Miracle-dz/UniFAR-FTUs huggingface dataset:
Complicating autoimmune diseases in myasthenia gravis: a review

Abstract Myasthenia gravis (MG) is a rare autoimmune disease of skeletal muscle endplates. MG subgroup is relevant for comorbidity, but usually not accounted for. MG patients have an increased risk for complicating autoimmune diseases, most commonly autoimmune thyroid disease, systemic lupus erythematosus and rheumatoid arthritis. In this review, we present concomitant autoimmune disorders associated with the different MG subgroups, and show how this influences treatment and prognosis. Concomitant MG should always be considered in patients with an autoimmune disorder and developing new neuromuscular weakness, fatigue or respiratory failure. When a second autoimmune disorder is suspected, MG should be included as a differential diagnosis.
Computational Strategies for a System-Level Understanding of Metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.
Composer attribution by quantifying compositional strategies

Taking a theory of musical style, developed by Leonard B. Meyer, as a starting point, an experiment is described in which statistical pattern recognition algorithms are used to characterize a particular musical style with respect to other styles. The resulting description can be used in authorship discussions. In the current study, a number of disputed organ works from the Bach catalog is used to illustrate the possibilities of this approach.
Highly Efficient Test Response Compaction Using a Hierarchical X-Masking Technique

This paper presents a highly effective compactor architecture for processing test responses with a high percentage of x-values. The key component is a hierarchical configurable masking register, which allows the compactor to dynamically adapt to and provide excellent performance over a wide range of x-densities. A major contribution of this paper is a technique that enables the efficient loading of the x-masking data into the masking logic in a parallel fashion using the scan chains. A method for eliminating the requirement for dedicated mask control signals using automated test equipment timing flexibility is also presented. The proposed compactor is especially suited to multisite testing. Experiments with industrial designs show that the proposed compactor enables compaction ratios exceeding 200x.
Impact analysis between observable degrees and estimation accuracy of Kalman filtering

It is well known that estimation performance of the Kalman filtering (KF) depends closely on systemic observability. Moreover, observable degree is usually used to measure the ability of observability on systemic state variables in control and estimation systems. Thereby, there should be a corresponding relation between the estimation performance of the KF and the observable degree. Unfortunately, value of the observable degree can tend to be infinite for most current computational ways and there must be a performance upper bound for the KF estimate. There is a clear impact between the observable degree and the filtering accuracy. Two common approaches to compute observable degree of estimation systems are briefly introduced in this paper, i.e., eigenvalues and eigenvectors analysis method for mean squared error (MSE) and singular value decomposition (SVD) method of observability matrix. Furthermore, the corresponding impact relation between the filtering performance and observable degree is expressly discussed by considering influences from system parameters to the observable degree and the estimation accuracy, respectively. Finally, two simulation examples are given to verify the analysis results obtained in this paper.
Link Prediction Based on Graph Topology: The Predictive Value of Generalized Clustering Coefficient

linkages among data objects is a fundamental data mining task in various application domains, including recommender systems, information retrieval, automatic Web hyperlink generation, record linkage, and communication surveillance. In many contexts link prediction is entirely based on the linkage information itself (a prominent example is the collaborative filtering recommendation). Link-structure based link prediction is closely related to a parallel and almost separate stream of research on topological modeling of large-scale graphs. Graph topological modeling builds on random graph theory to find parsimonious graph generation models reproducing empirical topological measures that summarize the global structure of a graph, such as clustering coefficient, average path length, and degree distribution. These well-studied topological measures and graph generation models have direct implications on link prediction. This paper represents initial efforts to explore the connection between link prediction and graph topology. The focus is exclusively on the predictive value of the clustering coefficient measure. The standard clustering coefficient measure is generalized to capture higher-order clustering tendencies. The proposed framework consists of a cycle formation link probability model, a procedure for estimating model parameters based on the generalized clustering coefficients, and model-based link prediction generation. Using the Enron email dataset we demonstrate that the proposed cycle formation model corresponded closely with the actual link probabilities and the link prediction algorithm based on this model outperformed existing algorithms.
Enhancing Small Cell Transmission Opportunity Through Passive Receiver Detection in Two-Tier Heterogeneous Networks

