Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving event detection using related videos and relevance degree support vector machines

In this paper, a new method that exploits related videos for the problem of event detection is proposed, where related videos are videos that are closely but not fully associated with the event of interest. In particular, the Weighted Margin SVM formulation is modified so that related class observations can be effectively incorporated in the optimization problem. The resulting Relevance Degree SVM is especially useful in problems where only a limited number of training observations is provided, e.g., for the EK10Ex subtask of TRECVID MED, where only ten positive and ten related samples are provided for the training of a complex event detector. Experimental results on the TRECVID MED 2011 dataset verify the effectiveness of the proposed method.
Self-Esteem and Optimism as Mediators in the Relationship Between Test Anxiety and Life Satisfaction Among a School-Based Sample of Adolescents

The aim of this research was to assess the mediating role of self-esteem and optimism in the correlation between test anxiety and life satisfaction. The study group comprised 201 females (50.6%), 185 males (46.6%) and 11 students without stated gender (2.8%) for a total of 397 high school students. The ages of adolescents included in the study group varied from 14 to 19, with a mean age of 16.09 (SD = 1.09). The research used the Test Anxiety Inventory, Satisfaction with Life Scale, Self-Esteem Scale, Life Orientation Test and a personal information form developed by the researchers as data collection tools. According to the results of correlation analysis, there were significant correlations between test anxiety, self-esteem, optimism and life satisfaction. According to the mediation test results, self-esteem and optimism have full mediating roles in the correlation between test anxiety and life satisfaction. The results obtained are discussed with reference to the literature.
Impaired Memory and Evidence of Histopathology in CA1 Pyramidal Neurons through Injection of Aβ1-42 Peptides into the Frontal Cortices of Rat

Introduction: Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders, which has much benefited from animal models to find the basics of its pathophysiology. In our previous work (Haghani, Shabani, Javan, Motamedi, & Janahmadi, 2012), a non-transgenic rat model of AD was used in electrophysiological studies. However, we did not investigate the histological aspects in the mentioned study. Methods: An AD model was developed through bilateral injection of amyloid-β peptides (Aβ) into the frontal cortices. Behavioral and histological methods were used to assess alterations in the memory and (ultra)structures. Furthermore, melatonin has been administered to assess its efficacy on this AD model. Results: Passive avoidance showed a progressive decline in the memory following Aβ injection. Furthermore, Nissl staining showed that Aβ neurotoxicity caused shrinkage of the CA1 pyramidal neurons. Neurodegeneration was clearly evident from Fluoro-jade labeled neurons in Aβ treated rats. Moreover, higher NF-κB immunoreactive CA1 pyramidal neurons were remarkably observed in Aβ treated rats. Ultrastructural analysis using electron microscopy also showed the evidence of subcellular abnormalities. Melatonin treatment in this model of AD prevented Aβ-induced increased NF-κB from immunoreaction and neurodegeneration. Discussion: This study suggests that injection of Aβ into the frontal cortices results in the memory decline and histochemical disturbances in CA1 pyramidal neurons. Furthermore, melatonin can prevent several histological changes induced by Aβ.
Flexible design of image classification rules using extended Fuzzy Oriented Classifier Evolution

We have previously proposed Fuzzy ORiented Classifier Evolution (FORCE), a method to construct fuzzy classification rules automatically with evolution of a directed graph composed of fuzzy conditions. In this work, we introduce (1) flexible optimization of Membership Functions (MFs) for each condition and (2) comparison operators between two input variables into FORCE. The original FORCE has used fixed MFs because optimizing MFs can decrease interpretability of rules. However optimizing MFs can also improve accuracy and compactness of rules. Hence we introduce flexible optimization of MFs into FORCE to investigate its effectiveness. The comparison operators between two input variables enable rules to consider relationships between two input variables easily, and can also improve performance of FORCE. We apply the proposed model to three different image classification tasks to investigate its performance in comparison with conventional methods.
On the Use of Games for Noninvasive EEG-Based Functional Brain Mapping

The use of statistical models and statistical inference for characterizing the interplay between brain structures and human behavior (functional brain mapping) is common in neuroscience. Statistical methods, however, require the availability of sufficiently large data sets. As a result, experimental paradigms used to collect behavioral trials from individuals are data centered and not user centered. This means that experimental paradigms are tuned to collect as many trials as possible, are generally rather demanding, and are not always motivating or engaging for individuals. Subject cooperation and their compliance with the task may decrease over time. Whenever possible, paradigms are designed to control for factors such as fatigue, attention, and motivation. In this paper, we propose the use of the Kinect motion tracking sensor (Microsoft, Inc., Redmond, WA, USA) in a game-based paradigm for noninvasive electroencephalogram (EEG)-based functional motor mapping. Results from an experimental study with able-bodied subjects playing a virtual ball game suggest that the Kinect sensor is useful for isolating specific movements during the interaction with the game, and that the computed EEG patterns for hand and feet movements are in agreement with results described in the literature.
Complement Receptor 2 Is Expressed in Neural Progenitor Cells and Regulates Adult Hippocampal Neurogenesis

