Miracle-dz/UniFAR-FTUs huggingface dataset:
Corpus-Based Analyses of Adjectives:Automatic Clustering

Similarity analysis is a substantial issue in both corpus-based researches and language usages. This paper focuses on the semantic usages of adjectives, and analyzes the similarities among adjectives. The adjective and the semantic tag of the head noun that it modifies in a noun phrase form a co-occurrence. A two-stage algorithm is applied to clustering the adjectives according to these co-occurrence relationships. Experimental results show that we break even the two issues of large data clustering and meaningful clustering.
Providing Recommendations in SCENS

SCENS (Secure Content Exchange Negotiation System) is a web based data sharing system that enables data owners to maintain control of their original data while negotiating the conditions under which sharing can be done. This allows for data tracking, data provenance and data copyright enforcement. In this paper, we introduce our current work on incorporating a recommendation component in SCENS. The objective of this component is to help users find the most reliable, valuable, important and interesting data quickly and easily. Four implemented recommendation algorithms (Naive item average, User-based, Item-based, and Singular Value Decomposition based) in our recommendation component are discussed.
An incremental approach to learning generalizable robot tasks from human demonstration

Dynamic Movement Primitives (DMPs) are a common method for learning a control policy for a task from demonstration. This control policy consists of differential equations that can create a smooth trajectory to a new goal point. However, DMPs only have a limited ability to generalize the demonstration to new environments and solve problems such as obstacle avoidance. Moreover, standard DMP learning does not cope with the noise inherent to human demonstrations. Here, we propose an approach for robot learning from demonstration that can generalize noisy task demonstrations to a new goal point and to an environment with obstacles. This strategy for robot learning from demonstration results in a control policy that incorporates different types of learning from demonstration, which correspond to different types of observational learning as outlined in developmental psychology.
Hope for the best, prepare for the worst: multi-tier control for adaptive systems

Most approaches for adaptive systems rely on models, particularly behaviour or architecture models, which describe the system and the environment in which it operates. One of the difficulties in creating such models is uncertainty about the accuracy and completeness of the models. Engineers therefore make assumptions which may prove to be invalid at runtime. In this paper we introduce a rigorous, tiered framework for combining behaviour models, each with different associated assumptions and risks. These models are used to generate operational strategies, through techniques such controller synthesis, which are then executed concurrently at runtime. We show that our framework can be used to adapt the functional behaviour of the system: through graceful degradation when the assumptions of a higher level model are broken, and through progressive enhancement when those assumptions are satisfied or restored.
Enabling remote access to a wireless sensor network by exploiting IPv6 capabilities

One of the most important applications of wireless sensor networks (WSNs) undoubtedly is the emergency management. In particular, sensor nodes can be helpful for both preventing and recovering emergency situations. During critical emergency situations, having a WSN easy to access and manage is a mandatory requirement. So far, sensor networks have been based on custom protocols that make difficult to access and configure sensors by users that are different from the original designers. Today, with the advent of IPv6, integration between a sensor network and Internet becomes feasible. In this paper, we discuss the IPv6 capabilities that can be effectively exploited jointly with 6LoWPAN, and we illustrate a testbed based on an implementation of IPv6 over IEEE 802.15. 4. We demonstrate that, by using an open source module we have purposely developed to run over 6LoWPAN, a user equipped with some few common network utilities can remotely access the WSN and change run-time the state and configuration of every single sensor node.
Regret-based Incremental Partial Revelation Mechanisms

Classic direct mechanisms suffer from the drawback of requiring full type (or utility function) revelation from participating agents. In complex settings with multi-attribute utility, assessing utility functions can be very difficult, a problem addressed by recent work on preference elicitation. In this work we propose a framework for incremental, partial revelation mechanisms and study the use of minimax regret as an optimization criterion for allocation determination with type uncertainty. We examine the incentive properties of incremental mechanisms when minimax regret is used to determine allocations with no additional elicitation of payment information, and when additional payment information is obtained. We argue that elicitation effort can be focused simultaneously on reducing allocation and payment uncertainty.
Empirical case-studies of state fusion via ellipsoidal intersection

This article presents a practical assessment of the recently developed state fusion method ellipsoidal intersection and focusses on distributed state estimation in sensor networks. It was already proven that this fusion method combines strong fundamental properties with attractive features in accuracy and computational requirements. However, these features were derived for linear processes with observability of the state vector in at least one of the local measurements. Therefore, several empirical case-studies are performed to assess ellipsoidal intersection with respect to three real-life limitations. A scenario of cooperative adaptive cruise control is used to analyze the absence of observability in any local measurement. Furthermore, the Van-der-Pol oscillator and a benchmark application of tracking shockwaves on highways assess the fusion method for nonlinear process models. The latter example is also used in a set-up where the employed state estimation methodology differs per node, so to meet different computational requirements per node.
Automatic Clustering of Faces in Meetings

