Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling Financial Time Series (Second Edition)

This book contains several innovative models for the prices of financial assets. First published in 1986, it is a classic text in the area of financial econometrics. It presents ARCH and stochastic volatility models that are often used and cited in academic research and are applied by quantitative analysts in many banks. Another often-cited contribution of the first edition is the documentation of statistical characteristics of financial returns, which are referred to as stylized facts.This second edition takes into account the remarkable progress made by empirical researchers during the past two decades from 1986 to 2006. In the new Preface, the author summarizes this progress in two key areas: firstly, measuring, modelling and forecasting volatility; and secondly, detecting and exploiting price trends.
Accuracy-Energy Configurable Sensor Processor and IoT Device for Long-Term Activity Monitoring in Rare-Event Sensing Applications

A specially designed sensor processor used as a main processor in IoT (internet-of-thing) device for the rare-event sensing applications is proposed. The IoT device including the proposed sensor processor performs the event-driven sensor data processing based on an accuracy-energy configurable event-quantization in architectural level. The received sensor signal is converted into a sequence of atomic events, which is extracted by the signal-to-atomic-event generator (AEG). Using an event signal processing unit (EPU) as an accelerator, the extracted atomic events are analyzed to build the final event. Instead of the sampled raw data transmission via internet, the proposed method delays the communication with a host system until a semantic pattern of the signal is identified as a final event. The proposed processor is implemented on a single chip, which is tightly coupled in bus connection level with a microcontroller using a 0.18 μm CMOS embedded-flash process. For experimental results, we evaluated the proposed sensor processor by using an IR- (infrared radio-) based signal reflection and sensor signal acquisition system. We successfully demonstrated that the expected power consumption is in the range of 20% to 50% compared to the result of the basement in case of allowing 10% accuracy error.
Cyclic nucleotides modulate genioglossus and hypoglossal responses to excitatory inputs in rats.

RATIONALE Previous studies modulating pharyngeal muscle activity with pharmacologic approaches have targeted membrane receptors on pharyngeal motoneurons. Whether modulation of intracellular pathways can increase pharyngeal muscle activity, however, has not been investigated but is relevant to pharmacologic treatments of obstructive sleep apnea. OBJECTIVES To determine if modulating the second messenger cyclic adenosine-3'-5'-monophosphate (cAMP) at the hypoglossal motor nucleus (HMN) will increase genioglossus activity across sleep- wake states. METHODS Forty-eight rats were implanted with electroencephalogram and neck electrodes to record sleep-wake states and genioglossus and diaphragm electrodes for respiratory muscle recordings. Microdialysis probes were inserted into the HMN to perfuse artificial cerebrospinal fluid and (1) forskolin (500 microM, adenylyl cyclase activator to increase cAMP), (2) a cAMP analog (500 microM), (3) iso-butyl-methylxanthine (IBMX; 300 microM, phosphodiesterase inhibitor), or (4) a cyclic guanosine-3'-5'-monophosphate (cGMP) analog (500 microM, 8-Br-cGMP). MEASUREMENTS AND MAIN RESULTS Forskolin and the cAMP analog at the HMN increased respiratory-related and tonic genioglossus activities in wakefulness and non-REM sleep but not REM sleep. IBMX did not affect genioglossus activity in awake or sleeping rats. However, IBMX abolished the robust excitatory responses to serotonin and phenylephrine at the HMN, but responses to non-N-methyl-D-aspartate receptor activation remained. These effects of IBMX were mimicked by 8-Br-cGMP. CONCLUSIONS Genioglossus responses to manipulation of cAMP at the HMN are differentially modulated by sleep-wake state. Selective abolition of serotonin and phenylephrine responses after IBMX suggests that under conditions of nonspecific phosphodiesterase inhibition the HMN is unresponsive to certain, otherwise potent, excitatory inputs. Similar responses with 8-Br-cGMP suggest this effect is likely mediated by cGMP pathways.
Distributed Processing Algorithms for Wireless Sensor Networks Having Fast Convergence and Robustness Against Coupling Noise

Designing sensor networks with decentralized and autonomous decisions capabilities, i.e., without the need to send all the collected data to a fusion center, is a big challenge that is receiving considerable attention. One of the major drawbacks of distributed algorithms is their iterative nature. This makes them prone to an energy consumption that depends on the convergence time and on the power transmitted by each node to guarantee the network connectivity. Furthermore, in a realistic environment, the interaction among sensor is inevitably corrupted by noise which affects the final decision. In this work, we describe decentralized algorithms for implementing various processing tasks, from spatial smoothing to distributed decision, characterized by fast convergence properties, for a given network topology, and resilience against inter-sensor communication noise.
Incorporating Latent Factors Into Knowledge Tracing To Predict Individual Differences In Learning

