Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the transmissibility of Trypanosoma congolense strains, isolated in a trypanosomiasis endemic area of eastern Zambia, by Glossina morsitans morsitans

Transmission experiments were conducted to compare the transmissibility of genetically different Trypanosoma congolense (Savannah subgroup) strains isolated from cattle in a trypanosomiasis endemic area of eastern Zambia. A total of 17 strains were compared. Three strains were extremely virulent with a short pre-patent period, high parasitaemia and a short median survival time (between 5 and 9 days) in mice. The remainder of the strains belonged to the moderate (6 strains) or low (8 strains) virulence categories with median survival times between 10 and 30 days and >30 days, respectively. Batches of 40 teneral Glossina morsitans morsitans (Diptera: Glossinidae) were offered a single bloodmeal on mice infected with one of those strains. Flies were dissected to determine their infection status 21 days later. The proportion of flies with procyclic and metacyclic infections differed significantly between trypanosome strains and were significantly higher in flies infected with extremely virulent strains (P=0·033 and P=0·016 for the differences in the procyclic infection rate of strains with moderate and low virulence, respectively and P=0·005 and P=0·019 for the differences in the metacyclic infection rate of strains with moderate and low virulence, respectively). On the other hand, moderately virulent strains had, in general, higher procyclic and metacyclic infection rates compared to low virulent strains. But the differences were not significant (P>0·05). The outcome of those experiments shows clear differences in transmissibility of trypanosome strains associated with their virulence. This observation confirms the theory for the evolution and maintenance of virulence in a parasite population and may explain the persistence of virulent trypanosome strains in a susceptible host population.
Short answer question examination using an automatic off-line handwriting recognition system and a novel combined feature

Off-line automatic assessment systems can be an aid for teachers in the marking process. There has been no recent work in the development of off-line automatic assessment systems using handwriting recognition, even though such systems will clearly benefit the education sector. The reason is many schools and universities in many parts of the world still use paper-based examination. This research proposes the use of a newly developed feature extraction technique called the Modified Water Reservoir, Loop and Gaussian Grid Feature, as well as other feature extraction techniques. These techniques were investigated employing artificial neural networks and support vector machines as classifiers to develop an automatic assessment system for marking short answer questions. The system has high assessment accuracy (up to 94.75% for hand printed, 96.09% for cursive handwritten, and 95.71% for hand printed and cursive handwritten combined). The proposed system also includes assessment criteria to augment its accuracy.
Cardiac Inter-Beat Interval Complexity Is Influenced By Physical Activity

The complexity of physiological signals may be a more sensitive indicator of health than standard or average measurements. We examined cardiac inter-beat intervals of healthy subjects who are either physically active or sedentary to determine whether measures of complexity are more sensitive to subtle cardiac changes than standard measures. Subjects were pre-screened by self-report, and qualifying subjects were placed in either the active group (n = 10) or sedentary group (n = 10). Cardiac inter-beat intervals were recorded and subsequently analyzed using standard time and frequency domain heart rate variability measurements as well as multiscale entropy and the detrended fluctuation analysis, both measures of complexity. Of the measurements, the detrended fluctuation analysis was the only tool that significantly (P = 0.04) differentiated between the active and sedentary groups. This suggests that the complexity of physiologic signals is a more sensitive indicator of cardiac health than standard measures.
Mindfulness Meditation Improves Mood, Quality of Life, and Attention in Adults with Attention Deficit Hyperactivity Disorder

Objective. Adults with attention deficit hyperactivity disorder (ADHD) display affective problems and impaired attention. Mood in ADHD can be improved by mindful awareness practices (MAP), but results are mixed regarding the enhancement of attentional performance. Here we evaluated MAP-induced changes in quality of life (QoL), mood, and attention in adult ADHD patients and controls using more measures of attention than prior studies. Methods. Twenty-one ADHD patients and 8 healthy controls underwent 8 weekly MAP sessions; 22 similar patients and 9 controls did not undergo the intervention. Mood and QoL were assessed using validated questionnaires, and attention was evaluated using the Attentional Network Test (ANT) and the Conners Continuous Performance Test (CPT II), before and after intervention. Results. MAP enhanced sustained attention (ANT) and detectability (CPT II) and improved mood and QoL of patients and controls. Conclusion. MAP is a complementary intervention that improves affect and attention of adults with ADHD and controls.
Analysis of Faults in an N-Version Software Experiment

