Miracle-dz/UniFAR-FTUs huggingface dataset:
A new Schmitt trigger circuit in a 0.13-μm 1/2.5-V CMOS process to receive 3.3-V input signals

A new Schmitt trigger circuit, which is implemented by low-voltage devices to receive the high-voltage input signals without gate-oxide reliability problem, is proposed. The new proposed circuit, which can be operated in a 3.3-V signal environment without suffering high-voltage gate-oxide overstress, has been fabricated in a 0.13-/spl mu/m 1/2.5-V 1P8M CMOS process. The experimental results have confirmed that the measured transition threshold voltages of the new proposed Schmitt trigger circuit are about 1 and 2.5 V, respectively. The new proposed Schmitt trigger circuit is suitable for mixed-voltage input-output interfaces to receive input signals and reject input noise.
Generalized gray level dependence method for prostate cancer classification

In this paper, we present a new approach for multi-spectral texture classification. Therefore, we aim to add spectral information to classical texture analysis methods that only treat gray-level spatial variations. To achieve this goal, we propose a Spatial and Spectral Gray Level Dependence Method (SSGLDM) in order to extend the concept of spatial gray level dependence method by assuming texture joint information between spectral bands. In addition, the new texture features measurement related to (SSGLDM) which define the image properties have been also proposed. Extensive experiments have been carried out on many multispectral images for use in prostate cancer diagnosis and quantitative results showed the efficiency of this method compared to the Gray Level Co-occurrence Matrix (GLCM). The results indicate a significant improvement in classification accuracy.
Process-variation-aware Iddq Diagnois for nano-scale CMOS designs - the firt step

Along with the shrinking CMOS process and rapid design, scaling, both Iddq values and their variation of chips increase. As a result, the defect leakages become less significant when compared to the full-chip currents, making them more in-distinguishable for traditional Iddq diagnosis. Therefore, in this paper, a new approach called σ-Iddq duagnosis is proposed for reinterpreting original data and diagnosing failing chips, intelligently. The overall flow consists of two key components, (1) σ-Iddq transformation and (2) defect-syndrome matching: σ-Iddq transformation first manifests defect leakages by excluding both the process-variation and design-scaling impacts. Later, defect-syndrome matching applies data mining with a pre-built library to identify types and locations of defects on the fly. Experimental result show that an average of 93.68% accuracy with a resolution of 1.75 defect suspects can be achieved on ISCAS'89 and IWLS'05 benchmark circuits using a 45nm technology, demonstrating the effectiveness of σIddq diagnosis.
Using Global Interpolation to Evaluate the Biot-Savart Integral for Deformable Elliptical Gaussian Vortex Elements

This paper introduces a new method for approximating the Biot-Savart integral for elliptical Gaussian functions using high-order interpolation and compares it to an existing method based on small aspect ratio asymptotics. The new evaluation technique uses polynomials to approximate the kernel corresponding to the integral representation of the streamfunction. We determine the polynomial coefficients by interpolating precomputed values from look-up tables over a wide range of aspect ratios. When implemented in a full nonlinear vortex method, we find that the new technique is almost three times faster and, unlike the asymptotic method, provides uniform accuracy over the full range of aspect ratios. As a proof-of-concept for large scale computations, we use the new technique to calculate inviscid axisymmetrization and filamentation of a two-dimensional elliptical fluid vortex. We compare our results with those from a pseudo-spectral computation and from electron vortex experiments, and find good agreement among the three approaches.
Improving task responsiveness with limited preemptions

The optimality of preemptive EDF scheduling with relation to the achievable system utilization is a clear advantage of this scheduling policy for single processor real-time systems. However, recent works suggested that the run-time behavior of EDF might be improved by limiting the preemption support only to particular time instants, dividing each task into a sequence of non-preemptive chunks of execution, without affecting the schedulability of the system. In this paper, we will take a closer look to limited preemption EDF scheduling (LP-EDF), evaluating the potential advantages offered by this policy in terms of response-time reduction and improved control performances. In particular, we will show how to increase the responsiveness of a control application by placing non-preemptive regions of maximal length at the end of the code of selected tasks. The effectiveness of the proposed method will be proved both analytically and by extensive simulations.
Improving the performance of the reinforcement learning model for answering complex questions

This paper addresses the task of answering complex questions using a multi-document summarization approach within a reinforcement learning setting. Given a set of complex questions, a list of relevant documents per question, and the corresponding human-generated summaries (i.e. answers to the questions) as training data, the reinforcement learning module iteratively learns a number of feature weights in order to facilitate the automatic generation of summaries i.e. answers to unseen complex questions. Previous works on this task have utilized a fully automatic reinforcement learning framework that selects the document sentences as the potential candidate (i.e. machine-generated) summary sentences by exploiting a relatedness measure with the available human-written summaries. In this paper, we propose an extension to this model that incorporates user interaction into the reinforcement learner to guide the candidate summary sentence selection process. Experimental results reveal the effectiveness of the user interaction component in the reinforcement learning framework.
Mandarin Juice Improves the Antioxidant Status of Hypercholesterolemic Children

