Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of Yeast Pheromone Pathway using Petri Nets

Yeast (Saccharomyces cerevisiae) is one of the most widely studied single celled organisms. Mating of yeast cells occur between cells of opposite mating types a and α. Pheromone secretion by a cell alerts the corresponding opposite type cell about its presence and eventually facilitates the process of mating between them. The details of how pheromones affect cells can be studied from the pheromone response pathway in a yeast cell. A response pathway typically depicts the chain of interactions that happens between the different proteins in the cells in response to the pheromone. In this thesis we model the yeast pheromone response pathway using Petri nets and simulate various conditions under which a cell will respond positively to the secreted pheromone. We also take a look at how different proteins in the cell might be able to facilitate the correct functionality of the pathway. The objective of this thesis is to serve as a guideline for performing lab experiments to further explore the yeast pheromone pathway. ACKNOWLEDGEMENTS My heart felt gratitude goes to my advisors, Prof. Stephen D. Scott and Prof. Jitender S. Deogun, for their continuous support, guidance and encouragement during the thesis. Without their patience, motivation, guidance, immense knowledge and encouragement it would not have been possible for me to complete the thesis. I would like to thank Prof. Steven Harris, for his support during the thesis. His expertise has been a guiding star in the development of this thesis. for her advise on setting up the experiments.
Simplified Algorithms for Optimizing Multiuser Multi-Hop MIMO Relay Systems

In this paper, we address the issue of multiaccess communication through multi-hop linear non-regenerative relays, where all users, all relay nodes, and the destination node may have multiple antennas. Using a linear minimal mean-squared error (MMSE) receiver at the destination node, we demonstrate that the optimal amplifying matrix at each relay node can be viewed as a linear MMSE filter concatenated with another linear filter. As a consequence, the MSE matrix of the signal waveform estimation at the destination node is decomposed into the sum of the MSE matrices at all relay nodes. We show that at a high signal-to-noise ratio (SNR) environment, this MSE matrix decomposition significantly simplifies the solution to the problem of optimizing the source precoding matrices and relay amplifying matrices. Simulation results show that even at the low to medium SNR range, the simplified optimization algorithms have only a marginal performance degradation but a greatly reduced computational complexity and signalling overhead compared with the existing optimal iterative algorithm, and thus are of great interest for practical relay systems.
Minimum replication min-cut partitioning

Logic replication has been shown to be very effective in reducing the number of cut nets in partitioned circuits. L.T. Liu et al. (1995) considered the circuit partitioning problem with logic replication for separating two given nodes and presented an algorithm to determine a partitioning of the minimum possible cut size. In general, there are many possible partitioning solutions with the minimum cut size and the difference of their required amounts of replication can be significant. Since there is a size constraint on each component of the partitioning in practice, it is desirable to also minimize the amount of replication. In this paper, we present a network-flow based algorithm to determine an optimum replication min-cut partitioning that requires minimum replication. We show that the algorithm can be generalized to separate two given subsets of nodes and determine an optimum partitioning of the minimum possible cut size using the least possible amount of replication. We also show that our algorithm can be used to improve the solutions produced by any heuristic replication min-cut partitioning algorithm by reducing the cut size and shrinking the replication set.
Effects of substrate bias voltage on adhesion of DC magnetron-sputtered copper films on E24 carbon steel: investigations by Auger electron spectroscopy

The adhesion strength of copper thin films on E24 carbon steel substrates was studied using the scratch test via the critical load. Coatings were deposited by a DC magnetron sputtering system. All substrates were mechanically polished; some of them were directly coated and others were ion-etched by argon ions prior to deposition process. The effects of substrate negative bias voltage during the film growth were investigated. Experimental results showed that the critical load depended on the bias voltage and that the higher bias voltage, the better adhesion. It was also observed that the deposition rate of deposited films gradually decreased with the increase of the substrate bias voltage. Furthermore, the working pressure during the substrate ion bombardment etching greatly affected the critical load. Scanning electron microscopy was used to observe the scratch tracks to accurately evaluate the critical load. Substrate surface profiles obtained by a mechanical profilometer showed that the critical load increased with the increase of the surface roughness. The analysis by Auger electron spectroscopy revealed that the interface, in case of an unbiased substrate, was relatively narrow and abrupt. However, in case of a bias voltage application, the interface was wider and more diffuse. These results suggest that the mechanisms involved in critical load enhancement are due firstly to the substrate surface roughness and the substrate temperature generated by the ion bombardment, secondly to the physical mixing in the interfacial domain and the densification of the deposited material created by the bias voltage.
LRFU: A Spectrum of Policies that Subsumes the Least Recently Used and Least Frequently Used Policies

