Miracle-dz/UniFAR-FTUs huggingface dataset:
Loanwords and stress in Tohono O'odham

Tohono O'odham (formerly Papago) assigns primary stress to the first syllable in content words; but there is an asymmetry in the distribution of secondary stress, where the facts are more complicated. Polymorphemic words stress all odd syllables, whereas monomorphemic words stress only nonfinal odd syllables. The implications of this distribution for metrical stress theory are discussed. It is especially intriguing that the crucial support for the asymmetry comes exclusively from loanwords.
Role of Prostaglandins in Neuroinflammatory and Neurodegenerative Diseases

Increasing data demonstrates that inflammation participates in the pathophysiology of neurodegenerative diseases. Among the different inflammatory mediators involved, prostaglandins play an important role. The effects induced by prostaglandins might be mediated by activation of their known receptors or by nonclassical mechanisms. In the present paper, we discuss the evidences that link prostaglandins, as well as the enzymes that produce them, to some neurological diseases.
Cricket/Mica2 Based Discrete Event Simulator for WiHoc Ver.1.0 Localization Analysis

The deployment of Wireless Sensor Networks (WSN) has enabled applications to operate at its optimum and maximum potential. This paper presents the enhancement of the Wireless Hockey (WiHoc) System ver.1. 0. The sensors are utilized to acquire the movement of field hockey players on a coaching strategy board. These are denoted and executed by the Cricket / Mica-2 sensors which have been utilized as the core hardware of the developed system. A software to enable coordinate derivation for the field hockey localization is further developed to relate the localization information to the hockey Grid. In addition, the details of a proposed and developed Discrete Event Simulator (DES) to analyze the performance analysis of WiHoc is deliberated extensively. The developed simulator is analyzed utilizing the accuracy metric. The correlation of the DES has enabled optimized operations of the WiHoc system, which have been demonstrated by the extensive experiments conducted.
On the impact of sequence and time in rich media advertising

Nowadays, commercial websites use an increasing amount of rich media ads, since these ads grab users' attention more easily. Ad overloading has a counterproductive effect and we advocate a more parsimonious and finely-tuned use of ads. In this paper we stress the importance of using ads in the right sequence and at the right time. We also present how to use our web adaptation system for a classic web marketing multivariable test. The experimental results show the usefulness of optimizing banners' sequences and timing.
Climate Vulnerability and Human Migration in Global Perspective

The relationship between climate change and human migration is not homogenous and depends critically on the differential vulnerability of population and places. If places and populations are not vulnerable, or susceptible, to climate change, then the climate–migration relationship may not materialize. The key to understanding and, from a policy perspective, planning for whether and how climate change will impact future migration patterns is therefore knowledge of the link between climate vulnerability and migration. However, beyond specific case studies, little is known about this association in global perspective. We therefore provide a descriptive, country-level portrait of this relationship. We show that the negative association between climate vulnerability and international migration holds only for countries least vulnerable to climate change, which suggests the potential for trapped populations in more vulnerable countries. However, when analyzed separately by life supporting sector (food, water, health, ecosystem services, human habitat, and infrastructure) and vulnerability dimension (exposure, sensitivity, and adaptive capacity), we detect evidence of a relationship among more, but not the most, vulnerable countries. The bilateral (i.e., country-to-country) migration show that, on average, people move from countries of higher vulnerability to lower vulnerability, reducing global risk by 15%. This finding is consistent with the idea that migration is a climate adaptation strategy. Still, ~6% of bilateral migration is maladaptive with respect to climate change, with some movement toward countries with greater climate change vulnerability.
LBRP: Geographic routing protocols for MANETs

In mobile ad hoc networks, since all nodes are mobile and there is no fixed infrastructure, the design of routing protocols becomes one of the most challenging issues. Due to the utilization of location information, geographic ad hoc routing present superiority in scalability compared with traditional topology-based routing in mobile ad hoc networks. In this paper a location-based routing protocol LBRP (Location-Based Routing Protocol) for ad hoc networks based on location system is introduced. The aim is extracting an optimum topology from the dynamic and irregular topology of a mobile ad hoc network to reach more quickly the destination applying for routing. The scheme operates in a loop free manner. The Network Simulator tool is used to analyze the performances of the protocol and compare its results to DSR (Dynamic Source Routing), DREAM (Distance Routing Effect Algorithm for Mobility). Also, investigate LBRP's performance taking into account and characteristics. Simulation results revealed that LBRP becomes more efficient when the topology changes rapidly reaching robustness and flooding minimization.
Hierarchical Semantic Labeling for Task-Relevant RGB-D Perception

