Miracle-dz/UniFAR-FTUs huggingface dataset:
RIP: run-based intra-query parallelism for scalable complex event processing

Recognition of patterns in event streams has become important in many application areas of Complex Event Processing (CEP) including financial markets, electronic health-care systems, and security monitoring systems. In most applications, patterns have to be detected continuously and in real-time over streams that are generated at very high rates, imposing high-performance requirements on the underlying CEP system. For scaling CEP systems to increasing workloads, parallel pattern matching techniques that can exploit multi-core processing opportunities are needed. In this paper, we propose RIP - a Run-based Intra-query Parallelism technique for scalable pattern matching over event streams. RIP distributes input events that belong to individual run instances of a pattern's Finite State Machine (FSM) to different processing units, thereby providing fine-grained partitioned data parallelism. We compare RIP to a state-based alternative which partitions individual FSM states to different processing units instead. Our experiments demonstrate that RIP's partitioned parallelism approach outperforms the pipelined parallelism approach of this state-based alternative, achieving near-linear scalability that is independent from the query pattern definition.
Serotypes of Aggregatibacter actinomycetemcomitans in relation to periodontal status and geographic origin of individuals-a review of the literature

Objectives: Several studies have focused on the relationship among serotype distribution, ethnical status and geographic populations, and periodontal conditions. Studies that have investigated the prevalence and the distribution of A. actinomycetemcomitans serotypes and the relation between the different serotypes of the bacterium and periodontal status were reviewed. Material and Methods: A systematic literature search for publications regarding the distribution of A. actinomycetemcomitans serotypes in subgingival samples of periodontitis patients and periodontally healthy subjects by employing polymerase chain reaction (PCR) was conducted. Results: From the 85 studies identified in the first analysis, only 12 met all inclusion and exclusion criteria. Clinical isolates from diverse geographic populations with different periodontal conditions were evaluated. Serotypes a, b and c were largely found, and serotype c was the most prevalent. They were isolated from various periodontal conditions, including aggressive periodontitis. Conclusions: The available literature suggests that serotypes a, b, and c are globally dominant, serotypes d and e are rare, and the prevalence of the most recently identified serotype fis still unknown. It is widely accepted that distribution patterns of A. actinomycetemcomitans vary among subjects of different ethnicity and geographic regions. The correlation of different serotypes with various periodontal conditions remains unclear. Key words:Aggregatibacter actinomycetemcomitans, serotypes, periodontal disease, prevalence.
A Curling Shot Tracking Program

We present an algorithm for tracking the position of a curling stone from broadcast footage of curling events. Due to the constant panning of the camera in addition to perspective changes in the image, this introduces a need for image rectification and tracking of the camera view on the ice. We break the algorithm in four distinct components: rectification, frame tracking, stone tracking, and localization. The resulting algorithm has good performance in tracking the stone as it travels down the ice. This has promising applications for advanced analysis of curling shots, including the prediction of stone paths and precise quantification of the effect sweeping has on the stone.
Uncertainty relation for mutual information

We postulate the existence of a universal uncertainty relation between the quantum and classical mutual informations between pairs of quantum systems. Specifically, we propose that the sum of the classical mutual information, determined by two mutually unbiased pairs of observables, never exceeds the quantum mutual information. We call this the complementary-quantum correlation (CQC) relation and prove its validity for pure states, for states with one maximally mixed subsystem, and for all states when one measurement is minimally disturbing. We provide results of a Monte Carlo simulation suggesting the CQC relation is generally valid. Importantly, we also show that the CQC relation represents an improvement to an entropic uncertainty principle in the presence of a quantum memory, and that it can be used to verify an achievable secret key rate in the quantum one-time pad cryptographic protocol.
A dynamic Peer-to-Peer traffic limiting policy for ISP networks

As a scalable paradigm for content distribution at Internet-wide scale, Peer-to-Peer (P2P) technologies have enabled a variety of networked services, such as distributed file-sharing and live video streaming. Most existing P2P systems employ non-intelligent peer selection algorithms for content swarming which greedily consume Internet bandwidth resources. As a result, Internet service providers (ISPs) need some efficient solutions for managing P2P traffic within their own networks. A common practice today is to block or shape P2P traffic in order to conserve bandwidth resources for carrying standard traffic from which revenue can be generated. In this paper, instead of looking at simple time-driven blocking/limiting approaches, we investigate how such types of limiting behaviors can be more gracefully performed by the ISP by taking into account the dynamics of both P2P traffic and of standard Internet traffic. Specifically, our approach is to adaptively limit excessive P2P traffic on critical network links that are prone to congestion, based on periodical link load/utilization measurements by the ISP. The ultimate objective is to guarantee non-P2P service capability while trying to accommodate as much P2P traffic as possible based on the available bandwidth resources. This approach can be regarded as a complementary solution to the recently proposed collaboration-based P2P paradigms such as P4P. Simulation results show that our approach not only eliminates performance degradation of non-P2P services that are caused by overwhelming P2P traffic, but also accommodates P2P traffic efficiently in both existing and future collaboration-based P2P network scenarios.
Teaching robots social autonomy from in situ human supervision

