Miracle-dz/UniFAR-FTUs huggingface dataset:
A Feature Selection Algorithm Based on Pairwise Constraints for Linked Social Media Data

Pairwise constraints, which specifies whether a pair of samples belong to the same class (must-link constraints) or different classes (cannot-link constraints). In this paper, we propose a new feature selection algorithm based on pairwise constraints for linked social media data, which integrate the linking information and pairwise constraints simultaneously to select robust features. As it known to all, the wide use of social media produces massive, high-dimensional and unlabeled social media data. Feature selection has been shown effective in dealing with high-dimensional data. The experiments on Flickr and BlogCatalog datasets evaluate that our method outperforms other conventional feature selection methods.
Paxinos and Watson's the rat brain in stereotaxic coordinates

Since its first edition in 1982, the Paxinos and Watson rat brain atlas has become the most trusted source of accurate coordinates and anatomical information in laboratories throughout the world. It has been cited over 60,000 times, making it the most cited publication in neuroscience. The seventh edition incorporates new findings on developmental gene expression and presents the boundaries of neuromeres in sagittal sections. * 161 thoroughly revised coronal diagrams and accompanying photographic plates spaced at 120 m intervals * 19 thoroughly revised sagittal diagrams and accompanying photographic plates * 27 thoroughly revised horizontal diagrams and accompanying photographic plates * Photographic plates printed from high resolution digital images in color * Diagrams of all spinal cord segments * The most accurate and virtually universally used stereotaxic coordinate system * Neuromeric segments delineated in sagittal sections * A 'mini-atlas' of 14 diagrams to assist beginning students of neuroanatomy * Over 1000 structures identified * Electronic diagrams available to purchasers of this book via a companion web site (http://booksite.elsevier.com/9780123919496/)
Travel Time-Dependent Maximum Entropy Inverse Reinforcement Learning for Seabird Trajectory Prediction

Trajectory prediction is a challenging problem in the fields of computer vision, robotics, and machine learning, and a number of methods for trajectory prediction have been proposed. Most methods generate trajectories that move toward a goal in a straight line (goal-directed) while avoiding obstacles. However, there are not only such goal-directed trajectories but also trajectories that taking detours to reach the goal (non-goal-directed). In this paper, we propose a method of predicting such non-goal-directed trajectories based on the maximum entropy inverse reinforcement learning framework. Our method introduces travel time as a state of the Markov decision process. As a practical example, we apply the proposed method to seabird trajectories measured using global positioning system loggers. Experimental results show that the proposed method can effectively predict non-goal-directed trajectories.
Person Re-identification by Saliency-Weighted Descriptor and Ranking Aggregation

Person re-identification which identifies the same person appeared in non-overlapping camera views is an important and challenging task in computer vision. Although most feature representation methods have significantly improved the person re-identification performance, they do not distinguish between pedestrian object and the environment in images in the process of extracting feature. In this paper, we present a novel feature representation called saliency-weighted descriptor (SWD) which intensifies the discrimination of pedestrian feature. Furthermore, we propose a ranking aggregation algorithm to combine SWD and unweighted descriptor for the purpose of mitigating the impact of inaccurate salient region. The experimental results on public person re-identification datasets (VIPeR, QMUL GRID, CUHK01, and CUHK03) demonstrate the effectiveness of our approach.
Text and Symbol Extraction in Traffic Panel from Natural Scene Images

Traffic panels contain rich text and symbolic information for transportation and scene understanding. In order to understand the information in panels, fast and robust extraction of the text and symbol is a crucial and essential step. This problem cannot be solved using generic scene text detection methods due to the special layout characteristics, especially in Chinese panels. In this paper, we propose a fast and robust approach for Chinese text and symbol extraction in traffic panels from natural scene images. Given a traffic panel in natural scene, Contrasting Extremal Region (CER) algorithm is applied to extract character candidates which are further filtered by boosting classifier using Histogram Orientation Gradient Features. Since Chinese characters often consist of multiple isolated strokes, a hierarchical clustering process of stroke components is carried out to group isolated strokes into characters using the detected characters as seeds. Next, the Chinese text lines are formed by Distance Metric Learning (DSL) method. In consideration that traffic symbols do not possibly appear in the location of texts, symbols are extracted using two stages boosting classifier after text detection. Experimental results on real traffic images from Baidu Street View demonstrate the effectiveness of the proposed method.
Translating Japanese Texts

