Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilization of Visual Information Perception Characteristics to Improve Classification Accuracy of Driver’s Visual Search Intention for Intelligent Vehicle

Understanding and predicting a driver’s behaviors in a vehicle is a prospective function embedded in a smart car. Beyond the patterns of observable behaviors, driver’s intention could be identified based on goal-driven behaviors. A computational model to classify driver intention in visual search which is finding a target with one’s eyes as moving selective attention across a search field, could improve the level of intelligence that a smart car could demonstrate. To develop a computational cognitive that explains the underlying cognitive process and reproduces drivers’ behaviors, particular parameters in human cognitive process should be specified. In this study, 2 issues are considered as influential factors on a driver’s eye movements: a driver’s visual information processing characteristics (VIPCs) and the purpose of visual search. To assess an individual’s VIPC, 4 psychological experiments—Donders’s reaction time, mental rotation, signal detection, and Stroop experiments—were utilized. Upon applying k-means clustering method, 114 drivers were divided into 9 driver groups. To investigate the influence of task goal on a driver’s eye movement, driving simulation was conducted to collect a driver’s eye movement data under the given purpose of visual search (perceptual and cognitive tasks). The empirical data showed that there were significant differences in a driver’s oculomotor behavior, such as response time, average fixation time, and average glance duration between the driver groups and the purposes of visual search. The effectiveness of using VIPC for grouping drivers was tested with task goal classification model by comparing the models’ performance when drivers were grouped by typical demographic data such as gender. Results show that grouping based on VIPC improves accuracy and stability of prediction of the model on a driver’s intention underlying visual search behaviors. This study would benefit future studies focusing on personalization and adaptive interfaces in the development of smart car.
Disjunctive temporal networks with uncertainty via SMT: Recent results and directions

Many Planning and Scheduling systems are designed assuming that the system under control is able to decide the duration of all the activities being executed. However, in many application scenarios this assumption is not acceptable because the actual timing of actions is not under direct control of the plan executor. Hence, new Planning and Scheduling techniques are needed to deal with this temporal uncertainty explicitly. In this paper, we summarize and systematize a series of works in which we addressed this uncertainty problem in the realm of temporal network scheduling. We show how Satisﬁability Modulo Theory (SMT) solvers can be exploited to quickly solve different kinds of query in this setting. In particular, we focus on the framework of Disjunctive Temporal Networks with Uncertainty and address the three degrees of controllability for the fully-disjunctive class of problems, solving several open problems in the literature and experimentally showing the performance of the developed techniques. Finally, we outline and discuss several foreseeable directions of research in this ﬁeld.
Community classification in decentralized social networks using local topological information

Decentralized Social Network (DSN) has attracted a lot of research and development interest in recent years. It is believed to be the solution to many problems of centralized services. Due to the data limitation imposed by common decentralized architectures, centralized algorithms that support social networking functions need to be re-designed. In this work, we tackle the problem of community detection for a given user under the constraint of limited local topology information. This naturally yields a classification formulation for community detection. As an initial study, we focus on a specific type of classifiers - classification by thresholding against a proximity measure between nodes. We investigated four proximity measures: Common Neighbours (CN), Adamic/Adar score (AA), Page Rank (PR), Personalized PageRank (PPR). Using data collected from a large-scale Online Social Network (OSN) in practice, we show that PPR can outperform the others with a few pre-known labels (37.5% to 64.97% relative improvement in terms of Area Under the ROC Curve). We further carry out extensive numerical evaluation of PPR, showing that more pre-known labels can linearly increase the capability of the single-feature classifier based on PPR. Users can thus seek for a trade-off between labeling cost and classification accuracy.
PII: S0165-0114(98)00337-6

In data analysis, objects are usually represented by feature vectors, each describing a state of an object at a point of time. Most methods for data analysis use only these feature vectors and do not take into account changes over time. They can therefore be called static, But often a "dynamic" approach, which utilizes the feature changes over time, seems to be more appropriate (e.g. supervision of patients in medical care, state-dependent maintenance of machines, classification of shares). In this paper, different criteria for structuring the field of "dynamic data analysis (DDA)" are proposed and one of the relevant approaches is investigated in more detail. This approach considers possible ways to handle dynamics within static methods for data analysis. In doing this, different types of similarity measures for trajectories are defined, which can be used to modify static methods for data analysis. One of the proposed similarity measures has been integrated into the fuzzy c-means. An application example is used to demonstrate the applicability of the modified fuzzy c-means. (~) 1999 Elsevier Science B.V. All rights reserved.
Changes in the Vascular Bed of the Marginal Periodontium in Periodontitis