TEACHING ROBOTS SOCIAL AUTONOMY FROM IN SITU HUMAN SUPERVISION Emmanuel Senft Traditionally the behaviour of social robots has been programmed. However, increasingly there has been a focus on letting robots learn their behaviour to some extent from example or through trial and error. This on the one hand excludes the need for programming, but also allows the robot to adapt to circumstances not foreseen at the time of programming. One such occasion is when the user wants to tailor or fully specify the robot’s behaviour. The engineer often has limited knowledge of what the user wants or what the deployment circumstances specifically require. Instead, the user does know what is expected from the robot and consequently, the social robot should be equipped with a mechanism to learn from its user. This work explores how a social robot can learn to interact meaningfully with people in an efficient and safe way by learning from supervision by a human teacher in control of the robot’s behaviour. To this end we propose a new machine learning framework called Supervised Progressively Autonomous Robot Competencies (SPARC). SPARC enables non-technical users to control and teach a robot, and we evaluate its effectiveness in Human-Robot Interaction (HRI). The core idea is that the user initially remotely operates the robot, while an algorithm associates actions to states and gradually learns. Over time, the robot takes over the control from the user while still giving the user oversight of the robot’s behaviour by ensuring that every action executed by the robot has been actively or passively approved by the user. This is particularly important in HRI, as interacting with people, and especially vulnerable users, is a complex and multidimensional problem, and any errors by the robot may have negative consequences for the people involved in the interaction. Through the development and evaluation of SPARC, this work contributes to both HRI and Interactive Machine Learning, especially on how autonomous agents, such as social robots, can learn from people and how this specific teacher-robot interaction impacts the learning process. We showed that a supervised robot learning from their user can reduce the workload of this person, and that providing the user with the opportunity to control the robot’s behaviour substantially improves the teaching process. Finally, this work also demonstrated that a robot supervised by a user could learn rich social behaviours in the real world, in a large multidimensional and multimodal sensitive environment, as a robot learned quickly (25 interactions of 4 sessions during in average 1.9 minutes) to tutor children in an educational game, achieving similar behaviours and educational outcomes compared to a robot fully controlled by the user, both providing 10 to 30% improvement in game metrics compared to a passive robot.
AN application of a dynamical model with ecological predator-prey approach to extensive livestock farming in uruguay: Economical assessment on forage deficiency

Extensive livestock farmers have to manage climate risk. Therefore, there is a need to generate quantitative tools to evaluate the biophysical and economic impacts on extensive farming based on native grasslands. We present an ecological model based on the predator-prey approach, used to simulate the effect of forage deficiency on the farm's economic performance. Different scenarios of animal stocking rate and carrying capacity of grassland are considered to assess the impact of forage deficiency in spring. Results suggest a cubic response of Gross product per hectare as function of Gross margin, according Mott's theoretical model for meat production on grassland systems in response to stocking rate. The maximum value of this cubic response function strongly depends on the initial grass height and climate scenarios. The initial grass height is critical to maximize secondary productivity and farm economic results. Scenarios including grass reserves can buffer the deficiency on grass growth rates and pasture offer, as occurs in drought periods at the time when farmers try to make animals gain liveweight. Our analysis reinforces the usefulness of forage assignment adjustment by modulating stocking rate to improve liveweight gain and economic results under climate change conditions.
Spectroscopic confirmation of a cluster of galaxies at z=1 in the field of the gravitational lens MG2016+112

We present new optical data on the cluster AX J2019+1127 identified by the X-ray satellite ASCA at z\sim 1 (Hattori et al. 1997). The data suggest the presence of a high-redshift cluster of galaxies responsible for the large separation triple quasar MG2016+112. Our deep photometry reveals an excess of z\sim 1 galaxy candidates, as already suspected by Benitez et al. (1999). Our spectroscopic survey of 44 objects in the field shows an excess of 6 red galaxies securely identified at z \sim 1, with a mean redshift of z =1.005 +/- 0.002. We estimate a velocity dispersion of \sigma = 771 (+430/-160) km s(-1) based on these 6 galaxies and a V-band mass-to-light ratio of 215 (+308/-77) h_50 M/L_sol. Our observations thus confirm the existence of a massive structure acting as the lens, which explains the unusual configuration of the triple quasar. Hence, there is no more need to invoke the existence of a ``dark cluster'' to understand this lens system.
MACROGEOGRAPHIC STRUCTURE AND PATTERNS OF GENETIC DIVERSITY IN HARBOR SEALS (PHOCA VITULINA) FROM ALASKA TO JAPAN

