Miracle-dz/UniFAR-FTUs huggingface dataset:
The Ideal Collaborative Environment

Network (ICON) Program Office was established in December 1998 to facilitate collaboration and the free flow of information across the intelligence community (IC). As its main starting point, the ICON Program Office initiated an IC collaboration forum in March 1999 to identify collaboration programs and systems within the IC and Department of Defense (DoD) and to achieve a consensus on common goals. During its first year, more than 150 IC members attended the forum. Its membership included representatives from the majority of intelligence agencies as well as the DoD. This paper highlights key findings of forum sessions from its inception to the present, defines common terms, and describes today's electronic collaboration capabilities, and the ideal collaboration environment for the future. At the forum outset, participants discovered a need for a common framework of collaboration terms. Basic terms such as "collaboration" and "interoperability" held different meanings for participants depending upon their organizational background. This section defines key terms. Collaboration is "the process of shared creation: two or more individuals with complementary skills interacting to create a shared understanding that none had previously possessed or could have come to on their own [1]. " The following list defines levels of collaboration: · Level 1: At a basic level, individuals operate independently and interact to accommodate their own specific needs passing documents back and forth and sharing information, but not as part of a working group or team [2]. · Level 2: At the next level, a group of individuals exchange information as part of a community of interest, but not to achieve a common goal [2]. · Level 3: At the highest level, collaborators operate as a team to achieve a common purpose by working together and gain ing new insights [2]. Asynchronous collaboration allows data (e.g., a message) to be sent as soon as it is ready, regardless of whether recipients are ready to receive it. Asynchronous collaboration includes e-mail with attachments, threaded discussion databases, bulletin boards, and persistent electronic "rooms" where members can store and access common documents and files at their convenience. Table 1 lists asynchronous collaboration capabilities. Asynchronous collaboration capabilities emphasize support for collaboration levels one and two. Synchronous collaboration manages (synchronizes) the sending and receiving of dynamic text, audio, and video, such that only a single user can dominate a discussion at a time. However, a group of users can work together to share ideas in real-time. …
Does Facial Amimia Impact the Recognition of Facial Emotions? An EMG Study in Parkinson’s Disease

According to embodied simulation theory, understanding other people’s emotions is fostered by facial mimicry. However, studies assessing the effect of facial mimicry on the recognition of emotion are still controversial. In Parkinson’s disease (PD), one of the most distinctive clinical features is facial amimia, a reduction in facial expressiveness, but patients also show emotional disturbances. The present study used the pathological model of PD to examine the role of facial mimicry on emotion recognition by investigating EMG responses in PD patients during a facial emotion recognition task (anger, joy, neutral). Our results evidenced a significant decrease in facial mimicry for joy in PD, essentially linked to the absence of reaction of the zygomaticus major and the orbicularis oculi muscles in response to happy avatars, whereas facial mimicry for expressions of anger was relatively preserved. We also confirmed that PD patients were less accurate in recognizing positive and neutral facial expressions and highlighted a beneficial effect of facial mimicry on the recognition of emotion. We thus provide additional arguments for embodied simulation theory suggesting that facial mimicry is a potential lever for therapeutic actions in PD even if it seems not to be necessarily required in recognizing emotion as such.
The Aspergillus fumigatus cell wall integrity signaling pathway: drug target, compensatory pathways, and virulence

Aspergillus fumigatus is the most important airborne fungal pathogen, causing severe infections with invasive growth in immunocompromised patients. The fungal cell wall (CW) prevents the cell from lysing and protects the fungus against environmental stress conditions. Because it is absent in humans and because of its essentiality, the fungal CW is a promising target for antifungal drugs. Nowadays, compounds acting on the CW, i.e., echinocandin derivatives, are used to treat A. fumigatus infections. However, studies demonstrating the clinical effectiveness of echinocandins in comparison with antifungals currently recommended for first-line treatment of invasive aspergillosis are still lacking. Therefore, it is important to elucidate CW biosynthesis pathways and their signal transduction cascades, which potentially compensate the inhibition caused by CW- perturbing compounds. Like in other fungi, the central core of the cell wall integrity (CWI) signaling pathway in A. fumigatus is composed of three mitogen activated protein kinases. Deletion of these genes resulted in severely enhanced sensitivity of the mutants against CW-disturbing compounds and in drastic alterations of the fungal morphology. Additionally, several cross-talk interactions between the CWI pathways and other signaling pathways are emerging, raising the question about their role in the CW compensatory mechanisms. In this review we focused on recent advances in understanding the CWI signaling pathway in A. fumigatus and its role during drug stress response and virulence.
Pricing the Option to Surrender in Incomplete Markets

