Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Emergence of the Interbank Networks

Interbank contagion has become a buzzword in the aftermath of the financial crisis that led to a series of shocks to the interbank market and to periods of pronounced market disruptions. However, little is known about how interbank networks are formed and about their sensitivity to changes in key bank parameters (for example, induced by common exogenous shocks or by regulatory initiatives). This paper aims to shed light on these issues by modelling endogenously the formation of interbank networks, which in turn allows for checking the sensitivity of interbank network structures and hence their underlying contagion risk to changes in market-driven parameters as well as to changes in regulatory measures such as large exposures limits. The sequential network formation mechanism presented in the paper is based on a portfolio optimisation model whereby banks allocate their interbank exposures while balancing the return and risk of counterparty default risk and the placements are accepted taking into account funding diversification benefits. The model offers some interesting insights into how key parameters may affect interbank network structures and can be a valuable tool for analysing the impact of various regulatory policy measures relating to banks' incentives to operate in the interbank market. JEL Classification: G21, C63, C78
Improving multi-objective clustering through support vector machine: Application to gene expression data

Microarray technology facilitates the monitoring of the expression profile of a large number of genes across different experimental conditions simultaneously. This article proposes a novel approach that combines a recently proposed multiobjective fuzzy clustering scheme with support vector machine (SVM), to yield improved solutions. The multiobjective technique is first used to produce a set of non-dominated solutions. The non-dominated set is then used to find some high-confidence points using a fuzzy voting technique. The SVM classifier is trained by this high-confidence points. Finally the remaining points are classified using the trained classifier. Results demonstrating the effectiveness of the proposed technique are provided for three real life gene expression data sets. Moreover statistical significance test has been conducted to establish the significant superiority of the proposed technique.
Application of the technical Frequency-Hopped to the localization of sources by array of sensors and application to the algorithm Music

In this paper, we present a different approach to the problem of estimating the angle of arrivals (AOA's) of D targets in frequency-hopped signaling sensors array for active systems, with D smaller than the number of sensors element, L. This method is based on the application of a new proposed model of received data available in different channels. The simulations results show that this approach improves the resolution in the estimation of the angle of arrivals compared with monotone-frequency signaling case
Diversity in genetic programming: an analysis of measures and correlation with fitness

Examines measures of diversity in genetic programming. The goal is to understand the importance of such measures and their relationship with fitness. Diversity methods and measures from the literature are surveyed and a selected set of measures are applied to common standard problem instances in an experimental study. Results show the varying definitions and behaviors of diversity and the varying correlation between diversity and fitness during different stages of the evolutionary process. Populations in the genetic programming algorithm are shown to become structurally similar while maintaining a high amount of behavioral differences. Conclusions describe what measures are likely to be important for understanding and improving the search process and why diversity might have different meaning for different problem domains.
Classically controlled quantum computation

It is reasonable to assume that quantum computations take place under the control of the classical world. For modelling this standard situation, we introduce a Classically controlled Quantum Turing Machine (CQTM), which is a Turing machine with a quantum tape for acting on quantum data, and a classical transition function for formalised classical control. In a CQTM, unitary transformations and quantum measurements are allowed. We show that any classical Turing machine can be simulated by a CQTM without loss of efficiency. Furthermore, we show that any $k$-tape CQTM can be simulated by a 2-tape CQTM with a quadratic loss of efficiency. In order to compare CQTMs with existing models of quantum computation, we prove that any uniform family of quantum circuits (Yao 1993) is efficiently approximated by a CQTM. Moreover, we prove that any semi-uniform family of quantum circuits (Nishimura and Ozawa 2002), and any measurement calculus pattern (Danos et al. 2004) are efficiently simulated by a CQTM. Finally, we introduce a Measurement-based Quantum Turing Machine (MQTM), which is a restriction of CQTMs in which only projective measurements are allowed. We prove that any CQTM is efficiently simulated by a MQTM. In order to appreciate the similarity between programming classical Turing machines and programming CQTMs, some examples of CQTMs are given.
A power-aware routing scheme for wireless sensor networks

Since the nodes in Wireless Sensor Networks (WSN) are typically very small in size and are powered by irreplaceable battery, efficient use of energy becomes one of the most challenging tasks while designing any protocol for WSN. In this paper, we propose a routing protocol named Reliable and Energy Efficient Protocol (REEP) for WSN. The performance of REEP has been evaluated with the help of simulations by considering different scenarios. REEP shows better performance compared to a popular data-centric routing protocol for WSN.
PIG WELFARE THROUGH BEHAVIOR LEARNING FROM CAMERA RECORDINGS

