Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Option Pricing Theory to Infer About Historical Equity Premiums

In this paper we make use of option pricing theory to infer about historical equity premiums. This we do by comparing the prices of an American perpetual put option computed using two different models: One is the standard model with continuous, zero expectation, Gaussian noise, the other is a very similar model, except that the zero expectation noise is of Poissonian type. Since a Poisson random variable is infinitely divisible, by the central limit theorem it is approximately normal. The interesting fact that makes this comparison worthwhile, is that the probability distribution under the risk adjusted measure turns out to depend on the equity premium in the Poisson model, while this is not so for the standard, Brownian motion version. This difference is utilized to find the intertemporal, equilibrium equity premium. We apply this technique to the US equity data of the last century, and find an indication that the risk premium on equity was about two and a half per cent if the risk free short rate was around one per cent. On the other hand, if the latter rate was about four per cent, we similarly find that this corresponds to an equity premium of around four and a half per cent. The advantage with our approach is that we only need equity data and option pricing theory, no consumption data was necessary to arrive at these conclusions. We round off the paper by investigating if the procedure also works for incomplete models.
Using Localization for Fault-tolerant Radio Coverage in Wireless Mesh Networks

Wireless Mesh Networks offer flexibility for industrial automation. However, in environments with changing propagation conditions it is challenging to guarantee the radio coverage. This paper contributes a localization-based method to achieve fault-tolerant radio coverage of wireless mesh networks in dynamic propagation environments. The idea is to use radio signal strength measurements from the mobile stations, find their positions with localization and use this information to automatically calibrate a radio propagation model to the real environment. This model is then used to automatically detect radio coverage errors at run-time and to repair them before the radio coverage fails. An evaluation in a real industrial scenario shows the practicability of our approach.
Load balancing strategies for distributed memory machines

Load balancing in large parallel systems with distributed memory is a diicult task often innuencing the overall eeciency of applications substantially. A number of eecient distributed load balancing strategies have been developed in the recent years. Although they are currently not generally available as part of parallel operating systems, it is often not diicult to integrate them into applications. This paper gives a classiication of diierent load balancing problems based on application characteristics. For the case of applications out of the eld of scientiic computing, useful methods are described in more detail.
Verso folio: Diversified Ranking for Large Graphs with Context-Aware Considerations

This work is pertaining to the diversified ranking of web-resources and interconnected documents that rely on a network-like structure, e.g. web-pages. A practical example of this would be a query for the k most relevant web-pages that are also in the same time as dissimilar with each other as possible. Relevance and dissimilarity are quantified using an aggregation of network distance and context similarity. For example, for a specific configuration of the problem, we might be interested in web-pages that are similar with the query in terms of their textual description but distant from each other in terms of the web-graph, e.g. many clicks away. In retrospect, a dearth of work can be found in the literature addressing this problem taking the network structure formed by the document links into consideration. In this work, we propose a hill-climbing approach that is seeded with a document collection which is generated using greedy heuristics to diversify initially. More importantly, we tackle the problem in the context of web-pages where there is an underlying network structure connecting the available documents and resources. This is a significant difference to the majority of works that tackle the problem in terms of either content definitions, or the graph structure of the data, but never addressing both aspects simultaneously. To the best of our knowledge, this is the very first effort that can be found to combine both aspects of this important problem in an elegant fashion by also allowing a great degree of flexibility on how to configure the trade-offs of (i) document relevance over result-items' dissimilarity, and (ii) network distance over content relevance or dissimilarity. Last but not least, we present an extensive evaluation of our methods that demonstrate the effectiveness and efficiency thereof.
Towards the Automated Generation of Hard Disk Models through Physical Geometry Discovery

As the High Performance Computing industry moves towards the exascale era of computing, parallel scientific and engineering applications are becoming increasingly complex. The use of simulation allows us to predict how an application's performance will change with the adoption of new hardware or software, helping to inform procurement decisions. In this paper, we present a disk simulator designed to predict the performance of read and write operations to a single hard disk drive (HDD). Our simulator uses a geometry discovery benchmark (Diskovery) in order to estimate the data layout of the HDD, as well as the time spent moving the read/write head. We validate our simulator against two different HDDs, using a benchmark designed to simulate common disk read and write patterns, demonstrating accuracy to within 5% of the observed I/O time for sequential operations, and to within 10% of the observed time for seek-heavy workloads.
Demonstration abstract: A MEMS-based airflow sensor network

