Miracle-dz/UniFAR-FTUs huggingface dataset:
Recursive State Estimation for Lane Detection Using a Fusion of Cooperative and Map Based Data

Modern automated and cooperative driver assistance systems (CoDAS) rely deeply on the position estimation. Regardless of absolute positioning accuracy, the relative position in regard to driving environment and other vehicles needs to be of high quality to enable sophisticated functions. Global Navigation Satellite Systems (GNSS) fulfill this demand only partially. In this paper we present an algorithm to accurately infer the driving lane by utilizing Dedicated Short Range Communication (DSRC) and map data alone. We evaluate our approach against simulated and real-life data from Europes largest cooperative vehicle Field Operational Test (FOT): simTD. This lane detection algorithm will be an enabler for CoDAS functions like collaborative driving and merging developed in the TEAM IP project.
From Glosses to Qualia: Qualia Extraction from Senso Comune

This paper describes a case study on meth-ods for automatically extracting qualia relations from dictionary glosses in Italian, namely the Senso Comune De Mauro Dictionary (SCDM). The qualia extraction has been addressed by means of a pattern-based approach and lexical match with an Italian generative lexicon based language resource, PAROLE-SIMPLE-CLIPS (PSC). The evaluation of the extraction approaches has been performed with respect to a manually built Gold Standard containig 174 different qualia. The results obtained are encouraging (P = 0.84, R = 0.08 for the pattern extraction approach and P=0.73 and R=0.16 for the merging of pattern extraction and lexical match) and suggest that the information contained in the SCDM glosses is complementary with that in PSC.
Using dynamic Bayesian networks for the prediction of mental deficiency in children with down syndrome

This work is located in the domain of the Knowledge Discovery from Data (KDD). The purpose of the KDD is the extraction of knowledge or of a knowledge starting from great number of data which evolve in a dynamic way. In this work we propose an approach for the temporal KDD. The Bayesian Network (BN) is one of the techniques used in KDD. Our objective comes back to fix the best algorithm of incremental learning of structure extracted by the Dynamic Bayesian Network (DBN) and using it in the decision making in a dynamic way. Our scope of application is the case of Down Syndrome (DS) also known as trisomy 21, the data are provided by the medical genetics and Child Psychiatry units of the university hospital Hedi Chaker Sfax, Tunisia.
A filter and refinement approach to RBIR

A promising trend in content based image retrieval (CBIR) is the incorporation of the notion of objects into the similarity evaluation. Images are automatically segmented into a dynamic number of regions that roughly correspond to objects. In region based image retrieval (RBIR), we compute standard feature characteristic atop those segments and evaluate the similarity between images based on the similarity of its segments. This requires the application of matching techniques, like the Hungarian algorithm, that are prohibitivly expensive for collections beyond a few thousand images. In this paper, we provide a filter and refinement search algorithm to solve RBIR queries in reasonable times for even large image collections. We describe the bounding functions as well as implementational issues of our approach. Experimental results show that retrieval performance surpasses alternative approaches by a factor of 5.
Combinatorial expression of three zebrafish genes related to distal- less: part of a homeobox gene code for the head

We describe analysis of zebrafish distal-less-related homeobox genes that may serve as specifiers of positional information in anterior regions of the CNS and in peripheral structures. We isolated three zebrafish genes, dlx2, dlx3, and dlx4, by screening embryonic cDNA libraries. Comparisons of the predicted sequences of the Dlx2, Dlx3, and Dlx4 proteins with distal-less proteins from other species suggest that vertebrate distal-less genes can be divided into four orthologous groups. We observed similarities but also unique features of the expression patterns of the zebrafish dlx genes. Among the three genes, dlx3 alone is expressed during gastrulation. Shortly after gastrulation, cells in the ventral forebrain rudiment express dlx2 and dlx4, but not dlx3, and hindbrain neural crest cells express only dlx2. Presumptive precursor cells of the olfactory placodes express dlx3 and dlx4 but not dlx2. Transcripts of dlx3 and dlx4 are present in overlapping subsets of cells in the auditory vesicle and in cells of the median fin fold, whereas dlx2 is never expressed in the auditory vesicle and only at low levels in localized regions of the median fin fold. Cells of the visceral arches and their primordia express all three dlx genes, but with different developmental time courses. We suggest that combinatorial expression of the dlx genes is part of a homeobox gene code specifying pattern formation or cell fate determination in the forebrain, in peripheral structures of the head, and in the fins.
Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model