Injury and inflammation are potent regulators of adult neurogenesis. As the complement system forms a key immune pathway that may also exert critical functions in neural development and neurodegeneration, we asked whether complement receptors regulate neurogenesis. We discovered that complement receptor 2 (CR2), classically known as a coreceptor of the B-lymphocyte antigen receptor, is expressed in adult neural progenitor cells (NPCs) of the dentate gyrus. Two of its ligands, C3d and interferon-α (IFN-α), inhibited proliferation of wild-type NPCs but not NPCs derived from mice lacking Cr2 (Cr2−/−), indicating functional Cr2 expression. Young and old Cr2−/− mice exhibited prominent increases in basal neurogenesis compared with wild-type littermates, whereas intracerebral injection of C3d resulted in fewer proliferating neuroblasts in wild-type than in Cr2−/− mice. We conclude that Cr2 regulates hippocampal neurogenesis and propose that increased C3d and IFN-α production associated with brain injury or viral infections may inhibit neurogenesis.
GPR based Wi-Fi radio map construction from real/virtual indoor dynamic surveying data

The scalable and cost-effective Wi-Fi radio-map construction is very important to revitalize indoor location-based service. Considering indoor dynamic surveying data for this purpose, the noble radio map construction method is necessary. With the limitation of indoor dynamic surveying, that is, only access to public area and sensitive signal strength reception, the GPR (Gaussian Process Regression) based radio-map construction method exploiting both realistic and virtual indoor dynamic surveying data is proposed and evaluated. The virtual indoor dynamic surveying data consists of a set of locations and its RSSI where the realistic surveying data is rare, by using access point's signal propagation model. The results show that this method can obtain not only high accuracy but also high availability in the whole indoor service area even though the realistic indoor surveying data is rare and its distribution is lack of uniformity.
Different Effects of Regulatory Genes (tat, nef) of Human Immunodeficiency Virus Type 1 (HIV-1) on the Proliferation and Differentiation of Mouse Embryonic Stem Cells in vitro

To examine the effects of the tat and nef regulatory genes of human immunodeficiency virus (HIV-1) on cell differentiation we used the mouse embryonic stem cells (ESC) as a model. Proliferation, embryoid bodies (EB) formation and subsequent differentiation into cardiomyocytes, glial and neuronal cells were investigated in ESC lines transfected with these genes. It has been shown that the transfection of ESC by the tat gene increased their proliferating activity, whereas the nef gene transfected ESC showed its decrease. The number of embryoid bodies formed was higher in the cultures of ESC transfected by the nef and lower in the cells transfected by the tat in comparison with controls. The percentage of embryoid bodies with contracting cardiomyocytes was higher against control in the nef transfected cells and lower in ESC transfected with the tat. There were no reliable differences in the appearance of glial cells between control and the nef and tat transfected cell lines. Spontaneous differentiation of ESC into neuronal cells was almost not observed in the nef transfected cells, in contrast to control and the tat transfected cells. However, addition of retinoic acid (RA) to the nef transfected cells caused even a slight increase in neuron formation as compared to control ESC treated with RA. Thus, for the first time we have shown that the tat and nef regulatory genes of HIV-1 had a visible effect on proliferation of ESC and some first steps of their differentiation. In general, the reverse correlation between the effects of these two viral genes on ESC proliferation and differentiation were observed.
Complete Parallax and Proper-Motion Solutions for Halo Binary-Lens Microlensing Events

A major problem in the interpretation of microlensing events is that the only measured quantity, the Einstein timescale tE, is a degenerate combination of the three quantities one would like to know: the mass, distance, and speed of the lens. This degeneracy can be partly broken by measuring either a "parallax" or a "proper motion," and can be completely broken by measuring both. Proper motions can easily be measured for caustic-crossing binary-lens events. Here we examine the possibility (first discussed by Hardy & Walker) that one could also measure a parallax for some of these events by comparing the light curves of the caustic crossing as seen from two observatories on Earth. We derive analytic expressions for the signal-to-noise ratio of the parallax measurement in terms of the characteristics of the source and the geometry of the event. For Galactic halo binary lenses seen toward the LMC, the light curve is delayed from one continent to another by a seemingly minuscule 15 s (compared to tE ~40 days). However, this is sufficient to cause a difference in magnification on the order of 10%. To actually extract complete parallax information (as opposed to merely detecting the effect) requires observations from three noncollinear observatories. Parallaxes cannot be measured for binary lenses in the LMC, but they can be measured for Galactic halo binary lenses seen toward M31. Robust measurements are possible for disk binary lenses seen toward the Galactic bulge, but are difficult for bulge binary lenses.
Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model

