Miracle-dz/UniFAR-FTUs huggingface dataset:
On the existence of the intrinsic anisotropies in the angular distributions of gamma-ray bursts

This article is concerned primarily with the intrin- sic anisotropy in the angular distribution of 2281 gamma-ray bursts (GRBs) collected in Current BATSE Gamma-Ray Burst Catalog until the end of year 1998, and, second, with intrin- sic anisotropies of three subclasses ("short", "intermediate", "long") of GRBs. Testing based on spherical harmonics of each class, in equatorial coordinates, is presented. Because the sky exposure function of BATSE instrument is not dependent on the right ascension , any non-zero spherical harmonic propor- tional either to P m n (sin )sin m or to P m n (sin )cos m with m= =0 ( is the declination), immediately indicates an intrinsic non-zero term. It is a somewhat surprising result that the "in- termediate" subclass shows an intrinsic anisotropy at the 97% significance level caused by the high non-zero P 1 3(sin )sin harmonic. The remaining two subclasses, and the full sample of GRBs, remain isotropic.
Optimization noise figure of fiber Raman amplifier based on bat algorithm in optical communication network

Designing Raman amplifier with high On-Off again and low noise figure is required in in optical communication networks, due to wide and tunable amplification and low nonlinearity. This paper proposes a new configuration design to the single mode fiber Raman amplifier using a multi-objective bat algorithm. The main aim of the proposed method is to preserve the values of noise figure and ripple of the amplifier as low as possible while keeping the values of laser wavelength and the amplifier powers are high. The simulation results show that increasing the number of iterations is required, which would result in a flat gain spectrum with a considerable enhancement in the noise figure and minimal gain ripple that reaches to less than 0.18 DB.
Female reproductive success decreases with display size in monkshood, Aconitum kusnezoffii (Ranunculaceae).

BACKGROUND AND AIMS Reduction in female fitness in large clones can occur as a result of increased geitonogamous self-fertilization and its influence through inbreeding depression. This possibility was investigated in the self-compatible, bee-pollinated perennial herb Aconitum kusnezoffii which varies in clone size. METHODS Field investigations were conducted on pollinator behaviour, flowering phenology and variation in seed set. The effects of self-pollination following controlled self- and cross-pollination were also examined. Selfing rates of differently sized clones were assessed using allozyme markers. KEY RESULTS High rates of geitonogamous pollination were associated with large display size. Female fitness at the ramet level decreased with clone size. Fruit and seed set under cross-pollination were significantly higher than those under self-pollination. The pre-dispersal inbreeding depression was estimated as 0.502 based on the difference in seed set per flower between self- and cross-pollinated flowers. Selfing rates of differently sized clones did not differ. CONCLUSIONS It is concluded that in A. kusnezoffii the negative effects of self-pollination causing reduced female fertility with clone size arise primarily from a strong early-acting inbreeding depression leading to the abortion of selfed embryos prior to seed maturation.
Face recognition based on subset selection via metric learning on manifold

With the development of face recognition using sparse representation based classification (SRC), many relevant methods have been proposed and investigated. However, when the dictionary is large and the representation is sparse, only a small proportion of the elements contributes to the l1-minimization. Under this observation, several approaches have been developed to carry out an efficient element selection procedure before SRC. In this paper, we employ a metric learning approach which helps find the active elements correctly by taking into account the interclass/intraclass relationship and manifold structure of face images. After the metric has been learned, a neighborhood graph is constructed in the projected space. A fast marching algorithm is used to rapidly select the subset from the graph, and SRC is implemented for classification. Experimental results show that our method achieves promising performance and significant efficiency enhancement.
Fast and accurate estimation of shortest paths in large graphs