An effective tutor—human or electronic—must determine what a student does and does not know. Inferring a student’s knowledge state is challenging because behavioral observations (e.g., correct vs. incorrect problem solution) provide only weak evidence. Two classes of models have been proposed to address the challenge. Latent-factor models employ a collaborative filtering approach in which data from a population of students solving a population of problems is used to predict the performance of an individual student on a specific problem. Knowledgetracing models exploit a student’s sequence of problem-solving attempts to determine the point at which a skill is mastered. Although these two approaches are complementary, only preliminary, informal steps have been taken to integrate them. We propose a principled synthesis of the two approaches that predicts student performance based on a theory of individual differences among students and among problems, as well as a theory of the temporal dynamics of learning. We present promising results using data from two intelligent tutoring systems.
Effect of Nano-sized Carbon Black Particles on Lung and Circulatory System by Inhalation Exposure in Rats

Objectives We sought to establish a novel method to generate nano-sized carbon black particles (nano-CBPs) with an average size smaller than 100 nm for examining the inhalation exposure risks of experimental rats. We also tested the effect of nano-CBPs on the pulmonary and circulatory systems. Methods We used chemical vapor deposition (CVD) without the addition of any additives to generate nano-CBPs with a particle size (electrical mobility diameter) of less than 100nm to examine the effects of inhalation exposure. Nano-CBPs were applied to a nose-only inhalation chamber system for studying the inhalation toxicity in rats. The effect on the lungs and circulatory system was determined according to the degree of inflammation as quantified by bronchoalveolar lavage fluid (BALF). The functional alteration of the hemostatic and vasomotor activities was measured by plasma coagulation, platelet activity, contraction and relaxation of blood vessels. Results Nano-CBPs were generated in the range of 83.3-87.9 nm. Rats were exposed for 4 hour/day, 5 days/week for 4 weeks to 4.2 × 106, 6.2 × 105, and 1.3 × 105 particles/cm3. Exposure of nano-CBPs by inhalation resulted in minimal pulmonary inflammation and did not appear to damage the lung tissue. In addition, there was no significant effect on blood functions, such as plasma coagulation and platelet aggregation, or on vasomotor function. Conclusion We successfully generated nano-CBPs in the range of 83.3-87.9 nm at a maximum concentration of 4.2 × 106 particles/cm3 in a nose-only inhalation chamber system. This reliable method can be useful to investigate the biological and toxicological effects of inhalation exposure to nano-CBPs on experimental rats.
RRLOC: A tool for iBGP Route Reflector topology planning and experimentation

The scalability of the global routing system is critical for the evolution of the Internet, and consequently, BGP scalability has been part of the Internet research agenda from long ago. The original concern was the growth of the routing table size, and more recently, the research community has also been considering the growth of the BGP churn, i.e. the number of messages that are generated by the BGP protocol. Some relevant studies have characterized the evolution of the external BGP (eBGP) churn for the last few years and have shown a moderated increase. There is evidence that part of such growth is caused by important changes in inter-AS connectivity patterns, along with some unexpected behaviour of internal BGP (iBGP), in particular, intra-AS duplication of BGP updates due to Route Reflectors (RR). There are many vantage points in the Internet that record BGP traces, which permit to study eBGP behaviour, but there exist no public iBGP traces nor internal AS topology repositories, limiting the exploration of iBGP influence in the global scalability problem. In this paper we analyse route reflection benefits and caveats, and present RRLOC, an Open Source tool developed to facilitate research on this matter. The tool supports the location of Route Reflectors in a given AS topology using well-known algorithms, enabling the experimentation on iBGP dynamics, with the aid of network simulators. It also allows network planners to test different scenarios for RR placement.
Finance : a quantitative introduction

1. Introduction 2. Fundamental concepts and techniques 3. Modern portfolio theory 4. Market efficiency 5. Capital structure and dividends 6. Valuing levered projects 7. Option pricing in discrete time 8. Option pricing in continuous time 9. Real options analysis 10. Selected option applications 11. Hedging 12. Agency problems and governance Solutions to exercises Glossary Index.
Formal Specification of Extra-Functional Properties for Component Based System

Inclusion of Extra-Functional Properties (EFPs) can enrich the quality of component based system design and development. However, such inclusion is a complex engineering task. It is even more complex when reuse possibility of components are high. Formal representation and proper validation mechanism of EFPs can ease the task of inclusion of Extra-functional properties in component based model at design level, subsequent realization at the development level. This paper proposed Z-notation based formal specification of EFPs that facilitates declaration, assignment and evaluation of such EFPs in the context of component-based applications. The proposed approach supported with the validation mechanism of EFPs towards effective design of Component Based Software.
A tagger-aided language model with a stack decoder