Background: Oxidative stress has been linked to such degenerative diseases as atherosclerosis, and it has been suggested that increased dietary intake of antioxidants may reduce its progression. Objective: To determine the effect of mandarin juice consumption on biomarkers related to oxidative stress in hypercholesterolemic children. Materials and Methods: The diet of 48 children with plasma cholesterol >200 mg/dL and low-density lipoprotein cholesterol >130 mg/dL was supplemented for 28 days with 500 mL/day of pure (100%) mandarin juice (Citrus clementina Hort. ex Tan.). The composition of the mandarin juice was analyzed, and its antioxidant antiradical activity was evaluated in vitro. Malondialdehyde, carbonyl groups, vitamins E and C, erythrocyte-reduced glutathione, and plasma lipids were measured at the onset and at the end of the supplementation period. The paired Student t test was used to compare values before and after supplementation. Results: Mandarin juice exerted a strong antioxidant effect mainly due to its high hydroxyl activity and, to a lesser extent, to its superoxide scavenger activity. At the end of the study, levels of the plasma biomarkers of oxidative stress were significantly decreased (malondialdehyde −7.4%, carbonyl groups −29.1%, P < 0.01), whereas the plasma antioxidants vitamin E and C (13.5%, P < 0.001 and 68.2%, P < 0.00001, respectively) and intraerythrocyte glutathione level (36.7%, P < 0.00001) were significantly increased. Plasma lipids and antibodies to oxidized low-density lipoproteins remained unchanged. Conclusions: Regular ingestion of mandarin juice significantly reduces plasma biomarkers of lipid and protein oxidation and enhances the antioxidant status of consumers.
Intelligent Time-Aware Query Translation for Text Sources

This paper describes a system called SITAC based on our proposed approach to discover concepts (called SITACs) in text archives that are identical semantically but alter their names over time. Our approach integrates natural language processing, association rule mining and contextual similarity to discover SITACs in order to answer historical queries over text corpora.
Do capital controls boost EME´s resilience to financial crises?

Capital controls are again in vogue as a number of emerging markets have reintroduced these measures in recent years in response to a 'flood' of international capital. Policymakers use these tools to buttress their economies against the 'sudden stop' risk that accompanies international capital flows. Using a panel VAR model, we show that capital controls appear to make emerging market economies (EMEs) more resistant to financial crises by showing that lower post-crisis output loss is correlated with stronger capital controls. However, EMEs that employ capital controls seem to be more crisis-prone. Thus, policymakers should carefully evaluate whether the benefits of capital controls outweigh their costs.
Therapeutic-assisted robot for children with autism

In this paper, we propose a therapeutic-assisted robot for children with autism to ameliorate their skill of joint attention. The robot conducts a goal-directed based interaction to establish engagement between the child and robot in order to establish a beneficial learning environment for autistic children. An unsupervised Mixture Gaussian-based cluster method is proposed to detect the child's intention in real time to process the goal-directed task smoothly. The novelty of this approach is that does not require the use of any training data or a trained model to detect the child's intention. Our autonomous robotic system is tested with several autistic children at a School for the Disabled in Nagoya, Japan. The results of the initial interaction showed that the children enjoyed interaction with and feedback from the robot, which confirmed that the robot can be used as mediator or an object of joint attention. The unsupervised approach was able to detect the children's intention at every time segment to process the goal-directed task with a higher accuracy rate. The results of the goal-directed task showed that the proposed interaction was highly effective in enhancing their joint attention. Since most of the children attempted to imitate the robot's gestural behaviors and used a variety of learning patterns to attend to the robot's fingered object in the environment to obtain joint attention with robot.
Transatlantic Investment by Multinational Firms: A Rivalistic Phenomenon?

The spread and growth of multinational corporations over the past two decades raise major public policy questions. Unfortunately, understanding of the economic behavior of the multinational corporation is incomplete. For example, the usual hypotheses of international investment may not be entirely adequate to explain direct investment by European multinational corporations in the United States. Thus, a new hypothesis, based upon rivalistic behavior among firms, is presented and tested. ' It is concluded that this hypothesis might add some additional explanatory power with policy implications for European direct investment in the United States.
A Robust Moving Object Detection Approach

Chang detection is one of the most important research issues in the field of video processing. This paper presents an adaptive method of objects and shadows detection in video streams based on the HSI color space. Bi-models of background is set up via the minimum, the maximum and the largest interframe absolute difference of per static pixel, which are adaptively updated by synthesizing pixel level, object level and frame level method. In addition, motion region are built by three-level region chain structure to deal with the errors' detection results. Finally, we remove cast shadows using the generic properties of luminance, chrominance and gradient density in HSI color space. Experimental results and their evaluation verify the effectiveness and the robustness of the proposed approach.
Survival Prediction and Treatment Recommendation with Bayesian Techniques in Lung Cancer