New international accounting standards require insurers to reflect the value of embedded options and guarantees in their products. Pricing techniques based on the Black and Scholes paradigm are often used; however, the hypotheses underneath this model are rarely met. We propose a framework that encompasses the most known sources of incompleteness. We show that the surrender option, joined with a wide range of claims embedded in insurance contracts, can be priced through our tool, and deliver hedging portfolios to mitigate the risk arising from their positions. We provide extensive empirical analysis to highlight the effect of incompleteness on the fair value of the option.
A Nested-PCR-Based Schizodeme Method for Identifying Leishmania Kinetoplast Minicircle Classes Directly from Clinical Samples and Its Application to the Study of the Epidemiology of Leishmania tropica in Pakistan

ABSTRACT A nested PCR was developed to amplify the variable region of the kinetoplast minicircles of all Leishmania species which infect mammals. Each Leishmania parasite contains approximately 10,000 kinetoplast DNA minicircles, which are unequally distributed among approximately 10 minicircle classes. The PCR primers were designed to bind within the 120-bp conserved region which is common to all minicircle classes; the remaining approximately 600 bp of each minicircle is highly conserved within each minicircle class but highly divergent between classes. The nested PCR generated a strong signal from a minimum of 0.1 fg of Leishmania DNA. Restriction digests of the amplicons from the highest dilutions suggested that minicircles from only a limited number of minicircle classes had acted as template in the reaction. One PCR product was directly sequenced and found to be derived from only one minicircle class. Since the primers amplify all minicircle classes, this indicated that as little as 1/10 of one Leishmania parasite was present in the PCR template. This demonstrated that the nested PCR achieved very nearly the maximum theoretically possible sensitivity and is therefore a potentially useful method for diagnosis. The nested PCR was tested for sensitivity on 20 samples from patients from the Timargara refugee camp, Pakistan. Samples were collected by scraping out a small amount of tissue with a scalpel from an incision at the edge of the lesion; the tissue was smeared on one microscope slide and placed in a tube of 4 M guanidine thiocyanate, in which the sample was stable for at least 1 month. DNA for PCR was prepared by being bound to silica in the presence of 6 M guanidine thiocyanate; washed in guanidine thiocyanate, ethanol, and acetone; and eluted with 10 mM Tris-HCl. PCR products of the size expected for Leishmania tropica were obtained from 15 of the 20 samples in at least one of three replicate reactions. The negative samples were from lesions that had been treated with glucantime or were over 6 months old, in which parasites are frequently scanty. This test is now in routine use for the detection and identification of Leishmaniaparasites in our clinical laboratory. Fingerprints produced by restriction digests of the PCR products were defined as complex or simple. There were no reproducible differences between the complex restriction patterns of the kinetoplast DNA of any of the parasites from Timargara camp with HaeIII and HpaII. The simple fingerprints were very variable and were interpreted as being the product of PCR on a limited subset of minicircle classes, and consequently, it was thought that the variation was determined by the particular minicircle classes that had been represented in the template. The homogeneity of the complex fingerprints suggests that the present epidemic of cutaneous leishmaniasis in Timargara camp may be due to the spread of a single clone of L. tropica.
A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions.
Use of relatives of cases as controls to identify risk factors when an interaction between environmental and genetic factors exists.

BACKGROUND The difficulty in detecting relevant risk factors for chronic diseases (such as breast and colon cancer) may be due to heterogeneity in the populations of studied cases, and one source of heterogeneity may be differential genetic susceptibility predisposing to differential environmental sensitivity. METHODS The purpose of this investigation is to determine whether the estimates of odds ratios are modified by using relatives as controls when risk factors interact with an underlying genetic factor. RESULTS We demonstrate that using relative controls in matched case-control studies produces odds ratios different from population-based odds ratios. This difference is dependent on the amount of interaction between the genetic and environmental factors and on the genetic correlation between relatives. CONCLUSIONS In the case of a common disease, the use of relatives as controls could be helpful in detecting interaction between an exposure and an underlying genetic factor when the genetic factor is common.
A Cognitive Analysis of the Preposition OVER: Image-schema transformations and metaphorical extensions