This contribution of this paper is to investigate the util-ity of exploiting words and predicted detailed semantic tags in the long history to enhance a standard trigram language model. The paper builds on earlier work in the (cid:12)eld that also used words and tags in the long history, but o(cid:11)ers a cleaner, and ultimately much more accurate system by integrating the application of these new features directly into the decoding algorithm. The features used in our models are derived using a set of complex questions about the tags and words in the history, written by a linguist. Maximum entropy modelling techniques are then used to com-binethese features with a standard trigram language model. We evaluate the technique in terms of word error rate, on Wall Street Journal test data.
CogWatch: Cognitive Rehabilitation for Apraxia and Action Disorganization Syndrome Patients

Neurological patients with symptoms of Apraxia and Action Disorganisation Syndrome commit cognitive errors during everyday goal-oriented tasks which premorbidly they used to perform automatically. CogWatch is a new cognitive rehabilitation system under development which uses instrumented objects, ambient and wearable sensors to monitor patients behavior and intervene with visual, auditory and haptic feedback to facilitate completion of Activities of Daily Living. Here, we present the CogWatch architecture and its technologies.
Effects of robot assisted gait training in progressive supranuclear palsy (PSP): a preliminary report

Background and Purpose: Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease clinically characterized by prominent axial extrapyramidal motor symptoms with frequent falls. Over the last years the introduction of robotic technologies to recover lower limb function has been greatly employed in the rehabilitative practice. This observational trial is aimed at investigating the changes in the main spatiotemporal following end-effector robot training in people with PSP. Method: Pilot observational trial. Participants: Five cognitively intact participants with PSP and gait disorders. Interventions: Patients were submitted to a rehabilitative program of robot-assisted walking sessions for 45 min, 5 times a week for 4 weeks. Main outcome measures: The spatiotemporal parameters at the beginning (T0) and at the end of treatment (T1) were recorded by a gait analysis laboratory. Results: Robot training was feasible, acceptable and safe and all participants completed the prescribed training sessions. All patients showed an improvement in the gait spatiotemporal index (Mean velocity, Cadence, Step length, and Step width) (T0 vs. T1). Conclusions: Robot training is a feasible and safe form of rehabilitation for cognitively intact people with PSP. The lack of side effects and the positive results in the gait parameter index in all patients support the recommendation to extend the trials of this treatment. Further investigation regarding the effectiveness of robot training in time is necessary. Trial registration: ClinicalTrials.gov NCT01668407.
A Methodology for Empirical Analysis of LOD Datasets

CoCoE stands for Complexity, Coherence and Entropy, and presents an extensible methodology for empirical analysis of Linked Open Data (i.e., RDF graphs). CoCoE can offer answers to questions like: Is dataset A better than B for knowledge discovery since it is more complex and informative? , Is dataset X better than Y for simple value lookups due its flatter structure? , etc. In order to address such questions, we introduce a set of well-founded measures based on complementary notions from distributional semantics, network analysis and information theory. These measures are part of a specific implementation of the CoCoE methodology that is available for download. Last but not least, we illustrate CoCoE by its application to selected biomedical RDF datasets.
Towards data driven decision support for financial institutions: Predicting small companies business volume in Switzerland

In Switzerland small and medium-sized enterprises represent more than 99% of all businesses. Therefore, prediction of their micro- and macroeconomic business development is of importance. In this paper, we propose a novel approach for predicting business volume using company characteristics and characteristics of the county the company operates in. We investigate which data sources can be combined to achieve this goal for small and midsized enterprises in Switzerland, building a model, irrespective of industry. We build our model based on the dataset obtained from an insurance company and combined the dataset with census data. We present two quantitative models, which allow to predict business volume in Swiss franks (CHF) and classify customers by size. Our results show that operational data from financial institutions (FI) customer relationship management (CRM) systems linked with census data are valuable to predict customer business volume.
Linear and Nonlinear Approximation of Fuzzy Random Variables

Since it is difficult to find the optimal solution of the vague data based models (1) in traditional ways, we use linear and nonlinear (hybrid) approximation methods simultaneously. We used the technique of simulation (2) and genetic algorithm (3) based on random fuzzy simulation to help find the optimal solution with hybrid algorithm and linear approximation to evaluate these methods efficiency in hybrid environment. Results showed that the probability of crossover and probability of mutation higher, the linear approximation better (in case of nonlinear integer programming). In this paper, we proposed fuzzy linear method and tested it on a portfolio model. We provided a fuzzy nonlinear integer programming model for portfolio selection. A numerical example demonstrates the application of the proposed method.
Transient growth in a shearing stratified atmosphere