In this paper, we investigate a number of Bayesian techniques for predicting 1-year- survival and making treatment selection recommendations for lung cancer. We have carried out two sets of experiments on the English Lung Cancer Dataset. For 1-year-survival prediction, the Naïve Bayes (NB) algorithm achieved an area under the curve value of 81%, outperforming the Bayesian Networks learned by the M(3) and K2 structure learning algorithms. For treatment recommendation, the Bayesian Network, whose structure was learned by the MC(3) algorithm, has marginally outperformed NB, based on producing concordant results with the recorded treatments in the dataset. We observed that in cases where the classifier recommendations were discordant with the recorded treatments, the 1-year-survival rate decreased by 15%. We also observed that discordance between the classifier and the dataset was more dominant in cases where the recorded treatment was non-curative or was not frequently encountered in the dataset.
Neuronal correlate of BOLD signal fluctuations at rest: Err on the side of the baseline

Functional MRI (fMRI) indirectly measures changes in neuronal activity because the blood oxygenation level-dependent (BOLD) signal is sensitive to changing concentrations of oxyhemoglobin (vs. deoxyhemoglobin) to support functional energy demand (1). Changes in the BOLD signal are usually interpreted from an unspecified baseline state (2). However, there is no true baseline because the brain is never actually at rest (3), measured either in terms of neuronal activity (4) or the energy that activities demand (5). Recently, however, investigators have begun to study the resting state using fMRI, but interpretation remains controversial because of questions about the relationship between the BOLD signal and neuronal activity. In PNAS, Scholvinck et al. (6) tackle this controversy by correlating slow modulations of neuronal activity in the resting state with spontaneous fluctuations in the BOLD signal.
Large Scale Video Conferencing : A Digital Amphitheater

In this paper we address the problem of large-scale video conferencing, both from a systems and user interface point of view. We present the architecture and current implementation of our video conferencing tool, the Digital Amphitheater, which facilitates large scale video conferencing for hundreds of participants in a natural and cohesive environment. We describe a unique user interface that aims to engender a feeling of presence, using background substitution, eliminating control functions from the screen, and allowing participants to view themselves in an amphitheater setting.
Migration and Lived Experiences of Racism: The Case of High-Skilled Migrants in Wrocław, Poland

This article examines migrants’ lived experiences with racism in Wrocław, Poland. Drawing on qualitative research conducted among high-skilled migrants, it analyzes various ways migrants encounter, understand, and cope with racism. Our case study broadens discussion about migration and racism by analyzing an Eastern European, post-socialist, predominantly “white” receiving society seldom researched in terms of racism. The article problematizes the assumption that high-skilled migrants experience only minor problems with incorporation. Furthermore, it suggests the importance of racial boundaries in a non-Western society that lacks a colonial background and long-standing relations with migrants. Therefore, our article contributes to a better understanding of how local settings inform the experiences of high-skilled migrants, which are often lost in abstract concepts of global flows and spaces.
Counting in Anonymous Dynamic Networks under Worst-Case Adversary

In this paper we investigate the problem of counting the size of a network where processes are anonymous (i.e, they share the same identifier) and the network topology constantly changes controlled by an adversary able to look internal process states and add and remove edges in order to contrast the convergence of the algorithm to the correct count. It is easy to show that, if the adversary can generate graphs without any constraint on the connectivity (i.e. it can generate topologies where there exist nodes not able to influence the others), counting is impossible. In this paper we consider a synchronous round based computation and the dynamicity is governed by a worst-case adversary that generates a sequence of graphs, one for each round, with the only constraint that each graph must be connected (1-interval connectivity property). It has been conjectured that counting in a finite time against such adversary is impossible and the existing solutions consider that each process has some knowledge about network topologies generated by the adversary, i.e. at each round, each node has a degree lesser than D. Along the path of proving the validity (or not) of the conjecture, this paper presents an algorithm that counts in a finite time against the worst-case adversary assuming each process is equipped with an oracle. The latter provides a process at each round r with an estimation of the process degree in the graph generated by the adversary at round r. To the best of our knowledge, this is the first counting algorithm (terminating in a finite time) where processes exploit the minimal knowledge about the behavior of the adversary. Interestingly, such oracle can be implemented in a wide range of real systems.
Domestic Hypermedia: - Folding Hyperspaces into Households

