Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion-based finger tracking for user interaction with mobile devices

A new motion-based tracking algorithm for user interaction with hand-held mobile devices is presented. The idea is to allow mobile phone users to control the device simply by moving a finger in front of a camera. A novel combination of Kalman filtering and the expectation maximization (EM) algorithm is utilized for estimation of two distinct motion components corresponding to the camera motion and the finger motion. The estimation is based on motion features, which are effectively extracted from the scene for each image frame. The performance of the technique is evaluated in experiments which show the usefulness of the approach. The method can be applied also when some conventional finger tracking techniques such as color segmentation and background subtraction cannot be used. (6 pages)
Improving color based video shot detection

Video shot detection plays a fundamental role in video access/analysis. Among many shot detection schemes, color based schemes seem to be the most widely used ones which can be applied to videos of different domains. However color based techniques are not always very efficient. For example, over-detection (high false-alarm rate) may occur, especially for difficult videos such as home video which often contain significant object motion and illumination change. In this paper we propose a new learning method to improve the color-based shot detection schemes by employing a better similarity measure obtained through a minmax optimization procedure. This new measure is not tied to a particular feature such as color, hence it can be applied to video browsing, indexing based on other features as well.
Theoretical and Factorial Validity of Shearerâ€™s Revised Logical-Mathematical Intelligence Scale and Its Relationship with Academic Achievement

The factorial validity of Logical Mathematical Intelligence Scale (LMIS) developed by Shearer (1994) was explored in this study by resorting to schema theory. To this end, its 17 interrogative sentences were rendered declarative and six heterogeneous alternatives offered for each sentence were reduced to four homogenous choices. The revised LMIS was then administered to 376 undergraduate and graduate students who had taken various courses offered in the English language. When the participantsâ€™ responses were subjected to Principal Axis Factoring and Varimax with Kaiser Normalization five factors appeared showing that the cognitive domain of logical-mathematical intelligence measured by the LMIS consists of five latent variables representing mathematical, logical, witty, ingenious, and inquisitive genera. Correlating the scale and its factors with the studentsâ€™ GPA established a significant but negative relationship between the logical genus and academic achievement. The results are discussed and suggestions are made for future research.
Baseline Monitoring of the Western Arctic Ocean Estimates 20% of Canadian Basin Surface Waters Are Undersaturated with Respect to Aragonite

Marine surface waters are being acidified due to uptake of anthropogenic carbon dioxide, resulting in surface ocean areas of undersaturation with respect to carbonate minerals, including aragonite. In the Arctic Ocean, acidification is expected to occur at an accelerated rate with respect to the global oceans, but a paucity of baseline data has limited our understanding of the extent of Arctic undersaturation and of regional variations in rates and causes. The lack of data has also hindered refinement of models aimed at projecting future trends of ocean acidification. Here, based on more than 34,000 data records collected in 2010 and 2011, we establish a baseline of inorganic carbon data (pH, total alkalinity, dissolved inorganic carbon, partial pressure of carbon dioxide, and aragonite saturation index) for the western Arctic Ocean. This data set documents aragonite undersaturation in ∼20% of the surface waters of the combined Canada and Makarov basins, an area characterized by recent acceleration of sea ice loss. Conservative tracer studies using stable oxygen isotopic data from 307 sites show that while the entire surface of this area receives abundant freshwater from meteoric sources, freshwater from sea ice melt is most closely linked to the areas of carbonate mineral undersaturation. These data link the Arctic Ocean’s largest area of aragonite undersaturation to sea ice melt and atmospheric CO2 absorption in areas of low buffering capacity. Some relatively supersaturated areas can be linked to localized biological activity. Collectively, these observations can be used to project trends of ocean acidification in higher latitude marine surface waters where inorganic carbon chemistry is largely influenced by sea ice meltwater.
Animated construction of line drawings

Revealing the sketching sequence of a line drawing can be visually intriguing and used for video-based storytelling. Typically this is enabled based on tedious recording of artists' drawing process. We demonstrate that it is often possible to estimate a reasonable drawing order from a static line drawing with clearly defined shape geometry, which looks plausible to a human viewer. We map the key principles of drawing order from drawing cognition to computational procedures in our framework. Our system produces plausible animated constructions of input line drawings, with no or little user intervention. We test our algorithm on a range of input sketches, with varying degree of complexity and structure, and evaluate the results via a user study. We also present applications to gesture drawing synthesis and drawing animation creation especially in the context of video scribing.
Physiological adaptations to interval training and the role of exercise intensity