According to previous studies (M. KINDLOVA and V. MATENA, J. dent. Res., 41:650, 1962; M. KINDLOVA, Arch. oral Biol. [in press]), the vascular bed of the marginal periodontium is divided into two segments. The first is the simple capillary network of the gingiva that supplies the gingival epithelium facing the oral cavity. The second lies close to the epithelial attachment and is formed by coiled capillaries closely connected with the vessels of the periodontal membrane, which have often been described as "glomerulus-like formations. " Their function is not yet clearly understood. The author considers it quite likely that they are in close relation to the epithelial attachment because she failed to find them in the marginal periodontium of the rat incisor, where the epithelial attachment probably has a different function due to the permanent eruption of the tooth. In order to study the vascular bed under pathological conditions, an experimental periodontitis, elicited by desalivation, was provoked in rats. The blood supply was followed in histological specimens and in corrosive specimens (M. KINDLoVA and V. MATiNA, Acta anal., 37:163, 1959). The experiment was carried out in thirty-five young Wistar rats. After 30 days a mild periodontitis developed in the molar region. In histological specimens it appeared as an edema of the gingiva, dilatation of the capillaries, and an increased infiltration. In relative pockets which formed between the swollen gingiva and the tooth wall, food debris and
Optimizing the capacitor placement based on 2D charge-delivery model of power delivery network

In high-speed electrical systems, power distribution network (PDN) with power ground planes (PGP) is widely used to gain effective high-speed decoupling. Rather than modelling such PDN from conventional steady-state analysis in the frequency domain, we propose its two-dimension (2-D) charge delivery model from charge delivery perspective in the time domain, which correlates better with the PDN real operations. With the model, by taking the synthesized effects from the interactions of the charge providers (such as voltage regulator module (VRM) and capacitors) into account, we propose two design rules to optimize the placement of the capacitors in preliminary PDN design.
AN ERGONOMIC INTERFACE TO COMPUTE 3 D MODELS USING PHOTOGRAMMETRY

Many commercial softwares offer to create 3D models of objects or scenes from photographs only. However professionals, including archaeologists and architects, have numerous constraints that severely restrict the choice of such software, such as limited financial resources, little knowledge in photogrammetry and computing, professional imperatives that limit the time of acquisition and processing (particularly manual treatments), and finally specific expectations in terms of quality, precision and completeness of the modeling. The MATIS laboratory has developed a set of free, open source tools, which can perform all stages of photogrammetric projects processing and that provide dense and precise 3D models thanks to the rigorous photogrammetric approach. A graphical interface, which will be presented in this paper, is being developed to provide a unified and simplified access to these tools. Through the interface, the user can manipulate the images of the same site and control every step of processing that includes : the automatic calculation of tie points between images, the estimation of camera poses and the 3D models calculation. The results can be converted into depth maps or dense 3D points clouds (in standard ply format), and can be displayed directly by the GUI. Particular attention is paid to the quality of documentation and to the product portability on the main types of operating systems (Linux, Mac and Windows). This interface, although not finalized, already enables to perform the full treatment of certain types of surveys and is available on-line.
A column approximate minimum degree ordering algorithm

Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero pattern of A such that the factorization remains as sparse as possible, regardless of the subsequent choice of P. The choice of Q can have a dramatic impact on the number of nonzeros in L and U. One scheme for determining a good column ordering for A is to compute a symmetric ordering that reduces fill-in in the Cholesky factorization of A'A. This approach, which requires the sparsity structure of A'A to be computed, can be expensive both in terms of space and time since A'A may be much denser than A. An alternative is to compute Q directly from the sparsity structure of A; this strategy is used by Matlab's COLAMD preordering algorithm. A new ordering algorithm, COLAMD, is presented. It is based on the same strategy but uses a better ordering heuristic. COLAMD is faster and computes better orderings, with fewer nonzeros in the factors of the matrix.
Applications of 3D morphable models for faces with expressions

In this paper, we present a framework to represent the face of any individual, dealing with identity and expression variation and some applications of this model. A 3D morphable model (3DMM) is a generative method capable to reconstruct the 3D shape of human faces from a small set of coefficients. It is used in many applications such as identity or expression recognition, 3D scans processing or face animation. Such a 3D face generative model is used to build any face with expression as linear combination of deformations. In order to determine these basis deformations and with the hypothesis of Gaussian distribution of the 3D human faces space, a PCA is computed on a registered dataset. The registration of training 3D faces is first achieved so that it is robust to expression. To build the 3DMM with identities variations separated from expressions variations, two PCA are computed. Using this morphable model, a new face can be represented as a linear combination of these principal modes of variations. We show in this paper that such a morphable model can be used as shape prior in many applications like denoising or occlusion recuperation tool.
Cannabis use and genetic predisposition for schizophrenia: a case-control study