Semantic labeling of RGB-D scenes is very important in enabling robots to perform mobile manipulation tasks, but different tasks may require entirely different sets of labels. For example, when navigating to an object, we may need only a single label denoting its class, but to manipulate it, we might need to identify individual parts. In this work, we present an algorithm that produces hierarchical labelings of a scene, following is-part-of and is-type-of relationships. Our model is based on a Conditional Random Field that relates pixel-wise and pair-wise observations to labels. We encode hierarchical labeling constraints into the model while keeping inference tractable. Our model thus predicts different specificities in labeling based on its confidence—if it is not sure whether an object is Pepsi or Sprite, it will predict soda rather than making an arbitrary choice. In extensive experiments, both offline on standard datasets as well as in online robotic experiments, we show that our model outperforms other stateof-the-art methods in labeling performance as well as in success rate for robotic tasks.
An improvement of the overlap complexity in the spaced seed searching problem between genomic DNAs

In homology search, finding optimal multiple spaced seeds in genomic DNA sequences is NP-hard but even finding good ones is very difficult. The exponential-time algorithm PatternHunter use optimal spaced seeds to increase both the sensitivity and the speed of homology search. The overlap complexity measure based on the overlaps between hits of a multiple seed are well correlated with sensitivity but is computable in polynomial time. Based on overlap complexity, we have improved polynomial-time algorithms to provide better multiple seeds. Our experimental results shows that these improvements significantly run faster and make better quality of spaced seeds than previous algorithms in almost all test cases.
Usefulness of MALDI-TOF MS as a Diagnostic Tool for the Identification of Streptococcus Species Recovered from Clinical Specimens of Pigs

The application of MALDI-TOF MS for identifying streptococcal isolates recovered from clinical specimens of diseased pigs was evaluated. For this proposal, the MALDI BDAL Database (Bruker Daltoniks, Germany) was supplemented with the main spectrum profiles (MSP) of the reference strains of S. porci, S. porcorum and S. plurextorum associated with pneumonia and septicemia. Although these three species showed similar MALDI profiles, several peaks were recognized that can be useful for their differentiation: S. porci (4113, 6133, 7975 and 8228 m/z Da), S. plurextorum (3979, 4078, 4665, 6164, 6491, 6812, 7959 and 9330 m/z Da) and S. porcorum (3385, 3954, 4190, 6772, 7908, and 8381 m/z Da). After adding these MSPs, an evaluation was conducted to determine the accuracy of MALDI-TOF MS for the identification of streptococci from diseased pigs using 74 field isolates. Isolates were identified as S. suis, S. porcinus, S. dysgalactiae, S. hyovaginalis, S. porcorum, S. alactolyticus, S. hyointestinalis and S. orisratti. This is the first time that the latter three species have been reported from clinical specimens of pigs. Overall, there was good concordance (95.9%) between the results obtained from MALDI-TOF MS identification (best hint) and those from genotyping. Our results demonstrate the good performance of MALDI-TOF MS (100% sensitivity and specificity) for identifying most of the species of streptococci that can frequently be isolated from diseased pigs. However, conflicting results were observed in the correct identification of some isolates of S. dysgalactiae and S. alactolyticus.
Unobservable Mix: Hiding Communication with Uniform Shape of Network Traffic

Unobservability in network communication is an important safeguard for hiding user's private activities. However, existing solutions including mix systems only offer anonymity, which is lower-level concept than unobservability. In this paper, we introduce a new mix model that effectively provides the unobservability by generating fake messages efficiently even in the environment where traffic volume may be low or the bounded transmission delay of messages is important. That is, users can hide their communication and determine the delay of message transmission. Also, it is possible to detect and isolate areas where blending attack occurs.
The Evolution of Human Language: Genetics and the evolution of language: what genetic studies reveal about the evolution of language

In this paper I argue that genetic studies of language provide insights about the evolution of language. The finding that genetic factors affect all aspects of language is consistent with theories that argue that language is at least partially the result of innate factors, and that the factors have evolved. Genetic factors appear to play a greater role for syntax and phonology than for the lexicon, suggesting that the evolutionary history of syntax and phonology differs from that of the lexicon. Genetic factors also affect the linguistic abilities of language-impaired children more than normal children, consistent with the evolutionary history of language in language-impaired and normal children being different. The high degree of genetic overlap for linguistic abilities and oral motor and fine motor abilities is consistent with evolutionary theories that argue that complex fine motor skills and non-linguistic oral motor skills were the precursors to language. Similarly, the high degree of genetic overlap for linguistic and social ability suggests that aspects of language could have evolved from social abilities. The greater genetic overlap for syntax and phonology than for either the lexicon and syntax or the lexicon and phonology suggests that the evolutionary history of syntax and phonology is more similar than that of the lexicon and either syntax or phonology. Taken as a whole, these genetic findings are most consistent with linguistic models and evolutionary theories of language that invoke both general and language-specific factors.
People detection based on co-occurrence of appearance and spatiotemporal features

