Miracle-dz/UniFAR-FTUs huggingface dataset:
Origin of abundance inhomogeneity in globular clusters

We numerically investigate abundance properties of the Galactic globular clusters (GCs) by adopting a new “external pollution” scenario. In this framework, GCs are assumed to originate in forming low-mass dwarfs embedded in dark matter subhalos at very high redshifts (z) and thus be chemically influenced by field AGB stars of the dwarfs during early GC formation processes. GCs within a dwarf galaxy therefore can be formed from the mixture of (i) gas ejected from the field AGB stars formed earlier in the dwarf and (ii) the interstellar gas infalling to the central region of the dwarf. In this external pollution scenario, the ratio of the total mass of infalling gas to that of AGB ejecta during GC formation in a dwarf (s) and the time scale of gas infall (σI) are the most important key parameters that can determine abundance properties of GCs. We mainly investigate the abundance inhomogeneity among light elements (e.g., C, N, O, Na, and Al) of stars in GCs by using the latest stellar yield models of metal-poor AGB stars with and without third dredge-up. Our principal results for the models with no third dredge-up, which are more consistent with observations, are as follows. (1) Both [N/Fe] and [C/Fe] can be diverse among stars within a GC owing to chemical pollution from field AGB stars. [N/Fe] distributions in some GCs can clearly show bimodality whereas [C/Fe] is monomodal in most models. [N/Fe] distributions depend on s such that models with smaller s (i.e., larger mass fraction of AGB ejecta used for GC formation) show the [N/Fe] bimodality more clearly. (2) N-rich, C-poor stars in GCs also have higher He abundances owing to pollution from massive AGB stars with He-rich ejecta. The number fraction of He-rich stars (Y > 0.30) is higher for the models with smaller s and shorter σI for 3 � s � 24 and 10 5 � σI � 10 7 yr. He abundances of stars correlate with [N/Fe] and [Al/Fe] and anticorrelate with [C/Fe], [O/Fe], and [Na/Fe] within GCs in our models. (3) Although our model can much better explain the observed C-N and Mg-Al anticorrelations than previous theoretical models, it is in strong disagreement with the observed O-Na anticorrelation. (4) This model naturally provides an explanation for the large fraction of CNstrong stars without recourse to an implausible IMF. Based on these results for the above external pollution scenario, we discuss the long-standing problem of the CN-bimodality prevalent in the Galactic GCs, the possible helium abundance inhomogeneity in these systems, and their horizontal branch morphologies.
Interest Group Lobbying , Corporate Strategy and Environmental Policy

We study three corporate non-market strategies designed to in‡uence the lobbying behavior of other special interest groups concerning environmental and/or health and safety issues: 1) “astroturf,” in which the . rm covertly subsidizes a group with similar views to lobby when it normally would not, 2) the “bearhug,” in which the . rm overtly subsidizes the lobbying activities of another interest group, and 3) self-regulation, in which the . rm voluntarily limits the potential social harm from its activities. All three strategies can be used to reduce the informativeness of lobbying, and all reduce the welfare of the public decision maker. Astroturf lobbying on environmental policy issues is growing in popularity in the United States and some law makers have attempted to pass disclosure laws which would weaken the e¤ectiveness of the strategy. We show that the decision maker would bene.t by requiring the public disclosure of funds spent on astroturf lobbying, but the availability of alternative in‡uence strategies limits the impact of such a policy. JEL Numbers: L51, Q28
Robust and real-time egomotion estimation using a compound omnidirectional sensor

We propose a new egomotion estimation algorithm for a compound omnidirectional camera. Image features are detected by a conventional feature detector and then quickly classified into near and far features by checking infinity on the omnidirectional image of the compound omnidirectional sensor. Egomotion estimation is performed in two steps: first, rotation is recovered using far features; then translation is estimated from near features using the estimated rotation. RANSAC is used for estimations of both rotation and translation. Experiments in various environments show that our approach is robust and provides good accuracy in real-time for large motions.
Prolonged Influenza Virus Shedding and Emergence of Antiviral Resistance in Immunocompromised Patients and Ferrets