The accurate measurement of airflow patterns at high spatial and temporal resolutions is of great importance for building design and operation. Microelectromechanical Systems (MEMS) technology can be used to develop miniaturized airflow sensors capable of detecting both airflow speed and direction at high temporal resolution. Wireless sensor networks (WSNs) are used extensively for large-scale monitoring at high spatial resolution. Thus, the integration of MEMS and WSN technologies enables the measurement of airflow patterns at high spatial and temporal resolution. In this work, we developed a prototype MEMS-based airflow sensor network and deployed it in a building environment.
Modeling impacts of process architecture on cost and schedule risk in product development

To gain competitive leverage, firms that design and develop complex products seek to increase the efficiency and predictability of their development processes. Process improvement is facilitated by the development and use of models that account for and illuminate important characteristics of the process. Iteration is a fundamental but often unaddressed feature of product development (PD) processes. Its impact is mediated by the architecture of a process, i.e., its constituent activities and their interactions. This paper integrates several important characteristics of PD processes into a single model, highlighting the effects of varying process architecture. The PD process is modeled as a network of activities that exchange deliverables. Each activity has an uncertain duration and cost, an improvement curve, and risks of rework based on changes in its inputs. A work policy governs the timing of activity execution and deliverable exchange (and thus the amount of activity concurrency). The model is analyzed via simulation, which outputs sample cost and schedule outcome distributions. Varying the process architecture input varies the output distributions. Each distribution is used with a target and an impact function to determine a risk factor. Alternative process architectures are compared, revealing opportunities to trade cost and schedule risk. Example results and applications are shown for an industrial process, the preliminary design of an uninhabited combat aerial vehicle. The model yields and reinforces several managerial insights, including: how rework cascades through a PD process, trading off cost and schedule risk, interface criticality, and occasions for iterative overlapping.
Modeling the language assessment process and result: Proposed architecture for automatic oral proficiency assessment

We outline challenges for modeling human language assessment in automatic systems, both in terms of the process and the reliability of the result. We propose an architecture for a system to evaluate learners of Spanish via the Computerized Oral Proficiency Instrument, to determine whether they have 'reached' or 'not reached' the Intermediate Low level of proficiency, according to the American Council on the Teaching of Foreign Languages (ACTFL) Speaking Proficiency Guidelines. Our system divides the acoustic and non-acoustic features, incorporating human process modeling where permitted by the technology and required by the domain. We suggest machine learning techniques applied to this type of system permit insight into yet unarticulated aspects of the human rating process.
Sequential Stochastic Dominance and the Robustness of Poverty Orderings

When comparing poverty across distributions, an analyst must select a poverty line to identify the poor, an equivalence scale to compare individuals from households of different compositions and sizes, and a poverty index to aggregate individual deprivation into an index of total poverty. A different choice of poverty line, poverty index or equivalent scale can of course reverse an initial poverty ordering. This paper develops sequential stochastic dominance conditions that throw light on the robustness of poverty comparisons to these important measurement issues.
Face recognition using training data with artificial occlusions

In face recognition for criminal identification, the training data are always clean while the probe data are occluded by sunglasses, scarf or other facial accessories. Occlusions in the probe data severely degrade the recognition performance. We find that introducing artificial occlusions into the training data is helpful in this situation. The incremental training data is decomposed into a class-specific dictionary, a non-class-specific dictionary and a sparse noise or corruption by the sparse and dense hybrid representation framework (SDR). The artificially introduced occlusions play an important role in building the discriminative faces for classification during SDR. Experimental results demonstrate that the proposed method can provide higher recognition accuracy under benchmark face database.
ℒ︁2‐Gain analysis and control of uncertain nonlinear systems with bounded disturbance inputs

This paper proposes a convex approach to regional stability and ℒ︁2‐gain analysis and control synthesis for a class of nonlinear systems subject to bounded disturbance signals, where the system matrices are allowed to be rational functions of the state and uncertain parameters. To derive sufficient conditions for analysing input‐to‐output properties, we consider polynomial Lyapunov functions of the state and uncertain parameters (assumed to be bounded) and a differential‐algebraic representation of the nonlinear system. The analysis conditions are written in terms of linear matrix inequalities determining a bound on the ℒ︁2‐gain of the input‐to‐output operator for a class of (bounded) admissible disturbance signals. Through a suitable parametrization involving the Lyapunov and control matrices, we also propose a linear (full‐order) output feedback controller with a guaranteed bound on the ℒ︁2‐gain. Numerical examples are used to illustrate the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.
Verifying system behaviors in EAST-ADL2 with the SPIN model checker