Efficient and effective buffering of disk blocks in main memory is critical for better file system performance due to a wide speed gap between main memory and hard disks. In such a buffering system, one of the most important design decisions is the block replacement policy that determines which disk block to replace when the buffer is full. In this paper, we show that there exists a spectrum of block replacement policies that subsumes the two seemingly unrelated and independent Least Recently Used (LRU) and Least Frequently Used (LFU) policies. The spectrum is called the LRFU (Least Recently/Frequently Used) policy and is formed by how much more weight we give to the recent history than to the older history. We also show that there is a spectrum of implementations of the LRFU that again subsumes the LRU and LFU implementations. This spectrum is again dictated by how much weight is given to recent and older histories and the time complexity of the implementations lies between O(1) (the time complexity of LRU) and {\rm O}(\log_2 n) (the time complexity of LFU), where n is the number of blocks in the buffer. Experimental results from trace-driven simulations show that the performance of the LRFU is at least competitive with that of previously known policies for the workloads we considered.
THE UNIVERSITY OF NEW SOUTH WALES

Most current learning algorithms require both positive and negative data. This is also the case for many of the recent ensemble learning techniques. Applications of Boosting , for example, rely on both positive and negative data to produce a hypothesis with high predictive accuracy. In this technical report, a learning methodology is presented that does not rely on negative examples. A learning method in this framework is described which shows remarkable similarities to boosting stumps. This is all the more surprising because learning from positive data has traditionally turned out to be very difﬁcult. Empirical results show that this technique successfully boosts stumps from positive data by paying only a small price in accuracy compared to learners that have access to both positive and negative data. Some theoretical justiﬁcation of the results is also provided.
C HAMELEON : A Hierarchical Clustering Algorithm Using Dynamic Modeling

Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. Existing clustering algorithms, such as K -means, PAM, CLARANS, DBSCAN, CURE, and ROCK are designed to find clusters that fit some static models. These algorithms can breakdown if the choice of parameters in the static model is incorrect with respect to the data set being clustered, or if the model is not adequate to capture the characteristics of clusters. Furthermore, most of these algorithms breakdown when the data consists of clusters that are of diverse shapes, densities, and sizes. In this paper, we present a novel hierarchical clustering algorithm called C HAMELEON that measures the similarity of two clusters based on a dynamic model. In the clustering process, two clusters are merged only if the inter-connectivity and closeness (proximity) between two clusters are high relative to the internal inter-connectivity of the clusters and closeness of items within the clusters. The merging process using the dynamic model presented in this paper facilitates discovery of natural and homogeneous clusters. The methodology of dynamic modeling of clusters used in C HAMELEON is applicable to all types of data as long as a similarity matrix can be constructed. We demonstrate the effectiveness of CHAMELEON in a number of data sets that contain points in 2D space, and contain clusters of different shapes, densities, sizes, noise, and artifacts. Experimental results on these data sets show that C HAMELEON can discover natural clusters that many existing state-of-the art clustering algorithms fail to find.
An adaptive history network method to improve the genetic optimization of pattern recognition systems

The existence of many pattern recognition systems (PRSs) and their relative merits and drawbacks highlights the need for a metalearning framework that can find the best PRS method for a given task. To address this issue, a hyperparameter evolutionary optimization (HPEO) framework was previously devised, initially using a genetic algorithm to tune external PRS parameters in a modular fashion, decoupled from its internal components. To further improve the effectiveness of HPEO and improve the diversity of the hyperparameter solutions found, this paper presents an extension that realizes cross-generation learning with an adaptive history network (AHN), which pro­motes exploring new regions in the search space while avoiding regions that have been searched extensively. The proposed ap­proach, termed HPEO-AHN, is particularly suitable for tuning powerful but complex PRSs such as neuro-fuzzy systems (NFS). Preliminary experiments with two state-of-the-art NFSs opti­mized using the new approach have shown encouraging results.
Antigen-B Cell Receptor Complexes Associate with Intracellular major histocompatibility complex (MHC) Class II Molecules*