The stability of fully compressible isothermal shear flows is studied in response to recent claims that such flows could be locally unstable to a linear instability. The behavior of the solution is characterized by the time-dependent Rayleigh quotient, which lies between the smallest and the largest eigenvalue. The imaginary part of the Rayleigh quotient (corresponding to a growth rate) is, for different initial conditions, always found to approach zero provided the flow is Rayleigh stable (angular momentum increasing outwards). The real part (corresponding to frequency) is usually growing linearly in time, which reflects the progressive increase in wavenumber for horizontally propagating sound waves.
Performance evaluation of parallel applications on next generation memory architecture with power-aware paging method

With increasing demand for low power high performance computing, reducing power of not only CPUs but also memory is becoming important. In typical general-purpose HPC environments, DRAM is installed in an over-provisioned fashion to avoid swapping, although in most cases not all such memory is used, leading to unnecessary and excessive power consumption, even in a standby state. We propose a next generation low power memory system that reduces required DRAM capacity while minimizing application performance degradation. In this system, both DRAM and MRAM, fast non-volatile memory, are used as main memory, while flash memory is used as a swap device. Our profile-based paging algorithm optimizes memory accesses by using faster memory as much as possible, reducing accesses to slower memory. Simulated results of our architecture show that the overall energy consumption of the memory system can be reduced to 25% by in the best case by reducing DRAM capacity, with only 17% performance loss in application benchmarks.
Visualizing Software Structure Understandability

Software architecture design is known to be driven by the quality attributes we may want to satisfy. Among them, modifiability plays an important role since software maintenance takes the lion's share in the software development costs. However, to successfully maintain a legacy system, the latter must be sufficiently understood so that the maintenance team will not introduce new bugs when correcting others. Then we present a software metric that we called the Autonomy Ratio (AR). We show this dynamic metric to be a good indicator of the system's structure understandability. Since we end up with hundreds of values for a single system, we represent these values as a hierarchical map: the "Autonomy Ratio Map". The contribution of the paper is to link the AR metric with theories of software comprehension, to show how the AR Map helps in assessing software structure understand-debility, and to present an empirical validation of it.
On the Origins of the Weak Folding Cooperativity of a Designed ββα Ultrafast Protein FSD-1

FSD-1, a designed small ultrafast folder with a ββα fold, has been actively studied in the last few years as a model system for studying protein folding mechanisms and for testing of the accuracy of computational models. The suitability of this protein to describe the folding of naturally occurring α/β proteins has recently been challenged based on the observation that the melting transition is very broad, with ill-resolved baselines. Using molecular dynamics simulations with the AMBER protein force field (ff96) coupled with the implicit solvent model (IGB = 5), we shed new light into the nature of this transition and resolve the experimental controversies. We show that the melting transition corresponds to the melting of the protein as a whole, and not solely to the helix-coil transition. The breadth of the folding transition arises from the spread in the melting temperatures (from ∼325 K to ∼302 K) of the individual transitions: formation of the hydrophobic core, β-hairpin and tertiary fold, with the helix formed earlier. Our simulations initiated from an extended chain accurately predict the native structure, provide a reasonable estimate of the transition barrier height, and explicitly demonstrate the existence of multiple pathways and multiple transition states for folding. Our exhaustive sampling enables us to assess the quality of the Amber ff96/igb5 combination and reveals that while this force field can predict the correct native fold, it nonetheless overstabilizes the α-helix portion of the protein (Tm = ∼387K) as well as the denatured structures.
Classification of smartcard attacks

The classification of smartcard attacks is a part of the learning process of different ways are used by attackers to challenge the system security. It really helps to enhance the smartcard security policies. In this paper, we study some types of taxonomies but we focus our attention upon the classification tree method (CTM). For this case study, we apply CTM to classify smartcard attacks by using the CTE tool.
Computer Aided Quantification of Pathological Features for Flexor Tendon Pulleys on Microscopic Images

Quantifying the pathological features of flexor tendon pulleys is essential for grading the trigger finger since it provides clinicians with objective evidence derived from microscopic images. Although manual grading is time consuming and dependent on the observer experience, there is a lack of image processing methods for automatically extracting pulley pathological features. In this paper, we design and develop a color-based image segmentation system to extract the color and shape features from pulley microscopic images. Two parameters which are the size ratio of abnormal tissue regions and the number ratio of abnormal nuclei are estimated as the pathological progression indices. The automatic quantification results show clear discrimination among different levels of diseased pulley specimens which are prone to misjudgments for human visual inspection. The proposed system provides a reliable and automatic way to obtain pathological parameters instead of manual evaluation which is with intra- and interoperator variability. Experiments with 290 microscopic images from 29 pulley specimens show good correspondence with pathologist expectations. Hence, the proposed system has great potential for assisting clinical experts in routine histopathological examinations.