Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithms for modeling and simulation of biological systems; applications to gene regulatory networks

Systems biology is an emergent field focused on developing a system-level understanding of biological systems. In the last decade advances in genomics, transcriptomics and proteomics have gathered a remarkable amount data enabling the possibility of a system-level analysis to be grounded at a molecular level. The reverse-engineering of biochemical networks from experimental data has become a central focus in systems biology. A variety of methods have been proposed for the study and identification of the system’s structure and/or dynamics. The objective of this dissertation is to introduce and propose solutions to some of the challenges inherent in reverse-engineering of biological systems. First, previously developed reverse engineering algorithms are studied and compared using data from a simulated network. This study draws attention to the necessity for a uniform benchmark that enables an objective comparison and performance evaluation of reverse engineering methods. Since several reverse-engineering algorithms require discrete data as input (e.g. dynamic Bayesian network methods, Boolean networks), discretization methods are being used for this purpose. Through a comparison of the performance of two network inference algorithms that use discrete data (from several different discretization methods) in this work, it has been shown that data discretization is an important step in applying network inference methods to experimental data. Next, a reverse-engineering algorithm is proposed within the framework of polynomial dynamical systems over finite fields. This algorithm is built for the identification of the underlying network structure and dynamics; it uses as input gene expression data and, when available, a priori knowledge of the system. An evolutionary algorithm is used as the heuristic search method for an exploration of the solution space. Computational algebra tools delimit the search space, enabling also a description of model complexity. The performance and robustness of the algorithm are explored via an artificial network of the segment polarity genes in the D. melanogaster. Once a mathematical model has been built, it can be used to run simulations of the biological system under study. Comparison of simulated dynamics with experimental measurements can help refine the model or provide insight into qualitative properties of the systems dynamical behavior. Within this work, we propose an efficient algorithm to describe the phase space, in particular to compute the number and length of all limit cycles of linear systems over a general finite field. This research has been partially supported by NIH Grant Nr. RO1GM068947-01.
Ontogenetic programming

In nature, the structure and behavior of a mature organism is determined not only by its genetic endowment, but also by complex developmental processes that the organism undergoes while immersed in its environment (ontogeny). By contrast, an individual in a genetic programming system is generally expected to solve problems immediately, without the benefit of a developmental phase. Various morphological systems have been used in previous genetic programming systems to allow programs to "grow" into more complex forms prior to evaluation. Runtime memory mechanisms allow evolved programs to acquire information from their environments while they solve problems, and to change their future behavior on the basis of such information. Ontogenetic programming combines these ideas to allow for runtime modification of program structure. In particular, an ontogenetic programming system includes program self-modification functions in the genetic programming function set, thereby allowing evolved programs to modify themselves during the course of a run. We demonstrate the ontogenetic programming methodology with the HiGP genetic programming system, and we show how ontogenetic programming can be used to solve problems that would not otherwise be solvable. We also discuss variants of the technique that could be used in other genetic programming systems.
CHINA'S ACCESSION TO THE WTO AND ITS IMPLICATIONS FOR THE FISHERY AND AQUACULTURE SECTOR

ABSTRACT This paper reviews recent developments in China's fishery and aquaculture sectors, as well as the policies affecting rural households in general and fisheries households in particular. It explores how China's policies may change as a result of the nation joining the World Trade Organization (WTO) in December 2001 and the likely impacts of these changes on China's fishery and aquaculture sector. It was found that the domestic fish markets are gradually integrating, suggesting that fish price shifts in one area will affect prices in most parts of the country. It was also found that, compared with the prices of other agricultural commodities, the domestic prices of most aquatic products are well below world prices. This suggests that exports of aquatic products would be able to expand now that the nation has joined the WTO and that fishers would gain from this move.
Kernel PCA Based Estimation of the Mixing Matrix in Linear Instantaneous Mixtures of Sparse Sources

Source sparsity based methods have become a common approach to blind source separation (BSS) problems, especially in the underdetermined case (more sources than sensors). If the sources are not sparse in the time-domain, in most cases, they can be mapped to a transformed domain (e.g., wavelets, time-frequency, Fourier) in which this assumption is verified. In this paper, we are solely interested in the estimation of the mixing matrix. As observed by Zibulevski and coauthors, the data represented in the scatter plot of the observations tend to cluster along the mixing matrix columns. Each column can be seen as one of the principal components of the data in a higher (possibly infinite) dimension space, and these components can be estimated with a kernel principal component analysis (KPCA) based approach. The theoretical framework is derived, and excellent performance is observed both on synthetic and audio signals
Investigation on College Students' Self-disclosure