Abstract We examined sequence variation in the control region of the mitochondrial genome from 778 seals sampled at 161 locations from northern Japan to southeastern Alaska to learn more about the evolutionary history and population structure of, and effects of recent declines on genetic diversity in, harbor seals (Phoca vitulina) in the northern Pacific Ocean. High haplotypic diversity (H = 0.975) and a poorly resolved mitochondrial genome (mtDNA) phylogeny suggest that harbor seals in the Pacific underwent a rapid expansion in population size in their recent evolutionary past, possibly after the retreat of Pleistocene ice sheets. Weak phylogeographic partitioning of lineages attests to a complex evolutionary and demographic history of contemporary Pacific populations. Extensive macrogeographic subdivision was evident among a subset of grouped localities that represent centers of abundance along the distributional continuum. Heterogeneity was influenced by population size and correlated with geographic distance, suggesting that dispersal occurs primarily among neighboring subpopulations. The 2 currently recognized subspecies of harbor seal in the Pacific, P. v. richardii of North America and P. v. stejnegeri of Asia, do not represent phylogenetically discrete mtDNA assemblages. The greatest differentiation detected was along the Commander–Aleutian Island chain, the region of the presumed subspecies boundary and a likely contact zone for expanding refugial populations of a number of marine mammal species after retreat of ice sheets. Differentiation between the Kodiak Archipelago and Prince William Sound, and between Bristol Bay and the Pribilof Islands, indicates that current management stocks are inappropriate and highlights the need for a detailed analysis of population and stock structure in Alaska. A decline in population size in Prince William Sound over the past few decades was accompanied by a discernible reduction in mtDNA diversity, manifested as a loss of rare haplotypes through random drift. A continued population decline will erode genetic diversity further, with potentially adverse effects on evolutionary potential and individual fitness.
Age, physical inactivity, obesity, health conditions, and health-related quality of life among patients receiving conservative management for musculoskeletal disorders

Background Musculoskeletal conditions and insufficient physical activity have substantial personal and economic costs among contemporary aging societies. This study examined the age distribution, comorbid health conditions, body mass index (BMI), self-reported physical activity levels, and health-related quality of life of patients accessing ambulatory hospital clinics for musculoskeletal disorders. The study also investigated whether comorbidity, BMI, and self-reported physical activity were associated with patients’ health-related quality of life after adjusting for age as a potential confounder. Methods A cross-sectional survey was undertaken in three ambulatory hospital clinics for musculoskeletal disorders. Participants (n=224) reported their reason for referral, age, comorbid health conditions, BMI, physical activity levels (Active Australia Survey), and health-related quality of life (EQ-5D). Descriptive statistics and linear modeling were used to examine the associations between age, comorbidity, BMI, intensity and duration of physical activity, and health-related quality of life. Results The majority of patients (n=115, 51.3%) reported two or more comorbidities. In addition to other musculoskeletal conditions, common comorbidities included depression (n=41, 18.3%), hypertension (n=40, 17.9%), and diabetes (n=39, 17.4%). Approximately one-half of participants (n=110, 49.1%) self-reported insufficient physical activity to meet minimum recommended guidelines and 150 (67.0%) were overweight (n=56, 23.2%), obese (n=64, 28.6%), severely obese (n=16, 7.1%), or very severely obese (n=14, 6.3%), with a higher proportion of older patients affected. A generalized linear model indicated that, after adjusting for age, self-reported physical activity was positively associated (z=4.22, P<0.001), and comorbidities were negatively associated (z=−2.67, P<0.01) with patients’ health-related quality of life. Conclusion Older patients were more frequently affected by undesirable clinical attributes of comorbidity, obesity, and physical inactivity. However, findings from this investigation are compelling for the care of patients of all ages. Potential integration of physical activity behavior change or other effective lifestyle interventions into models of care for patients with musculoskeletal disorders is worthy of further investigation.
eview pecies detection vs . habitat suitability : Are we biasing habitat suitability models ith remotely sensed data ?

Remotely sensed datasets are increasingly being used to model habitat suitability for a variety of taxa. We review habitat suitability models (HSMs) developed for both plants and animals that include remote sensing predictor variables to determine how these variables could affect model projections. For models focused on plant species habitat, we find several instances of unintentional bias in HSMs of vegetation due to the inclusion of remote sensing variables. Notably, studies that include continuous remote sensing variables could be inadvertently mapping actual species distribution instead of potential habitat due to unique spectral or temporal characteristics of the target species. Additionally, HSMs including categorical classifications are rarely explicit about assumptions of habitat suitability related to land cover, which could lead to unintended exclusion of potential habitat due to current land use. Although we support the broader application of remote sensing in general, we caution developers of HSMs to be aware of introduced model bias. These biases are more likely to arise when remote sensing variables are added to models simply because they improve accuracy, rather than considering how they affect the model results and interpretation. When including land cover classifications as predictors, we recommend that modellers provide more explicit descriptions of how habitat is defined (e.g., is deforested land considered suitable for trees? ). Further, we suggest that continuous remote sensing variables should only be included in habitat models if authors can demonstrate that their inclusion characterizes potential habitat rather than actual species distribution. Use of the term ‘habitat suitability model’ rather than ‘species distribution model’ could reduce confusion about modelling goals and improve communication between the remote sensing and ecological modelling communities. © 2012 Elsevier B.V. All rights reserved.
Design and implementation of an embedded monitor system for detection of a patient's breath by double Webcams