Black locust (Robinia pseudoacacia L.) is a tree species of high economic and ecological value, but is also considered to be highly invasive. Understanding the global potential distribution and ecological characteristics of this species is a prerequisite for its practical exploitation as a resource. Here, a maximum entropy modeling (MaxEnt) was used to simulate the potential distribution of this species around the world, and the dominant climatic factors affecting its distribution were selected by using a jackknife test and the regularized gain change during each iteration of the training algorithm. The results show that the MaxEnt model performs better than random, with an average test AUC value of 0.9165 (±0.0088). The coldness index, annual mean temperature and warmth index were the most important climatic factors affecting the species distribution, explaining 65.79% of the variability in the geographical distribution. Species response curves showed unimodal relationships with the annual mean temperature and warmth index, whereas there was a linear relationship with the coldness index. The dominant climatic conditions in the core of the black locust distribution are a coldness index of −9.8 °C–0 °C, an annual mean temperature of 5.8 °C–14.5 °C, a warmth index of 66 °C–168 °C and an annual precipitation of 508–1867 mm. The potential distribution of black locust is located mainly in the United States, the United Kingdom, Germany, France, the Netherlands, Belgium, Italy, Switzerland, Australia, New Zealand, China, Japan, South Korea, South Africa, Chile and Argentina. The predictive map of black locust, climatic thresholds and species response curves can provide globally applicable guidelines and valuable information for policymakers and planners involved in the introduction, planting and invasion control of this species around the world.
Journal of Cerebral Blood Flow and Metabolism Hydrogen Ions Kill Brain at Concentrations Reached in Ischemia

Elevation of brain glucose before the onset of nearly complete ischemia leads to increased lactic acid within brain. When excessive, such acidosis may be a necessary factor for converting selective neuronal loss to brain infarction from nearly complete ischemia. To ex­ amine the potential neurotoxicity of excessive lactic acid concentrations, we microinjected (0.5 ILl/min) 150 mM sodium lactate solutions (adjusted to 6.50-4.00 pH) for 20 min into parietal cortex of anesthetized rats. Intersti­ tial pH (pHo) was monitored with hydrogen ion-selec­ tive microelectrodes. Animals were allowed to recover for 24 h before injection zones were examined with the light microscope. Injectants produced brain necrosis in a Infarction occurs when brain glucose is elevated above normal before the onset of nearly complete ischemia (Myers, 1979; Kalimo et aI. , 1981; Pulsi­ nelli et aI., 1982), circumstances known to raise neocortical lactate values during ischemia to 19 � mmollkg (Pulsinelii et aI., 1982). Conversely, in neocortex only small-to medium-sized neurons are destroyed after nearly complete ischemia during normoglycemia (Pulsinelli and Brierley, 1979), which, in rat, is correlated with a neocortical lac­ tate content of up to 13 mmollkg (Pulsinelli and Duffy, 1983). These considerations have led to the postulate that infarction of brain from nearly com­ plete ischemia is thought to result from excessive tissue lactacidosis (Plum, 1983). Direct measurements of brain hydrogen ion con­ centration ([H+ ]) during ischemia have extended our understanding of the mechanisms by which aci-379 histological pattern resembling ischemic infarction only when pHo was � 5.30. Nonlethal injections showed only needle tract injuries. Abrupt deterioration of brain acid­ base homeostatic mechanisms correlated with necrosis since pHo returned to baseline more slowly after lethal tissue injections than after nonlethal ones. The slowed return of pHo to baseline after the severely acidic injec­ tions may reflect altered function of plasma membrane antiport systems for pH regulation and loss of brain hy­ drogen ion buffers. dosis can lead to infarction. Thorn and Heitmann (1954), using surface H +-selective electrodes, first showed that brain becomes acidotic during isch­ emia. Twenty-seven years later Siemkowicz and Hansen (1981) documented with H+-selective mi­ croelectrodes that acidic shifts occurred in intersti­ tial pH (pHo) during nearly complete ischemia. Ne­ moto and Frinak (1981) also showed that a sec­ ondary acidosis occurs in interstitial space shortly after reperfusion from nearly complete ischemia. Kraig and colleagues (1985b) reported that this sec­ ondary acidosis can reach a pH …
A New PAPR Reduction Scheme Using Efficient Peak Cancellation for OFDM Systems