This is a practical textbook and a precise introduction to problems of and strategies for translating Japanese texts, aimed at both students and teachers of translation, and professional translators. The theoretical foundation is that texts are created by means of interplay between different levels of linguistic material and pragmatic, cognitive and cultural mechanisms. We claim that all translation should hence take such factors into consideration. The book focuses on Japanese and English and attempts to highlight systematic differences between these two languages, but will be useful when translating Japanese into other languages than English and for translation in general. The book will be of interest not only for students and professionals of translation and language studies, but also for people generally interested in Asian languages, cultures and worlds of thought, seen through the lens of translation from Japanese into English.
Process monitoring method

Local and remote processes can be controlled from a data processing system performance monitor. Multiple processes can be controlled concurrently with a single action selected. Processes to be controlled can be ranked when presented to a user, to assist in determining problematic processes that need attention. Process data is captured dynamically at local and remote processes using a daemon to minimize system overhead in monitoring and controlling processes.
The perceptions of rural women doctors about their work

ABSTRACT Background: Recruitment and retention of medical staff are important issues in rural health. The aim of this study was to describe and understand the perceptions of women doctors working in rural hospitals in South Africa about their work. Methods: This was a descriptive study, using a qualitative methodology. Free attitude interviews were conducted with 14 women doctors. Themes were identified and tested against the data and comments from the research diary. Results: The main theme was balance. A rural woman doctor has to juggle different issues, including running the household and responsibilities at work. Other themes that were identified included the reason for working at a rural hospital, attitudes to rural life, opportunities for personal and professional growth, the feeling of being needed in a rural hospital, advantages and disadvantages for children and family, the impact of relationships on the rural woman doctor, issues regarding the environment and security, and that the proximity of home and work gives a rural woman doctor far more connection with her family. Conclusions: Some of the themes identified in this study agreed with international research, e.g. the importance of a job for the spouse, family considerations influencing the choice to specialise, and balancing responsibilities at home and work. The advantage of accommodation close to the hospital is a theme that has not been documented before. Based on the findings, recommendations are made to attract women doctors to rural areas.
Human Driving Patterns Modeling using Hidden Markov Models and GMM-based Clustering

In this work the use of two stochastic methods, hidden Markov models (HMM) and Gaussian mixture models (GMM), is presented to construct a reliable model for human driving patterns. After collecting driving signals including velocity and heading of the vehicle using a simulated driving task, translational acceleration and rotational velocity as two most important features are extracted. Then a segmentation method based on detection the local extrema of the velocity and heading signals, divides driving data into a number of segments. By studying histogram plot of the extracted features, it is found a mixture of three and four univariate Gaussian mixture models can be fitted onto translational acceleration and rotational velocity respectively. Considering each mixture component as a cluster, in total we have twelve different clusters in combination. Segmented data are categorized into these clusters; forming training data set for HMMs. The parameters of one HMM for each cluster are optimized using the obtained training data set. The experimental results reveal that the trained HMMs can recognize correct clusters with about 71% accuracy.
Periodic boundary conditions for three dimensional dislocation dynamics

The boundary conditions in three dimensional Dislocation Dynamics (DD) simulations have always been a matter of concern. Two types of boundary conditions, quasi-free-surface and reflection boundaries are currently being used by groups in Grenoble, France and Pullman, Washington. In this paper, we present a mathematical transformation that enables simulations of dislocation evolution processes in single crystals using periodic boundary conditions (PBCs). The idea is graphically demonstrated with transformation matrices given for BCC crystal systems. Extension to other crystal structures is also discussed. Comparing to the existing boundary conditions, the new approach (1) balances the dislocation flux in and out of a computational cell; and (2) does not require artificial termination of dislocations in the bulk. 3 refs., 2 figs., 1 tab.
Association rules incremental updating method based on cloud computing

To deal with the problem in true environment caused by data mining tasks with larger amount of data,complex processing and intensive computing,to improve the association rules incremental updating mining efficiency,to change the existing algorithm of serial implementation methods,and to solve mass data mining problems,an association rules incremental updating method is proposed on the basis of cloud computing.Firstly,concepts concerning cloud computing,the cloud model and operating process and so on are introduced.Then,a new association rules incremental updating mining algorithm(IUM)is proposed.Lastly,new association rules incremental updating mining algorithm(CIUM)is designed on the basis of cloud platform.After large number of experiments,the results show that the paralleled algorithm is feasible,highly efficient,expandable,and the algorithm can mining mass data effectively.
Efficient receiver structures for wideband wireless data access systems employing dynamic packet assignment techniques

High spectral efficiency, flexible data rate access, reconfigurability, highly integrated terminal devices and scalability are key areas of focus in the development of third generation wireless systems. Also central to the eventual deployment of these broadband wireless systems is the ability to develop cost effective receiver structures that are both flexible and robust. The following article presents a receiver structure which, when deployed in macrocellular environments, supports dynamic packet assignment, frequency hopping, OFDM and interference suppression using digital beamforming techniques. An architecture is described whereby channel survey and packet assignment can be efficiently performed. Results are presented showing the performance of this receiver structure in terms of packet loss, complexity and sensitivity.
Conflict-driven symbolic execution : how to learn to get better