Meetings are an integral part of business life for any organization. In previous work, we have developed a physical awareness system called CAMEO (camera assisted meeting event observer) to record and process the audio/visual information of a meeting. An important task in meeting understanding is to know who and how many people are attending the meeting. In this paper, we present an automatic approach to detect, track, and cluster people's faces in long video sequences. This is a challenging problem due to the appearance variability of people's faces (illumination, expression, pose,.). Two main novelties are presented: a robust real-time adaptive subspace face tracker which combines color and appearance. A temporal subspace clustering algorithm. The effectiveness and robustness of the proposed system is demonstrated over a data set of long videos (i.e. 1 hour).
Optical Imaging Reveals Elevated Intracellular Chloride in Hippocampal Pyramidal Neurons after Oxidative Stress

The accumulation of reactive oxygen species (ROS) in the brain is associated with several neurodegenerative conditions. ROS can affect ionic homeostasis leading to impaired neurotransmission. Here, we determined the ability of H2O2, a membrane permeant ROS, to alter intraneuronal Cl−, an important regulator of neuronal excitability. Real-time alterations in intracellular chloride, [Cl−]i, were measured with UV laser scanning confocal microscopy in hippocampal slices loaded with the cell-permeant form of 6-methoxy-N-ethylquinolium iodide (MEQ), a Cl−-sensitive fluorescent probe. In slices superfused with H2O2 for 10 min, there was a significant decrease in MEQ fluorescence (elevation in [Cl−]i) in area CA1 pyramidal cell soma but not in interneurons located in stratum radiatum. Alterations in [Cl−]i induced by H2O2were prevented by the iron chelator deferoxamine and the vitamin E analog Trolox, suggesting the involvement of free radicals. The influx of Cl− probably occurred through the GABA-gated Cl− channel because the effects of H2O2 were blocked by picrotoxin. In addition, HPLC analysis of the superfusates indicated that GABA and glutamate accumulated extracellularly after H2O2exposure. Excitatory amino acid receptor antagonists 2-amino-5-phoshopentanoic acid and 1,2,3,4-tetrahydro-6-nitro-2, 3-dioxo-benzo[f]quinoxaline-7-sulfonamide also attenuated the effect of H2O2 on MEQ fluorescence. The changes in [Cl−]i induced by H2O2were Ca2+-dependent and Na+-independent. After exposure of slices to H2O2, the ability of the GABA agonist muscimol to increase [Cl−]i was attenuated. Thus, ROS, like H2O2, may impair transmembrane Cl− gradients and reduce inhibitory neurotransmission, further promoting neuronal damage in oxidative stress-related disease and in aging.
An Extended Two-Phase Method for Accessing Sections of Out-of-Core Arrays

A number of applications on parallel computers deal with very large data sets that cannot fit in main memory. In such applications, data must be stored in files on disks and fetched into memory during program execution. Parallel programs with large out-of-core arrays stored in files must read/write smaller sections of the arrays from/to files. In this article, we describe a method for accessing sections of out-of-core arrays efficiently. Our method, the extended two-phase method, uses collective l/O: Processors cooperate to combine several l/O requests into fewer larger granularity requests, to reorder requests so that the file is accessed in proper sequence, and to eliminate simultaneous l/O requests for the same data. In addition, the l/O workload is divided among processors dynamically, depending on the access requests. We present performance results obtained from two real out-of-core parallel applications - matrix multiplication and a Laplace's equation solver - and several synthetic access patterns, all on the Intel Touchstone Delta. These results indicate that the extended two-phase method significantly outperformed a direct (noncollective) method for accessing out-of-core array sections.
1 Training Status Diverges Muscle Diacylglycerol Accumulation During Free Fatty Acid 2 Elevation 3 4 Short title : Training Effects on Muscle Lipid During FFA Elevation 5 6 7

