Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Learning and Sequential Anomaly Detection in Trajectories

Detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms typically suffer from one or more limitations: They are not designed for sequential analysis of incomplete trajectories or online learning based on an incrementally updated training set. Moreover, they typically involve tuning of many parameters, including ad-hoc anomaly thresholds, and may therefore suffer from overfitting and poorly-calibrated alarm rates. In this article, we propose and investigate the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) for online learning and sequential anomaly detection in trajectories. This is a parameter-light algorithm that offers a well-founded approach to the calibration of the anomaly threshold. The discords algorithm, originally proposed by Keogh et al. , is another parameter-light anomaly detection algorithm that has previously been shown to have good classification performance on a wide range of time-series datasets, including trajectory data. We implement and investigate the performance of SHNN-CAD and the discords algorithm on four different labeled trajectory datasets. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning during unsupervised online learning and sequential anomaly detection in trajectories.
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task. We show that properly combining saliency and attention maps allows us to obtain reliable cues capable of significantly boosting the performance. First, we propose a simple yet powerful hierarchical approach to discover the class-agnostic salient regions, obtained using a salient object detector, which otherwise would be ignored. Second, we use fully convolutional attention maps to reliably localize the class-specific regions in a given image. We combine these two cues to discover class-specific pixels which are then used as an approximate ground truth for training a CNN. While solving the weakly supervised semantic segmentation task, we ensure that the image-level classification task is also solved in order to enforce the CNN to assign at least one pixel to each object present in the image. Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of 60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to the published state-of-the-art results. The code is made publicly available.
Teacher performance assessment in teacher education: an example in Malaysia

As part of a cross-cultural collaboration, a teacher performance assessment (TPA) was implemented during 2009 in three Malaysian institutes of teacher education. This paper reports on the TPA for graduating primary teachers in Malaysia. The investigation focused on the pre-service teachers’ perceptions about whether the TPA provided them with an opportunity to document successfully their professional learning and professional practice. Successful completion of the Malaysian TPA was closely aligned to successful relationships, support and collaboration between Malaysian lecturers and pre-service teachers, and between pre-service teachers and their classroom teachers. Overall, the TPA did provide pre-service teachers with an opportunity to focus on the connection between theory and professional learning during field-work, and to become reflective evidence-based practitioners. Recommendations for improving the assessment of pre-service teachers are discussed.
Möglichkeiten und Grenzen der Frühförderung aus entwicklungspsychologischer Sicht

Fruhforderung hat seit einigen Jahren wieder einmal Hochkonjunktur. Viele der dort verwendeten Ansatze werden mit entwicklungspsychologischen Argumenten begrundet. Im vorliegenden Beitrag wird der Frage nachgegangen, welche Moglichkeiten und Grenzen der Fruhforderung sich aus entwicklungpsychologischer Sicht ergeben. Ausgehend von den Zielen der Fruhforderung wird dabei auf indizierte und selektive Fruhfordermasnahmen eingegangen, die sich in der Vergangenheit als wirksam erwiesen haben. Insgesamt ist zurzeit ein gedampfter Optimismus fur die Moglichkeiten der Fruhforderung angebracht. Die Einschrankungen entstehen nicht zuletzt daraus, dass eine systematische differentielle entwicklungspsychologische Trainingsforschung bis dato noch weitgehend unterentwickelt ist. (DIPF/Orig.) For several years now, preschool education has again been experiencing a boom. Many of the approaches taken in this context are grounded on arguments pertaining to developmental psychology. The present contribution inquires into the question of which possibilities and limits of preschool education present themselves from the point of view of developmental psychology. Starting from the aims of preschool education, the author discusses indicated and selective approaches of preschool education which have proven to be efficient in the past. All in all, a restrained optimism seems to be appropriate at the moment as far as the possibilities of preschool education are concerned. The limitations are not least due to the fact that, in the field of developmental psychology, the systematic differential research into training is as yet rather underdeveloped. (DIPF/Orig.)
Reconstruction of the surface of the human body from 3D scanner data using B-splines

There are an increasing number of applications that require the construction of computerized human body models. Recently, Hamamatsu Photonics have developed an accurate and fast scanner based on position-sensitive photon detectors, capable of providing in a few seconds a dense representation of the body. The work presented here is designed to exploit such a scanner's capabilities. An algorithm is introduced that deals with the surface-from-curves problem and can be combined with an existing curves-from-points algorithm to solve the surface-from-points problems. The algorithm takes as input a set of B-spline curves and uses them to drive a fast and robust surface generation process. This is done by adequately sampling the curves, in a manner that incorporates explicit assumptions about human body geometry and topology. The result is a compound, multi-segment, and yet entirely smooth surface, that my be used to calculate body volume and surface area.
Dream characteristics in a Brazilian sample: an online survey focusing on lucid dreaming