This paper analyses the use of media and material in private homes based on empirical studies in a project on designing interactive home environments. Material in homes with hypermedia characteristics includes photos, music, play-lists, as well as physical media such as newspapers, messages and letters. Based on the analyses we propose a Domestic Hypermedia infrastructure (DoHM) combining spatial, context-aware and physical hypermedia to support collaborative structuring and ubiquitous presentation of materials in private homes. With DoHM we propose establishing new relationship between digital and physical hyperspaces, folding hyperspaces into the physical space of the household. Thus we strive to combine the qualities of physical domestic materials and spaces with the flexibility and dynamics of digital hyperspaces. The DoHM infrastructure may be utilized on a variety of new ubiquitous home appliances called MediaWall, MediaTable, MediaTray and MediaPort, which are discussed in the paper.
DigitEyes: vision-based hand tracking for human-computer interaction

Computer sensing of hand and limb motion is an important problem for applications in human-computer interaction (HCI), virtual reality, and athletic performance measurement. Commercially available sensors are invasive, and require the user to wear gloves or targets. We have developed a noninvasive vision-based hand tracking system, called DigitEyes. Employing a kinematic hand model, the DigitEyes system has demonstrated tracking performance at speeds of up to 10 Hz, using line and point features extracted from gray scale images of unadorned, unmarked hands. We describe an application of our sensor to a 3D mouse user-interface problem. <<ETX>>
Applying independent component analysis to heart rate and blood pressure variations

The variations of heart rate (HR) and blood pressure (BP) reflect autonomic control. Most studies used spectral analysis and time-domain statistics to assess autonomic functions. Such methods provide some parameters to represent sympathetic and vagal activities. Independent component analysis (ICA) is a statistical signal processing method for blind separation. Assume that HR and BP pressure variations are linearly composed by some independent hidden signals and these hidden signals represent some meaningful physiological signals such as cardiac nervous outflow and hormonal level. Applying ICA to HR and BP variations signals will be expected to extract these hidden signals. In this study, the HR and BP variations data of six subjects were measured and the beat-to-beat RR intervals, systolic BP, and diastolic BP were considered as the mixed signals to be decomposed. The results from ICA showed that these signals were decomposed to noise component, dominate oscillation component and slow-changed component. Dominate oscillation component is similar to the spectral component observed from traditional spectral analysis but show a de-noised form. The physiological meaning of slow-changed component remains to be further studied. This study shows that ICA will be helpful for HR and BP variation analysis
Building Performance Visualization Using Augmented Reality

Magnetic motion trackers have been widely used for tracking user head / hand pose information owing to their advantages such as size, occlusion-less tracking environment and high sample rate. Yet, issues such as latency and jitter leave magnetic tracker technology unfavorable as compared to other tracker technologies. While latency is due to tracker hardware employed, jitter is due to magnetic field distortion caused by the presence of metals nearby. These issues permit the emergence of registration errors when employed for Virtual Environment (VE) systems, specifically for interactive Virtual Reality (VR) or Augmented Reality (AR) applications. This paper discusses the integration of prediction-smoothing algorithms to achieve accurate registration for a magnetic tracker-based immersive Augmented Reality – Computational Fluid Dynamics (CFD) environment. In this project, Kalman and Gaussian filters are utilized to remove latency and jitter effects. In addition, to allow efficient control of head-pose data prediction, a control variable is appended to the Kalman dynamic equation. Furthermore, to permit real-time latency calibration during immersive visualization, speechrecognition is integrated with the system. Such integration, apart from enabling accurate calibration of AR setup, permits robust on-the-fly latency calibration, thereby facilitating effective user experience during interactive, immersive AR visualization of CFD datasets of indoor spaces.
Malaysian Lifelogging over Twitter

Twitter can be used to exchange a variety of data such as sensitive data, political news, commercial messages, real-time events, and life logging. Life logging is the process of recording aspects of one's everyday life in digital form. This paper presents an empirical study to investigate whether Twitter is used for life logging by Malaysian users and to what extent. In this study, ten million Malaysian Twitter messages were gathered via human agents and Twitter's API. These messages were analyzed to assess the extent of their life logging activity according to two main aspects: 1) geolocation of users, and 2) subject of messages. Our results indicate that Malaysians have a high tendency to capture and share their day to day life via Twitter.
A New Method of Vehicle Activity Perception from Live Video

In this paper, we describe an unsupervised model of activity perception by vehicles trajectories in a visual surveillance scene. We introduce a novel trajectory similarity measure based on (5) for comparing trajectories to cluster them. Then using the result of clustering, a dynamic probabilistic network model is constructed and behavior patterns of normal vehicle's trajectories are obtained. At last, MAP is used to estimate the parameters of abnormal activity model for abnormal detection. The effectiveness and robustness of our approach are shown by experiments using noisy dataset from real world scene. The experimental results show that the novel method can obtain the categories and samples of normal activity patterns automatically and exactly to establish the normal activity model. The novel method has high reliability and adaptability.