To investigate self-disclosure among college students. 424 college students received JSDQ assessment. There are significant differences between gender and grade in self-disclosure to different targets and different topics. (1) Girls show more than boys on different topics of self-disclosure; (2) There is the most self-disclosure within those of the female, the middle self-disclosure within those of the male, and the least self-disclosure within those of opposite sex; (3) There is more self-disclosure of senior undergraduates than of junior undergraduates; (4) Graduate students' self-disclosure to their male and female friends is more than that to their parents; (5) There is more in the topics of studies, tastes and attitudes than in the topics of personality, body and money.
Prediction of First-Year Corrosion Losses of Carbon Steel and Zinc in Continental Regions

Dose-response functions (DRFs) developed for the prediction of first-year corrosion losses of carbon steel and zinc (K1) in continental regions are presented. The dependences of mass losses on SO2 concentration, K = f([SO2]), obtained from experimental data, as well as nonlinear dependences of mass losses on meteorological parameters, were taken into account in the development of the DRFs. The development of the DRFs was based on the experimental data from one year of testing under a number of international programs: ISO CORRAG, MICAT, two UN/ECE programs, the Russian program in the Far-Eastern region, and data published in papers. The paper describes predictions of K1 values of these metals using four different models for continental test sites under UN/ECE, RF programs and within the MICAT project. The predictions of K1 are compared with experimental K1 values, and the models presented here are analyzed in terms of the coefficients used in the models.
TCP fairness in ethernet over passive optical networks (EPON)

The Ethernet Passive Optical Network (EPON) is a promising solution of metropolitan optical networks. In this paper, we model the performance of TCP over EPON in terms of fairness. We identify the fairness problem suffered by EPON using the existing polling scheme. We then derive two tight bounds of fairness indices for downloading users, and propose two polling mechanisms to improve unfair resource sharing among multiple TCP flows. The performance of the proposed mechanisms is evaluated by simulations via ns2. The results show that our mechanisms can improve fairness among the downloading flows without degrading the aggregate throughput and the fairness of the uploading flows.
Journal of Theoretical and Applied Information Technology a Novel Algorithm for Minimum Spanning Clustering Tree

The minimum spanning tree clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose minimum spanning tree based clustering algorithm. The algorithm produces k clusters with Minimum Spanning Clustering Tree (MSCT), a new data structure which can be used as search tree. Our algorithm works in two phases. The first phase produces subtree (cluster). The second phase converts the subtree into binary tree called MSCT. 1. INTRODUCTION Given a connected, undirected graph G = (V, E) , where V is the set of nodes, E is the set of edges between pairs of nodes, and a weight w (u , v) specifying weight of the edge (u, v) for each edge
Comparison of ADA and WHO Criteria in Detecting Pattern of Glucose Disorders in a Population-Based Study: Tehran Lipid and Glucose Study

any studies have been performed to compare WHO and ADA criteria for detection of diabetic patients. This study aims to compare these two criteria in a community-based epidemiological survey in an urban population of Tehran, Iran. Materials and Methods: Subjects were chosen from among 15005 urban individuals, 3 years old and over, selected by cluster random sampling in the cross-sectional phase of a longitudinal study conducted in the east of Tehran; there were 3870 men and 5359 women aged 20 years and over. Those with known diabetes were excluded. Blood samples were taken after 12-14 hours overnight fast and 2 hours post 75gr glucose taken orally. Prevalence of glucose tolerance categories and the level of agreement (ĸ statistic) were obtained using WHO and ADA criteria. Results: Based on WHO criteria 6.0% (0.95 CI, 5.5-6.5) had type-2 diabetes and 13.0% (12.3-13.7) had IGT (Impaired glucose tolerance). Using ADA, 3.3% (2.9-3.7) had type-2 diabetes and 4.8% (4.4-5.2) had IFG (Impaired fasting glucose); p<0.05 and <0.001 for diabetes and IGT, respectively and 7355 (84%) had concordance with both criteria. Among 7105 normal subjects classified according to WHO criteria, 153 (2.2%) had IFG or diabetes by ADA, whereas from 8068 normal subjects according to ADA criteria, 1116 (13.8%) had IGT or diabetes based on WHO criteria. The level of agreement (ĸ statistic) between the two criteria was 35% (p<0.001). Sensitivity and specificity of ADA criteria were 45.5 and 100%, respectively, considering WHO as the gold standard. Conclusion: Our data shows a low level of agreement between WHO and ADA diagnostic criteria for detection of diabetes. Patients with unknown diabetes, glucose disorders are detected more frequently using WHO criteria.
Subgrouping in Chronic Fatigue Syndrome Based on Actigraphy and Illness Severity

