Miracle-dz/UniFAR-FTUs huggingface dataset:
Bacterial carbon sources in coastal sediments: a cross-system analysis based on stable isotope data of biomarkers

Coastal ecosystems are typically highly produc- tive, and the sediments in these systems receive organic mat- ter from a variety of local and imported sources. To as- sess if general patterns are present in the origin of carbon sources for sedimentary bacteria and their relation to the origin of the sediment organic carbon pool, we compiled both literature and new data on 13 C of bacterial biomark- ers (the phospholipid derived fatty acids i+a15:0), along with 13 C data on sediment organic carbon ( 13 CTOC) and macro- phyte biomass from a variety of typical near-coastal systems. These systems included mangroves, salt marshes (both C3 and C4-dominated sites), seagrass beds, and macroalgae- based systems, as well as unvegetated sediments. First, our 13 Ci+a15:0 data showed large variability over the en- tire range of 13 CTOC, indicating that in many settings, bac- teria may depend on carbon derived from various origins. Secondly, systems where local macrophyte production is the major supplier of organic carbon for in situ decomposi- tion are generally limited to organic carbon-rich, peaty sites (TOC>10 wt%), which are likely to make up only a small part of the global area of vegetated coastal systems. These carbon-rich sediments also provided a field based estimate of isotopic fractionation between bacterial carbon sources and biomarkers ( 3.7±2.1‰), which is similar to the ex- pected value of about 3‰ associated with the biosynthesis of fatty acids. Thirdly, only in systems with low TOC (be- low 1 wt%), we consistently found that bacteria were selec- tively utilizing an isotopically enriched carbon source, which may be root exudates but more likely is derived from micro- phytobenthos. In other systems with between 1 and 10 wt% TOC, bacteria appear to show on average little selectivity and 13 Ci+a15:0 data generally follow the 13 CTOC, even in sys-
Homoclinic Stripe Patterns

In this paper, we study homoclinic stripe patterns in the two-dimensional generalized Gierer--Meinhardt equation, where we interpret this equation as a prototypical representative of a class of singularly perturbed monostable reaction-diffusion equations. The structure of a stripe pattern is essentially one-dimensional; therefore, we can use results from the literature to establish the existence of the homoclinic patterns. However, we extend these results to a maximal domain in the parameter space and establish the existence of a bifurcation that forms a new upper bound on this domain. Beyond this bifurcation, the Gierer--Meinhardt equation exhibits self-replicating pulse, respectively, stripe patterns in one, respectively, two dimension(s). The structure of the self-replication process is very similar to that in the Gray--Scott equation. We investigate the stability of the homoclinic stripe patterns by an Evans function analysis of the associated linear eigenvalue problem. We extend the recently develope.
Interference alignment aided by relays for the quasi-static X channel

In this paper, two relay-aided interference alignment (IA) schemes are proposed for the quasi-static M×2 X channel. The first scheme utilizes one full-duplex relay and the second scheme utilizes two half-duplex relays. In the proposed schemes, transmitters transmit in every time slot and relays operate in amplify-and-forward (AF) mode. By confirming the linear independence between the desired signals and interference and also among the desired signals, it is shown that the proposed schemes can achieve 2M/(M + 1) degrees of freedom (DoF) which is the maximum DoF of M × 2 X channel.
SensorScheme: Supply chain management automation using Wireless Sensor Networks

The supply chain management business can benefit greatly from automation, as recent developments with RFID technology shows. The use of Wireless Sensor Network technology promises to bring the next leap in efficiency and quality of service. However, current WSN system software does not yet provide the required functionality, flexibility and safety. This paper discusses a scenario showing how WSN technology can benefit supply chain management, and presents SensorScheme, a platform for realizing the scenario. SensorScheme is a general purpose WSN platform, providing a safe execution environment for dynamically loaded programs. It uses high level programming primitives like marshalled communication, automatic memory management, and multiprocessing facilities. SensorScheme makes efficient use of the little available memory present in WSN nodes, to allow larger and more complex programs than the state of the art. We present a SensorScheme implementation and provide experimental results to show its compactness, speed of operation and energy efficiency.
Learning Projections for Hierarchical Sparse Coding

Olshausen and Field (1996) suggest a method for learning a set of Gabor-like bases that activate sparsely to reconstruct natural images. We extended this approach by learning a sparse representation of these filter activations using an adapted version of the sparse coding algorithm (TISC). We trained our models on a simplified dataset with three shapes, and then on a subset of the Caltech 101 dataset. We found that for the shapes data, SVM classification accuracy improved when using either the first or second layer responses compared to the original images, while for the Caltech data, only the first layer yielded improvement. We also attempted to learn a projection of the first layer responses for the Caltech data to use for learning a second layer, but the algorithm failed to converge. We discuss reasons why this may have occurred, and also evaluate all representations that we learn by applying SVM classification.
Special Interests and Anthropomorphism