A new computationally efficient peak-to-average power ratio (PAPR) reduction scheme for orthogonal frequency division multiplexing (OFDM) signals is proposed, which utilizes the parabolic peak cancellation (PPC) using the truncated kernel signal generated from the inverse fast Fourier transform (IFFT) of the shaped peak reduction tones (PRTs). The proposed scheme only repeats peak canceling in the time domain without iteratively performing IFFT and FFT. Also, the application of the proposed PPC scheme to active constellation extension (ACE) can reduce the number of iterations of IFFT and FFT. Moreover, a transmit power allocation scheme is also suggested to relieve the degradation of bit error rate (BER) of the proposed PAPR reduction scheme. Numerical analysis shows that if the shaping parameters of PRTs are chosen properly, out-of-band (OOB) radiation and BER can be improved while the PAPR reduction performance is maintained.
Fixed point Dual Carrier Modulation performance for wireless USB

Dual Carrier Modulation (DCM) is currently used as the higher data rate modulation scheme for Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) in the ECMA-368 defined Ultra-Wideband (UWB) radio platform. ECMA-368 has been chosen as the physical radio platform for many systems including Wireless USB (W-USB), Bluetooth 3.0 and Wireless HDMI; hence ECMA-368 is an important issue to consumer electronics and the user's experience of these products. In this paper, Log Likelihood Ratio (LLR) demapping method is used for the DCM demaper implemented in fixed point model. Channel State Information (CSI) aided scheme coupled with the band hopping information is used as the further technique to improve the DCM demapping performance. The receiver performance for the fixed point DCM is simulated in realistic multi-path environments1.
Energy Saving in Wireless Sensor Networks

A wireless sensor network (WSN) consists of a large number of sensor nodes which are deployed over an area to perform local computations based on information gathered from the surroundings. Each node in the network is equipped with a battery, but it is almost very difficult to change or recharge batteries; therefore, the crucial question is: “how to prolong the network lifetime to such a long time? ” Hence, maximizing the lifetime of the network through minimizing the energy is an important challenge in WSN; sensors cannot be easily replaced or recharged due to their ad-hoc deployment in hazardous environment. Considering that energy saving acts as one of the hottest topics in wireless sensor networks, we will survey the main techniques used for energy conservation in sensor networks. The main focus of this article is primarily on duty cycling schemes which represent the most compatible technique for energy saving and we also focus on the data-driven approaches that can be used to improve the energy efficiency. Finally, we will make a review on some communication protocols proposed for sensor networks.
IS ADAPTIVE ESTIMATION USEFUL FOR PANEL MODELS WITH HETEROSKEDASTICITY IN THE INDIVIDUAL SPECIFIC ERROR COMPONENT? SOME MONTE CARLO EVIDENCE