Immunocompromised individuals tend to suffer from influenza longer with more serious complications than otherwise healthy patients. Little is known about the impact of prolonged infection and the efficacy of antiviral therapy in these patients. Among all 189 influenza A virus infected immunocompromised patients admitted to ErasmusMC, 71 were hospitalized, since the start of the 2009 H1N1 pandemic. We identified 11 (15%) cases with prolonged 2009 pandemic virus replication (longer than 14 days), despite antiviral therapy. In 5 out of these 11 (45%) cases oseltamivir resistant H275Y viruses emerged. Given the inherent difficulties in studying antiviral efficacy in immunocompromised patients, we have infected immunocompromised ferrets with either wild-type, or oseltamivir-resistant (H275Y) 2009 pandemic virus. All ferrets showed prolonged virus shedding. In wild-type virus infected animals treated with oseltamivir, H275Y resistant variants emerged within a week after infection. Unexpectedly, oseltamivir therapy still proved to be partially protective in animals infected with resistant virus. Immunocompromised ferrets offer an attractive alternative to study efficacy of novel antiviral therapies.
Quantum mechanics of damped systems

We show that the quantization of a simple damped system leads to a self-adjoint Hamiltonian with a family of complex generalized eigenvalues. It turns out that they correspond to the poles of energy eigenvectors when continued to the complex energy plane. Therefore, the corresponding generalized eigenvectors may be interpreted as resonant states. We show that resonant states are responsible for the irreversible quantum dynamics of our simple model. Mathematical Subject Classifications (2000): 46E10, 46F05, 46N50, 47A10.
From Passive Overeating to “Food Addiction”: A Spectrum of Compulsion and Severity

A psychobiological dimension of eating behaviour is proposed, which is anchored at the low end by energy intake that is relatively well matched to energy output and is reflected by a stable body mass index (BMI) in the healthy range. Further along the continuum are increasing degrees of overeating (and BMI) characterized by more severe and more compulsive ingestive behaviours. In light of the many similarities between chronic binge eating and drug abuse, several authorities have adopted the perspective that an apparent dependence on highly palatable food—accompanied by emotional and social distress—can be best conceptualized as an addiction disorder. Therefore, this review also considers the overlapping symptoms and characteristics of binge eating disorder (BED) and models of food addiction, both in preclinical animal studies and in human research. It also presents this work in the context of the modern and “toxic” food environment and therein the ubiquitous triggers for over-consumption. We complete the review by providing evidence that what we have come to call “food addiction” may simply be a more acute and pathologically dense form of BED.
Partitioning the flags of PG(2, q) into strong representative systems

In this paper we show a natural extension of the idea used by Ill\'es, Sz\H{o}nyi and Wettl \cite{swi} which proved that the flags of ${\rm PG} (2,q)$ can be partitioned into $(q-1)\sqrt q+3q$ strong representative systems for $q$ an odd square. From a generalization of the Buekenhout construction of unitals \cite{kozoscikk} their idea can be applied for any non-prime $q^h$ to yield that $q^{2h-1}+2q^h$ strong representative systems partition the flags of ${\rm PG} (2,q^{h})$. In this way we also give a solution to a question of Gy\'arf\'as \cite{FSGT} about the strong chromatic index of the bipartite graph corresponding to ${\rm PG} (2,q)$, for $q$ non-prime.
Multiparty differential privacy over vertically partitioned data

Data integration methods enable different data providers to flexibly integrate their expertise and deliver highly customizable services to their customers. Data mining of private data is one of the key to success for an organization, it is challenging task to implement data mining in distributed database. Collaboration of different organization brings mutual benefits to the party involved. So different organizations want to collaborate and execute efficient data mining algorithm. Privacy preserving data publishing addresses the problem of disclosing sensitive data where different attributes for the same set of individuals are held by multi parties. Differential privacy guarantees strongest privacy among multi parties. To ensure differential privacy, distributed Gaussian mechanism is proposed for vertically partitioned data for multi parties in the trusted third party model. To achieve differential privacy noise are added with true function using Gaussian mechanism. Random value protocol and Yao’s protocol are used to provide the security, according to the definition of secure multiparty computation. KeywordsDifferential privacy, secure data integration, classification analysis
What is optimal scheduling in wireless networks?