This paper presents a method for detecting people based on the co-occurrence of appearance and spatiotemporal features. Histograms of oriented gradients(HOG) are used as appearance features, and the results of pixel state analysis are used as spatiotemporal features. The pixel state analysis classifies foreground pixels as either stationary or transient. The appearance and spatiotemporal features are projected into subspaces in order to reduce the dimensions of the vectors by principal component analysis(PCA). The cascade AdaBoost classifier is used to represent the co-occurrence of the appearance and spatiotemporal features. The use of feature co-occurrence, which captures the similarity of appearance, motion, and spatial information within the people class, makes it an effective detector. Experimental results show that the performance of our method is about 29% better than that of the conventional method.
Neue Entwicklungen in der neuropathologischen Diagnostik fokaler Epilepsien

Zusammenfassung Mit zunehmender Verfügbarkeit Epilepsie-chirurgischer Therapieverfahren stellen sich besondere Anforderungen an die neuropathologische Diagnostik. Die Durchsicht von 4 840 am Neuropathologischen Referenzzentrum für Epilepsie-Chirurgie gesammelten Fällen ergibt ein ungewöhnlich breites Spektrum histopathologischer Läsionen. Die häufige mesiale Temporallappensklerose ist durch segmentale Nervenzelluntergänge im Hippocampus gekennzeichnet. Eine neue histopathologische Klassifikation ist für die Prädiktion der postoperativen Anfallsfreiheit hilfreich. Bei Epilepsie-assoziierten Tumoren steht die Charakterisierung neuer Varianten und die Einstufung ihrer biologischen Wertigkeit im Mittelpunkt. Fehlbildungen der kortikalen Entwicklung finden sich in 14% der untersuchten Fälle. Bisherige Klassifikationsschemen berücksichtigen nur unzureichend die unterschiedlichen Läsionsmuster, vor allem in der Abgrenzung fokaler kortikalen Dysplasien. Weiterführende interdisziplinäre Studien sollen eine klinisch-genetische und pathologische Klassifikation zum besseren Verständnis der Ätiologie erarbeiten und die zielgerichtete Entwicklung neuer medikamentöser Therapiestrategien fördern.
A new PSO approach to constraint satisfaction

Constraint satisfaction arises in many domains in different forms. Search and inference compete for solving constraint satisfaction problems (CSPs) but the most successful approaches are those which benefit from both techniques. Based on this idea, this article introduces a new scheme for solving the general Max-CSP problem. The new approach exploits the simplicity and efficiency of a modified Particle Swarm Optimization and the advantage of adaptable inference levels offered by the Mini-Bucket Elimination algorithm. Experiments conducted on binary CSPs using different levels of inference are illustrative for the inference/search trade-off. Comparative studies highlight the differences between our stochastic population-based method and the systematic search performed by a Branch and Bound algorithm.
Towards an explanation of Irish rural-based social enterprises

ABSTRACT In Ireland social enterprises are seen primarily as non-profit organisations driven by social objectives. This article presents qualitative data gathered through a series of in depth interviews with representatives from five rural-based Irish social enterprises. The objectives of the article are to provide short descriptions of these social enterprises, to assess the critical success factors and barriers facing them, to discuss their potential for replication in other rural areas and some of the lessons to be learned from these initiatives.
Defining a role for novel biomarkers in acute coronary syndromes.

BACKGROUND Biomarkers play a pivotal role in the diagnosis and treatment of patients with cardiovascular disease. Active investigation has brought forward an increasingly large number of novel candidate markers; however, few of these markers have yet to be incorporated into routine clinical use. CONTENT This review discusses biomarkers currently used in the setting of acute coronary syndromes. In this context, we assess the contemporary unmet needs for novel biomarkers in acute ischemic heart disease and the related challenges faced in developing new biomarkers to the point of integration into clinical practice. In particular, we address the impact of the availability of increasingly sensitive biomarkers of myocardial necrosis on the potential roles for novel biomarkers of inflammation, thrombosis, and ischemia. SUMMARY Although active investigation has produced a growing list of candidate novel biomarkers for the care of patients with cardiovascular disease, it has become increasingly challenging to find appreciable incremental clinical benefit for their addition to existing markers, in particular newer, more analytically sensitive cardiac troponin assays. A major challenge for researchers and clinicians will be to demonstrate whether candidate novel markers are useful in improving diagnosis and guiding clinical treatment.
Co-Orientation of Replication and Transcription Preserves Genome Integrity