Neural adaptation underlies the ability of neurons to maximize encoded information over a wide dynamic range of input stimuli. Recent spiking neuron models like the adaptive Spike Response Model implement adaptation as additive fixed-size fast spike-triggered threshold dynamics and slow spike-triggered currents. Such adaptation accurately models neural spiking behavior over a limited dynamic input range. To extend efficient coding over large changes in dynamic input range, we propose a multiplicative adaptive Spike Response Model where the spike-triggered adaptation dynamics are scaled multiplicatively by the adaptation state at the time of spiking. We show that, unlike the additive adaptation model, the firing rate in our multiplicative adaptation model saturates to a realistic maximum spike-rate regardless of input magnitude. Additionally, when simulating variance switching experiments, the model quantitatively fits experimental data over a wide dynamic range. Dynamic threshold models of adaptation furthermore suggest a straightforward interpretation of neural activity in terms of dynamic differential signal encoding with shifted and weighted exponential kernels. We show that when thus encoding rectified filtered stimulus signals, the multiplicative adaptive Spike Response Model achieves a high coding efficiency and maintains this efficiency over changes in the dynamic signal range of several orders of magnitude, without changing model parameters.
Three Decades of Research on Database Design at PUC-Rio

Research on database design at PUC-Rio dates back to the late seventies and covers a broad range of topics, from the early development of the relational model to recent applications of semiotic concepts to the design and specification of information systems. This paper briefly reviews some of the major contributions of the group, from the perspective of the authors. It organizes the contributions according to the data model or to the underlying disciplines that they are based on. Within each section, the presentation follows a chronological order as much as possible.
A Hybrid Scheme against Pollution Attack to Network Coding

Network coding allows intermediate nodes to encode data packets, which increases network throughput and enhances robustness. However, once a node injects corrupted data blocks into a network, the polluted data blocks will propagate quickly with network coding. Based on linear subspace and key pre-distribution, this paper proposes a HYBRID scheme against pollution attacks to network coding. Some typical properties of HYBRID are: (1) all nodes are able to detect polluted data blocks; (2) it prevents tag pollution attacks; (3) It does not need a very large field to implement and is computationally efficient; (4) It does not need a time synchronization of all nodes in a network and the correctness of each data packet can be verified immediately when it arrives at a node.
Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images

An important first step in diagnosis and treatment planning using tomographic imaging is differentiating and quantifying diseased as well as healthy tissue. One of the difficulties encountered in solving this problem to date has been distinguishing the partial volume constituents of each voxel in the image volume. Most proposed solutions to this problem involve analysis of planar images, in sequence, in two dimensions only. We have extended a model-based method of image segmentation which applies the technique of iterated conditional modes in three dimensions. A minimum of user intervention is required to train the algorithm. Partial volume estimates for each voxel in the image are obtained yielding fractional compositions of multiple tissue types for individual voxels. A multispectral approach is applied, where spatially registered data sets are available. The algorithm is simple and has been parallelized using a dataflow programming environment to reduce the computational burden. The algorithm has been used to segment dual echo MRI data sets of multiple sclerosis patients using lesions, gray matter, white matter, and cerebrospinal fluid as the partial volume constituents. The results of the application of the algorithm to these datasets is presented and compared to the manual lesion segmentation of the same data.
A Novel Ex Vivo Isolation and Expansion Procedure for Chimeric Antigen Receptor Engrafted Human T Cells

Genetically engineered T lymphocytes are a promising option for cancer therapy. Prior to adoptive transfer they have to be expanded in vitro to reach therapeutically sufficient numbers. So far, no universal method exists for selective in vitro expansion of engineered T lymphocytes. In order to overcome this problem and for proof of concept we incorporated a novel unique peptide sequence of ten amino acids as epitope (E-Tag) into the binding domains of two novel chimeric antigen receptors (ECARs) directed against either prostate stem cell antigen (PSCA) for the treatment of prostate cancer (PCa) or CD33 for the treatment of acute myeloide leukemia (AML). The epitope tag then was utilized for expanding ECAR engrafted T cells by triggering the modified T cells via a monoclonal antibody directed against the E-Tag (Emab). Moreover, the E-Tag served as an efficient selection epitope for immunomagnetic isolation of modified T cells to high purity. ECAR engrafted T cells were fully functional and mediated profound anti-tumor effects in the respective models of PCa or AML both in vitro and in vivo. The method can be integrated straightforward into clinical protocols to improve therapeutic efficiency of tumor treatment with CAR modified T lymphocytes.
Teaching Bandits How to Behave Manuscript