During sleep, humans experience the offline images and sensations that we call dreams, which are typically emotional and lacking in rational judgment of their bizarreness. However, during lucid dreaming (LD), subjects know that they are dreaming, and may control oneiric content. Dreaming and LD features have been studied in North Americans, Europeans and Asians, but not among Brazilians, the largest population in Latin America. Here we investigated dreams and LD characteristics in a Brazilian sample (n = 3,427; median age = 25 years) through an online survey. The subjects reported recalling dreams at least once a week (76%), and that dreams typically depicted actions (93%), known people (92%), sounds/voices (78%), and colored images (76%). The oneiric content was associated with plans for the upcoming days (37%), memories of the previous day (13%), or unrelated to the dreamer (30%). Nightmares usually depicted anxiety/fear (65%), being stalked (48%), or other unpleasant sensations (47%). These data corroborate Freudian notion of day residue in dreams, and suggest that dreams and nightmares are simulations of life situations that are related to our psychobiological integrity. Regarding LD, we observed that 77% of the subjects experienced LD at least once in life (44% up to 10 episodes ever), and for 48% LD subjectively lasted less than 1 min. LD frequency correlated weakly with dream recall frequency (r = 0.20, p < 0.01), and LD control was rare (29%). LD occurrence was facilitated when subjects did not need to wake up early (38%), a situation that increases rapid eye movement sleep (REMS) duration, or when subjects were under stress (30%), which increases REMS transitions into waking. These results indicate that LD is relatively ubiquitous but rare, unstable, difficult to control, and facilitated by increases in REMS duration and transitions to wake state. Together with LD incidence in USA, Europe and Asia, our data from Latin America strengthen the notion that LD is a general phenomenon of the human species.
What representations and computations underpin the contribution of the hippocampus to generalization and inference?

Empirical research and theoretical accounts have traditionally emphasized the function of the hippocampus in episodic memory. Here we draw attention to the importance of the hippocampus to generalization, and focus on the neural representations and computations that might underpin its role in tasks such as the paired associate inference (PAI) paradigm. We make a principal distinction between two different mechanisms by which the hippocampus may support generalization: an encoding-based mechanism that creates overlapping representations which capture higher-order relationships between different items [e.g., Temporal Context Model (TCM): Howard et al., 2005]—and a retrieval-based model [Recurrence with Episodic Memory Results in Generalization (REMERGE): Kumaran and McClelland, in press] that effectively computes these relationships at the point of retrieval, through a recurrent mechanism that allows the dynamic interaction of multiple pattern separated episodic codes. We also discuss what we refer to as transfer effects—a more abstract example of generalization that has also been linked to the function of the hippocampus. We consider how this phenomenon poses inherent challenges for models such as TCM and REMERGE, and outline the potential applicability of a separate class of models—hierarchical Bayesian models (HBMs) in this context. Our hope is that this article will provide a basic framework within which to consider the theoretical mechanisms underlying the role of the hippocampus in generalization, and at a minimum serve as a stimulus for future work addressing issues that go to the heart of the function of the hippocampus.
SDF-1&agr; Induction in Mature Smooth Muscle Cells by Inactivation of PTEN Is a Critical Mediator of Exacerbated Injury-Induced Neointima Formation

Objective—PTEN inactivation selectively in smooth muscle cells (SMC) initiates multiple downstream events driving neointima formation, including SMC cytokine/chemokine production, in particular stromal cell-derived factor-1&agr; (SDF-1&agr;). We investigated the effects of SDF-1&agr; on resident SMC and bone marrow–derived cells and in mediating neointima formation. Methods and Results—Inducible, SMC-specific PTEN knockout mice (PTEN iKO) were bred to floxed-stop ROSA26-&bgr;-galactosidase (&bgr;Gal) mice to fate-map mature SMC in response to injury; mice received wild-type green fluorescent protein–labeled bone marrow to track recruitment. Following wire-induced femoral artery injury, &bgr;Gal(+) SMC accumulated in the intima and adventitia. Compared with wild-type, PTEN iKO mice exhibited massive neointima formation, increased replicating intimal and medial &bgr;Gal(+)SMC, and enhanced vascular recruitment of bone marrow cells following injury. Inhibiting SDF-1&agr; blocked these events and reversed enhanced neointima formation observed in PTEN iKO mice. Most recruited green fluorescent protein(+) cells stained positive for macrophage markers but not SMC markers. SMC-macrophage interactions resulted in a persistent SMC inflammatory phenotype that was dependent on SMC PTEN and SDF-1&agr; expression. Conclusion—Resident SMC play a multifaceted role in neointima formation by contributing the majority of neointimal cells, regulating recruitment of inflammatory cells, and contributing to adventitial remodeling. The SMC PTEN-SDF-1&agr; axis is a critical regulator of these events.
Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures

Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should be learned not prescribed. Here, we develop a novel search algorithm, applicable across domains, that discovers an optimal architecture which simultaneously learns general genomic patterns and identifies the most important sequence motifs in predicting functional genomic outcomes. The architectures we find using this algorithm succeed at using only RNA expression data to predict gene regulatory structure, learn human-interpretable visualizations of key sequence motifs, and surpass state-of-the-art results on benchmark genomics challenges.
ARA-the ant-colony based routing algorithm for MANETs

A mobile ad-hoc network (MANET) is a collection of mobile nodes which communicate over radio. These kind of networks are very flexible, thus they do not require any existing infrastructure or central administration. Therefore, mobile ad-hoc networks are suitable for temporary communication links. The biggest challenge in this kind of networks is to find a path between the communication end points, what is aggravated through the node mobility. In this paper we present a new on-demand routing algorithm for mobile, multi-hop ad-hoc networks. The protocol is based on swarm intelligence and especially on the ant colony based meta heuristic. These approaches try to map the solution capability of swarms to mathematical and engineering problems. The introduced routing protocol is highly adaptive, efficient and scalable. The main goal in the design of the protocol was to reduce the overhead for routing. We refer to the protocol as the ant-colony-based routing algorithm (ARA).
Arabidopsis Cortical Microtubules Are Initiated along, as Well as Branching from, Existing Microtubules[W]

The principles by which cortical microtubules self-organize into a global template hold important implications for cell wall patterning. Microtubules move along bundles of microtubules, and neighboring bundles tend to form mobile domains that flow in a common direction. The bundles themselves move slowly and for longer than the individual microtubules, with domains describing slow rotary patterns. Despite this tendency for colinearity, microtubules have been seen to branch off extant microtubules at ∼45°. To examine this paradoxical behavior, we investigated whether some microtubules may be born on and grow along extant microtubule(s). The plus-end markers Arabidopsis thaliana end binding protein 1a, AtEB1a-GFP, and Arabidopsis SPIRAL1, SPR1-GFP, allowed microtubules of known polarity to be distinguished from underlying microtubules. This showed that the majority of microtubules do branch but in a direction heavily biased toward the plus end of the mother microtubule: few grow backward, consistent with the common polarity of domains. However, we also found that a significant proportion of emergent comets do follow the axes of extant microtubules, both at sites of apparent microtubule nucleation and at cross-over points. These phenomena help explain the persistence of bundles and counterbalance the tendency to branch.
Viral Etiology and Clinical Profiles of Children with Severe Acute Respiratory Infections in China

Background No comprehensive analysis is available on the viral etiology and clinical characterization among children with severe acute respiratory infection (SARI) in China during 2009 H1N1 pandemic and post-pandemic period. Methods Cohort of 370 hospitalized children (1 to 72 months) with SARI from May 2008 to March 2010 was enrolled in this study. Nasopharyngeal aspirate (NPA) specimens were tested by a commercial assay for 18 respiratory viral targets. The viral distribution and its association with clinical character were statistically analyzed. Results Viral pathogen was detected in 350 (94.29%) of children with SARI. Overall, the most popular viruses were: enterovirus/rhinovirus (EV/RV) (54.05%), respiratory syncytial virus (RSV) (51.08%), human bocavirus (BoCA) (33.78%), human parainfluenzaviruse type 3 (PIV3) (15.41%), and adenovirus (ADV) (12.97%). Pandemic H1N1 was the dominant influenza virus (IFV) but was only detected in 20 (5.41%) of children. Moreover, detection rate of RSV and human metapneumovirus (hMPV) among suburb participants were significantly higher than that of urban area (P<0.05). Incidence of VSARI among suburb participants was also significant higher, especially among those of 24 to 59 months group (P<0.05). Conclusion Piconaviruses (EV/RV) and paramyxoviruses are the most popular viral pathogens among children with SARI in this study. RSV and hMPV significantly increase the risk of SARI, especially in children younger than 24 months. Higher incidence of VSARI and more susceptibilities to RSV and hMPV infections were found in suburban patients.
Energy Efficient In-Network Data Processing in Sensor Networks

We assume a sensor network with data-centric storage, where sensor data is stored within the sensor network and adhoc queries are disseminated to and processed in the network. In such minimizing energy consumption has been a major objective at all levels in sensor networks. In this paper, we present an in-network aggregation scheme that maintains the user-specified quality of data requirement while significantly reducing the overall energy consumption. Specifically, since communication dominates power usage in sensor networks, in-network aggregation scheme exploits end-user temporal coherency tolerances to reduce the amount of information transmitted by individual nodes. Further, we show that in- network aggregation scheme, by using temporal coherency tolerances, can allow for better quality of data when the time given to perform readings is too short for all data to be propagated up through the network. We compare our proposed scheme against an existing in-network aggregation scheme with a local sensor cache. We present experimental results measuring both power savings and also the quality of data for group-by queries. These results show that in- network aggregation scheme can reduce power consumption without any loss in the quality of data and extend the life of the sensor network. Keywords—Aggregation, Energy Consumption, Query Plan, Network Lifetime.
Vertical GaN p-n Junction Diodes With High Breakdown Voltages Over 4 kV