Participants with chronic fatigue syndrome were categorized into subtypes based on actigraphy and illness self- report symptom severity data. Each method identified two groups of patients, one with severe and one with less severe manifestations of the illness. For both subtypes, those in the more severe category had more physical functioning problems than those in the less severe categories. However, for the illness self-report symptom group, those in the more severe category had significantly more impairment in sleep, anxiety, depression, and pain, and more concurrent psychiatric status and Fibromyalgia than those in the less severe category. In contrast, those in the more severe actigraphy subtype group in comparison to the less severe group had more impairment in quality of life and cortisol readings. These findings suggest that CFS subtypes based on symptom severity and amount of activity identify different groups of patients with varying types of impairments.
Inductive Principles for Restricted Boltzmann Machine Learning

Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with intractable partition functions. In this paper, we study learning methods for binary restricted Boltzmann machines (RBMs) based on ratio matching and generalized score matching. We compare these new RBM learning methods to a range of existing learning methods including stochastic maximum likelihood, contrastive divergence, and pseudo-likelihood. We perform an extensive empirical evaluation across multiple tasks and data sets.
An Analysis of V-blast Detection Schemes for MIMO Wireless Communications

Vertical Bell Laboratories Layered Space time (V-BLAST) is a Multiple input Multiple output (MIMO) wireless communication system that uses multiple antenna elements at transmit and receive to offer high spectral efficiency and increased capacity in a rich multipath environment without increasing the use of the spectrum. Whereas the detection schemes employed in such systems are computationally expensive as the number of transmitting and receiving antennas increases. Reduction in computational cost is essential for time critical application of high speed packet transfer using MIMO for 3GPP and 3GPP-2 standards. This paper compares the performance between various detection scheme which include the conventional detection, An efficient square root algorithm, An improved square root algorithm and An improved square root algorithm based on cholesky factorization when subjected to multi-media application with 4x4 and 12x12 array for varying SNR. Performance parameters considered include bit error rate (BER), symbol error rate (SER), peak signal-to-noise ratio (PSNR), number of number of floating point operations (FLOPS) and Time required for detection. Among the Modulation scheme optimum performance is achieved with M-QAM, whereas among the detection schemes nonlinear detection schemes with interference cancellation (IC) with MMSE performed better than ZF schemes in terms of BER, SER PSNR but at the cost of increase in FLOPS. In an attempt to reduce computational complexity the number of FLOPS required for detection with an improved square root algorithm based on efficient inverse cholesky factorization with MMSE for 12 transmitting and 12 receiving antennas is 0.3 x 10 6 , a reduction of 0.7x10 6 , 0.9x10 6 , 1.5x10 6 and 1.7 x10 6 flops is achieved when compared to improved square-root algorithm, the efficient square root algorithm and the conventional detection scheme employing Zero forcing (ZF) and MMSE filter respectively. This algorithm is faster than the existing efficient V-BLAST algorithms.
Predictors of use of hearing protection among a representative sample of farmers.

Farmers experience higher rates of noise-induced hearing loss (NIHL) than workers in most other industries. We developed a model of farmers' use of hearing protection, and tested it with a random sample (n = 532) of farmers from the upper Midwest. Barriers to using hearing protection (e.g., difficulty communicating; OR = .44, p < .003) were negatively related to use. Greater access/availability of hearing protectors (OR = 1.75, p < .010) and male gender (OR = .43, p < .019) were positively related to use. The model correctly predicted use of hearing protection for 74% of the cases. Overall, farmers demonstrated low hearing protector use, and results were similar to those from previous studies of non-farm workers. Findings from this study will be useful in designing interventions to increase farmers' hearing protector use and decrease their rates of NIHL.
Observability of complex systems

A quantitative description of a complex system is inherently limited by our ability to estimate the system’s internal state from experimentally accessible outputs. Although the simultaneous measurement of all internal variables, like all metabolite concentrations in a cell, offers a complete description of a system’s state, in practice experimental access is limited to only a subset of variables, or sensors. A system is called observable if we can reconstruct the system’s complete internal state from its outputs. Here, we adopt a graphical approach derived from the dynamical laws that govern a system to determine the sensors that are necessary to reconstruct the full internal state of a complex system. We apply this approach to biochemical reaction systems, finding that the identified sensors are not only necessary but also sufficient for observability. The developed approach can also identify the optimal sensors for target or partial observability, helping us reconstruct selected state variables from appropriately chosen outputs, a prerequisite for optimal biomarker design. Given the fundamental role observability plays in complex systems, these results offer avenues to systematically explore the dynamics of a wide range of natural, technological and socioeconomic systems.
Prioritisation of network security services