This study investigated the extent to which animals and objects of special interest to the participant were reported to have anthropomorphic or ‘humanlike’ qualities. It is a natural human tendency to be inclined toward faces and social activities. Deficits in face recognition and social processing are characteristic of Autism Spectrum Disorder (ASD). Another major characteristic of this disorder is preoccupation with objects or topics of interest. This preoccupation with ‘special interests’, and their co-occurrence with communication deficits may be indicative of a compromise between the two. The hypothesis of this study is that social cognitive systems have been repurposed in ASD to represent objects of special interest. A prediction of this hypothesis is, individuals with ASD may show more evidence of (potentially inappropriately) mixing social and special interest stimuli. This thesis structured a questionnaire aimed at quantifying the mixing of intense special interests, in individuals with ASD in comparison to NT individuals. Participants (N = 68) responded to questions about personal interests including categories in which those interests fell. For this thesis, mixing was examined for object and animal interests. Of the fifteen participants who met this criterion, none of indicated an ASD diagnosis. This thesis instead characterized the extent of anthropomorphization in individuals who were above and below threshold on an autism-screening questionnaire. Results did not support the hypothesis of this thesis. This may be explained by the limited number of responses available for analysis. Future work will include additional data collection. SPECIAL INTERESTS AND ANTHROPOMORPHISM
A Social Choice Function Proposal to Aggregate Preferences in a Group Decision Making Process

— In order to generate a single preference order for the society, a set of operations must be made on the preferences of the individuals forming that society. The aim of this paper is to propose a new Social Choice Function – SCF which will do this procedure. There are numerous functions that do this based on different intuitions or ideas. The function proposed in this paper has been developed with the intuitive approach that is based on the distances of the candidates to the ranks retrieved from the voters' ranking (as in the function of Cook & Seiford). The proposed function has a rather simple algorithm which, at each step, assigns a candidate to the closest possible rank while considering that distance has been covered for the next iteration. The function's process is designed in such way that the function will try to solve as much as possible the cases where there is indifference between candidates.
Robust Motion Controller for Mobile Robot Navigation in Indoor Environment

The problem of controlling the pose of a mobile robot with respect to a target position by means of visual feedback is investigated. The proposed method enables a mobile robot to identify its own position using visual features of environment. At the same time, the robot performs an orientation recognition using the same recognition method of position identification in order to follow a path in environment. One of the advantages of the proposed method is that both position and orientation recognition uses the same task where this situation will help for lowering the cost of the robot system development. Furthermore, the calculation method is simple, no precise and accurate measurement is used in the proposed navigation method. Experimental results demonstrate the effectiveness of our proposed method.
Reception state estimation of GNSS satellites in urban environment using particle filtering

The reception state of a satellite is an unavailable information for Global Navigation Satellite System receivers. His knowledge or estimation can be used to evaluate the pseudorange error. This article deals with the problem using three reception states: direct reception, alternate reception and blocked situation. This parameter, estimated using a Dirichlet distribution, is included in a particle filtering algorithm to improve the GNSS position in urban area. The algorithm takes into account three observation noise models depending on the reception of each satellite. Gaussian probability distribution is used with a direct path whereas a Gaussian mixture model is used in the alternate case.
The Role of Microglia in the Healthy Brain

Microglia were recently shown to play unexpected roles in normal brain development and adult physiology. This has begun to dramatically change our view of these resident “immune” cells. Here, we briefly review topics covered in our 2011 Society for Neuroscience minisymposium “The Role of Microglia in the Healthy Brain. ” This summary is not meant to be a comprehensive review of microglia physiology, but rather to share new results and stimulate further research into the cellular and molecular mechanisms by which microglia influence postnatal development, adult neuronal plasticity, and circuit function.
Neonatal White Matter Abnormalities an Important Predictor of Neurocognitive Outcome for Very Preterm Children

