Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacodynamic model of neuroendocrine controlled blood pressure

In this present work, the authors proposed a pharmacodynamic model which reflects the effects of both antihypertensive and the antidepressant agents on the signalling pathways associated with blood pressure concomitant with the action of neuroendocrine messengers. There are large numbers of signalling and metabolic pathways associated with blood pressure. The parameters associated with the neuroendocrine signaling pathways are altered in diseased state of a subject. Here the authors endeavoured to develop a model how the pharmacodynamic effects of antihypertensive or antidepressant agents change the dynamics of certain signalling pathways which affect the blood pressure.
A state-dependent vector control for a West Nile Virus model from mosquitoes to birds

In this paper, a novel West Nile Virus model looking upon the infected birds as monitoring threshold, for the mosquitoes and birds with impulsive state feedback control is considered. We obtain sufficient conditions of the global asymptotical stability of the system without impulsive state feedback control via comprehensively qualitative analysis. By using the Poincar\'e map, we obtain that the system with impulsive state feedback control has a positive periodic solution of order-1 or order-2 which is asymptotical stability due to the analogue of Poincar\'e criterion, theory of differential inequalities, differential equation geometry and so on. What's more, sufficient conditions for existence and stability of the order one periodic solution are given by the existence and uniqueness of the limit. Our results show that the control measure is effective and feasible by means of numerical simulations.
Fast and Robust Reconstruction for Fluorescence Molecular Tomography via L 1-2 Regularization

Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means L 1 minimization may not produce the sparsest solution. In this paper, we propose a novel reconstruction method by minimization of the difference of L 1 and L 2 norms. To solve the nonconvex L 1-2 minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an L 1 minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. We investigated the performance of the proposed method with both simulated data and in vivo experimental data. The results demonstrate that the DCA for L 1-2 minimization outperforms the representative algorithms for L 1, L 2, L 1/2, and L 0 when the system matrix is highly coherent.
Male mate preference as an agent of fecundity selection in a polymorphic salamander

Abstract Color polymorphisms are associated with variation in other traits which may affect individual fitness, and these color‐trait associations are expected to contribute to nonrandom mating in polymorphic species. The red‐backed salamander (Plethodon cinereus) exhibits a polymorphism in dorsal pattern: striped and unstriped, and previous studies have suggested that they may mate nonrandomly. However, the mechanism(s) contributing to this behavior remain unclear. Here we consider the role that male preference may have in driving mating behavior in P. cinereus. We limit our focus to striped individuals because this morph is most likely to be choosy given their dominant, aggressive behavioral profiles relative to unstriped males. Specifically, we evaluated (a) whether striped males preferentially associate with females with respect to her dorsum color, size, and body condition and (b) if so, whether female traits are evaluated via visual or chemical cues. We also considered whether the frequency of another male social behavior, nose taps, was associated with mate preferences. We found that striped male P. cinereus nose tapped more often to preferred females. However, males only assessed potential mates via chemical cues, preferring larger females overall. Reproductive phenology data on a sample of gravid females drawn from the same population indicated that the color morphs do not differ in reproductive traits, but larger females have greater fecundity. Given our findings, we conclude that female P. cinereus are under fecundity selection, mediated by male preference. In this manner, male mating behavior contributes to observations of nonrandom mate associations in this population of P. cinereus.
Neural Networks for Time Perception and Working Memory

Time is an important concept which determines most human behaviors, however questions remain about how time is perceived and which areas of the brain are responsible for time perception. The aim of this study was to evaluate the relationship between time perception and working memory in healthy adults. Functional magnetic resonance imaging (fMRI) was used during the application of a visual paradigm. In all of the conditions, the participants were presented with a moving black rectangle on a gray screen. The rectangle was obstructed by a black bar for a time period and then reappeared again. During different conditions, participants (n = 15, eight male) responded according to the instructions they were given, including details about time and the working memory or dual task requirements. The results showed activations in right dorsolateral prefrontal and right intraparietal cortical networks, together with the anterior cingulate cortex (ACC), anterior insula and basal ganglia (BG) during time perception. On the other hand, working memory engaged the left prefrontal cortex, ACC, left superior parietal cortex, BG and cerebellum activity. Both time perception and working memory were related to a strong peristriate cortical activity. On the other hand, the interaction of time and memory showed activity in the intraparietal sulcus (IPS) and posterior cingulate cortex (PCC). These results support a distributed neural network based model for time perception and that the intraparietal and posterior cingulate areas might play a role in the interface of memory and timing.
A Large-Scale Agent-Based Traffic Microsimulation Based On Queue Model