Background Cannabis use may be a risk factor for schizophrenia. Part of this association may be explained by genotype–environment interaction, and part of it by genotype–environment correlation. The latter issue has not been explored. We investigated whether cannabis use is associated with schizophrenia, and whether gene–environment correlation contributes to this association, by examining the prevalence of cannabis use in groups with different levels of genetic predisposition for schizophrenia. Method Case-control study of first-episode schizophrenia. Cases included all non-Western immigrants who made first contact with a physician for schizophrenia in The Hague, The Netherlands, between October 2000 and July 2005 (n=100; highest genetic predisposition). Two matched control groups were recruited, one among siblings of the cases (n=63; intermediate genetic predisposition) and one among immigrants who made contact with non-psychiatric secondary health-care services (n=100; lowest genetic predisposition). Conditional logistic regression analyses were used to predict schizophrenia as a function of cannabis use, and cannabis use as a function of genetic predisposition for schizophrenia. Results Cases had used cannabis significantly more often than their siblings and general hospital controls (59, 21 and 21% respectively). Cannabis use predicted schizophrenia [adjusted odds ratio (OR) cases compared to general hospital controls 7.8, 95% confidence interval (CI) 2.7–22.6; adjusted OR cases compared to siblings 15.9 (95% CI 1.5–167.1)], but genetic predisposition for schizophrenia did not predict cannabis use [adjusted OR intermediate predisposition compared to lowest predisposition 1.2 (95% CI 0.4–3.8)]. Conclusions Cannabis use was associated with schizophrenia but there was no evidence for genotype–environment correlation.
Ultrawideband characteristics of fractal dipole antennas integrated on Si for ULSI wireless interconnects

Ultrawideband characteristics of Sierpinski carpet fractal antennas fabricated on silicon substrates with the resistivities of 2290, 79.6, and 10 /spl Omega//spl middot/cm were investigated. The return losses lower than -10 dB and high transmission gains of approximately -14 dB were obtained for the antennas with 10-mm distance on the Si substrate with the resistivity of 2290 /spl Omega//spl middot/cm in the frequency range from 18 to 26.5 GHz. Gaussian monocycle pulses with 70 ps pulsewidth were transmitted in the Si substrates successfully and the corresponding voltage gains were -23, -26, and -39 dB for the Si resistivities of 2290, 79.6, and 10 /spl Omega//spl middot/cm, respectively.
Fish Oil Inhibits Human Lung Carcinoma Cell Growth by Suppressing Integrin-Linked Kinase

We previously showed that synthetic peroxisome proliferator-activated receptor γ (PPARγ) ligands inhibit non–small cell lung carcinoma (NSCLC) cell growth through multiple signaling pathways. Here, we show that dietary compounds, such as fish oil (which contains certain kinds of fatty acids like ω3 and ω6 polyunsaturated fatty acids), also inhibit NSCLC cell growth by affecting PPARγ and by inhibiting the expression of integrin-linked kinase (ILK). Exogenous expression of ILK overcame, whereas silencing ILK enhanced the inhibitory effect of fish oil on cell growth. The inhibitor of p38 mitogen-activated protein kinase, SB239023, abrogated the inhibitory effect of fish oil on ILK expression, whereas the inhibitor of extracellular signal-regulated kinase, PD98059, had no effect. Transient transfection experiments showed that fish oil reduced ILK promoter activity, and this effect was abolished by AP-2α small interfering RNA and SB239023 and by deletion of a specific portion of the ILK gene promoter. Western blot analysis and gel mobility shift assay showed that fish oil significantly induced AP-2α protein expression and AP-2 DNA-binding activity in the ILK gene promoter and that this was dependent on PPARγ activation. Blockade of AP-2α abrogated the effect of fish oil on ILK expression and on cell growth, whereas exogenous expression of AP-2α enhanced cell growth in the setting of fish oil exposure. Taken together, these findings show that fish oil inhibits ILK expression through activation of PPARγ-mediated and p38 mitogen-activated protein kinase–mediated induction of AP-2α. In turn, this leads to inhibition of NSCLC cell proliferation. This study unveils a novel mechanism by which fish oil inhibits human lung cancer cell growth. (Mol Cancer Res 2009;7(1):108–17)
A Recommendation System for Collaborative Visualization Platforms

As the popular use of technologies within Industry 4.0, such as cloud manufacturing, collaboration and IoT, datasets researchers can collect have been rapidly growing in terms of complexity and scale. In order to dig the information hidden behind it or solving cross-disciplinary problems involving multiple datasets, datasets are now often accessed and analyzed by a team consist of researchers in different fields. For that reason, collaborative visualization becomes a new research branch. However, when it comes to the sorting and distributing of information, previous studies always depend on the subjective initiative of researchers. This article first introduces the basic theories on collaborative visualization and recommendation systems, then discusses the architecture of a prototype platform that supports sorting and recommendations of information to all users and propose an algorithm for the recommendation system of the platform.
Effect of length on laterally-wedged insoles in knee osteoarthritis.