Animal Science students along with the farm staff have to monitor the behavior of pigs in order to assure their welfare. The video systems are used by our educational software and new methods of pig observation, evaluation and treatment are applied much faster and more efficient compared to the classical intervention. Each recording is stored as a media file and each frame taken at 0.1 seconds is stored as a Bitmap image. The Bitmap images are processed in parallel using the MapReduce programming model from Apache Hadoop. The contour of the image is automatically analyzed and based on it the presence of pigs is detected, as well as their location can be determined. The location is important because it can be denoting that the pig eats or that it stays aside. Pig limp was also detected. It was observed based on the recordings that 83% of the time the pigs spend it lying down, 7% is spent eating and 10% of the time they walk and sit. Video monitoring and automatic interpretation facilitates the learning of new intervention approaches and boosts the responsiveness among the students. The students can learn from the critical situations and benefit from these cases while learning.
The Journal of Histochemistry and Cytochemistry Carbonic Anhydrase Activity in Skeletal Muscle Fiber Types, Axons, Spindles, and Capillaries of Rat Soleus and Extensor Digitorum Longus Muscles'

Carbonic anhydrase (CA) activities were studied in soluble extracts and cryostat sections of skeletal muscles from pre-pubertal and postpuhertal rats. Acetazolamide inhibition was utilized to distinguish between activities of the ace-tazolamide-sensitive (CA 1 and II) and acetazolamide-resistant (CA III) forms ofthe enzyme. The inhibition studies i ndicated that fast-twitch oxidative-glycolytic muscle fi-hers contained both the sensitive and resistant forms of CA. Acetazolamide-sensitive activity was localized within muscle fibers, axons, myelin, and capillaries. Axoplasmic staining was restricted to subpopulations of myelinated axons in both the dorsal and ventral roots. Soleus muscles exhibited significantly greater activity of CA Ill than ex
Partitioning Regular Applications for Cache -

In all massively parallel systems (MPPs), whether message-passing or shared-address space, the memory is physically distributed for scalability and the latency of accessing remote data is orders of magnitude higher than the processor cycle time. Therefore, the programmer/compiler must not only identify parallelism but also specify the distribution of data among the processor memories in order to obtain reasonable eeciency. Shared-address MPPs provide an easier paradigm for programmers than message passing systems since the communication is automatically handled by the hardware and/or operating system. However, it is just as important to optimize the communication in shared-address systems if high performance is to be achieved. Since communication is implied by the data layout and data reference pattern of the application, the data layout scheme and data access pattern must be controlled by the compiler in order to optimize communication. Machine speciic parameters, such as cache size and cache line size, describing the memory hierarchy of the shared-address space machine must be used to tailor the optimization of the application to the memory hierarchy of the MPP. This report focuses on a partitioning methodology to optimize application performance on cache-coherent multiprocessors. We give an algorithm for choosing block-cyclic partitions for scientiic programs with regular data structures such as dense linear algebra applications and PDE solvers. We provide algorithms to compute the cache state on exiting a parallel region given the cache state on entry; and methods to compute the overall cache-coherency traac and choose block-cyclic parameters to optimize cache-coherency traac. Our approach is demonstrated on two applications. We show that the optimal partition computed by our algorithm matches the experimentally observed optimum and we show the eeect of cache line size on partition performance.
Heart rate monitoring during combat sports matches: a brief review

ABSTRACT The aims of the present short review were to (i) summarise data relating to the heart rate (HR) response to combat sports matches according to many factors and (ii) determine the cardiovascular demand and intensity of combat sports matches. The present review shows that HR increased significantly across rounds during male taekwondo, boxing and kickboxing matches. HR and maximal heart rate (HRmax) increased significantly across successive karate and judo combat matches in young men and boy athletes. It has also been shown that there are no significant differences between winners and losers and males and females in HR response during official and simulated striking combat sports matches. Furthermore, combat HR intensity as a percentage of HRmax varied from 90% to 94%, from 86% to 100%, from 83% to 94%, and approximately 95% during judo, taekwondo, karate and Muay Thai matches, respectively. Coaches, sports scientists and athletes can also use HR to (i) assess athletes’ cardiovascular demand and (ii) monitor exercise intensity during combat sports matches.
USING MULTIMODAL IMAGING TO EXAMINE CLINICAL AND COGNITIVE CORRELATES OF MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE

Mild cognitive impairment (MCI) may interact with depression to mediate the risk of developing Alzheimer’s disease (AD). Investigating the relationship between depression-related and AD-associated pathological changes such as beta-amyloid (Aβ), tau, and hippocampal volume loss may help to explain the mechanistic links between depression and the cognitive and functional impairment underlying AD. We employed different neuroimaging techniques, including positron emission tomography (PET) and magnetic resonance imaging, to investigate several in-vivo markers of AD pathology, allowing us to examine their associations with clinical (e.g. symptoms of depression) and cognitive correlates of MCI and AD. The thesis covers five themes. The first theme seeks to explore the relationship between a lifetime history of depression and cortical Aβ burden using PET. The second theme investigates the association between late-life depressive symptoms and cortical Aβ, also using PET. The third theme examines hippocampal volume atrophy to observe how depressive symptoms and progressive hippocampal volume loss mediate the conversion process from MCI to dementia. The fourth iii theme focuses on the longitudinal and structural trajectories of MCI patients with suspected non-Alzheimer’ disease in order to examine how their pathological and cognitive profiles differ from other MCI groups. The final theme seeks to investigate the link between usage of the psychoactive agent benzodiazepine and cerebral Aβ levels using participants’ longitudinal cognitive profile and PET. Overall, a history of depression appears to be more strongly associated with increased Aβ burden than late-life depressive symptoms. However, late-life depression, in conjunction with hippocampal atrophy, may accelerate the conversion process to AD. Furthermore, this thesis emphasizes that Aβ level is not required to promote cognitive and functional deterioration; thus Aβ levels only partially explain mechanisms underlying AD pathogenesis. Lastly, this thesis elucidates the importance of early and preventative interventions to reduce the future incidence of AD.
An encryption algorithm (T-key) based on a 4D tesseract

In this paper, innovative 4D tesseract-based solutions (T-key) are suggested, implemented, tested and shown to overcome the drawbacks of most existing algorithms. They are based on a symmetric block cipher technique and have a large key-space (e.g. 384-bit). Three key size are suggested as examples, which are tested and evaluated: T-{0,1}128, T-{0,1}256 and T- {0,1}512. Moreover, four lightweight coding rounds are applied to create these keys from the 4D tesseract key containers. The execution speed of the T-key was compared with the AES cipher , and the results indicated that the first two encryption key sizes suggested here are faster than the first two encryption keys of AES. Furthermore, the security evaluations carried out on the suggested algorithm showed fully resistance the statistical attacks and pass the correlation coefficient test.
Molecular Chaperones in the Yeast Endoplasmic Reticulum Maintain the Solubility of Proteins for Retrotranslocation and Degradation

Endoplasmic reticulum (ER)-associated degradation (ERAD) is the process by which aberrant proteins in the ER lumen are exported back to the cytosol and degraded by the proteasome. Although ER molecular chaperones are required for ERAD, their specific role(s) in this process have been ill defined. To understand how one group of interacting lumenal chaperones facilitates ERAD, the fates of pro–α-factor and a mutant form of carboxypeptidase Y were examined both in vivo and in vitro. We found that these ERAD substrates are stabilized and aggregate in the ER at elevated temperatures when BiP, the lumenal Hsp70 molecular chaperone, is mutated, or when the genes encoding the J domain–containing proteins Jem1p and Scj1p are deleted. In contrast, deletion of JEM1 and SCJ1 had little effect on the ERAD of a membrane protein. These results suggest that one role of the BiP, Jem1p, and Scj1p chaperones is to maintain lumenal ERAD substrates in a retrotranslocation-competent state.
Long-Term Survival of Patients With Radiation Heart Disease Undergoing Cardiac Surgery: A Cohort Study

Background— Thoracic radiation results in radiation-associated heart disease (RAHD), often requiring cardiothoracic surgery (CTS). We sought to measure long-term survival in RAHD patients undergoing CTS, to compare them with a matched control population undergoing similar surgical procedures, and to identify potential predictors of long-term survival. Methods and Results— In this retrospective observational cohort study of patients undergoing CTS, matched on the basis of age, sex, and type/time of CTS, 173 RAHD patients (75% women; age, 63±14 years) and 305 comparison patients (74% women; age, 63±4 years) were included. The vast majority of RAHD patients had prior breast cancer (53%) and Hodgkin lymphoma (27%), and the mean time from radiation was 18±12 years. Clinical and surgical parameters were recorded. The preoperative EuroSCORE and all-cause mortality were recorded. The mean EuroSCOREs were similar in the RAHD and comparison groups (7.8±3 versus 7.4±3, respectively; P=0.1). Proximal coronary artery disease was higher in patients with RAHD versus the comparison patients (45% versus 38%; P=0.09), whereas redo CTS was lower in the RACD versus the comparison group (20% versus 29%; P=0.02). About two thirds of patients in either group had combination surgical procedures. During a mean follow-up of 7.6±3 years, a significantly higher proportion of patients died in the RAHD group than in the comparison group (55% versus 28%; P<0.001). On multivariable Cox proportional hazard analysis, RAHD (2.47; 95% confidence interval, 1.82–3.36), increasing EuroSCORE (1.22; 95% confidence interval, 1.16–1.29), and lack of &bgr;-blockers (0.66; 95% confidence interval, 0.47–0.93) were associated with increased mortality (all P<0.01). Conclusions— In patients undergoing CTS, RAHD portends increased long-term mortality. Alternative treatment strategies may be required in RAHD to improve long-term survival.
Computing Periodic Orbits and their Bifurcations with Automatic Differentiation