The authors have conducted a large-scale experiment in N-version programming. A total of 27 versions of a program were prepared independently from the same specification at two universities. The results of executing the versions revealed that the versions were individually extremely reliable but that the number of input cases in which more than one failed was substantially more than would be expected if they were statistically independent. After the versions had been executed, the failures of each version were examined and the associated faults located. It appears that minor differences in the software development environment would not have a major impact in reducing the incidence of faults that cause correlated failures. >
Neighbor Attack And Detection Mechanism In Mobile Ad-Hoc Networks

In Mobile Ad-Hoc Networks (MANETs), security is one of the most important concerns because a MANETs system is much more vulnerable to attacks than a wired or infrastructure-based wireless network. Designing an effective security protocol for MANET is a very challenging task. This is mainly due to the unique characteristics of MANETs, namely shared broadcast radio channel, insecure operating environment, lack of central authority, lack of association among users, limited availability of resources, and physical vulnerability. In this paper we present simulation based study of the impact of neighbor attack on mesh-based Mobile Ad-Hoc Network (MANET). And also we study the number of attackers and position affects the performance metrics such as packet delivery ratio and throughput. The study enables us to propose a secure neighbor detection mechanism (SNDM). A generic detection mechanism against neighbor attack for On Demand Routing Protocols is simulated on GlomoSim environment.
Gaze contingent articulated robot control for robot assisted minimally invasive surgery

This paper introduces a novel technique for controlling an articulated robotic device through the eyes of the surgeon during minimally invasive surgery. The system consists of a binocular eye-tracking unit and a robotic instrument featuring a long, rigid shaft with an articulated distal tip for minimally invasive interventions. They have been integrated into a daVinci surgical robot to provide a seamless and non-invasive localization of eye fixations of the surgeon. By using a gaze contingent framework, the surgeonpsilas fixations in 3D are converted into commands that direct the robotic probe to the desired location. Experimental results illustrate the ability of the system to perform real-time gaze contingent robot control and opens up a new avenue for improving current human-robot interfaces.
On the Scalability of Fair Queueing

The scalability of fair queueing depends on the number of flows that have, or recently have had, packets in the queue. Applying known facts about statistical traffic characteristics at flow level, we demonstrate that this number is typically very small for any link capacity. This suggests fair queueing may be perfectly feasible opening interesting possibilities for the development of the Internet architecture. The evaluation is based on trace simulations and an analytical traffic model.
Information Extraction and Sentence Classification applied to Clinical Trial MEDLINE Abstracts

In this paper, firstly we report experimental results on applying information extraction (IE) methodology to the task of summarizing clinical trial design information in focus on “Compared Treatment”, “Endpoint” and “Patient Population” from clinical trial MEDLINE abstracts. From these results, we have come to see this problem as one that can be decomposed into a sentence classification subtask and an IE subtask. By classifying sentences from clinical trial abstracts and only performing IE on sentences that are most likely to contain relevant information, we hypothesize that the accuracy of information extracted from the abstracts can be increased. As preparation for testing this theory in the next stage, we conducted an experiment applying state-of-the-art sentence classification techniques to the clinical trial abstracts and evaluated its potential in the original task of the summarization of clinical trial design information.
Acoustic scene classification using sparse feature learning and event-based pooling

Recently unsupervised learning algorithms have been successfully used to represent data in many of machine recognition tasks. In particular, sparse feature learning algorithms have shown that they can not only discover meaningful structures from raw data but also outperform many hand-engineered features. In this paper, we apply the sparse feature learning approach to acoustic scene classification. We use a sparse restricted Boltzmann machine to capture manyfold local acoustic structures from audio data and represent the data in a high-dimensional sparse feature space given the learned structures. For scene classification, we summarize the local features by pooling over audio scene data. While the feature pooling is typically performed over uniformly divided segments, we suggest a new pooling method, which first detects audio events and then performs pooling only over detected events, considering the irregular occurrence of audio events in acoustic scene data. We evaluate the learned features on the IEEE AASP Challenge development set, comparing them with a baseline model using mel-frequency cepstral coefficients (MFCCs). The results show that learned features outperform MFCCs, event-based pooling achieves higher accuracy than uniform pooling and, furthermore, a combination of the two methods performs even better than either one used alone.
CUNTZ-KRIEGER ALGEBRAS OF DIRECTED GRAPHS

We associate to each row-nite directed graph E a universal Cuntz-Krieger C-algebra C(E), and study how the distribution of loops in E aects the structure of C(E) . W e prove that C(E) is AF if and only if E has no loops. We describe an exit condition (L) on loops in E which allows us to prove an analogue of the Cuntz-Krieger uniqueness theorem and give a characterisation of when C(E) is purely innite. If the graph E satises (L) and is conal, then we have a dichotomy: if E has no loops, then C(E) is AF; if E has a loop, then C(E) is purely innite. If A is an n n f0; 1g-matrix, a Cuntz-Krieger A-family consists of n partial isometries Si on Hilbert space satisfying
Layered data representation for visual simulation of terrain erosion