EAST-ADL2 is a domain-specific architecture description language to support the model-based development of automotive embedded systems. It emerged to manage the complexity of software and electronics in advanced automotive applications. The language focuses on the structural definition for functional specifications. Behavior is defined only on the component level, in terms of functional blocks and their triggers and interfaces. The behavioral definition inside each functional block is not prescribed. This paper shows one approach to augment the language with precise syntax and semantics for behavior, and develops a procedure that transforms the composed behavioral model to the SPIN model for logic model checking. The contribution improves the modeling and verification capability of EAST-ADL2.
Self-Delimiting Neural Networks

Self-delimiting (SLIM) programs are a central concept of theoretical computer science, particularly algorithmic information & probability theory, and asymptotically optimal program search (AOPS). To apply AOPS to (possibly recurrent) neural networks (NNs), I introduce SLIM NNs. Neurons of a typical SLIM NN have threshold activation functions. During a computational episode, activations are spreading from input neurons through the SLIM NN until the computation activates a special halt neuron. Weights of the NN's used connections define its program. Halting programs form a prefix code. The reset of the initial NN state does not cost more than the latest program execution. Since prefixes of SLIM programs influence their suffixes (weight changes occurring early in an episode influence which weights are considered later), SLIM NN learning algorithms (LAs) should execute weight changes online during activation spreading. This can be achieved by applying AOPS to growing SLIM NNs. To efficiently teach a SLIM NN to solve many tasks, such as correctly classifying many different patterns, or solving many different robot control tasks, each connection keeps a list of tasks it is used for. The lists may be efficiently updated during training. To evaluate the overall effect of currently tested weight changes, a SLIM NN LA needs to re-test performance only on the efficiently computable union of tasks potentially affected by the current weight changes. Future SLIM NNs will be implemented on 3-dimensional brain-like multi-processor hardware. Their LAs will minimize task-specific total wire length of used connections, to encourage efficient solutions of subtasks by subsets of neurons that are physically close. The novel class of SLIM NN LAs is currently being probed in ongoing experiments to be reported in separate papers.
Development and reliability of a correction factor for family-reported medication adherence: pediatric inflammatory bowel disease as an exemplar.

OBJECTIVES To examine the issue of accurate adherence assessment and illustrate methodologies for correcting parent-reported medication adherence. METHODS 40 children with inflammatory bowel disease provided medication adherence data using electronic monitoring. Parents provided subjective reports of medication adherence. Receiver operating characteristic analyses were used to examine the detection of non-adherence at several adherence cut-points. 2 methods for empirically deriving a correction factor for subjectively reported adherence were applied. RESULTS Although parent-report and EM adherence were significantly correlated, parent-reported adherence was significantly higher than EM adherence. A 90% cut-point provided the highest sensitivity and specificity. Both correction factors reliably adjusted parent-reported adherence based on EM adherence. CONCLUSIONS Application of an empirically derived correction factor for parent-reported adherence using methodologies, such as those illustrated in the current study, could yield more accurate adherence assessment. Obtaining more accurate adherence assessments based on parent-report will have implications for self-management interventions, clinician prescribing behavior, and medication safety.
Rental Expectations and the Term Structure of Lease Rates

We consider the term structure of lease rates in a general setting where both the interest rate and the short rent are stochastic. Our framework is applicable to any leasing market, but we focus on real estate. We find that the “expectations hypothesis” of lease rates, i.e. that the forward rent is an unbiased estimator of the future short rent, requires similar assumptions as in interest rate theory to hold. To study the magnitude of the bias we parameterize our general framework. The simulations show that different realistic parameter values for risk aversion and interest rate stochastics can generate widely different shapes of the rental term structure, holding the objective rental expectations constant. As a result, an expected increase in rent may very well be consistent with a downward-sloping term structure and vice versa.
FORM: a frame-oriented representation method for digital telecommunication system design

This paper proposes a new design method called FORM (Frame-Oriented Representation Method) for digital telecommunication systems with the aim of efficient system design and development. FORM has the unique feature wherein timing design and function design are performed independently. FORM can translate system-level specifications at the behavioral level into RTL. This method is applied to the SDH/ATM interface and it is proved that FORM relaxes design complications and simplifies system design.
APoD eXplorer: Recommendation System and Interactive Exploration of a Dynamic Image Collection