This paper formulates several algorithms for the direct computation of periodic orbits as solutions of boundary value problems. The algorithms emphasize the use of coarse meshes and high orders of accuracy. Convergence theorems are given in the limit of increasing order with a fixed mesh. The algorithms are implemented with the use of MATLAB and ADOL-C, a software package for automatic differentiation. Automatic differentiation enables accurate computation of high-order derivatives of functions without the truncation errors inherent in finite difference calculations. We embed the algorithms in a continuation framework and extend them to compute saddle-node bifurcations of periodic orbits directly. We present data from numerical studies of four test problems, making some comparisons with other methods for computing periodic orbits. These results demonstrate that high-order methods based upon automatic differentiation are capable of high precision with small meshes.
Rotational velocities of the giants in symbiotic stars – II. Are S‐type symbiotics synchronized?★

We have measured the projected rotational velocities (v sin i) of the mass donors for 29 S-type symbiotic stars (SSs) using high-resolution spectroscopic observations and the cross-correlation function (CCF) method. The results of the CCF have been controlled with synthetic spectra. The typical rotational velocity of the K and M giants in S-type symbiotics appeared to be 4.5 99 per cent) only for the recurrent nova RS Oph. For 22 S-type symbiotics we give clues as to what their orbital periods could be.
OBJECT MODELLING, DETECTION AND LOCALISATION IN MOBILE VIDEO: A STATE-OF-THE-ART

This report is part of the state-of-the-art deliverable of the ITEA2 project SPY “Surveillance imProved System: Intelligent situation awareness” whose purpose is to develop new urban surveillance systems using video cameras embedded within mobile security vehicles. This report is dedicated to the problem of finding objects of interest in a video. “Object” is understood in its familiar (i.e. semantic) sense: e.g. car, tree, human, road. and the system is supposed to automatically find the location of such objects in the captured video. To be consistent with the project technological level, we shall exclude the “developmental” approaches, where the system does not know the objects in advance, and constructs incrementally its own internal representation. We then suppose that the system operates with a provided representation of the objects and its environment that has been constructed (learned) off-line, and that may evolve on-line. Such representation includes a set of object classes that the system is then expected to recognize and localise in every image, either by attributing accordingly a label to every location in the image (task referred to as “semantic segmentation”), or by localising –more or less precisely– instances of each class in the video and tagging every image accordingly (referred to as “semantic indexing”). We thus present a state-of-the-art of the video analysis methods for object and environment modelling and semantic indexing or segmentation with respect to the corresponding model.
Polynomial Homotopies for Dense, Sparse and Determinantal Systems

Numerical homotopy continuation methods for three classes of polynomial sys- tems are presented. For a generic instance of the class, every path leads to a solution and the homotopy is optimal. The counting of the roots mirrors the resolution of a generic system that is used to start up the deformations. Software and applications are discussed. AMS Subject Classification. 14N10, 14M15, 52A39, 52B20, 52B55, 65H10, 68Q40.
DataGopher: Context-based search for research datasets

Scientific datasets play a crucial role in data-driven research. While, several search tools are developed for searching documents, blogs, images, videos and various other information needs, important scientific artifacts like research datasets lack this prerogative. The main challenge faced in developing an effective search tool for datasets is to determine the content representation of the raw data. Dataset description provided by users is often very content-specific and short. Moreover, even the public datasets generally have very limited description about the various research problems/applications that used them. Given the ever expanding variety of datasets on the web and the lack of representative content for the purpose of indexing, the task of developing an effective search engine for dataset is computationally very challenging. In this work, we propose a novel `context' based paradigm of search for dataset to overcome the problem of limited representative content for research datasets. In contrast to any general purpose search engine which index the `little' text information about the dataset sources, we hypothesized that the proposed paradigm of `context' based search is more effective for dataset search. The hypothesis is tested by conducting a user study. The performance of the context based search (DataGopher) is compared with a popular general purpose search engine. The study was conducted in a real world setting where user are free to use the search engine as per the information need. Based on the user study, we find that the performance of DataGopher was favored for 58% of the total context based user queries whereas the baseline was only 26%.
Feature Learning via Mixtures of DCNNs for Fine-Grained Plant Classification

We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2016 plant task. Our system learns two deep convolutional neural network models. The first is a domain-specific model and the second is a mixture of content specific models, one for each of the plant organs such as branch, leaf, fruit, flower and stem. We combine these two models and experiments on the PlantCLEF2016 dataset show that this approach provides an improvement over the baseline system with the mean average precision improving from 0.603 to 0.629 on the test set.