Abstract Dewell (1994), following Brugman (1981) and Lakoff (1987), provides a semantic analysis of over by relying more exclusively on image-schema transformations than did Brugman and Lakoff. The Brugman-Lakoff-Dewell analysis, however, can be improved by using simpler image-schemas, more natural image-schema transformations, and metaphorical extensions. A key idea adopted in the present article is to capture both trajectors and landmarks three-dimensionally and topologically. This modification brings about the elimination of unessential features such as the shape and size of the trajector and the landmark, contact/non-contact between the trajector and the landmark, and physical properties of the trajector. Its main advantage is that a central image-schema for a semicircular path provides the basis for explaining all of the senses of over using natural image-schema transformations and metaphorical extensions. The proposed image-schema transformations include: segment profiling, profiling the endpoint of access paths, the profiled peak position of the semicircular path with the constraint that the rest of the semicircular path is excluded, and the extension of the semicircular path-trajectory to an image of covering. The proposed metaphorical senses are time, means, and control. In addition, the radial category relating each sense of over is presented. Résumé Dewell (1994), après Brugman (1981) et Lakoff (1987), a fourni une analyse sémantique de over ‘dessus’ en s'appuyant plus exclusivement que Brugman et Lakoff sur les transformations de schémas d'image. L'analyse de Brugman-Lakoff-Dewell peut cependant être améliorée en utilisant des schémas d'image plus simples, des transformations de schémas d'image plus naturelles et des extensions métaphoriques. Une idée clé adoptée dans le présent article concerne les trajectoires et les points de repère capturés en trois dimensions et topologiquement. Cette modification permet d’éliminer des caractéristiques non essentielles telles que la forme et la taille du trajet et du point de repère, le contact ou non-contact entre le trajet et le point de repère, et les propriétés physiques du trajecteur. Son principal avantage est que le schéma d'image central d'une trajectoire semi-circulaire constitue la base pour expliquer tous les sens de over, en utilisant des transformations de schémas d'image naturelles et des extensions métaphoriques. Les transformations de schéma d'image proposées comprennent: le profilage de segment, le profil du point d'extrémité des chemins d'accès, la position maximale profilée du chemin semi-circulaire avec la contrainte d'exclusion du reste du chemin semi-circulaire, et l'extension de la trajectoire semi-circulaire à une image de couverture. Les sens métaphoriques proposés sont: temps, moyens, et contrôle. En outre, la catégorie radiale relative à chaque sens de over est présentée.
Unknown heterogeneity , the EC-EM algorithm

We study a panel structure with n subjects/entities being observed over T periods. We consider a class of models for each subject's data generating process, and allow for unknown heterogeneity. In other words, we do not know how many types we have, what the types are, and which subjects belong to each type. We propose a large T approximation to the posterior mode on the unknowns through the Estimation/Classication (EC) algorithm of El-Gamal and Grether (1995) which is linear in n, T, and the unknown number of types. If our class of models (likelihood functions) allows for a consistent asymptotically normal estimator under the assumption of homogeneity (number of types = 1), then the estimators obtained by our EC algorithm inherit those asymptotic properties as T " 1 and then as n " 1 (with a block-diagonal covariance matrix facilitating hypothesis-testing). We then propose a large T approximation to the EM algorithm to obtain posteriors on the subject classiications and diagnostics for the goodness of the large T approximation in the EC stage. If the large T approximation does not seem to be appropriate, then we suggest the use of the more computationally costly EM algorithm, or the-even more costly-full Bayesian updating. We illustrate the procedure with two applications to experimental data on probability assessments within a class of Probit and a class of Tobit models.
Research of RSSI indoor ranging algorithm based on Gaussian - Kalman linear filtering

Considering the complex propagation of RSSI signal along with perturbations, the accuracy of RSSI based indoor location cannot be guaranteed and it cannot achieve accurate positioning. To address this issue, this study proposes a filtering algorithm to process the RSSI signal. In particular, the CC2540 chip of TI company is used as the transmitter station. Then, a platform is constructed using the BLE low-power dissipation based Bluetooth technology. Subsequently, the RSSI signal can be collected under different environment and different locations from the station. Thereby, the formula of distance measuring can be obtained based on the parameters determined using the linear regression. Consequently, the RSSI signal is processed using the Gauss-Kalman filtering algorithm. Simulation results show that the performance of the proposed filtering algorithm is improved with respect to the perturbation rejection and accuracy as well as stability.
Robust beamforming for SWIPT systems with non-linear energy harvesting model