The amount of captured images has increased exponentially these last years. Online available image collections are becoming common thanks to social networks or institutes digitization programs. The context of our work falls into the need to explore such image collections. The literature paradigms are leveraged to meet three constraints: (i) handling medium to large image collections, (ii) handling dynamic image collections and (iii) providing interactive visualisations. In this paper, we describe how our work has been used to build a recommendation system and an interactive exploration platform for dynamic image collection. To illustrate the relevance of such tools, we present APoD eXplorer, an online available platform that enhances the exploration of the NASA Astronomy Picture of the Day image collection. The platform is available at http://frederic.rayar. free.fr/apod/.
Two appendages homologous between basal bodies and centrioles are formed using distinct Odf2 domains

Ciliogenesis is regulated by context-dependent cellular cues, including some transduced through appendage-like structures on ciliary basal bodies called transition fibers and basal feet. However, the molecular basis for this regulation is not fully understood. The Odf2 gene product, ODF2/cenexin, is essential for both ciliogenesis and the formation of the distal and subdistal appendages on centrioles, which become basal bodies. We examined the effects of Odf2 deletion constructs on ciliogenesis in Odf2-knockout F9 cells. Electron microscopy revealed that ciliogenesis and transition fiber formation required the ODF2/cenexin fragment containing amino acids (aa) 188-806, whereas basal foot formation required aa 1-59 and 188-806. These sequences also formed distal and subdistal appendages, respectively, indicating that the centriole appendages are molecularly analogous to those on basal bodies. We used the differential formation of appendages by Odf2 deletion constructs to study the incorporation and function of molecules associated with each appendage type. We found that transition fibers and distal appendages were required for ciliogenesis and subdistal appendages stabilized the centrosomal microtubules.
An algorithm with high decoding success probability based on LT codes

The declining channel quality by chance would bring about the rise of error rate in wireless transmission. Moreover, automatic repeat request mechanisms would give rise to the tremendous increasement of transmission delay. Therefore, research on the scheme of low transmission delay is of great importance. In this paper, we propose the novel decoding algorithm to obtain a tradeoff between Belief Propagation and Gaussian Elimination decoding. Results show that the proposed decoding algorithm has higher DSP (decoding success probability) and lower decoding time.
Relationship between Research Outcomes and Risk of Bias, Study Sponsorship, and Author Financial Conflicts of Interest in Reviews of the Effects of Artificially Sweetened Beverages on Weight Outcomes: A Systematic Review of Reviews

Background Artificially sweetened beverage consumption has steadily increased in the last 40 years. Several reviews examining the effects of artificially sweetened beverages on weight outcomes have discrepancies in their results and conclusions. Objectives To determine whether risk of bias, results, and conclusions of reviews of effects of artificially sweetened beverage consumption on weight outcomes differ depending on review sponsorship and authors’ financial conflicts of interest. Methods We performed a systematic review of reviews of the effects of artificially sweetened beverages on weight. Two assessors independently screened articles for inclusion, extracted data, and assessed risks of bias. We compared risk of bias, results and conclusions of reviews by different industry sponsors, authors’ financial conflict of interest and journal sponsor. We also report the concordance between review results and conclusions. Results Artificial sweetener industry sponsored reviews were more likely to have favorable results (3/4) than non-industry sponsored reviews (1/23), RR: 17.25 (95% CI: 2.34 to 127.29), as well as favorable conclusions (4/4 vs. 15/23), RR: 1.52 (95% CI: 1.14 to 2.06). All reviews funded by competitor industries reported unfavorable conclusions (4/4). In 42% of the reviews (13/31), authors’ financial conflicts of interest were not disclosed. Reviews performed by authors that had a financial conflict of interest with the food industry (disclosed in the article or not) were more likely to have favorable conclusions (18/22) than reviews performed by authors without conflicts of interest (4/9), RR: 7.36 (95% CI: 1.15 to 47.22). Risk of bias was similar and high in most of the reviews. Conclusions Review sponsorship and authors’ financial conflicts of interest introduced bias affecting the outcomes of reviews of artificially sweetened beverage effects on weight that could not be explained by other sources of bias.
A self-paced online BCI for mobile robot control

This paper presents the design and online experiments of a self-paced online brain-computer interface (BCI) for controlling a simulated robot in an indoor environment. Three one-vs-rest linear discriminant analysis (LDA) classifiers are combined to control the switching between automatic control (AC) and subject control (SC) modes. The hierarchical structure of the controller allows the most reliable class (mental task) in a specific subject to play a dominant role in the robot control. A group of simple rules triggered by local sensor signals are designed for safety and obstacle avoidance in the AC mode. Due to the intuitive nature of the controller and the small number of AC rules, a subject has much flexibility and full control of the robot. Online experiments have shown that subjects successfully control the robot to circumnavigate obstacles and reach some specified targets in separate rooms by motor imagery of their hands and feet.