Interval exercise typically involves repeated bouts of relatively intense exercise interspersed by short periods of recovery. A common classification scheme subdivides this method into high‐intensity interval training (HIIT; ‘near maximal’ efforts) and sprint interval training (SIT; ‘supramaximal’ efforts). Both forms of interval training induce the classic physiological adaptations characteristic of moderate‐intensity continuous training (MICT) such as increased aerobic capacity ( V̇O2 max ) and mitochondrial content. This brief review considers the role of exercise intensity in mediating physiological adaptations to training, with a focus on the capacity for aerobic energy metabolism. With respect to skeletal muscle adaptations, cellular stress and the resultant metabolic signals for mitochondrial biogenesis depend largely on exercise intensity, with limited work suggesting that increases in mitochondrial content are superior after HIIT compared to MICT, at least when matched‐work comparisons are made within the same individual. It is well established that SIT increases mitochondrial content to a similar extent to MICT despite a reduced exercise volume. At the whole‐body level, V̇O2 max is generally increased more by HIIT than MICT for a given training volume, whereas SIT and MICT similarly improve V̇O2 max despite differences in training volume. There is less evidence available regarding the role of exercise intensity in mediating changes in skeletal muscle capillary density, maximum stroke volume and cardiac output, and blood volume. Furthermore, the interactions between intensity and duration and frequency have not been thoroughly explored. While interval training is clearly a potent stimulus for physiological remodelling in humans, the integrative response to this type of exercise warrants further attention, especially in comparison to traditional endurance training.
Emotional Processing in a Salient Motion Context: Integration of Motion and Emotion in Both V5/hMT+ and the Amygdala

Recent studies have suggested that the extent to which primary task demands draw on attentional resources determines whether or not task-irrelevant emotional stimuli are processed. Another important factor that can bias task-relevant and task-irrelevant stimulus competition is the bottom-up factor of stimulus salience. Here, we investigated the effect of stimulus salience associated with a primary motion task on the processing of emotional face distractors. Faces of different emotional valences were presented within a context of randomly moving dots. Subjects had to detect short intervals of coherent motion while ignoring the background faces. Task salience was manipulated by the level of motion coherence of the dots with high motion coherence being associated with high salience. Using functional magnetic resonance imaging, we show that emotional faces, compared with neutral faces, more strongly interfered with the primary task, as reflected in significant signal decreases in task-related motion area V5/hMT+. In addition, these faces elicited significant signal increases in the left amygdala. Most importantly, task salience was found to further increase amygdala's activity when presented together with an emotional face. Our data support a more general role of the amygdala as a behavioral relevance detector, which flexibly integrates behavioral relevant, salient context information to decode the emotional content of a visual scene.
Feedforward-feedback P-PID control of elbow joint angle for functional electrical stimulation: A simulation study

Functional electrical stimulation (FES) is the most commonly used system for restoring functions after spinal cord injury (SCI). In this study, we used a combined feedforward-feedback PID controller for regulating the elbow joint angle. Feedforward controller is unable to make corrections if the actual angle deviates from the desired angle. Feedback controller monitors the system output, so it can correct the error. So, a combination of feedforward and feedback control is the prefered control method in FES system designs. The controller was tuned using a model contains one joint, two links with one degree of freedom and two muscles in the sagital plane which simulated in MATLAB using SimMechanics and Simulink Toolboxes. The muscle model is based on Zajac musculotendon model. At First, the P-PID coefficients were tuned with error and effort, and then a genetic algorithm (GA) was used to optimize them. The controller optimized by GA is much better in terms of the rise time and settling time and has no overshoot and oscillation but the classical tuned controller response has a big overshoot and oscillations before getting the desired angle and has much less rise time and settling time. A comparison between the results of suggested controlling method and the results of genetic-PID feedback controller has been done. The results show that however the feedforward-feedback controller is better in terms of the rise time and the settling time, the feedback controller has less overshoot.
A machine learning technique for predicting the productivity of practitioners from individually developed software projects

Context: Productivity management of software developers is a challenge in Information and Communication Technology. Predictions of productivity can be useful to determine corrective actions and to assist managers in evaluating improvement alternatives. Productivity prediction models have been based on statistical regressions, statistical time series, fuzzy logic, and machine learning. Goal: To propose a machine learning model termed general regression neural network (GRNN) for predicting the productivity of software practitioners. Hypothesis: Prediction accuracy of a GRNN is better than a statistical regression model when these two models are applied for predicting productivity of software practitioners who have individually developed their software projects. Method: A sample obtained from 396 software projects developed between the years 2005 and 2011 by 99 practitioners was used for training the models, whereas a sample of 60 projects developed by 15 practitioners in the first months of 2012 was used for testing the models. All projects were developed based upon a disciplined development process within a controlled environment. The accuracy of the GRNN was compared against that of a multiple regression model (MLR). The criteria for evaluating the accuracy of these two models were the Magnitude of Error Relative to the estimate and a t-paired statistical test. Results: Prediction accuracy of an GRNN was statistically better than that of an MLR model at the 99% confidence level. Conclusion: An GRNN could be applied for predicting the productivity of practitioners when New and Changed lines of code, reused code, and programming language experience of practitioners are used as independent variables.
Research on Katakana phrase translation based on bi-directional integration