Background Cerebral white matter abnormalities on term MRI are a strong predictor of motor disability in children born very preterm. However, their contribution to cognitive impairment is less certain. Objective Examine relationships between the presence and severity of cerebral white matter abnormalities on neonatal MRI and a range of neurocognitive outcomes assessed at ages 4 and 6 years. Design/Methods The study sample consisted of a regionally representative cohort of 104 very preterm (≤32 weeks gestation) infants born from 1998–2000 and a comparison group of 107 full-term infants. At term equivalent, all preterm infants underwent a structural MRI scan that was analyzed qualitatively for the presence and severity of cerebral white matter abnormalities, including cysts, signal abnormalities, loss of white matter volume, ventriculomegaly, and corpus callosal thinning/myelination. At corrected ages 4 and 6 years, all children underwent a comprehensive neurodevelopmental assessment that included measures of general intellectual ability, language development, and executive functioning. Results At 4 and 6 years, very preterm children without cerebral white matter abnormalities showed no apparent neurocognitive impairments relative to their full-term peers on any of the domain specific measures of intelligence, language, and executive functioning. In contrast, children born very preterm with mild and moderate-to-severe white matter abnormalities were characterized by performance impairments across all measures and time points, with more severe cerebral abnormalities being associated with increased risks of cognitive impairment. These associations persisted after adjustment for gender, neonatal medical risk factors, and family social risk. Conclusions Findings highlight the importance of cerebral white matter connectivity for later intact cognitive functioning amongst children born very preterm. Preterm born children without cerebral white matter abnormalities on their term MRI appear to be spared many of the cognitive impairments commonly associated with preterm birth. Further follow-up will be important to assess whether this finding persists into the school years.
Psychometric Validation of the Multiple Sclerosis Environmental Supports Scale: A Brief Report

The main objective of this study was to evaluate the measurement structure of the Multiple Sclerosis Environmental Supports Scale (MSESS) with 248 individuals with multiple sclerosis (MS). Exploratory factor analysis yielded three reliable factors (health and mental health services; rehabilitation, social, and support services; and independent living supports). The MSESS appears to be a promising rehabilitation assessment tool for individuals with MS, one that can be used by rehabilitation counselors to evaluate environmental support factors in their interview, assessment, and rehabilitation planning protocols.
Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors.
Fears and Difficulties Experienced by Ukrainian Nationals During Their Period of Study in Poland

The overall aim of this study is to review problems relating to the acculturation of Ukrainian students during their initial period of study abroad in Poland. More importantly, our objective was to recognize the fears and difficulties these students experience and examine the strategies of coping with major stressors. Based on group interviews we identified four themes, which mainly related to the language barrier, social isolation, discrimination, and financial issues. Our results show that the main acculturation strategy among the students in our sample was integration rather than assimilation, marginalization, or separation. Our respondents actively sought contacts with the locals, for example, by giving up their living quarters in the socially isolated and remote university campus and renting shared accommodation in the city, which made it possible for them to maintain closer links with their Ukrainian friends and to receive social support while also staying open to establishing relationships with Poles and having more opportunities to improve their Polish. At the same time, these students maintained their contacts with family and friends abroad through the Internet. This study also suggests the ways for higher education institutions to improve the services rendered to international students and to support their integration into a new community.
The urban labour market during structural adjustment: Ethiopia 1990-1997

The paper examines the effects of reform and structural adjustment on the urban labour market in Ethiopia using a combination of cross-section and panel data based on surveys conducted both pre- and post- reform. During this period Ethiopia has seen impressive growth in GDP but little in the way of private investment. Meanwhile, the labour market has remained remarkably unresponsive to the pressures of reform despite the growing queues of the educated unemployed. While the public sector has contracted over the period, real wages have been re-adjusted to almost pre-reform levels; furthermore, real wages have grown in the private sector, while returns to education have remained largely unaffected.
A NEW THRESHOLD UNIT LEARNING ALGORITHMH

A new algorithm for learning a threshold unit is proposed. The Barycentric Correction Procedure (BCP) is an eecient substitute for the Perceptron and its enhanced versions such as the Thermal Perceptron or the Pocket algorithm. Based on geometrical concepts, the BCP is much more eecient than the Perceptron for learning linearly separable mappings. To deal with linearly nonseparable mappings, extensions are proposed leading to two versions of the BCP. The rst version is designed to minimize the number of misclassiied patterns and the second, to maximize the number of excluded patterns. Using the rst version in lieu of the Thermal Perceptron or the Pocket algorithm in constructive algorithms (such as Upstart, Tiling or OOset) dramatically enhances the speed of the construction process and the generalization ability of the networks constructed. The second version used with the Sequential Learning strategy leads to an eecient algorithm quickly learning complex mappings with real valued inputs. Together with various constructive algorithms, the BCP appears as a promising approach to neural network design for classiication tasks.
Removal of Eye Blink Artifacts From EEG Signals Based on Cross-Correlation