This paper investigates resource allocation for simultaneous wireless information and power transfer (SWIPT) downlink systems based on a non-linear energy harvesting model. The resource allocation algorithm design is formulated as a non-convex optimization problem for the maximization of the total harvested power. The proposed problem formulation not only takes into account imperfect channel state information (CSI) but also guarantees the quality-of-service (QoS) of information transfer. A novel iterative algorithm is proposed to obtain the globally optimal solution of the considered non-convex optimization problem. In each iteration, a rank-constrained semidefinite program (SDP) is solved optimally by SDP relaxation. Simulation results demonstrate the significant gains in harvested power and the robustness against CSI imperfection for the proposed optimal resource allocation, compared to a baseline scheme designed for perfect CSI and the conventional linear energy harvesting model.
Chapter 1 Interchanging geometry conventions in 3 DEM : mathematical context for the development of standards

The specification of the information on the three-dimensional orientation of an image with respect to a given coordinate system is at the heart of our ability to reconstruct a three-dimensional object from sets of its two-dimensional projection images. Transferring this information from one package to another is important to structural biologists wanting to get the best from each software suite. In this chapter, we review in depth the main considerations and implications associated with the unambiguous specification of geometrical specifications, in this way paving the way to the future specifications of standards in the field of three-dimensional electron microscopy. This is the case of EMX in which affine transformations have been adopted as the means to communicate geometrical information.
Commercialisation of modelling predictions at Swinburne Sports Statistics

Universities are under increasing pressure to increase funding from non government sources. Swinburne University is no exception, and has as one of its strategic themes to be an entrepreneurial university and to encourage innovation and commercialisation of research results. Swinburne Sports Statistics is part of the School of Mathematical Sciences, and one of its areas of research is in the prediction of sporting events. This paper details some efforts at commercialisation of this activity
An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential

Two-dimensional cursor control is an important and challenging issue in EEG-based brain-computer interfaces (BCIs). To address this issue, here we propose a new approach by combining two brain signals including Mu/Beta rhythm during motor imagery and P300 potential. In particular, a motor imagery detection mechanism and a P300 potential detection mechanism are devised and integrated such that the user is able to use the two signals to control, respectively, simultaneously, and independently, the horizontal and the vertical movements of the cursor in a specially designed graphic user interface. A real-time BCI system based on this approach is implemented and evaluated through an online experiment involving six subjects performing 2-D control tasks. The results attest to the efficacy of obtaining two independent control signals by the proposed approach. Furthermore, the results show that the system has merit compared with prior systems: it allows cursor movement between arbitrary positions.
DISCRETE MECHANICS AND OPTIMAL CONTROL: AN ANALYSIS ∗

The optimal control of a mechanical system is of crucial importance in many application areas. Typical examples are the determination of a time-minimal path in vehicle dynamics, a minimal energy trajectory in space mission design, or optimal motion sequences in robotics and biomechanics. In most cases, some sort of discretization of the original, infinite-dimensional optimization problem has to be performed in order to make the problem amenable to computations. The approach proposed in this paper is to directly discretize the variational description of the system's motion. The resulting optimization algorithm lets the discrete solution directly inherit characteristic structural properties from the continuous one like symmetries and integrals of the motion. We show that the DMOC (Discrete Mechanics and Optimal Control) approach is equivalent to a finite difference discretization of Hamilton's equations by a symplectic partitioned Runge-Kutta scheme and employ this fact in order to give a proof of convergence. The numerical performance of DMOC and its relationship to other existing optimal control methods are investigated.
rsonal Learning Apprentice

Personalized knowledge-based systems have not yet become widespread, despite their potential for valuable assistance in many daily tasks. This is due, in part, to the high cost of developing and maintaining customized knowledge bases. The construction of personal assistants as learning apprentices -interactive assistants that learn continually from their users -is one approach which could dramatically reduce the cost of knowledge-based advisors. We present one such personal learning apprentice, called CAP, which assists in managing a meeting calendar. CAP has been used since June 1991 by a secretary in our work place to manage a faculty member’s meeting calendar, and is the first instance of a fielded learning apprentice in routine use. This paper describes the organization of CAP, its performance in initial field tests, and more general lessons learned from this effort about learning apprentice systems. knowledge constraints Abstract