In order to solve the problem of Katakana reduced to English in Japanese-English translation, we employ the phrase-based statistical machine translation model to perform Katakana phrase (or word) translation from Japanese to English. The katakana phrase is segmented into words by CRF, and then Japanese-English and English-Japanese bi-directional integration translation is carried out on those segmented results. The translated results of all the segmented words are comprehensively scored to obtain the best English phrase translation result. The experimental results indicate that the katakana phrase translation precision reaches 76%, effectively addresses the problem of the katakana reduced to English.
Semantic Web and Information Visualization

The paper augments the results achieved in the research of Semantic Web technologies for semantic search application, underlining the importance of integrating Information Visualization into semantic web technologies. We present the results of a preliminary investigation of user needs in the activity of information search and an overview of the most used visualization tools in the web and tools for the ontology visualization. Our goal is to identify different types of information search problems and to demonstrate the advantages given by the adoption of information visualization.
Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics

Bacterial growth environment strongly influences the efficacy of antibiotic treatment, with slow growth often being associated with decreased susceptibility. Yet in many cases, the connection between antibiotic susceptibility and pathogen physiology remains unclear. We show that for ribosome-targeting antibiotics acting on Escherichia coli, a complex interplay exists between physiology and antibiotic action; for some antibiotics within this class, faster growth indeed increases susceptibility, but for other antibiotics, the opposite is true. Remarkably, these observations can be explained by a simple mathematical model that combines drug transport and binding with physiological constraints. Our model reveals that growth-dependent susceptibility is controlled by a single parameter characterizing the ‘reversibility’ of ribosome-targeting antibiotic transport and binding. This parameter provides a spectrum classification of antibiotic growth-dependent efficacy that appears to correspond at its extremes to existing binary classification schemes. In these limits, the model predicts universal, parameter-free limiting forms for growth inhibition curves. The model also leads to non-trivial predictions for the drug susceptibility of a translation mutant strain of E. coli, which we verify experimentally. Drug action and bacterial metabolism are mechanistically complex; nevertheless, this study illustrates how coarse-grained models can be used to integrate pathogen physiology into drug design and treatment strategies.
Decentralized Power Control for Slotted Spread Spectrum Aloha with Successive Interference Cancellation

In this paper, we study slotted Spread Spectrum Aloha with Successive Interference Cancellation at the receiver over a Gaussian channel. We consider a decentralized power control setting in which each user chooses its transmit power independently at random according to a power distribution with continuous support. In this setting, we derive an analytical expression for the expected interference power experienced by a user. This allows us to derive analytically the power distribution that, during the Successive Interference Cancellation process leads to a constant signal to noise plus interference ratio for all users. We consider both perfect and imperfect interference cancellation.
Antibodies against PfEMP1, RIFIN, MSP3 and GLURP Are Acquired during Controlled Plasmodium falciparum Malaria Infections in Naïve Volunteers

Antibodies to polymorphic antigens expressed during the parasites erythrocytic stages are important mediators of protective immunity against P. falciparum malaria. Therefore, polymorphic blood stage antigens like MSP3, EBA-175 and GLURP and variant surface antigens PfEMP1 and RIFIN are considered vaccine candidates. However, to what extent these antibodies to blood stage antigens are acquired during naive individuals' first infections has not been studied in depth. Using plasma samples collected from controlled experimental P. falciparum infections we show that antibodies against variant surface antigens, PfEMP1 and RIFIN as well as MSP3 and GLURP, are acquired during a single short low density P. falciparum infection in non-immune individuals including strain transcendent PfEMP1 immune responses. These data indicate that the immunogenicity of the variant surface antigens is similar to the less diverse merozoite antigens. The acquisition of a broad and strain transcendent repertoire of PfEMP1 antibodies may reflect a parasite strategy of expressing most or all PfEMP1 variants at liver release optimizing the likelihood of survival and establishment of chronic infections in the new host.
Approximate Characterization of Capacity in Gaussian Relay Networks