Due to software complexity, manual and automatic testing are not enough to guarantee the correct behavior of software. One alternative to this limitation is known as Symbolic Execution. Symbolic Execution is a formal verification method that simulates software execution using symbolic values instead of concrete ones. The execution starts with all input variables unconstrained, and assignments that use any input variable are encoded as logical expressions. Whenever a branch is reached, the symbolic execution engine checks which values the branch condition can assume. If more than one valid evaluation is possible, the execution forks, and a new process is created for each possibility. In cases where the program execution is finite, symbolic execution is complete, and potentially executes every reachable program path. However, the number of paths is exponential in the number of branches in the program, and this approach suffers from a problem know as path explosion. This thesis presents a novel algorithm that can dynamically reduce the number of paths explored during symbolic execution in order to prove a given set of properties. The algorithm is capable of learning from conflicts detected while symbolically executing a path. I have named this algorithm Conflict-Driven Symbolic Execution (CDSE), since it was inspired by the conflict-driven clause learning (CDCL) insights introduced by modern boolean satisfiability solvers. The proposed algorithm takes advantage of two features responsible for the success of CDCL solvers: conflict analysis and non-chronological backtracking. In a nutshell, CDSE prunes the search space every time a certain branch is proven infeasible by learning the reason why there is a conflict. In order to assess the proposed algorithm, this thesis presents a proof-of-
Calibration of Hand-held Instruments for Whole Body Measurements of Caesium-137

The responses of eight hand-held instruments to 137 Cs internal contamination in people are determined. The caesium was presented in the form of a bottle manikin absorption (BOMAB) phantom filled with 137 Cs in solution. The phantom was assembled to represent the ICRP 23 Reference Man and five smaller configurations representing a 16, 14, 8, and 4 year old, and a 3 month old infant. Measurements were taken at 30 cm, 1 m, and 2 m above the surface of the part/s representing the chest with the phantom arranged horizontally. This corresponds to positioning the instrument at the abdominal end of the sternum in a human subject. Minimum detectable activity (MDA) values were calculated for whole body and lung measurements. The MDAs for whole body measurement of an adult at 30 cm ranged from 140 kBq to 2 MBq, but were ≈ 10 kBq for the three instruments with a region of interest (ROI) set up for the 137 Cs photopeak. Generally, MDAs decreased with decreasing age/size, with those for the 3 month old infant at about 55% of those for the adult. For lung measurements at 30 cm MDA values were about 70% of those for whole body. MDAs at 1 m were about 5x higher than at 30 cm, and at 2 m about 15x higher. Committed effective doses corresponding to the MDA values, 24 hours after intake of soluble 137 Cs, ranged from 2 to 30 mSv for an adult measured at 30 cm, or about 0.15 mSv when ROI facilities were used. Corresponding doses to children, 16 to 4 years of age, decreased with decreasing age, but those for the 3 month old infant were similar to those for the adult. Many of the instruments tested were readily capable of detecting 137 Cs activities in whole body, which correspond to a committed effective dose of 10 mSv, for intake by inhalation, 24 hours before measurement. The procedures and instruments described in this report could be used to rapidly monitor a large group of people for 137 Cs internal contamination. The results of this screening could be used to identify people for possible decorporation therapy.
Optimal congestion control algorithm for ad hoc networks: Penalty function-based approach

In this paper, based on the inherent characteristic of the contention relation between flows in ad hoc networks, we introduce the notion of the link’s interference set, extend the utility maximization problem representing congestion control in wireline networks to ad hoc networks, apply the penalty function approach and the subgradient method to solve this problem, and propose the congestion control algorithm Penalty function-based Optical Congestion Control (POCC) which is implemented in NS2 simulator. Specifically, each link transmits periodically the information on its congestion state to its interference set; the session at each source adjusts the transmission rate based on the optimal tradeoff between the utility value and the congestion level which the interference set of the links that this session goes though suffers from. MATLAB-based simulation results showed that POCC can approach the globally optimal solution. The NS2-based simulation results showed that POCC outperforms default TCP and ATCP to achieve efficient and fair resource allocation in ad hoc networks.
Evaluation of a Motion Seat System for Reduction of a Driver’s Passive Task-Related (TR) Fatigue