Training Status Diverges Muscle Diacylglycerol Accumulation During Free Fatty Acid 2 Elevation 3 4 Short title: Training Effects on Muscle Lipid During FFA Elevation 5 6 7 Lisa S Chow1 8 Douglas G. Mashek2 9 Erin Austin 3 10 Lynn E. Eberly 3 11 Xuan-Mai Persson 12 Mara T. Mashek 2 13 Elizabeth R. Seaquist 1 14 Michael D. Jensen 15 16 1 Department of Medicine, University of Minnesota, Minneapolis MN , Department of Food 17 Science and Nutrition, University of Minnesota, Minneapolis MN, Division of Biostatistics, 18 University of Minnesota, Minneapolis MN, Department of Medicine, Mayo Clinic College of 19 Medicine, Rochester MN 20 21 Corresponding author: 22 Lisa S Chow MD, MS 23 MMC 101 24 420 Delaware St SE 25 Minneapolis, MN 55455 26 612-625-8934 27 Fax 612-626-3133 28 Email: chow0007@umn.edu 29 30 31 Author contributions: 32 LSC and ES designed the research. LSC, DM, XMP, MM, MJ provided essential data for 33 analysis. LSC, EA, LE analyzed the data. LSC, EA, LEE, XMP wrote the manuscript. LSC, 34 DM, EA, LE, XMP, MM, ES, MJ contributed to the discussion and reviewed/edited the 35 manuscript. LSC has primary responsibility for the final content. All authors have read and 36 approved the final manuscript. 37 38 39 Disclosure: 40 No conflict of interest to report 41 42 43 Articles in PresS. Am J Physiol Endocrinol Metab (May 20, 2014). doi:10.1152/ajpendo.00166. 2014
Performance of Forest Bryophytes with Different Geographical Distributions Transplanted across a Topographically Heterogeneous Landscape

Most species distribution models assume a close link between climatic conditions and species distributions. Yet, we know little about the link between species' geographical distributions and the sensitivity of performance to local environmental factors. We studied the performance of three bryophyte species transplanted at south- and north-facing slopes in a boreal forest landscape in Sweden. At the same sites, we measured both air and ground temperature. We hypothesized that the two southerly distributed species Eurhynchium angustirete and Herzogiella seligeri perform better on south-facing slopes and in warm conditions, and that the northerly distributed species Barbilophozia lycopodioides perform better on north-facing slopes and in relatively cool conditions. The northern, but not the two southern species, showed the predicted relationship with slope aspect. However, the performance of one of the two southern species was still enhanced by warm temperatures. An important reason for the inconsistent results can be that microclimatic gradients across landscapes are complex and influenced by many climate-forcing factors. Therefore, comparing only north- and south-facing slopes might not capture the complexity of microclimatic gradients. Population growth rates and potential distributions are the integrated results of all vital rates. Still, the study of selected vital rates constitutes an important first step to understand the relationship between population growth rates and geographical distributions and is essential to better predict how climate change influences species distributions.
A geological metaphor for geospatial-temporal data analysis

To provide visual access to geospatial-temporal data, existing systems usually highlight the data's spatial, temporal and topical distribution individually in separated, but linked views. Because this design often complicates queries that concern multiple data aspects and also involves more user interaction, in this paper, we present a geological metaphor that aims to combine relations between orthogonal data aspects. We describe how our adopted landscape metaphor intuitively depicts global and local relationships based on its surface, glyph augmentation and inner sediment structure. We validate the geological metaphor with case studies, compare it with existing systems and describe how it can be integrated into those as an alternative map view.
Routing IPv6 over wireless networks with low-memory devices

Future wireless Internet envisions that thousands of embedded devices will be integrated to the Internet. These devices, which can be micro-controllers with radio and sensor components, require communication protocols to establish a network. But, some embedded devices have scarce RAM resource. Our work shows that it is possible to implement an IPv6 stack over Low power Wireless Personal Area Networks (6LowPAN) for RAM constrained systems. We are able to implement the RPL routing protocol with less than 512 bytes of RAM. To handle the hard RAM constraint, we develop a circular queue and a swap content mechanism from RAM to Flash memory. We develop a platform and tested the implementation with realist experiments. Our research has the potential for a great impact since it enables interconnecting many lower cost devices to the Internet.
Pyroelectric IR sensor arrays for fall detection in the older population