ABSTRACT This paper first derives an adaptive estimator when heteroskedasticity is present in the individual specific error in an error component model and then compares the finite sample performance of the proposed estimator with various other estimators. While the Monte Carlo results show that the proposed estimator performs adequately in terms of relative efficiency, its performance on the basis of empirical size is quite similar to the other estimators considered.
A Simulation-Based Decision Support System for Managing Information Technology Project Portfolios

The complicatedness in conducting software development derives from the probabilistic assessment of future conditions or unclear requirements and project implications. Information technology project portfolio management is more complex and presents program managers with the challenge of dynamic decision-making in the context of target revisions and project selection continuation of active projects at various budgeting levels, termination of other active projects and launching new projects. The authors propose a simulation-based decision support system for managing information technology project portfolios. At each control decision point, this data support system enables 'optimizing' each project planning subject to alternative determinations of time and cost chance constraints, and evaluating the up-to-date chance of alternative project selections being within the project portfolio budget. These 'optimizations' and evaluations are provided by a stochastic procedure based on Monte Carlo simulations. In contrast with deterministic procedures that are highly inadequate since they mostly provide erroneous results, the stochastic procedure is appropriate since it analyses a higher level of information and provides more accurate results. The proposed system enables the program manager to decipher highly complex simulation results and make decisions based on up-to-date data. It can be easily implemented even by non-experts.
INTERACTIVE COLLISION DETECTION FOR DEFORMABLE AND GPU OBJECTS

If two closed polygonal objects with outfacing normals intersect each other there exist one or more lines that intersect these objects at at least two consecutive front or back facing object points. In this work we present a method to efficiently detect these lines using depth-peeling and simple fragment operations. Of all polygons only those having an intersection with any of these lines are potentially colliding. Polygons not intersected by the same line do not intersect each other. We describe how to find all potentially colliding polygons and the potentially colliding pairs using a mipmap hierarchy that represents line bundles at ever increasing width. To download only potentially colliding polygons to the CPU for polygon-polygon intersection testing, we have developed a general method to convert a sparse texture into a packed texture of reduced size. Our method exploits the intrinsic strength of GPUs to scan convert large sets of polygons and to shade billions of fragments at interactive rates. It neither requires a bounding volume hierarchy nor a pre-processing stage, so it can efficiently deal with very large and deforming polygonal models. The particular design makes the method suitable for applications where geometry is modified or even created on the GPU.
Rice Pi5-Mediated Resistance to Magnaporthe oryzae Requires the Presence of Two Coiled-Coil–Nucleotide-Binding–Leucine-Rich Repeat Genes

Rice blast, caused by the fungus Magnaporthe oryzae, is one of the most devastating diseases of rice. To understand the molecular basis of Pi5-mediated resistance to M. oryzae, we cloned the resistance (R) gene at this locus using a map-based cloning strategy. Genetic and phenotypic analyses of 2014 F2 progeny from a mapping population derived from a cross between IR50, a susceptible rice cultivar, and the RIL260 line carrying Pi5 enabled us to narrow down the Pi5 locus to a 130-kb interval. Sequence analysis of this genomic region identified two candidate genes, Pi5-1 and Pi5-2, which encode proteins carrying three motifs characteristic of R genes: an N-terminal coiled-coil (CC) motif, a nucleotide-binding (NB) domain, and a leucine-rich repeat (LRR) motif. In genetic transformation experiments of a susceptible rice cultivar, neither the Pi5-1 nor the Pi5-2 gene was found to confer resistance to M. oryzae. In contrast, transgenic rice plants expressing both of these genes, generated by crossing transgenic lines carrying each gene individually, conferred Pi5-mediated resistance to M. oryzae. Gene expression analysis revealed that Pi5-1 transcripts accumulate after pathogen challenge, whereas the Pi5-2 gene is constitutively expressed. These results indicate that the presence of these two genes is required for rice Pi5-mediated resistance to M. oryzae.
Evaluating the linear dielectric constant-electrical conductivity model using time-domain reflectometry