In this paper, we investigate how to embed small cells into a macro cell to enhance the performance of two-tier heterogeneous networks. Conventionally, overlay and underlay schemes are adopted by small cells, where the former enables a small cell to use the macro cell idle bands with transmitter detection, while the latter enables a small cell to use the macro cell busy bands with a certain access probability. To enhance the performance of small cells, we propose to exploit more busy band opportunities for small cells through passive receiver detection, i.e., identifying the location of an active macro user that occupies a certain band. Then, a small cell may access the busy band when the active macro user is far away from the small cell. In our method, the small cell uses the energy of the received signal from the macro cell as the test statistic. We obtain the closed-form distribution of the test statistic and design two detectors with one and two thresholds, respectively. Our results demonstrate that the proposed detectors achieve about 100% to 300% more transmission opportunities than the conventional energy detector in typical two-tier heterogeneous networks without requiring any prior information of the macro user signal.
Early life cycle design : Methods for visualising the object-oriented design process and the impact of task characteristics on design quality

The object-oriented methodology is now used more and more in industry without scientific evidence supporting its claimed benefit of applicability to a wide range of tasks. Prior research in human problem solving suggests that if the characteristics of the task and tool match, a better result is achieved than if there is a mismatch. To investigate this, we replicated an experiment by having two groups of subjects solve two different tasks; one object-oriented and one process-oriented. The prediction that the object-oriented methodology should match better with the object-oriented task and result in better performance was not supported; there was no significant difference in the quality of the solutions from the two groups. Even though the type of task did not influence the quality of the solutions, there were differences between the best and worst performances among the subjects. To gain insight into how these different solutions were achieved, the design process was explored by developing methods for visualisation of the design process. In this exploratory research we found two useful models to visualise design growth and design status. Design growth shows how the quality of the solution grows with time and design status show the status of the user; if he/she is working with the structure-part or the behaviour-part of the task. To be able to use these models, fine grain data from the design process was needed. This was possible through the use of an instrumented CASE-tool that captured every action made by the user. When the models were used on our data and the best solutions compared with the worst, the findings indicated that modeling all of the structure-part of the task before modeling the behaviour-part gives a higher quality solution. First of all we would like to thank our two supervisors Prof. for their support and positive feedback during the writing of the thesis. A special thanks to Prof. Ross Jeffery for helping us with our English and guiding us through problems and difficulties. Thanks to Prof. Claes Wohlin (from Lund University, Sweden) for establishing the link to Prof. Ross Jeffery and therefore enabling us to experience this enriching semester in Sydney. Thanks to Dr. Andrew Brooks (from University of Strathclyde, Scotland) for the initial guidance and thanks to the students participating in the experiments. We also thank Derek Renouf and Fiona Walkerdon at Adaptive Arts for the instrumentation of Simply Objects which was a key item …
Hardware acceleration for similarity computations of feature vectors

In Web mining applications, an enormous amount of data needs to be processed quickly and efficiently. Thus, hardware support is crucial to enhance the performance of these operations. In this work, chip-level hardware support for similarity measure calculation is introduced and then extended to similarity matrix computation. Both software and hardware versions of various similarity computations are implemented with a hierarchical platform-based design approach to facilitate component reuse at different levels of abstraction. Software modules are executed on a reconfigurable soft processor core on the same field-programmable gate array (FPGA) as the hardware implementations for the purpose of performance comparison. Preliminary results using parallel hardware are also presented. In addition, performance gain from hardware as opposed to a general-purpose processor is examined. Experimental results show that the performance of similarity computation for a set of feature vectors could be significantly enhanced by using on-chip hardware.
Still Cognitive After All These Years? Perspectives for a Cognitive Behavioural Theory of Obsessions and Where We Are 30 Years Later: A Commentary

Salkovskis and Millar (this issue) observe a number of shortcomings in our paper entitled “New Perspectives for a Cognitive Theory of Obsessions” (Clark & Purdon, 1993), which include: (a) an under‐emphasis on the role of responsibility, (b) an under‐emphasis on the influence of neutralisation; and (c) an over‐emphasis on the relative importance of mental control in the pathogenesis of obsessions. They also contend that our emphasis on ego‐dystonicity as a defining feature of obsessions has been discredited. We agree that certain aspects of our original cognitive model of obsessions may have been overstated in light of the past 22 years of research but several key points have been upheld, especially the importance of failed mental control in the persistence of obsessional problems. This paper addresses each of these criticisms in turn.
Multi-resource energy-efficient routing in cloud data centers with network-as-a-service