Uncooled pyroelectric sensor arrays have been studied over many years for their uses in thermal imaging applications. These arrays will only detect changes in IR flux and so systems based upon them are very good at detecting movements of people in the scene without sensing the background, if they are used in staring mode. Relatively-low element count arrays (16x 16) can be used for a variety of people sensing applications, including people counting (for safety applications), queue monitoring etc. With appropriate signal processing such systems can be also be used for the detection of particular events such as a person falling over. There is a considerable need for automatic fall detection amongst older people, but there are important limitations to some of the current and emerging technologies available for this. Simple sensors, such as I or 2 element pyroelectric infra-red sensors provide crude data that is difficult to interpret; the use of devices worn on the person, such as wrist communicator and motion detectors have potential, but are reliant on the person being able and willing to wear the device; video cameras may be seen as intrusive and require considerable human resources to monitor activity while machine-interpretation of camera images is complex and may be difficult in this application area. The use of a pyroelectric thermal array sensor was seen to have a number of potential benefits. The sensor is wall-mounted and does not require the user to wear a device. It enables detailed analysis of a subject's motion to be achieved locally, within the detector, using only a modest processor. This is possible due to the relative ease with which data from the sensor can be interpreted relative to the data generated by alternative sensors such as video devices. In addition to the cost-effectiveness of this solution, it was felt that the lack of detail in the low-level data, together with the elimination of the need to transmit data outside the detector, would help to avert feelings intrusiveness on the part of the end-user. The main benefits of this type of technology would be for older people who spend time alone in unsupervised environments. This would include people living alone in ordinary housing or in sheltered accommodation (apartment complexes for older people with local warden) and non-communal areas in residential/nursing home environments (e.g. bedrooms and ensuite bathrooms and toilets). This paper will review the development of the array, the pyroelectric ceramic material upon which it is based and the system capabilities. It will present results from the Framework 5 SIMBAD project, which used the system to monitor the movements of elderly people over a considerable period of time.
A cooperative Reinforcement Learning approach for Inter-Cell Interference Coordination in OFDMA cellular networks

Inter-Cell Interference Coordination (ICIC) is commonly identified as a key radio resource management mechanism to enhance system performance of 4G networks. This paper addresses the problem of ICIC in the downlink of cellular OFDMA (LTE and WiMAX) systems in the context of Self-Organizing Networks (SON). The problem is posed as a cooperative MultiAgent control problem. Each base station is an agent that dynamically changes power masks on a subset of its bandwidth to control interference it produces to its neighbouring cells. The agent learns the optimal coordinated power allocation strategy using information from its own and its neighbouring cells. A Fuzzy Inference System (FIS) is used to handle continuous state space defined by the input quality indicators to the controller performing the ICIC. The FIS is optimized using Reinforcement Learning (RL) with a Fuzzy Q-Learning (FQL) implementation. Simulation results illustrate the important performance gain brought about by the proposed ICIC scheme.
Supporting predictability in real-time database systems

Real-time database systems (RTDBSs) have timing constraints in their specifications, such as read times, deadlines and other temporal constraints. In addition, RTDBSs must adapt to changes in the operating environment and guarantee the completion of critical transactions. Previous research efforts in RTDBSs have been focused on scheduling transactions with soft or firm deadlines with serializability as the sole correctness criterion. Few results have been reported for supporting predictable transaction execution and guaranteeing the temporal consistency of data. The goal has been to minimize the deadline miss ratio and to maintain the logical consistency of data. In this paper, we address the issues of predictability and temporal consistency in RTDBSs. We briefly discuss the characteristics and requirements of RTDBSs, and present a transaction processing scheme that supports multiple levels of predictability for real-time transactions. The performance of the proposed scheme and the cost of achieving a high level of predictability is studied by using simulation.
Evidence-Based Assessment of Autism Spectrum Disorders in Children and Adolescents

This article reviews evidence-based criteria that can guide practitioners in the selection, use, and interpretation of assessment tools for autism spectrum disorders (ASD). As Mash and Hunsley (2005) discuss in this special section, evidence-based assessment tools not only demonstrate adequate psychometric qualities, but also have relevance to the delivery of services to individuals with the disorder (see also Hayes, Nelson, & Jarrett, 1987). Thus, we use what is known about the symptoms, etiologies, developmental course, and outcome of ASD to evaluate the utility of particular assessment strategies and instruments for diagnosis, treatment planning and monitoring, and evaluation of outcome. The article begins with a review of relevant research on ASD. Next we provide an overview of the assessment process and some important issues that must be considered. We then describe the components of a core (minimum) assessment battery, followed by additional domains that might be considered in a more comprehensive assessment. Domains covered include core autism symptomatology, intelligence, language, adaptive behavior, neuropsychological functions, comorbid psychiatric illnesses, and contextual factors (e.g., parent well-being, family functioning, quality of life). We end with a discussion of how well the extant literature meets criteria for evidence-based assessments.
An Improved Partial Adaptive Narrow-Band Beamformer Using Concentric Ring Array

Partial adaptation is often used to reduce the computation and improve the tracking ability of an adaptive array. In some practical situations, the received signal to be processed contains some interferences whose characteristics are known. The previously proposed partially adaptive concentric ring array is not able to utilize the prior information of known interferences without sacrificing the number of degrees of freedom, which causes higher steady state error and smaller number of interferences that can be cancelled. We propose in this paper an improved partially adaptive concentric ring array that can utilize the prior knowledge to improve the performance and maintain the same number of degrees of freedom. The proposed method designs the non-adaptive weights to remove the known interferences, and is shown to provide much faster convergence speed and lower steady state error than the original method