We consider a wirelessnetwork consisting of multiple transmitters with multicast traffic destined for a set of receivers. We are interested in the problem of joint scheduling and rate control under two performanceobjectives; the objective of maximizing the total sum throughput of the network and of being proportionally fair with respect to the received rate at each receiver. We first consider static wirelessnetworks, and then extend our analysis for the more general and more realistic case of time-varying networks. We finally verify our analytical results througha set of simulations.
A sparse Bayesian learning algorithm with dictionary parameter estimation

This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous parameter space to a fixed grid of points, thus restricting the solution space. In this work, we avoid discretization by working directly with the signal model containing parameterized atoms. Inspired by the “fast inference scheme” by Tipping and Faul we develop a novel sparse Bayesian learning (SBL) algorithm, which estimates the atom parameters along with the model order and weighting coefficients. Numerical experiments for spectral estimation with closely-spaced frequency components, show that the proposed SBL algorithm outperforms state-of-the-art subspace and compressed sensing methods.
Hierarchical game theory analysis in subscribers cooperative relaying network with OFDMA orthogonal channels

A two-user decision making game is studied in the OFDM based subscriber cooperative relaying network, in which subscribers transmit their own data in the first phase, while helping to retransmit their partner's or choosing to freeride in the second phase. In the paper, we study a hierarchical game (HG) for a two user coalition, where cooperation is preferred and total payoff is transferable. Instead of consulting to a global optimal solution, we decouple the cooperation resource allocation into two level subproblems: a user level Nash Equilibrium game for distributed cooperation decision and an Access Point (AP) level coalition game for centralized resource allocation. Meanwhile, due to the consideration of efficiency and immediacy, we reduce a repeated game theory into a scheduling evaluation across two scheduling time slots—the second cooperation phase in the first time slot and the first self-transmission phase in the next slot. The simulation results show that the proposed scheme achieves a well tradeoff between fairness and efficiency, and greatly reduces the system complexity compared to the centralized resource allocation strategies.
Norms that Locally Depend on Countably Many Linear Functionals

Mathematical Institute, Czech Academy of Sciences, Zitn¶• a 25, 11567, Prague 1, CzechRepublic, e-mail: fabian@math.cas. czMathematical Institute, Czech Academy of Sciences, Zitn¶• a 25, 11567, Prague 1, CzechRepublic and Department of Mathematics, University of Alberta, T6G 2G1, Edmon-ton, Canada (Prof. Emeritus), e-mail: zizler@math.cas. czAMS Subject Class. (1991): 46B03, 46B26, 46B20IntroductionNorms and bump functions on a Banach space Xthat locally depend on ﬂnitely manyelements of the dual space X
Performance detection of an embedded system using Boosting Algorithm

The boosting algorithm which we introduce is a representational ensemble classification methodology. It is well known that the Boosting algorithm can improve the accuracy of any given learning algorithm and train the strong classifiers efficiently. A specific embedded hardware is designed for target identification task. This paper also evaluates the performance and implementation issues for the Boosting classification on the embedded hardware. Considering the limited source and the characteristics of the embedded system, this paper proposed an optimal memory allocation method for system optimization which is combining the general software optimization methods. And the method we proposed can also be used for the other embedded system which is support the cache configuration. Some testing samples show the effectiveness of the proposed technique.
A Method for Identification of Basmati Rice grain of India and Its Quality Using Pattern Classification