A passive task-related (TR) fatigue that occurs monotonous driving environment can degrade driver's alertness and performance, thereby impairing driving safety. This study evaluated the driver's passive TR fatigue reduction effect of the motion seat system in terms of driving performance, physiological response, and subjective fatigue. 17 Korean drivers (6 females and 11 males) measured the driving performance (standard deviation of lane position, SDLP; break reaction time, BRT), percentage of eye closure (PERCLOS), and standard deviation of NN interval (SDNN) of the ECG during simulated driving for 90 minutes on a monotonous highway. The evaluation of the driving consisted of the first half (45 min) and the second half (45 min), while static seat condition in the first half and seat motion (bow, wave motion profile) condition in the second half. During static seat condition driving, SDLP, BRT, and PERCLOS were significantly higher (α = .05) in the second half compared with first half by 6.0 cm, 92.8 msec and 1.3%, respectively. However, there was no significant difference between first half and second half under motion seat conditions. In addition, subjective passive mental fatigue was observed to be 1.2 times lower during motion seat conditions than static seat condition (p < 0.01). The results of this study indicated that motion seat system have some effect on the driver’s passive TR fatigue reduction. Our findings may not extend to on road driving condition because we tested only simulation driving condition. Therefore, effect of motion seat system on driver’s passive TR fatigue need to be evaluated in future studies under real road condition.
Bubbles and particles in a cylindrical rotating flow

To predict the behavior of bubbles and particles moving in fluids, the hydrodynamics forces acting on them need to be known. They are often written in the form of dimensionless coefficients. For some forces, in particular the force at right angles with the velocity, i.e. the lift force, the parameterization of these coefficients is not clear. In this thesis the forces on bubbles and particles in a cylindrical solid body rotating flow are studied, both experimentally and numerically. Experimentally the flow is realized by rotating a horizontal liquid filled cylinder around its axis. A bubble or a buoyant particle will reach a sable equilibrium position in the setup and from this position the drag and lift force can be determined. Some phenomena observed in the flow type under consideration differ from those in many other widely occurring flow types. Examples are a reversal on the lift fore at low Reynolds numbers for a bubble, a freely spinning sphere rotating faster than the surrounding fluid, a lift coefficient which rises with the Reynolds number for a particle, asymmetrical particles and bubbles that drift along the axis of a cylinder. The phenomena and the underlying physics explaining them are discussed.
Development and Validation of High Performance Liquid Chromatography Method for Simultaneous Estimation of Ambroxol and Doxofylline in Their Combined Tablet Dosage Form

The present study described a new, simple, accurate, and precise high performance liquid chromatography method for the simultaneous determination of Ambroxol and Doxofylline in combined tablet dosage form. The chromatographic method was standardized using a BDS hypersil C18, 250 mm × 4.6 mm, 5 μ (particle size), Thermo scientific from Germany with isocratic conditions, and mobile phase containing potassium dihydrogen orthophosphate buffer-pH 4.5 (0.05 M KH2PO4): acetonitrile (60 : 40) at flow rate of 1 ml/min using UV detection at 254 nm. The retention times of Ambroxol and Doxofylline were 3.510 min and 7.247 min, respectively. The method was linear over the concentration range for Ambroxol 3.75–11.25 μg/mL and for Doxofylline 50-150 μg/mL. The recovery of Ambroxol and Doxofylline was found to be in the range of 99.42–101.18% and 99.37–100.28%, respectively. The validation of method was carried out using ICH guidelines. The described HPLC method was successfully employed for the analysis of pharmaceutical formulations containing combined dosage form.
Diagnosis of cardiac involvement in systemic amyloidosis by state-of-the-art echocardiography: where are we now?

ABSTRACT Introduction: Echocardiography is the first-line imaging modality used to characterize heart compromise in systemic infiltrative diseases and to evaluate response to therapy. Cardiac amyloidosis results in progressive heart failure due to restriction of ventricular filling. However, in early stages, typical findings may not be evident, even when symptoms and signs of heart failure with preserved ejection fraction are already present. Identification of early compromise is now made possible with the advent of new echocardiography techniques as two-dimensional strain imaging. Areas covered: Diagnostic and prognostic parameters that can be assessed by echocardiography are discussed, including M-mode, two-dimensional and Doppler echocardiography, as well as by new tools as tissue Doppler imaging and strain imaging. Expert opinion: Systemic amyloidosis is a rare disorder resulting from the production of misfolded proteins, primarily light-chain (AL) immunoglobulins and transthyretin (ATTR). The prognosis of affected subjects depends mainly on the presence and the degree of cardiac involvement. Early diagnosis and treatment are mandatory in order to improve survival. We present here a proposal for structured diagnosis, an algorithm based peak systolic longitudinal left ventricular strain, combined with parameters including typical myocardial texture, increased ventricular wall thickness and the presence of some degree of left ventricular diastolic dysfunction.