We use the so-called queue model introducted by Gawron as the base of the traffic dynamics in our micro-simulation. The queue model describes the links with a flow capacity that limits the number of agents that can leave the link and a space constraint which defines the limit of the number of agents that can be on a link at the same time. Free flow speed is the third key component of traffic dynamics in the model. Flow capacity and space constraint together model physical queues, which can spill back beyond the end of the link. A consequence of this is that fairness between the incoming traffic streams becomes an issue, since in a spill-back situation they cannot be served at their full rate. We implement and verify a simple solution to this; the solution is much simpler than the one chosen in many other models. The traffic micro-simulation is “large-scale” which means the simulation is capable of modeling the behavior of millions of agents simultaneously. We utilize a parallel implementation to speed up the computation. In this implementation, the data is distributed onto a number of computing node, each of which runs a smaller portion of the data. Data distribution and communication among the computing nodes are achieved by freely available software libraries. We test this simulation on two different scenarios using the road network of Switzerland. One of them is aimed to see how the simulation handles the congestion whereas the other one is based on real data of the daily activities of the Swiss people. The parallel version on the bigger scenario gives a runtime that is about 800 times faster than real time on 64 computing nodes using Myrinet. The maximum number of vehicles simultaneously in that simulation is about 160 000.
A Mobile Augmented Reality Service Model Based on Street Data, and Its Implementation

The popularity of smart devices and Location Based Services (LBSes) is increasing in part due to users demand for personalized information associated with their location. These services provide intuitive and realistic information by adopting Augmented Reality(AR) technology. This technology utilizes various sensors embedded in the mobile devices. However, these services have inherent problems due to the devices small screen size and the complexity of the real world environment, overlapping content on a small screen and placing icons without considering the user's possible movement. In order to solve these problems, this paper proposes a Mobile Augmented Reality Model with the application of Street Data. The model consists of two layers "Real-world layer" and "Information layer". In the model, a user creates a query by scanning the nearby street with a camera in real space and searches accessible content along the street through the use of the information space. Furthermore, the results are placed on both sides of the street to solve the issue of Overlapping. Also, the proposed model is implemented for region "Aenigol", and the efficiency and usefulness of the model are verified.
Driver Gaze Region Estimation Without Using Eye Movement

Automated estimation of the allocation of a driver's visual attention may be a critical component of future Advanced Driver Assistance Systems. In theory, vision-based tracking of the eye can provide a good estimate of gaze location. In practice, eye tracking from video is challenging because of sunglasses, eyeglass reflections, lighting conditions, occlusions, motion blur, and other factors. Estimation of head pose, on the other hand, is robust to many of these effects, but cannot provide as fine-grained of a resolution in localizing the gaze. However, for the purpose of keeping the driver safe, it is sufficient to partition gaze into regions. In this effort, we propose a system that extracts facial features and classifies their spatial configuration into six regions in real-time. Our proposed method achieves an average accuracy of 91.4% at an average decision rate of 11 Hz on a dataset of 50 drivers from an on-road study.
Rasdiscriminatie op de Amsterdamse arbeidsmarkt

Situation testing was used to investigate race discrimination in hiring personnel on the Amsterdam labor market. A black Surinamer or a Spanish migrant worker applied for 278 jobs, each time immediately followed by a white Dutchman. Race discrimination did occur; there was no significant difference between its extent for the black excolonial & the migrant worker, but it was highest on the lowest job level, & its extent varied strongly with rates of supply & demand for particular jobs. 5 Tables. Modified HA.
A 1.25 GHz 0.35 /spl mu/m monolithic CMOS PLL clock generator for data communications

A 1.25 GHz monolithic CMOS PLL clock synthesis unit was designed for data communications. The monolithic PLL consists of a ring oscillator, divider, phase/frequency detector, charge pump and on-chip loop filter. The voltage controlled oscillator incorporates a quadrature output ring structure with sub-feedback loop embedded to speed up the circuit. The design accommodates process, supply voltage and temperature variations. The PLL has been fabricated in a 0.35 /spl mu/m CMOS process, occupies an active area of 1 mm/sup 2/ and consumes 100 mW power at 3.3 V.
A 2 . 4 GHz Voltage Controlled Oscillator