Vertical structured GaN power devices have recently been attracting a great interest because of their potential on extremely high-power conversion efficiency. This letter describes increased breakdown voltages in the vertical GaN p-n diodes fabricated on the free-standing GaN substrates. By applying multiple lightly Si doped n-GaN drift layers to the p-n diode, the record breakdown voltages (VB) of 4.7 kV combined with low specific differential ON-resistance (RON) of 1.7 mΩcm2 were achieved. With reducing the Si-doping concentration of the top n-GaN drift layer adjacent to the p-n junction using well-controlled metal-organic vapor phase epitaxy systems, the peak electric field at the p-n junction could be suppressed under high negatively biased conditions. The second drift layer with a moderate doping concentration contributed to the low RON. A Baliga's figure of merit (VB2/RON) was 13 GW/cm2. These are the best values ever reported among those achieved by GaN p-n junction diodes on the free-standing GaN substrates.
Robust speech recognition with on-line unsupervised acoustic feature compensation

An on-line unsupervised hybrid compensation technique is proposed to reduce the mismatch between training and testing conditions. It combines multi-environment model based linear normalization with cross-probability model based on GMMs (MEMLIN CPM) with a novel acoustic model adaptation method based on rotation transformations. Hence, a set of rotation transformations is estimated with clean and MEMLIN CPM-normalized training data by linear regression in an unsupervised process. Thus, in testing, each MEMLIN CPM normalized frame is decoded using a modified Viterbi algorithm and expanded acoustic models, which are obtained from the reference ones and the set of rotation transformations. To test the proposed solution, some experiments with Spanish SpeechDat Car database were carried out. MEMLIN CPM over standard ETSI front-end parameters reaches 83.89% of average improvement in WER, while the introduced hybrid solution goes up to 92.07%. Also, the proposed hybrid technique was tested with Aurora 2 database, obtaining an average improvement of 68.88% with clean training.
The social and clinical characteristics of patients on antiretroviral therapy who are ‘lost to follow-up’ in KwaZulu-Natal, South Africa: a prospective study

A significant proportion of those initiating antiretroviral treatment (ART) for HIV infection are lost to follow-up. Causes (including HIV symptoms, quality of life, depression, herbal treatment and alcohol use) for discontinuing ART follow-up in predominantly rural resource-limited settings are not well understood. This is a prospective study of the treatment-naïve patients recruited from three (one urban, one-semi-urban and one rural) public hospitals in Uthukela health district in KwaZulu-Natal from October 2007 to February 2008. The aim of this study was to investigate predictors of loss to follow-up or all caused attrition from an ART programme within a cohort followed up for over 12 months. A total of 735 patients (217 men and 518 women) prior to initiating ART completed a baseline questionnaire and 6- and 12-months' follow-up. At 12-months follow-up 557 (75.9%) individuals continued active ART, 177 (24.1%) were all cause attrition, there were 82 deaths (13.8%), 58 (7.9%) transfers, 7 (1.0%) refused participation, 8 (1.1%) were not yet on ART and 22 (3.0%) could not be traced. Death by 12-months of follow-up was associated with lower CD4 cell counts (risk ratio, RR=2.05, confidence intervals, CI=1.20 – 3.49) and higher depression levels (RR=1.05, CI=1.01 – 1.09) at baseline assessment. The high early mortality rates indicate that patients are enrolling into ART programmes with far too advanced immunodeficiency; median CD4 cell counts 119 (IQR=59 – 163). Causes of late access to the ART programme, such as delays in health care access (delayed health care seeking), health system delays, or inappropriate treatment criteria, need to be addressed. Differences in health status (lower CD4 cell counts and higher depression scores) should be taken into account when initiating patients on ART. Treating depression at ART initiation is recommended to improve treatment outcome.
Attention-Sensitive Alerting

We introduce utility-directed procedures for mediating the flow of potentially distracting alerts and communications to computer users. We present models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption. We describe the challenge of reasoning about such costs under uncertainty via an analysis of user activity and the content of notifications. After introducing principles of attention-sensitive alerting, we focus on the problem of guiding alerts about email messages. We dwell on the problem of inferring the expected criticality of email and discuss work on the PRIORITIES system, centering on prioritizing email by criticality and modulating the communication of notifications to users about the presence and nature of incoming email.