Introduction Knee osteoarthritis (OA) is a prevalent musculoskeletal condition (1). It imposes a significant economic burden and is associated with considerable pain, disability, and loss of quality of life. Although the causative mechanisms of knee OA are not entirely clear, increased joint loads during walking have been associated with the initiation and progression of the disease and its symptoms (2). The external knee adduction moment, which reflects the dynamic load borne on the medial tibiofemoral joint compartment, has become a biomechanical marker for the risk of progression for medial compartment OA (2). Accordingly, much research is focused on devising and evaluating interventions that may reduce the peak adduction moment with a view to alleviating symptoms and reducing the risk of disease progression in knee OA. Laterally-wedged insoles are an in-shoe orthotic recommended to manage medial knee OA (3). Lateral wedges are hypothesized to reduce the moment arm of the ground reaction force vector relative to the knee center during walking (4), leading to reduced knee pain. Biomechanical studies have demonstrated that lateral wedges can reduce the peak adduction moment in patients with OA and healthy subjects (4–7). However, this finding is not consistent across all studies, with some showing no effect of the wedge on the adduction moment (8,9). Although differences in study samples and measurement methodologies may account for the conflicting findings, it is possible that the varying results may be attributable to differences in the designs of the lateral wedges tested. The most apparent difference in wedge design that is evident across studies relates to the length of the lateral wedge. Most studies that have demonstrated a beneficial effect of the wedge on the adduction moment have tested a full-length insole that wedged the foot from the heel to the forefoot (4–7). In contrast, the studies that demonstrated no effect on knee loading tested insoles that wedged only the rearfoot (8,9). The only clinical trial to evaluate the efficacy of lateral wedges for knee OA used rearfoot wedges and found no significant effect on symptoms or disease progression over time (10,11). Excluding the degree of wedging (which can mediate the effect of a wedge on the adduction moment) (5,7), the design features of laterally-wedged insoles are not considered to influence their efficacy. Evidence suggests that wedging of the entire foot is necessary for a beneficial effect, yet no study has evaluated the influence of wedge length on the adduction moment. The aim of this laboratory study was to compare the effect of a full-length, laterally-wedged insole on the adduction moment with that of a rearfoot wedge in patients with medial knee OA.
Improved algorithms for cancellation carrier optimization to suppress the OFDM OOB spectrum

Cancellation carriers (CCs) are known to be effective for suppressing the out-of-band (OOB) spectrum of an orthogonal frequency-division multiplexing (OFDM) signal and are useful to realize OFDM-based overlay systems. We show that the known minimum-mean-square-error (MMSE) algorithm is not sufficient to reduced the OOB spectral peaks in the vicinity of transmission band boundaries compared to a direct minmax algorithm and show that a weighted MMSE (WMMSE) algorithm can attain almost the same suppression performance as the direct min-max algorithm with the same computational cost as the MMSE algorithm.
Multi-Dimensional Complete Ensemble Empirical Mode Decomposition With Adaptive Noise Applied to Laser Speckle Contrast Images

Laser speckle contrast imaging (LSCI) is a noninvasive full-field optical technique which allows analyzing the dynamics of microvascular blood flow. LSCI has attracted attention because it is able to image blood flow in different kinds of tissue with high spatial and temporal resolutions. Additionally, it is simple and necessitates low-cost devices. However, the physiological information that can be extracted directly from the images is not completely determined yet. In this work, a novel multi-dimensional complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) is introduced and applied in LSCI data recorded in three physiological conditions (rest, vascular occlusion and post-occlusive reactive hyperaemia). MCEEMDAN relies on the improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and our algorithm is specifically designed to analyze multi-dimensional data (such as images). Over the recent multi-dimensional ensemble empirical mode decomposition (MEEMD), MCEEMDAN has the advantage of leading to an exact reconstruction of the original data. The results show that MCEEMDAN leads to intrinsic mode functions and residue that reveal hidden patterns in LSCI data. Moreover, these patterns differ with physiological states. MCEEMDAN appears as a promising way to extract features in LSCI data for an improvement of the image understanding.