Consider a setting in which an agent selects an action in each time period and there is an interested party who seeks to induce a particular action. The interested party can associate incentives with actions to perturb their value to the agent. The agent’s decision problem is modeled as a multi-armed bandit process where the intrinsic value for an action updates independently of the state of other actions and only when the action is selected. The agent selects the action in each period with the maximal perturbed value. In particular, this models the problem of a learning agent with the interested party as a teacher. For inducing the goal action as soon as possible, or as often as possible over a fixed time period, it is optimal for an interested party with a per-period incentive budget to assign the budget to the goal action and wait for the agent to learn to want to make that choice. Teaching is easy in this case. In contrast, with an across-period budget, no algorithm can provide good performance on all instances without knowledge of the agent’s update process, except in the particular case in which the goal is to induce the agent to select the goal action
Video-based recognition of unmarked lanes via texture interpretation and N-level-set-fitting

In modern driver assistance systems the environment perception plays a decisive role in order to evaluate the current traffic scene. The reliable recognition of the drivable area provides essential information for lane departure warning systems which in turn contribute to active road safety. Most systems on lane recognition do reliable work on well marked roads and under good weather and lighting conditions but they fail in more difficult scenarios especially in the absence of lane markings. In this paper we present a two-stage algorithm for the recognition of unmarked lanes using only a single monochrome camera. In the first step our approach transforms texture differences into gray value information in order to intensify the separation of on-road and off-road parts. Then a N-level-set-fitting is applied on this new image which uses a clothoid road model as underlying set model. Minimizing the error function yields to the parameter estimation of the road model. The method is independent of the availability of lane markings or any other landmarks. The parameter propagation to the next frame is done via a standard kalman filter. The given results show the performance of our approach in robust lane recognition under various weather and lighting conditions.
Sensor selection and placement for failure diagnosis in networked aerial robots

Unmanned aerial vehicles (UAVs) represent an important class of networked robotic applications that must be both highly dependable and autonomous. This paper addresses sensor selection and placement problems for distributed failure diagnosis in such networks where multiple vehicles must agree on the fault status of another UAV. An integer linear programming (ILP) approach is proposed to solve these problems. The ILP models of interest are developed and solved using two different solvers. Experimental results indicate that the proposed models are tractable for medium-sized topologies
Strategies to minimise the total run time of cyclic graph based genetic programming with GPUs

In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (GP) can be accelerated on one machine using currently available mid-range Graphics Processing Units (GPUs). Cyclic graphs pose different problems for evaluation than do trees and we describe how our CUDA based, "population parallel" evaluator tackles these problems. Previous similar work has focused on the evaluation alone. Unfortunately large reductions in the evaluation time do not necessarily translate to similar reductions in the total run time because the time spent on other tasks becomes more significant. We show that this problem can be tackled by having the GPU execute in parallel with the Central Processing Unit (CPU) and with memory transfers. We also demonstrate that it is possible to use a second graphics card to further improve the acceleration of one machine. These additional techniques are able to reduce the total run time of the GPU system by up to 2.83 times. The combined architecture completes a full cyclic GP run 434.61 times faster than the single-core CPU equivalent. This involves evaluating at an average rate of 3.85 billion GP operations per second over the course of the whole run.
Modeling for hue shift effect of human visual system on high luminance display

This paper proposes a color correction method based on modeling the hue shift phenomenon of human visual system (HVS). Observers tend to perceive same color stimuli, but of different intensity, as different hues, what is referred to as the hue shift effect. Although the effect can be explained with the Bezold-Brücke (B-B) effect, it is not enough to apply the B-B model on high luminance displays because most displays have a broad-band spectrum distribution and results vary according to type of display. In the proposed method, the quantities of hue shift between a high luminance display and a normal luminance display were first modeled by a color matching experiment with color samples along the hue angle of the LCH color space. Based on the results, the hue shift was then modeled piecewise and was finally applied to the inverse characterization of display to compensate the original input image. From evaluating the proposed method using the psychophysical experiment with some test images, we confirmed that the proposed modeling method is effective for color correction on high luminance displays.