Background: Mechanisms that preferentially deliver B cell receptor-antigen complexes to receptive MHC class II molecules are poorly defined. Results: Internalized B cell receptor-antigen complexes associate with class II molecules. Conclusion: In B cells, B cell receptor-antigen complexes associate with peptide-loaded class II molecules, potentially defining sites of class II ligand acquisition. Significance: Antigen-specific B cells control the loading of cognate antigen-derived peptide onto class II molecules. Antigen processing and MHC class II-restricted antigen presentation by antigen-presenting cells such as dendritic cells and B cells allows the activation of naïve CD4+ T cells and cognate interactions between B cells and effector CD4+ T cells, respectively. B cells are unique among class II-restricted antigen-presenting cells in that they have a clonally restricted antigen-specific receptor, the B cell receptor (BCR), which allows the cell to recognize and respond to trace amounts of foreign antigen present in a sea of self-antigens. Moreover, engagement of peptide-class II complexes formed via BCR-mediated processing of cognate antigen has been shown to result in a unique pattern of B cell activation. Using a combined biochemical and imaging/FRET approach, we establish that internalized antigen-BCR complexes associate with intracellular class II molecules. We demonstrate that the M1-paired MHC class II conformer, shown previously to be critical for CD4 T cell activation, is incorporated selectively into these complexes and loaded selectively with peptide derived from BCR-internalized cognate antigen. These results demonstrate that, in B cells, internalized antigen-BCR complexes associate with intracellular MHC class II molecules, potentially defining a site of class II peptide acquisition, and reveal a selective role for the M1-paired class II conformer in the presentation of cognate antigen. These findings provide key insights into the molecular mechanisms used by B cells to control the source of peptides charged onto class II molecules, allowing the immune system to mount an antibody response focused on BCR-reactive cognate antigen.
Discriminative Training of Markov Logic Networks

Many machine learning applications require a combination of probability and first-order logic. Markov logic networks (MLNs) accomplish this by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can be learned by maximizing the likelihood of a relational database, but this can be quite costly and lead to suboptimal results for any given prediction task. In this paper we propose a discriminative approach to training MLNs, one which optimizes the conditional likelihood of the query predicates given the evidence ones, rather than the joint likelihood of all predicates. We extend Collins’s (2002) voted perceptron algorithm for HMMs to MLNs by replacing the Viterbi algorithm with a weighted satisfiability solver. Experiments on entity resolution and link prediction tasks show the advantages of this approach compared to generative MLN training, as well as compared to purely probabilistic and purely logical approaches.
Circuit-Switching Physical Layer Based on Time-Frequency Coding for Multi-Band UWB Wireless Networks

The article describes physical layer (PHY) for high data-rate self-organized wireless networks based on the Ultra Wideband (UWB) technology with frequency band [3.1 - 10.6] GHz. The developed transceiver scheme has wide range adaptation features and cross-layer elements based on convergence of maximum likelihood sequence estimation (MLSE) equalizer and frequency hopping (FH). Circuit switching technology for multiply access and data transmission is one of key-notes of developed PHY architecture.
acACS: Improving the Prediction Accuracy of Protein Subcellular Locations and Protein Classification by Incorporating the Average Chemical Shifts Composition

The chemical shift is sensitive to changes in the local environments and can report the structural changes. The structure information of a protein can be represented by the average chemical shifts (ACS) composition, which has been broadly applied for enhancing the prediction accuracy in protein subcellular locations and protein classification. However, different kinds of ACS composition can solve different problems. We established an online web server named acACS, which can convert secondary structure into average chemical shift and then compose the vector for representing a protein by using the algorithm of auto covariance. Our solution is easy to use and can meet the needs of users.
An 8–11b 320kS/s resolution scalable noise shaping SAR ADC

This work presents an 8 – 11b resolution scalable and energy efficient ADC, using the oversampled and noise shaping SAR architecture in 90nm UMC CMOS process. Further, the proposed ADC simplifies the design of the noise shaping filter to enable the use of a first order switched capacitor low pass filter for shaping the comparator noise and the in band quantization noise. The ADC design alleviates the matching concerns by using only an 8b capacitive DAC, and allows to configure the ADC architecture from an 8b traditional SAR ADC up to an 11b ADC by enabling the oversampling and noise shaping loops within the SAR architecture. This ADC is designed to operate with 8–11b resolution, up to 320kS/s, achieving a power scaling from 80nW to 1. 5µW, resulting in an average FoM of 30fJ/conv-step.
Characterization of penetration depth as a function of optical fiber separation at various absorption and scatter coefficients for a noninvasive metabolic sensor

A visible-near IR (500-1,000nm) fiber optic sensor is under development that is intended to non-invasively assess muscle metabolism through the measurement of tissue pH and oxygen partial pressure. These parameters are calculated from the spectra of hemoglobin and myoglobin in muscle. The sensor consists of transmit (illumination) fibers and receive (detection) fibers that are coupled to a spectrometer. Light from the probe must penetrate below the surface of the skin and into a 5-10mm thick layer of muscle. A study was conducted to quantify the relationship between transmit and receive fiber separation and sensor penetration depth below the surface of the skin. A liquid phantom was created to replicate the absorption (μa) and reduced scatter coefficient (μs') profiles typically found in human blood and tissue. The phantom consisted of a solution of Intralipid and India ink in the appropriate concentrations to achieve desired reduced scatter coefficient and absorption profiles. The reduced scatter coefficient of the liquid phantom was achieved to an accuracy of +/-10% compared to previously published data. A fixed illumination fiber and translatable detector fiber were placed in the liquid phantom, and the fiber separation was varied from 3-40mm. Values of μa and μs' varied from 0.03-0.40 cm-1 and 5.0-15.0 cm-1 respectively. Results from the experiment demonstrate a strong correlation between penetration depth and fiber separation. Additionally, it was found that penetration depth was not substantially influenced by absorption and scatter concentration. As signal-to-noise is an important parameter in many non-invasive biomedical applications, the relative signal as a function of fiber separation was determined to follow an exponential relationship.
Current control of unbalanced electrical systems

