mechanisms. In this paper, we develop computationally efficient iterative auction mechanisms for solving combinatorial exchanges. Our mechanisms satisfy Individual rationality (IR) and budget-nonnegativity (BN) properties. We also show that the exchange problem can be reduced to combinatorial auction problem when either the buyers or the sellers are single minded. Our numerical experiments show that our algorithm produces good quality solutions and is computationally efficient.
Springs-based simulation for image retargeting

In this paper an efficient method for image retargeting is proposed. It relies onto a mechanical model based on springs network. Each pixel displacement (compression or expansion) is given by the network response, according to the springs stiffness. The properties of the springs are determined as function of the visual relevance of the pixels. Such model does not require any optimization, since its solution is obtained simply from a linear system of equations, allowing real-time application even for large images. The approach is fully automatic, though can be improved by interactively providing cues such as geometric constraints and/or manual relevant object labeling. The results prove that the presented method achieves results comparable or superior to reference methods, while improving efficiency.
A subset-search and ranking based feature-selection for histology image classification using global and local quantification

Biopsy remains the gold standard for the diagnosis of chronic liver diseases. However, the variability in the diagnostic between readers leads to define a method to objectively describe histologic tissue. A complete framework has been implemented to analyze images of any tissue. Based on subset selection and feature ranking approaches, a feature selection computes the most relevant subset of descriptors in terms of classification from a wide initial list of descriptors. In comparison with equivalent methods, this implementation can find lists of descriptors which are significantly shorter for an equivalent accuracy. Furthermore, it enables the classification of slides using combinations of global and local measurements. The results have pointed that it could reach an accuracy of 90.5% (ROC-AUC=81.1%) in a human liver fibrosis grading approach by selecting 3 of the 457 global and local descriptors. The feature ranking approach gave less accurate subsets than the subset selection.
Segmentation of Striatal Brain Structures from High Resolution PET Images

We propose and evaluate an automatic segmentation method for extracting striatal brain structures (caudate, putamen, and ventral striatum) from parametric 11C-raclopride positron emission tomography (PET) brain images. We focus on the images acquired using a novel brain dedicated high-resolution (HRRT) PET scanner. The segmentation method first extracts the striatum using a deformable surface model and then divides the striatum into its substructures based on a graph partitioning algorithm. The weighted kernel k-means algorithm is used to partition the graph describing the voxel affinities within the striatum into the desired number of clusters. The method was experimentally validated with synthetic and real image data. The experiments showed that our method was able to automatically extract caudate, ventral striatum, and putamen from the images. Moreover, the putamen could be subdivided into anterior and posterior parts. An automatic method for the extraction of striatal structures from high-resolution PET images allows for inexpensive and reproducible extraction of the quantitative information from these images necessary in brain research and drug development.