With the rapid development of software defined networking and network function virtualization, researchers have proposed a new cloud networking model called Network-as-a-Service (NaaS) which enables both in-network packet processing and application-specific network control. In this paper, we revisit the problem of achieving network energy efficiency in data centers and identify some new optimization challenges under the NaaS model. Particularly, we extend the energy-efficient routing optimization from single-resource to multi-resource settings. We characterize the problem through a detailed model and provide a formal problem definition. Due to the high complexity of direct solutions, we propose a greedy routing scheme to approximate the optimum, where flows are selected progressively to exhaust residual capacities of active nodes, and routing paths are assigned based on the distributions of both node residual capacities and flow demands. By leveraging the structural regularity of data center networks, we also provide a fast topology-aware heuristic method based on hierarchically solving a series of vector bin packing instances. Extensive simulations show that the proposed routing scheme can achieve significant gain on energy savings and the topology-aware heuristic can produce comparably good results while reducing the computation time to a large extent.
Complexity reduction in BICM-ID systems through selective log-likelihood ratio updates

Bit-interleaved coded modulation with iterative decoding (BICM-ID) performs well under a variety of channel conditions. Based on a factor graph representation of BICM-ID, we propose a modification of the BICM-ID receiver that can reduce the computational complexity significantly without BER degredation. The basic idea is to avoid updates of code bit log-likelihood ratios whose magnitude is already sufficiently large. Simulation results illustrate the excellent performance-complexity trade-off of our method.
PHY Layer Security Based on Protected Zone and Artificial Noise

We address physical layer security in multiple- input-multiple-output (MISO) communications in the presence of an unknown passive eavesdropper. Beamforming and artificial noise broadcasting are chosen to increase communications security. We first study the effect of a close eavesdropper on security and then we define a “Protected Zone” in the transmitter's vicinity. We present an optimisation strategy that intelligently sets the transmission power and the size of the protected zone to probabilistically achieve secrecy at a specified target secrecy rate. The results show that this strategy can achieve a high probability of secrecy by efficiently prioritising the use of the available resources.
COMPASSS Working Paper 2013-72 Fuzzy-Set Coincidence Analysis : the Hidden Asymmetries

Despite being conceptually and algebraically straightforward, set coincidence has never received great attention within the framework of set-relational research, which has essentially focused on subset/superset relations. In this article, I advance a novel procedure called fuzzy-set coincidence analysis to systematically assess the degree of overlapping of two or multiple sets. Individual-level analyses, and those in social stratification in particular, are deemed to be a fertile field to apply fuzzy-set coincidence analysis. A main comparative advantage with respect to statistical techniques lies in its ability to uncover hidden asymmetrical patterns. This will be shown with an empirical application that explores patterns of overlapping inequalities for students of different migratory
The origins of spontaneous grain refinement in deeply undercooled metallic melts

Spontaneous grain refinement in undercooled metallic melts has been a topic of enduring interest within the solidification community since its discovery more than 50 years ago. Here we present a comparative study of the solidification microstructures and velocity-undercooling behaviour in two dilute Cu-Ni alloys (3.98 & 8.90 wt.% Ni), which have been undercooled by a melt encasement (fluxing) method. Cu-3.98 wt. % Ni shows grain refinement at both low and high undercooling, with a dendritic growth regime separating the two grain refined regions. Within the grain refined region dendritic fragments are clearly evident in the centres of the refined grains and on the surface of the undercooled droplet, suggesting a dendritic fragmentation mechanism. Cu-8.90 wt. % Ni displays also grain refinement at both high and low undercoolings. In the low undercooling grain refined region the samples display curved grain boundaries with a dendritic substructure that extends across grains, indicative of a recovery and recrystallisation mechanism. Conversely, prior to the onset of the high undercooling grain refinement transition extensive regions of dendritic seaweed are observed, suggesting that it is remelting of a dendritic seaweed that gives rise to this structure. Consequently, in two closely related Cu-based systems we have strong microstructural evidence for the operation of all three mechanisms currently considered to give rise to grain refinement. This may help to resolve the grain refinement controversy, although it remains to be determined what factors determine which mechanism operates in any given system.
The Canadian underground and measured economies: Granger causality results