The research work deals with an approach to perform texture and morphological based retrieval on a corpus of Basmati rice grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The approach has been tested on sufficient number of basmati rice grain images of rice based on intensity, position and orientation. A digital image analysis algorithm based on color, morphological and textural features was developed to identify the six varieties of basmati rice seeds which are widely planted in India. Nine color and nine morphological and textural features were used for discriminant analysis. A back propagation neural network-based classifier was developed to identify the unknown grain types. The color and textural features were presented to the neural network for training purposes. The trained network was then used to identify the unknown grain types. It is also to find the percentage purity of hulled basmati rice grain sample by image processing technique. Commercially the purity test of basmati rice sample is done according to the size of the grain kernel (full, half or broken). By image processing we can also identify any broken basmati rice grains. Here we discuss the various procedures used to obtain the percentage quality of basmati rice grains.
Chemical fate and settling of mineral dust in surface seawater after atmospheric deposition observed from dust seeding experiments in large mesocosms

We report here the elemental composition of sinking particles in sediment traps and in the water column following four artificial dust seeding experiments (each representing a flux of 10 g m−2). Dry or wet dust deposition were simulated during two large mesocosms field campaigns that took place in the coastal water of Corsica (NW Mediterranean Sea) representative of oligotrophic conditions. The dust additions were carried out with fresh or artificially aged dust (i.e., enriched in nitrate and sulfate by mimicking cloud processing) for various biogeochemical conditions, enabling us to test the effect of these parameters on the chemical composition and settling of dust after deposition. The rates and mechanisms of total mass, particulate organic carbon (POC) and chemical elements (Al, Ba, Ca, Co, Cu, Fe, K, Li, Mg, Mn, Mo, N, Nd, P, S, Sr and Ti) transfer from the mesocosm surface to the sediment traps installed at the base of the mesocosms after dust deposition show that (1) 15% of the initial dust mass was dissolved in the water column in the first 24 h after seeding. Except for Ca, S and N, the elemental composition of dust particles was constant during their settling, showing the relevance of using interelemental ratios, such as Ti/Al as proxy of lithogenic fluxes. (2) Whatever the type of seeding (using fresh dust to simulate dry deposition or artificially aged dust to simulate wet deposition), the particulate phase both in the water column and in the sediment traps was dominated by dust particles. (3) Due to the high Ba content in dust, Ba/Al cannot be used as productivity proxy in the case of high dust input in the sediment traps. Instead, our data suggests that the ratio Co/Al could be a good productivity proxy in this case. (4) After 7 days, between 30 and 68% of added dust was still in suspension in the mesocosms. This difference in the dust settling was directly associated with a difference in POC export, since POC fluxes were highly correlated to dust lithogenic fluxes signifying a ballast effect of dust. The highest fraction of remaining dust in suspension in the mesocosm at the end of the experiment was found inversely correlated to Chl a increase. This suggests that the fertilizing effect of dust on autotrophs organisms, the ballast effect, and POC fluxes are strongly correlated. (5) Our data emphasize a typical mass ratio Lithogenic/POC fluxes around 30 which could be used as reference to estimate the POC export triggered by wet dust deposition event.
Positioning Technologies and Mechanisms for mobile Devices

Services for which it is necessary to know the position of a mobile phone and a mobile user are commonly known as location-based services (LBS). These services become more and more popular and telecommunication companies (telcos) hope for big revenues through them. In the US, telecommunication companies are obligated by law to localize a mobile station (MS) with a certain accuracy due to the E911 mandate. The hope for big revenues and the law are the reasons for telcos to invest in research in this area since many years. In the European Union the counterpart is called E112 but it does not give the telecommunication companies any guidelines for the accuracy with which they have to localize an MS in case of an emergency. The goal of this paper is to look at the different approaches no matter if the are based on the GSM, UMTS or even other infrastructures. The goal of this work is to look at the different localization approaches that exsits nowadays for GSM, UMTS and other relevant wireless infrastructures.
Statistical modelling for enhanced outlier detection