Abstract Recently, Hilhorst (2000) presented a theoretical model describing a linear relationship between bulk electrical conductivity, [sgrave]p, and dielectric constant, K a, in moist soil. Hilhorst (2000) found that using this linear relationship, measurements of the pore water electrical conductivity, [sgrave]p can be made in a wide range of soil types without soil-specific calibrations. If accurate measurements were to be made, only one parameter had to be optimized. In the present study, the linear model was evaluated using detailed time-domain reflectometry (TDR) measurements in coarse sand, medium sand and loamy sand. It was shown that the measured slope of the linear model was lower than expected. A reason for this might be that the Ka of the pore water is smaller than that of free water due to the presence of bound water. The results showed that the linear model was as good as other commonly used models for [sgrave]p predictions.
Processing and analysis of polarimetric ground penetrating radar landmine signatures

The paper presents an analysis of landmine signatures acquired with a polarimetric ground penetrating radar (GPR). First, preprocessing was applied to isolate the target signatures from other reflection events. This included a frequency-wavenumber (f-k) filter to remove unwanted side reflections, followed by background subtraction to remove the direct wave and the ground reflection. The obtained polarimetric signatures of the landmines indicated that these can contain significant cross-polar components, which is in contrast to expectation for a target with a cylindrical casing and most likely a result of internal structure. Making use of the reciprocity property of the scattering matrix, the polarimetric signatures were transformed into the target frame, yielding signatures that are independent of target orientation. From the transformed signatures target impulse responses were estimated using a newly developed deconvolution algorithm. The novelty of the deconvolution algorithm lies in the fact that it computes impulse responses, which adhere to a physical model. Accordingly, the computed impulse responses can be related to mine properties such as size and dielectric permittivity. An interesting application of this is the estimation of a mine's permittivity from its impulse response and the impulse response of a calibration target, of which an example is given.
Hes1 Increases the Invasion Ability of Colorectal Cancer Cells via the STAT3-MMP14 Pathway

The Notch pathway contributes to self-renewal of tumor-initiating cell and inhibition of normal colonic epithelial cell differentiation. Deregulated expression of Notch1 and Jagged1 is observed in colorectal cancer. Hairy/enhancer of split (HES) family, the most characterized targets of Notch, involved in the development of many cancers. In this study, we explored the role of Hes1 in the tumorigenesis of colorectal cancer. Knocking down Hes1 induced CRC cell senescence and decreased the invasion ability, whereas over-expression of Hes1 increased STAT3 phosphorylation activity and up-regulated MMP14 protein level. We further explored the expression of Hes1 in human colorectal cancer and found high Hes1 mRNA expression is associated with poor prognosis in CRC patients. These findings suggest that Hes1 regulates the invasion ability through the STAT3-MMP14 pathway in CRC cells and high Hes1 expression is a predictor of poor prognosis of CRC.
Discriminating DDoS Attack traffic from Flash Crowds on Internet Threat Monitors (ITM) Using Entropy variations

Internet threat Monitoring (ITM) is a monitoring system in the internet to detect, measure, characterize and track the security attacks against attack sources. Distributed Denial of Service (DDoS) is a serious threat to the internet. Attacker uses botnets to launch DDoS attack by sending malicious traffic and the goal is to exhaust ITM network resources such as utilization of network bandwidth, computing power of victim system, data structures used in victim operating systems. The attacker or the botmasters attempt to disable the ITMs by sending the traffic in flash crowd pattern. The Flash Crowd flows are from legitimate users and they are absolutely normal requests, the generated results are similar to the effect of DDoS attacks. Hence, it is important to distinguish DDoS attack flows from flash crowd flows in the internet traffic, for those who defend against DDoS attacks. Based on this, we used a discrimination algorithm based on entropy variations as a similarity metric among suspicious flows. We formulated the problem in the internet with botnets, and presented theoretical proofs for the feasibility of the proposed discrimination method.