Voltage controlled oscillators are essential building blocks in communication systems and are used as local oscillators to up and down-convert signals in an RF mixer. Despite the continuous improvement in the state of the art of CMOS VCO’s, they still remain the most critical blocks of RF transceivers. The most important parameters of a VCO are phase noise, power consumption and frequency tuning ranges. The monolithic integration of all components of a VCO is also very challenging. A 2.4 GHz voltage controlled oscillator (VCO) has been designed in a standard 0.25m CMOS process.
An automatic ontology population with a machine learning technique from semi-structured documents

The manual design of an ontology usually defines the concepts for the domain, but the individual instances of the concepts are often missing though they are important in using the ontology as a knowledge base. This is due to high cost of the manual construction of individuals. In order to tackle this problem, this paper proposes an automatic method for ontology population. The knowledge source for ontology population used in this paper is the web tables of which structure is relatively well organized. Since a web table can be analyzed into a parse tree, the most appropriate concept within the ontology for a given web table is determined by a kernel method, so-called a parse tree kernel. Then, the table is populated as an individual of the concept. According to the experimental results on a large ontology with a great number of concepts, the proposed method achieves 62.35% of accuracy for a number of web tables.
FEATURE EXTRACTION BASED ON OBJECT ORIENTED ANALYSIS

: Most traditional pixel-based classification approaches are based exclusively on the digital number of the pixel itself. Thereby only the spectral information is used for the classification. The situation comes into worse when extracting certain interested features only. As a result, it produces the salt-and-pepper-effect pseudocolor layer. An object-oriented classification is suggested in order to overcome the limitations stated above. The existing software, eCognition allows the polygon based classification process. It is based on fuzzy logic, allows the integration of a broad spectrum of different object features, such as spectral values, shape and texture. This study demonstrated the extraction of Penang Bridge from the Landsat TM image even with its’ moderate resolution. The land use classification result as well as the spatial information can be exported to GIS environment for further analysis.
Integrating Document Features for Expert Finding

Expert finding is a key task in enterprise search a nd has recently attracted lots of attention from bo th research and industry communities. The Text REtrieval Conference 2 (TREC) has organized an expert search task for 200 5, 2 06, and 2007. Given a search topic, a prominent existing approach is to a pply some information retrieval (IR) system to retr ieve top ranking documents, which will then be used to derive associations betw e n experts and the search topic based on co-occurr en es. However, we argue that expert finding is more sensitive to multiple d ocument features that current expert finding system insufficiently address, including: (1) multiple levels of associations betw en experts and search topics, (2) document interna l structure and (3) document authority. We propose a novel approach whi ch integrates the above three aspects as well as a query expansion technique in a two-stage model for expert finding. A systematic evaluation is conducted on the TREC200 6 and TREC2005 expert search collections to test the performance o f our approach and the effects of different aspects of document features and query expansion. These experimental results show th at query expansion can dramatically improve expert finding performance. Both document internal structure and our novel mult iple window based approach for taking into account multiple levels of associations improve expert finding over the direct use of a number of well-known IR models for both w ith and without query expansion.
Stoichiometry of HLA Class II-Invariant Chain Oligomers

Background The HLA gene complex encodes three class II isotypes, DR, DQ, and DP. HLA class II molecules are peptide receptors that present antigens for recognition by T lymphocytes. In antigen presenting cells, the assembly of matched α and β subunits to heterodimers is chaperoned by invariant chain (Ii). Ii forms a homotrimer with three binding sites for class II heterodimers. The current model of class II and Ii structure states that three αβ heterodimers bind to an Ii trimer. Methology/Principal Findings We have now analyzed the composition and size of the complexes of class II and Ii using epitope tagged class II subunits and density gradient experiments. We show here that class II-Ii oligomers consist of one class II heterodimer associated with one Ii trimer, such that the DR, DQ and DP isotypes are contained within separate complexes with Ii. Conclusion/Significance We propose a structural model of the class II-Ii oligomer and speculate that the pentameric class II-Ii complex is bent towards the cell membrane, inhibiting the binding of additional class II heterodimers to Ii.
Merging element fuzzy cognitive maps

Importance degree and difference degree of keywords in different topics have been computed by the weights in Element Fuzzy Cognitive Maps (E-FCMs). Logic "and" operation is introduced to roughly evaluate the similarities between mass E-FCMs in order to form similar communities of E-FCMs. Based on the weights computing and the logic "and" operation, an E-FCMs-based knowledge merging algorithm is proposed to inspect the noisy and the redundancy information hidden in the original E-FCMs belonging to one similar community. Shannon entropy is employed as an indicator to measure the loss of textual information during the merging process of E-FCMs. The merging algorithm and the indicator provide a concise representation of text knowledge that can be used in understanding-based text automatic classification and clustering, as well as relevant knowledge aggregation and integration. The proposed algorithm has very good application prospects in the fields of e-Science knowledge gird and e-Learning.
Low-Rank Matrix Recovery Approach for Clutter Rejection in Real-Time IR-UWB Radar-Based Moving Target Detection