One of the distinguishing features of wireless communication is its broadcast nature. This creates complicated signal interactions when there are multiple transmitting and receiving nodes, due to superposition of signals at the receivers. In this paper, we focus on wireless relay networks where (relay) nodes facilitate communications between a source and a destination. We review some recent progress on this problem. The progress is made by developing simpler models which give insight and facilitate an approximate characterization of network capacity. We have developed deterministic models that (approximately) capture these interactions and show that for such models, one can obtain an information-theoretic max-flow min-cut result, in analogy to the classic wire-line Ford-Fulkerson result. We have extended these ideas to networks with general deterministic (interaction) functions. Using insights from the analysis of deterministic networks, we develop an approximate characterization for noisy Gaussian relay networks showing that the achievable rate is within a constant number of bits from the information- theoretic cut-set upper bound on the capacity of these networks. This constant depends on the topology of the network, but not on the values of the channel gains.
An e-Learning System Supporting the Problem-Based-Learning Approach: the Case of "Virtual eBMS"

This paper presents a case study of applying the Problem Based Learning (PBL) approach in a Web-based environment. The application domain concerns Business Management and Technology Management topics. At first, the paper describes the rationale and the main features behind the PBL for creating Business Leaders in the Emerging Competitive Environment. Then, it introduces the Web-based system called "Virtual eBMS" that has been designed and implemented at the e-Business Management Section (eBMS) of the Scuola Superiore ISUFI. The system is illustrated by highlighting the operational framework allowing an author to design PBL-based curricula; moreover the two main learning approaches (structured and unstructured) referred to the learner access to the curricula are proposed, allowing him to capture the most suitable learning resources for solving complex problems. Finally, the main benefits from the learner perspective, together with some indications for future research will end the paper.
Self-regulated active galactic nuclei heating in elliptical galaxies

We study the effect of active galactic nuclei (AGN) heating on the chemodynamical evolution of elliptical galaxies and their X-ray and optical properties using high-resolution Λ-dominated cold dark matter cosmological simulations. Our model considers an AGN as being ‘active’ when a convergent gas inflow condition exists within the nucleus of a galaxy; otherwise, the AGN is assumed to remain dormant. This induces a self-regulated activity for the AGN, the result of which leads to a stable hot corona and the suppression of significant late-time star formation – characteristics not encountered in traditional chemodynamical models of ellipticals. These properties of our AGN heating model lead to a system consistent with both the X-ray and optical properties of comparable elliptical galaxies.
Role of ARPC2 in Human Gastric Cancer

Gastric cancer continues to be the second most frequent cause of cancer deaths worldwide. However, the exact molecular mechanisms are still unclear. Further research to find potential targets for therapy is critical and urgent. In this study, we found that ARPC2 promoted cell proliferation and invasion in the human cancer cell line MKN-28 using a cell total number assay, MTT (3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide) assay, cell colony formation assay, migration assay, invasion assay, and wound healing assay. For downstream pathways, CTNND1, EZH2, BCL2L2, CDH2, VIM, and EGFR were upregulated by ARPC2, whereas PTEN, BAK, and CDH1 were downregulated by ARPC2. In a clinical study, we examined the expression of ARPC2 in 110 cases of normal human gastric tissues and 110 cases of human gastric cancer tissues. ARPC2 showed higher expression in gastric cancer tissues than in normal gastric tissues. In the association analysis of 110 gastric cancer tissues, ARPC2 showed significant associations with large tumor size, lymph node invasion, and high tumor stage. In addition, ARPC2-positive patients exhibited lower RFS and OS rates compared with ARPC2-negative patients. We thus identify that ARPC2 plays an aneretic role in human gastric cancer and provided a new target for gastric cancer therapy.
Design and Optimization of Fuel Injection System in Diesel Engine Using Biodiesel – A Review

Fuel injection is systems for supplying high pressurize fuel to maximum mixing of fuel with air in an internal combustion engine. Direct Injection (DI) Systems as used in DI engines, in which the fuel is injected directly into a combustion chamber formed in the clylinder itself. The fuel injector directly injects fuel into the direct fuel injection system. The injector is a very complicated part, and massive research has been done to improve it.In my work indicating the development of fuel injector system to reduce chocking problem which is generally happen in bio diesel engine. The injection nozzles and their respective nozzle holders are vitally important components situated between the in-line injection pump and the diesel engine, its functions are as metering the injection of fuel, management of the fuel, defining the rate-of-discharge curve, Sealing-off against the combustion, chamber. Mechanical type injectors used in direct injection system. When biodiesel is used in the diesel engine choking problem is created in fuel injector. Therefore, we optimize the design of fuel injector component, and tried to prevent the chocking problem. The diesel fuel injector system directly injects fuel into the system without chocking.