In many bacteria, there is a genome-wide bias towards co-orientation of replication and transcription, with essential and/or highly-expressed genes further enriched co-directionally. We previously found that reversing this bias in the bacterium Bacillus subtilis slows replication elongation, and we proposed that this effect contributes to the evolutionary pressure selecting the transcription-replication co-orientation bias. This selection might have been based purely on selection for speedy replication; alternatively, the slowed replication might actually represent an average of individual replication-disruption events, each of which is counter-selected independently because genome integrity is selected. To differentiate these possibilities and define the precise forces driving this aspect of genome organization, we generated new strains with inversions either over ∼1/4 of the chromosome or at ribosomal RNA (rRNA) operons. Applying mathematical analysis to genomic microarray snapshots, we found that replication rates vary dramatically within the inverted genome. Replication is moderately impeded throughout the inverted region, which results in a small but significant competitive disadvantage in minimal medium. Importantly, replication is strongly obstructed at inverted rRNA loci in rich medium. This obstruction results in disruption of DNA replication, activation of DNA damage responses, loss of genome integrity, and cell death. Our results strongly suggest that preservation of genome integrity drives the evolution of co-orientation of replication and transcription, a conserved feature of genome organization.
A learning automata-based algorithm for determination of the number of hidden units for three-layer neural networks

There is no method to determine the optimal topology for multi-layer neural networks for a given problem. Usually the designer selects a topology for the network and then trains it. Since determination of the optimal topology of neural networks belongs to class of NP-hard problems, most of the existing algorithms for determination of the topology are approximate. These algorithms could be classified into four main groups: pruning algorithms, constructive algorithms, hybrid algorithms and evolutionary algorithms. These algorithms can produce near optimal solutions. Most of these algorithms use hill-climbing method and may be stuck at local minima. In this article, we first introduce a learning automaton and study its behaviour and then present an algorithm based on the proposed learning automaton, called survival algorithm, for determination of the number of hidden units of three layers neural networks. The survival algorithm uses learning automata as a global search method to increase the probability of obtaining the optimal topology. The algorithm considers the problem of optimization of the topology of neural networks as object partitioning rather than searching or parameter optimization as in existing algorithms. In survival algorithm, the training begins with a large network, and then by adding and deleting hidden units, a near optimal topology will be obtained. The algorithm has been tested on a number of problems and shown through simulations that networks generated are near optimal.
Models and algorithms of brain connectivity, spatial sparsity, and temporal dynamics for the MEG/EEG inverse problem

Magnetoencephalography (MEG) and electroencephalography (EEG) are noninvasive functional neuroimaging techniques that provide high temporal resolution recordings of brain activity, offering a unique means to study fast neural dynamics in humans. Localizing the sources of brain activity from MEG/EEG is an ill-posed inverse problem, with no unique solution in the absence of additional information. In this dissertation I analyze how solutions to the MEG/EEG inverse problem can be improved by including information about temporal dynamics of brain activity and connectivity within and among brain regions. The contributions of my thesis are: 1) I develop a dynamic algorithm for source localization that uses local connectivity information and Empirical Bayes estimates to improve source localization performance (Chapter 1). This result led me to investigate the underlying theoretical principles that might explain the performance improvement observed in simulations and by analyzing experimental data. In my analysis, 2) I demonstrate theoretically how the inclusion of local connectivity information and basic source dynamics can greatly increase the number of sources that can be recovered from MEG/EEG data (Chapter 2). Finally, in order to include long distance connectivity information, 3) I develop a fast multi-scale dynamic source estimation algorithm based on the Subspace Pursuit and Kalman Filter algorithms that incorporates brain connectivity information derived from diffusion MRI (Chapter 3). Overall, I illustrate how dynamic models informed by neurophysiology and neuroanatomy can be used alongside advanced statistical and signal processing methods to greatly improve MEG/EEG source localization. More broadly, this work provides an example of how advanced modeling and algorithm development can be used to address difficult problems in neuroscience and neuroimaging. Thesis Supervisor: Patrick L. Purdon Title: Associate Professor of Anesthesia at Harvard Medical School
Sparse regression analysis for object recognition

This paper proposes a new method named Sparse Regression Analysis (SRA) for object representation and recognition. In SRA, ℓ1-norm minimization is combined with regression analysis to represent the input signal. The discriminative ability of SRA derives from the fact that the subset which most compactly expresses the input signal is activated in the regression analysis. To achieve a further improvement, Kernelized SRA (KSRA) is developed to make a nonlinear extension of SRA. The experiments are conducted on both palmprint and face recognition, which show that the proposed methods achieve a much better performance than sparse representation classifier, principal component analysis, and linear discriminant analysis.