In this paper we use both an embedded system and double Webcams to design an embedded monitor system for breath detection (EMSFBD) which monitors and records the patient's breath and sends the information to a specific server through the Internet. Our design uses image processing methods to monitor and record human breath fluctuation and to calculate the breath rate. If the breath rate is too low, too fast or if an individual's breathing stops for more than 10 seconds, our design sends out an alarm signal. Our EMSFBD consists of two parts. For the first part double Webcams are used to capture images and to transmit them to an embedded board. For the second part an image processing program using a temporal differencing algorithm to detect chest expansion and contraction to determine the breath rate is installed in the embedded board.
A Sparse Symmetric Indefinite Direct Solver for GPU Architectures

In recent years, there has been considerable interest in the potential for graphics processing units (GPUs) to speed up the performance of sparse direct linear solvers. Efforts have focused on symmetric positive-definite systems for which no pivoting is required, while little progress has been reported for the much harder indefinite case. We address this challenge by designing and developing a sparse symmetric indefinite solver SSIDS. This new library-quality LDLT factorization is designed for use on GPU architectures and incorporates threshold partial pivoting within a multifrontal approach. Both the factorize and the solve phases are performed using the GPU. Another important feature is that the solver produces bit-compatible results. Numerical results for indefinite problems arising from a range of practical applications demonstrate that, for large problems, SSIDS achieves performance improvements of up to a factor of 4.6 × compared with a state-of-the-art multifrontal solver on a multicore CPU.
Transceiver energy consumption models for the design of low power wireless sensor networks

It is generally desired to have a wireless sensor network (WSN) that will run on little power (if possible, none at all) thereby saving cost, and the inconveniences of having to replace batteries in some difficult to access areas of usage. Despite several researches on WSN energy consumption, there is no clear transceiver energy consumption model to use when designing very low power WSN. By studying the energy consumption map of the transceiver of a WSN node in different states and within state transitions, we propose in this paper the energy consumption model of the transceiver unit of a typical sensor node. The contribution of this paper is an innovative energy consumption model based on finite automata which reveals the relationship between the aggregate energy consumption and power characteristics of the transceiver components.
For Leakage and Process Variation Effects in Current Testing on Future Cmos Circuits

tions, supply and threshold voltages will have to scale together to sustain high performance, limit energy consumption, control power dissipation, and maintain reliability. These continual scaling requirements pose several technology, circuit design, and testing challenges. Controlling process variation and leakage has become critical in designing and testing ICs. Die-to-die and intradie parameter variations—which are worsening with technology scaling—affect IC clock frequency and leakage power distributions. These effects are more pronounced at low supply voltages (VCC). Technology scaling also affects various aspects of VLSI testing.1 Specifically, elevated transistor leakage and excessive parameter variations in scaled process technologies threaten the feasibility of leakagebased (IDDQ) tests. Researchers have proposed several methods for adapting IDDQ testing to scaled technologies—reverse body bias (RBB), current signatures, ∆ IDDQ testing, and transient current testing (see the sidebar, “Other IDDQ test methods”). Our data suggests adopting a correlative multiparameter test solution. For high-performance IC applications, we propose looking at leakage in the context of the circuit’s maximum operating frequency. This approach doesn’t rely on the absolute value of the current. Added parameters of temperature and body bias further improve our test technique’s defect detection sensitivity. Leakage averaging and variance Leakage and Process Variation Effects in Current Testing on Future CMOS Circuits Defect-Oriented Testing in the Deep-Submicron Era
Embracing the Peer Next Door: Proximity in Kademlia

At present, the probability of selecting "the peer next door" as an overlay neighbour in Kademlia is fairly small. Prior research has been concerned with reducing the lookup latency by means of proximity neighbour and route selection, but focused on recursive routing algorithms. This work leverages location data about peers and extends Kademlia's iterative routing algorithm to reduce cross-network traffic at the level of the distributed hash table. Evaluation with real-world measurement data gives evidence that locality of traffic tends to reduce lookup latencies as well. In turn, mechanisms that aim at reducing lookup latencies do not necessarily reduce cross-network traffic to the same extent.