The vector modeling approach is employed to describe the behavior of unbalanced three-phase (three-wire), unbalanced two-phase and single-phase systems. In the case of single-phase systems, a fictitious circuit is introduced in order to create the vector model. Continuous-time and discrete-time current controllers based on the vector approach are presented. The current control scheme is based on two controllers, one for the positive sequence and another for the negative sequence. The controllers are implemented in the synchronous reference frame and in the stationary reference frame. In the stationary reference frame, the use of the same integral gains for both controllers simplifies the equations of the control law especially for single-phase systems. Simulation and experimental results obtained with a pulsewidth modulation voltage-source inverter supplying unbalanced systems are presented.
Evaluation of wild and commercial types of Pleurotus strains for their ability to decolorize cibacron black W-NN textile dye

Biological decolorisation of Cibacron Black W-NN, was comparatively studied using five commercial (Pleurotus djamor, P. ostreatus, P. citrinopileatus, P. eryngiiand P. sajor-caju) and three wild types of (Pleurotus ostreatus, P. ostreatus and P. eryngii) white rot fungi strains on agar plates. The initial dye concentrations in the medium were 20, 50, 100, 200 and 500 mg/L, respectively. All P. ostreatus strains and P. sajor-caju fully decolorized Cibacron Black W-NN without any problem. Other organisms were found to be negatively affected from increasing dye concentrations and were able to decolorize the dye used up to a certain concentration (50 mg/L for P. citrinopileatus; 100 mg/L for commercial P. eryngiiand 200 mg/L for wild P. eryngii). Key words: Agar plate screening, biological decolorisation, Pleurotus spp. , textile dye
gp25L/emp24/p24 Protein Family Members of the cis-Golgi Network Bind Both COP I and II Coatomer

Abstract. Five mammalian members of the gp25L/ emp24/p24 family have been identified as major constituents of the cis-Golgi network of rat liver and HeLa cells. Two of these were also found in membranes of higher density (corresponding to the ER), and this correlated with their ability to bind COP I in vitro. This binding was mediated by a K(X)KXX-like retrieval motif present in the cytoplasmic domain of these two members. A second motif, double phenylalanine (FF), present in the cytoplasmic domain of all five members, was shown to participate in the binding of Sec23 (COP II). This motif is part of a larger one, similar to the F/YXXXXF/Y strong endocytosis and putative AP2 binding motif. In vivo mutational analysis confirmed the roles of both motifs so that when COP I binding was expected to be impaired, cell surface expression was observed, whereas mutation of the Sec23 binding motif resulted in a redistribution to the ER. Surprisingly, upon expression of mutated members, steady-state distribution of unmutated ones shifted as well, presumably as a consequence of their observed oligomeric properties.
Dynamic community detection based on distance dynamics

Dynamic community detection has been of great significance on analyzing network structure and community evolution. Among state-of-the-art methods, incremental algorithms based on modularity have been used widely, for the fully utilization of both current and historical information. Unfortunately, they are difficult to uncover small community due to problem called “resolution limit” and also sensitive to the sequence of network increments' arrival. In this paper, we propose a novel dynamic community detection algorithm based on distance dynamics, which detects community in near-linear time. Meanwhile, the proposed algorithm overcomes the shortcomings of traditional methods by replacing modularity with local interaction model. In a sense, it is assumed that increments can be treated as disturbance of network. It can be limited to a certain “local area” by disturbing factor. Experiments show that the proposed algorithm has achieved well balance between efficiency and effectiveness both in synthetic and real world networks.
The reference lattice concept and its application to the analysis of diffraction patterns

Abstract The concept of the reference lattice has been thoroughly discussed in physical and in higher-dimensional spaces. Such an approach has been applied to diffraction pattern analysis of different periodic, modulated and other aperiodic structures. It has been shown that two periodicities can be found in diffraction patterns of the above structures, namely the periodicity of the envelope curves describing the diffraction peak intensities, and the periodicity of peak positions ascribed to each envelope curve. The formula for the envelope curve has been given and it depends on the statistical moments of a specially defined real-space variable describing spatial fluctuations in atomic positions. Full equivalence of the presented physical space approach to the higher-dimension analysis of perfect quasicrystals has been shown.