New data structure for visual simulation of 3D terrains is introduced. The representation is inspired by real geological measurements and presents good trade-off between commonly used inexpensive, but inaccurate, height fields and memory demanding voxel representation. The representation is based on horizontal stratified layers consisting of one material. The layers are captured in certain positions of the landscape. This representation is then discretized into a 2D array. We demonstrate that the classical algorithm simulating thermal erosion (F.K. Musgrave, 1989), can run on this representation and we can even simulate some new properties. The simulation has been done on artificial data as well as on real data from Mars.
Nanoemulsions in Translational Research—Opportunities and Challenges in Targeted Cancer Therapy

Nanoemulsion dosage form serves as a vehicle for the delivery of active pharmaceutical ingredients and has attracted great attention in drug delivery and pharmacotherapy. In particular, nanoemulsions act as an excellent vehicle for poorly aqueous soluble drugs, which are otherwise difficult to formulate in conventional dosage forms. Nanoemulsions are submicron emulsions composed of generally regarded as safe grade excipients. Particle size at the nanoscale and larger surface area lead to some very interesting physical properties that can be exploited to overcome anatomical and physiological barriers associated in drug delivery to the complex diseases such as cancer. Along these lines, nanoemulsions have been engineered with specific attributes such as size, surface charge, prolonged blood circulation, target specific binding ability, and imaging capability. These attributes can be tuned to assist in delivering drug/imaging agents to the specific site of interest, based on active and passive targeting mechanisms. This review focuses on the current state of nanoemulsions in the translational research and its role in targeted cancer therapy. In addition, the production, physico-chemical characterization, and regulatory aspects of nanoemulsion are addressed.
Neural Network and Jacobian Method for Solving the Inverse Statics of a Cable-Driven Soft Arm With Nonconstant Curvature

The solution of the inverse kinematics problem of soft manipulators is essential to generate paths in the task space. The inverse kinematics problem of constant curvature or piecewise constant curvature manipulators has already been solved by using different methods, which include closed-form analytical approaches and iterative methods based on the Jacobian method. On the other hand, the inverse kinematics problem of nonconstant curvature manipulators remains unsolved. This study represents one of the first attempts in this direction. It presents both a model-based method and a supervised learning method to solve the inverse statics of nonconstant curvature soft manipulators. In particular, a Jacobian-based method and a feedforward neural network are chosen and tested experimentally. A comparative analysis has been conducted in terms of accuracy and computational time.
REGION-BASED ANALYSIS OF DIGITAL SURFACE MODELS

This general statement is also if not in particular valid for acquiring and processing Digital Elevation Models. On one hand, important developments like radar-interferometric sensors (e.g., the Shuttle Radar Topographic Mission, SRTM) or laser scanners (e.g., the operational systems TopoSys, TopScan or ALTM) have become available to the market. On the other hand, important tasks along the data processing chain like the normalization of Digital Surface Models (DSMs) or the extraction of object types using elevation information very often do not lead to satisfying and reliable results as obtained with human operators. This is the motivation to present a novel region-based, multi-scale approach which can be applied on elevation data for the above mentioned processing tasks. Chapter 2 will describe the underlying methodology in more detail while Chapter 3 will present results using elevation data from laserscanning.
Are the Scaling Properties of Bull and Bear Markets Identical? Evidence from Oil and Gold Markets

In this study, the scaling properties of the oil and gold return volatilities have been analyzed in the context of bull and bear periods. In the determination of bull and bear turning points, we used the Modified Bry-Boschan Quarterly (MBBQ) algorithm. Results showed that the business cycle phase shapes of the bear periods in the oil market are almost linear, whereas the bull and bear periods of the gold and bull period of the oil market are convex. This means that there are sharper declines in the bear period of the oil market. Following the detection of bull and bear periods, scaling exponent H analysis was performed via the aggregated variance, Higuchi’s statistic, Peng’s statistic, rescaled range, boxed periodogram and wavelet fit models, which are from the time, frequency and wavelet domains. As there are conflicts about the credibility of these methods in the literature, we have used the shuffling procedure in order to determine the most robust methods. According to the results, bear periods have higher volatility persistency than bull periods.
Order p 6 chiral couplings from the scalar Kπ form factor