Using new time-series data for the size of the Canadian underground economy, the relationship between unreported and measured GDP in that country is examined. Granger causality tests are conducted, with a proper allowance for the non-stationarity of the data. It is found that there is clear evidence of such causality from measured GDP to ‘hidden’ output, but only very mild evidence of Granger causality in the reverse direction. This result supports similar evidence for New Zealand reported by the first author, and has several interesting policy implications.
PALAIS : A 3 D Simulation Environment for Artificial Intelligence in Games

In this paper we present PALAIS — a virtual simulation environment for Artificial Intelligence (AI) in games. The environment provides functionality for prototyping, testing, visualisation and evaluation of game AI. It allows definition and execution of arbitrary, three-dimensional game scenes and behaviors. Additionally, PALAIS incorporates a plugin system that supports swift integration of custom AI algorithms. As a result, PALAIS effectively reduces the effort necessary to research, develop, prototype and showcase behaviors used for non-player characters in games. Finally, we demonstrate the power of the provided plugin system by exemplarily extending the functionality of PALAIS with an external module. PALAIS is available at http://www.palais. io.
E-Procurement Management in State Governments: Diffusion of E-Procurement Practices and Its Determinants

State governments have begun to introduce various eprocurement tools to improve inefficient and ineffective procurement management; however, few empirical studies have documented the diffusion of e-procurement. This study uses data collected from state procurement agencies to examine which e-procurement tools state governments are using as well as to explore what factors affect the adoption of these tools. This study confirms that simple innovations are more rapidly diffused than those that are technically or legally complex. State governments that are more likely to adopt e-procurement tools tend to be larger, managerially innovative, and to have a strong centralized procurement office. Overall, e-procurement is promising technology, but managerial and technical challenges still remain.
Improving generalization in backpropagation networks with distributed bottlenecks

The primary goal of any adaptive system that learns by example is to generalize from the training examples to novel inputs. The backpropagation learning algorithm is popular for its simplicity and landmark cases of generalization. It has been observed that backpropagation networks sometimes generalize better when they contain a hidden layer that has considerably fewer units than previous layers. The functional properties of such hidden-layer bottlenecks are analyzed, and a method for dynamically creating them, concurrent with backpropagation learning, is described. The method does not excise hidden units; rather, it compresses the dimensionality of the space spanned by the hidden-unit weight vectors and forms clusters of weight vectors in the low-dimensional space. The result is a functional bottleneck distributed across many units. The method is a gradient descent procedure, using local computations on simple lateral Hebbian connections between hidden units. <<ETX>>
Stability Analysis of Discrete Time Delay Control for Nonlinear Systems

This paper presents a solution to the long standing problem of the stability of time delay control (TDC) for nonlinear systems. Ever since it was first introduced, TDC has rapidly drawn attentions owing to its unusually robust performance and yet its extraordinarily compact form. The existing stability analyses have been made based on the assumption that the TDC is continuous and time delay L rarr 0- The assumption, however, not only fails to reflect the reality that TDC is usually implemented in a digital processor, but also leads to a stability criterion in which important parameters, such as L, that play crucial roles are absent. In this paper, therefore, we present our theoretical investigation on the stability of TDC with the premise that TDC is discrete and L is nonzero and finite. Specifically, stability criteria based on the premise are derived, so that one may clearly grasp which parameters affect stability and how. For the analysis of the closed-loop stability, we have first derived its approximate discrete model (approximate discrete plant model with the discrete TDC). Then by using the model and the concepts of consistency and Lyapunov stability, we have analyzed the stability of the exact discrete model of closed loop systems. The analysis results in a stability criteria consisting of L and other parameters that affect the performance of TDC. The suggested stability analysis has been verified by simulation results.
Synthesis of partial rankings of points of interest using crowdsourcing

The web is increasingly being accessed from mobile devices, and studies suggest that a large fraction of keyword-based search engine queries have local intent, meaning that users are interested in local content and that the underlying ranking function should take into account both relevance to the query keywords and the query location. A key challenge in being able to make progress on the design of ranking functions is to be able to assess the quality of the results returned by ranking functions. We propose a model that synthesizes a ranking of points of interest from answers to crowdsourced pairwise relevance questions. To evaluate the model, we propose an innovative methodology that enables evaluation of the quality of synthesized rankings in a simulated setting. We report on an experimental evaluation based on the methodology that shows that the proposed model produces promising results in pertinent settings and that it is capable of outperforming an approach based on majority voting.