Matching of local features is an uncertain process which may provide wrong associations due to several reasons that include, among other factors, the uncertainty in locating the keypoint position. Since the statistics of the Log Distance Ratio (LDR) for pairs of incorrect matches are significantly different from those of correct matches, we propose a noniterative scheme for outlier detection that includes in the distance calculation the location uncertainty of the keypoint, specifically modeled by a covariance matrix: the LDR is then evaluated relying on Mahalanobis distance. By statistically modeling the wrong associations, inlier matches can thus be rapidly identified by solving an eigenvalue problem. The method is general enough to be applied both in 2D (i.e., texture) and 3D (i.e., texture + depth) scenarios. The effectiveness of the proposed method is assessed in the field of RGB-D SLAM, showing significant improvements with respect to state of the art methods.
The Roles of Familiarity Design in Active Ageing

The elderly often struggle when interacting with technologies. This is because the software and hardware components of the technologies are not familiar to the elderly's mental model. This is a lack of empirical studies about how the concept of familiarity can be infused into the design of interactive technology systems to bridge the digital divide preventing today's elderly people from actively engaging with such technologies. In this paper, a multi pronged approach is utilized. We investigate the Effects of Familiarity in Design on the Adoption of Wellness Games by the Elderly, familiarity in productive ageing, familiarity in efficient collaborative crowdsourcing, productive ageing through familiarity based Intelligent Personalized Crowdsourcing and familiarity based Agent Augmented Inter-generational Crowdsourcing. The results show that familiarity in design improves the perceived satisfaction and adoption likelihood significantly among the elderly users. These results can potentially benefit intelligent interface agent design when such agents need to interact with elderly users. A Crowdsourcing algorithm, CrowdAsm is developed. By using CrowdAsm we are able to dynamically assemble teams of workers considering the budgets, the availability of workers with the required skills and their track records to complete crowdsourcing tasks requiring collaboration among workers with heterogeneous skills. Theoretical analysis has shown that CrowdAsm can achieve close to optimal profit for a collaborative crowdsourcing system if workers follow the recommendations.
Parallel, Distributed and Multi-Agent Production Systems - A Research Foundation for Distributed Artificial Intelligence

Production systems have been widely used as expert system building tools and recognize-and-act models in cognitive science. This paper is intended to introduce parallel/distributed/multiagent production systems, and to reveal their possibilities as research foundations for distributed artificial intelligence: a parallel production system as an agent reactive architecture, a distributed production system as an adaptive agent organization, and a multi-agent production system for organizational learning. Production systems already have been equipped with clear syntax/semantics and efficient pattern matching algorithms. Their functions can be further strengthened with recent algorithms such as realtime search and reinforcement learning.
Endoscopic ultrasound for staging of colonic cancer proximal to the rectum: A systematic review and meta-analysis

Background and Objectives: Treatment of colonic cancer patients is highly dependent on the depth of tumor invasion (T-stage) as well as the extension of lymph node involvement (N-stage). We aimed to systematically review the accuracy of endoscopic ultrasound (EUS) for staging of colonic cancer proximal to the rectum. Patients and Methods: Men and women with colonic adenocarcinomas were included in the study. EUS staging was compared to histopathology as the gold standard. Outcome measures were T- and N-staging accuracies. Articles were searched in PubMed, Web of Science, The Cochrane Library, and EMBASE. Results: Six studies were identified comparing EUS staging of colonic cancer to histopathology. The pooled-staging sensitivity and specificity were 0.90 and 0.98 for T1 tumors, 0.67 and 0.96 for T2 tumors, and 0.97 and 0.83 for T3/T4 tumors, respectively. Sensitivity and specificity for N + disease were 0.59 and 0.78, respectively. Conclusions: EUS is a feasible method for T-staging of cancers of the colon proximal to the rectum. The accuracy of lymph node staging needs to be verified by prospective multicenter studies including larger patient populations.