On large networks security administration tasks such as patch management and event log analysis can take many hours and even days to successfully complete even with automated solutions. Currently it is left to the systems administrators' discretion to choose in which order to protect individual devices. In light of the rapidly decreasing time between vulnerabilities being discovered and maliciously exploited by malware, such an arbitrary method introduces an unacceptable level of risk to the security of those devices, which are critical to business processes. An information risk management approach needs to be adopted to ensure the protection of the network with a high likelihood; this can be achieved through the prioritisation of critical devices. In this introductory paper a generic prioritisation technique for individual devices in a network is described offering a methodical alternative to the current ambiguity of a systems administrator's operations. The technique is based upon compromise path analysis, which identifies critical paths in a network from a security viewpoint and is relevant in a wide range of operations from the application of security services to analysing their results. The vulnerability period metric is introduced, as a mechanism to control the risk exposure to individual devices through prioritisation.
Expectation Hypothesis Bias: Risk Aversion versus Stochastic Adjustments

For a long time the classical expectation hypothesis has been challenged from both empirical and theoretical perspective. Still no one could explain entirely the existent bias between expected future spot rates and forward rates, the so called puzzle in the expectation hypothesis.In this work we will address this issue through arbitrage theory, in particular, focusing our attention in a connection of the classical expectation hypothesis to a certain probability measures where the relation between expected future spot rates and forward rates holds. We will approach this applying certain instantaneous spot rate models, verifying in these models that through changes from "real world" probability measure to other probability measures, we will find adjustments that will able us to explain this bias in the expectation hypothesis.
Containment for Rule-Based Ontology-Mediated Queries

Many efforts have been dedicated to identifying restrictions on ontologies expressed as tuple-generating dependencies (tgds), a.k. a. existential rules, that lead to the decidability of answering ontology-mediated queries (OMQs). This has given rise to three families of formalisms: guarded, non-recursive, and sticky sets of tgds. We study the containment problem for OMQs expressed in such formalisms, which is a key ingredient for solving static analysis tasks associated with them. Our main contribution is the development of specially tailored techniques for OMQ containment under the classes of tgds stated above. This enables us to obtain sharp complexity bounds for the problems at hand.
Multi-Criteria Generation Optimal Mix Planning for Malaysia ‟ s Additional Capacity

announced that an additional 2000MW needs to be constructed to meet the projected electricity demand in Peninsular Malaysia. The proposed generation technology for the 2000MW capacity is coal technology. This paper considers other generation technology options, namely nuclear, solar thermal and biomass. These four technologies, along with coal technology will be compared based on three criteria, economic cost, reliability and socio-environmental cost. The proposed comparison algorithm is the two-phase K-best dynamic programming trade-off method.
TWO APPROACHES TO OPTIMIZING FOR POWER IN THE SPIRAL FRAMEWORK

This paper evaluates two methods for generating power or energy optimized Digital Signal Processing (DSP) transforms. Both approaches integrate power models into the SPIRAL code generator. SPIRAL decomposes a transform into an algorithmic formula representation, which is then implemented in C and timed. The timing data is used as feedback to control the parameters of the next algorithm generated. By replacing the timing method with power estimation models, we direct SPIRAL to search for a poweroptimized implementation. We implement and evaluate two different methods: a processor simulator and power model, and a hardware based method that models power based upon data collected directly from on-chip Pentium 4 performance counters.
Concurrency in CoOperative objects

CoOperative Objects (COOs) are a concurrent object oriented modelling and programming language, which uses the Petri net theory to define the concurrency within each object, among objects, and also the inter object asynchronous communication. SYROCO is a C++ implementation of CoOperative Objects which proves to be efficient and convenient enough to support real size applications. The paper aims to show that intra object concurrency is of paramount importance for models of object oriented concurrency, and that intra object concurrency is smartly supported by the integration of the object oriented and Petri net paradigms. Namely, it discusses the model of concurrency adopted by COOs, and details how it is implemented in SYROCO.
MTAF: An Adaptive Design for Keyword-Based Content Dissemination on DHT Networks

Beyond offering the widely used keyword search function, many peer-to-peer systems nowadays support the subscription function. For example, Vuze allows users to create subscription filters based on the keyword search. Given the subscription, episodic or related content will be delivered to the users whenever new episodes are available. Unfortunately, these applications suffer from the downsides, for example, high network traffic in the nodes maintaining popular terms. In this paper, we propose the MTAF mechanism to overcome the issues. The key of MTAF is to carefully select a subset of terms without incurring false negatives and to forward the content item toward the home nodes of such selected terms for low content forwarding cost. Experimental results based on real datasets indicate that the proposed solutions are efficient compared to existing approaches. In particular, the similarity-based replication of filters is shown to mitigate the effect of hot spots that arise due to the fact that some document terms are substantially more popular than the others.