The detection of a moving target using an IR-UWB Radar involves the core task of separating the waves reflected by the static background and by the moving target. This paper investigates the capacity of the low-rank and sparse matrix decomposition approach to separate the background and the foreground in the trend of UWB Radar-based moving target detection. Robust PCA models are criticized for being batched-data-oriented, which makes them inconvenient in realistic environments where frames need to be processed as they are recorded in real time. In this paper, a novel method based on overlapping-windows processing is proposed to cope with online processing. The method consists of processing a small batch of frames which will be continually updated without changing its size as new frames are captured. We prove that RPCA (via its Inexact Augmented Lagrange Multiplier (IALM) model) can successfully separate the two subspaces, which enhances the accuracy of target detection. The overlapping-windows processing method converges on the optimal solution with its batch counterpart (i.e., processing batched data with RPCA), and both methods prove the robustness and efficiency of the RPCA over the classic PCA and the commonly used exponential averaging method.
Robust Catheter and Guidewire Tracking Using B-Spline Tube Model and Pixel-Wise Posteriors

In endovascular surgery and cardiology, robotic catheters are emerging as a promising technology for enhanced catheter manipulation and navigation while reducing radiation exposure. For robotic catheter systems especially with tendon actuation, a key challenge is the localisation of the catheter shape and position within the anatomy. An effective approach is through image-based catheter/guidewire detection and tracking. However, these are difficult problems due to the thin appearance of the instruments in the image and the low signal-to-noise ratio of fluoroscopy. In this letter, we propose a deformable B-spline tube model, which can effectively represent the shape of a catheter and guidewire. The model allows fitting using a region-based probabilistic algorithm, which does not rely on intensity gradients but exploits a signed distance function and the nonparametric distributions of measurements. Unlike previous B-spline fitting approaches, which optimise the spline with respect to control points, we propose a knot-driven scheme with an equidistance prior to better fit complex curves. Our probabilistic framework shows promising results for catheter and guidewire tracking in different procedures even with handling overlapping instrument segments. We present empirical studies using phantom model data and in vivo fluoroscopic sequences with annotated ground truth. Our results indicate that the proposed approach can precisely model the catheter and guidewire contours in near real time, and this information can be embedded in a robotic catheter control loop or utilised for image-guidance.
FEATURE EXTRACTION OF MAMMOGRAMS

Breast cancer is the second leading cause of cancer deaths in women today. Early detection of the cancer can reduce mortality rate. Studies have shown that radiologists can miss the detection of a significant proportion of abnormalities in addition to having high rates of false positives. Pattern recognition in image processing requires the extraction of features from regions of the image and the processing of these features with a pattern recognition algorithm. We consider the feature extraction part of this processing; with a focus on the problem of micro calcification detection in digital mammography. For every pattern classification problem, the most important stage is feature extraction. The accuracy of the classification depends on the feature extraction stage. We have extracted textural, statistical and structural features which show promising results than most of the existing technology.
Zhen Gan Xi Feng Decoction, a Traditional Chinese Herbal Formula, for the Treatment of Essential Hypertension: A Systematic Review of Randomized Controlled Trials

Objectives. To assess the clinical effectiveness and adverse effects of Zhen Gan Xi Feng Decoction (ZGXFD) for essential hypertension (EH). Methods. Five major electronic databases were searched up to August 2012 to retrieve any potential randomized controlled trials designed to evaluate the clinical effectiveness of ZGXFD for EH reported in any language, with main outcome measure as blood pressure (BP). Results. Six randomized trials were included. Methodological quality of the trials was evaluated as generally low. Four trials compared prescriptions based on ZGXFD with antihypertensive drugs. Meta-analysis showed that ZGXFD was more effective in BP control and TCM syndrome and symptom differentiation (TCM-SSD) scores than antihypertensive drugs. Two trials compared the combination of modified ZGXFD plus antihypertensive drugs with antihypertensive drugs. Meta-analysis showed that there is significant beneficial effect on TCM-SSD scores. However, no significant effect on BP was found. The safety of ZGXFD is still uncertain. Conclusions. ZGXFD appears to be effective in improving blood pressure and hypertension-related symptoms for EH. However, the evidence remains weak due to poor methodological quality of the included studies. More rigorous trials are warranted to support their clinical use.