Artifacts such as eye blinks cause interference signals within electroencephalogram (EEG) measurements. Therefore, we propose a method for artifact removal based on certain power spectrum features of independent components extracted from the EEGs by fusing ensemble average and cross-corrasion parameter. First, each independent component which was assumed to be a source was searched by applying cross-corrasion parameter to EEG which involved artifacts relevant to the eye blinks of a normal person. Next, the signal which was assumed to be blink artifacts can be removed using high-pass filtering. The removal results of the eye blink artifacts are fed into the interpretation system.
An integrated approach for Noise Reduction and Dynamic Range Compression in hearing aids

Hearing aids typically use a serial concatenation of Noise Reduction (NR) and Dynamic Range Compression (DRC). However, the DRC in such a concatenation negatively affects the performance of the NR stage: the residual noise after NR is amplified by the DRC, resulting in a signal-to-noise-ratio (SNR) degradation. In this paper, we present an integrated solution for NR and DRC. The solution is based on an estimate of the amount of speech and noise in each time segment. In case the speech is dominant, the NR is less active and it is desirable to have as much DRC as possible, whereas in a noise dominant segment the NR is more active and the idea is not to compromise this operation by applying DRC. Experimental results confirmed that a serial concatenation of NR and DRC degrades the SNR improvement, and that the proposed solution offers a better SNR improvement compared to a serial concatenation.
Low Concordance between Gene Expression Signatures in ER Positive HER2 Negative Breast Carcinoma Could Impair Their Clinical Application

Background Numerous prognostic gene expression signatures have been recently described. Among the signatures there is variation in the constituent genes that are utilized. We aim to evaluate prognostic concordance among eight gene expression signatures, on a large dataset of ER positive HER2 negative breast cancers. Methods We analysed the performance of eight gene expression signatures on six different datasets of ER+ HER2- breast cancers. Survival analyses were performed using the Kaplan–Meier estimate of survival function. We assessed discrimination and concordance between the 8 signatures on survival and recurrence rates The Nottingham Prognostic Index (NPI) was used to to stratify the risk of recurrence/death. Results The discrimination ability of the whole signatures, showed fair discrimination performances, with AUC ranging from 0.64 (95%CI 0.55–0.73 for the 76-genes signatures, to 0.72 (95%CI 0.64–0.8) for the Molecular Prognosis Index T17. Low concordance was found in predicting events in the intermediate and high-risk group, as defined by the NPI. Low risk group was the only subgroup with a good signatures concordance. Conclusion Genomic signatures may be a good option to predict prognosis as most of them perform well at the population level. They exhibit, however, a high degree of discordance in the intermediate and high-risk groups. The major benefit that we could expect from gene expression signatures is the standardization of proliferation assessment.
EXTRACTING WINDOWS FROM TERRESTRIAL LASER SCANNING

With the high density point clouds obtained with te rrestrial laser scanning, detailed building facade structures, such as windows, can be recovered. There are usually only a few laser po ints available for windows, because window frames are small parts on walls, and window glass reflects no laser beam. Insufficient r aw laser information makes it very difficult to rec over reliable geometry of a window without human interaction. In this paper, we describe an approach to automatically extract wind ows from terrestrial point clouds. First, a segmentation process will group la ser points in planar segments. Walls, doors and ext rusions will be detected by applying feature constraints. Then, two detection s trategies for windows are presented, depending on w hether a window is covered with curtains or not. Windows which are not covered with curtains reflect no laser beam during the sca nning process, and therefore cause holes on the wall segments. Laser points are available for windows which are covered with curtai ns, but they usually are not on the same plane as its wall and will therefore be gr ouped into other segments than wall segments. This again results in holes on wall segments. Holes on the wall are recognized by searc hing long edges from the TIN, generated for wall se gments. After filtering out the holes caused by doors and extrusions, the remai ning holes are believed to be caused by windows, an d hence fitted to rectangles. The result from our approach is evaluated and discu ssed with examples of reconstructed building facade s.
Probabilistic path planning for cooperative target tracking using aerial and ground vehicles

In this paper, we present a probabilistic path planning algorithm for tracking a moving ground target in urban environments using UAVs in cooperation with UGVs. The algorithm takes into account vision occlusions due to obstacles in the environments. The target state is modeled using the dynamic occupancy grid and the probability of the target location is updated using Bayesian Altering. Based on the probability of the target's current and predicted locations, the path planning algorithm is designed to generate paths for a single UAV or UGV maximizing the sum of probability of detection over a finite look-ahead. For target tracking using multiple vehicle collaboration, a decentralized planning algorithm using an auction scheme generates paths maximizing the sum of joint probability of detection over the finite look ahead horizon. Simulation results show the proposed algorithm is successful in solving the target tracking problem in urban environments.