Employing results from a recent determination of the scalar Kπ form factor F 0 Kπ within a coupled channel dispersion relation analysis [1], in this work we calculate the slope and curvature of F 0 Kπ(t) at zero momentum transfer. Knowledge of the slope and curvature of the scalar Kπ form factor, together with a recently calculated expression for F 0 Kπ(t) in chiral perturbation theory at order p 6 , enable to estimate the (p 6) chiral constants C 12 r (M ρ) = (0.3±5.4). 10−7 and (C 12 r +C 34 r )(M ρ) = (3.2±1.5). 10−6 . Our findings also allow to estimate the contribution coming from the C i to the vector form factor F + Kπ(0) which is a crucial ingredient for a precise determination of |V us | from K l3 decays. Our result F + Kπ(0)| C i r = − 0.018±0.009, though inflicted with large uncertainties, is in perfect agreement with a previous estimate by Leutwyler and Roos already made twenty years ago.
General Theorems on the Equivalence of Group Velocity and Energy Transport

It is shown that under very general conditions there is a rigorous identity between the group velocity and the velocity of energy transport in nonhomogeneous media with or without anomalous dispersion. The medium is assumed nondissipative and the parameters such as density, rigidity, dielectric constant, etc., vary from point to point but are independent of the Cartesian coordinate lying in the direction of the mode propagation. This covers propagation in any type of wave guide, surface waves, propagation in stratified media, etc. The identity is established for fluids, for isotropic and anisotropic solids with or without prestress, and for electromagnetic waves. Anomalous dispersion is assumed to result from hidden coordinates such as electron oscillators. A new variational formulation of field theory is introduced. An interesting application is to wave propagation in an electron gas and it is shown that such wave propagation obeys the relativistic Schr\"odinger equation for a mass particle.
Network-side positioning of cellular-band devices with minimal effort

We address the problem of network-side localization where cellular operators are interested in localizing cellular devices by means of signal strength measurements alone. While fingerprinting-based approaches have been used recently to address this problem, they require significant amount of geo-tagged (`labeled') measurement data that is expensive for the operator to collect. Our goal is to use semi-supervised and unsupervised machine learning techniques to reduce or eliminate this effort without compromising the accuracy of localization. Our experimental results in a university campus (6 sq. km) demonstrate that sub-100m median localization accuracy is achievable with very little or no labeled data so long as enough training is possible with `unlabeled' measurements. This provides an opportunity for the operator to improve the model with time. We present extensive analysis of the error characteristics to gain insight and improve performance, including understanding spatial properties and developing confidence measures.
Fast Log Writes using Disk Mimic

We investigate a suite of algorithms for reducing the Small synchronous disk writesoverhead, which are an important & frequently occurring workload in journallling filesystems and transactional database systems. The studied algorithms try to optimize the performance by trying to predict the disk head position. We find the current state-of-art algorithms/systems either memory ineffi cient or bad at predicting the actual disk head position. We carry out an emulation based performance study to understand the trade-offs between memory overhead and accuracy. We then design algorithms with configurable memory and accuracy parameters (interdependent). Finally we design and implement a pseudo driver for Linux based systems, which can be integrated with the existing Linux based journalling filesystems.
Lungs Nodule Detection by Using Fuzzy Morphology from CT Scan Images

In this paper we have proposed a method for lungs nodule detection from computed tomography (CT) scanned images by using Fuzzy C-Mean (FCM) and morphological techniques. First of all, fuzzy have been used for automated segmentation of lungs. Region of interests (ROIs) have been extracted by using 8 directional searches slice by slice and then 3D ROI image have been constructed. A 3D template has been constructed and convolves with the 3D ROI image. Finally FCM have been used to extract ROI that contain nodule. The proposed system is capable to perform fully automatic segmentation and nodule detection from CT Scan Lungs images, based solely on information contained by the image itself. The technique was tested against the 50 datasets of different patients received from Aga Khan Medical University, Pakistan and Lung Image Database Consortium (LIDC) dataset.
The geometry of uniserial representations of finite dimensional algebras III: Finite uniserial type

A description is given of those sequences ${\Bbb S}= (S(0),S(1),\dots,S(l))$ of simple modules over a finite dimensional algebra for which there are only finitely many uniserial modules with consecutive composition factors $S(0),\dots,S(l)$. Necessary and sufficient conditions for an algebra to permit only a finite number of isomorphism types of uniserial modules are derived. The main tools in this investigation are the affine algebraic varieties parametrizing the uniserial modules with composition series ${\Bbb S}$.