Computing shortest paths between two given nodes is a fundamental operation over graphs, but known to be nontrivial over large disk-resident instances of graph data. While a number of techniques exist for answering reachability queries and approximating node distances efficiently, determining actual shortest paths (i.e. the sequence of nodes involved) is often neglected. However, in applications arising in massive online social networks, biological networks, and knowledge graphs it is often essential to find out many, if not all, shortest paths between two given nodes. In this paper, we address this problem and present a scalable sketch-based index structure that not only supports estimation of node distances, but also computes corresponding shortest paths themselves. Generating the actual path information allows for further improvements to the estimation accuracy of distances (and paths), leading to near-exact shortest-path approximations in real world graphs. We evaluate our techniques - implemented within a fully functional RDF graph database system - over large real-world social and biological networks of sizes ranging from tens of thousand to millions of nodes and edges. Experiments on several datasets show that we can achieve query response times providing several orders of magnitude speedup over traditional path computations while keeping the estimation errors between 0% and 1% on average.
Springbrook: Challenges in developing a long-term, rainforest wireless sensor network

We describe the design, development and learnings from the first phase of a rainforest ecological sensor network at Springbrook - part of a World Heritage precinct in South East Queensland. This first phase is part of a major initiative to develop the capability to provide reliable, long-term monitoring of rainforest ecosystems. We focus in particular on our analysis around energy and communication challenges which need to be solved to allow for reliable, long-term deployments in these types of environments.
Message Indexing for Document-Oriented Integration Processes

The integration of heterogeneous systems is still an evolving research area. Due to the complexity of integration processes, there are challenges for the optimization of integration processes. Message-based integration systems, like EAI servers and workflow process engines, are mostly document-oriented, using XML technologies to achieve suitable data independence from the different and particular proprietary data representations of the supported external systems. However, such an approach causes large costs for single-value evaluations within the integration processes. At this point, message indexing, adapting extended database technologies, can be applied in order to achieve better performance. In this paper, we introduce our message indexing structure MIX and discuss and evaluate immediate as well as deferred indexing concepts.
Building Modular Surveillance Systems based on Multiple Sources of Information - Architecture and Requirements

Intelligent surveillance is becoming increasingly important for the enhanced protection of facilities such as airports and power stations from various types of threats. We propose a surveillance system architecture based on multiple sources of information to apply on large scale surveillance networks. The main contribution of this paper is the definition of the requirements for a flexible and scalable architecture that supports intelligent surveillance using, alongside video, different sources of information, such as audio or other sensors.
INTELLIGENT PROCESS CONTROL FOR UASB REACTORS

The copyright law of the United States (Title 17, United States Code) governs the making of photocopies or other reproductions of copyrighted material. Under certain conditions specified in the law, libraries and archives are authorized to furnish a photocopy or other reproduction. One of these specified conditions is that the photocopy or reproduction is not to be " used for any purpose other than private study, scholarship, or research. " If a, user makes a request for, or later uses, a photocopy or reproduction for purposes in excess of " fair use " that user may be liable for copyright infringement, This institution reserves the right to refuse to accept a copying order if, in its judgment, fulfillment of the order would involve violation of copyright law. Please Note: The author retains the copyright while the New Jersey Institute of Technology reserves the right to distribute this thesis or dissertation Printing note: If you do not wish to print this page, then select " Pages from: first page # to: last page # " on the print dialog screen The Van Houten library has removed some of the personal information and all signatures from the approval page and biographical sketches of theses and dissertations in order to protect the identity of NJIT graduates and faculty. The Upflow Anaerobic Sludge Blanket (UASB) reactor is widely used for the anaerobic treatment of concentrated municipal / industrial wastewaters and sludges. Ability to handle high organic loading rates and cost effectiveness are often the most cited merits of the UASB process. Numerous mathematical models have been developed to describe the process and mechanistic phenomena in these systems. However, evidence in the literature of these models having been applied, either as control or diagnostic tools is limited. The use of intelligent process control mechanisms can greatly ease problems associated with operating these reactor systems. The objective of this study is to develop a Human Machine Interface (HMI) module to assist UASB operators to optimize process conditions based on input from transducers, analytical data and a knowledgebase. The module makes extensive use of algorithms developed for modeling UASB systems in evaluation of reactor performance. The module is part of an intelligent process control software which uses information from sensors monitoring process parameters in real time, analytical laboratory data and historical databases to make process adjustments automatically and advise operators on current process conditions and corrective action …
Preference transfer model in collaborative filtering for implicit data

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.
Adaptive High-Gain Observer for Joint State and Parameter Estimation: A Comparison to Extended and Unscented Kalman Filter

Abstract An adaptive High-Gain observer (AHG) as well as an Extended (EKF) and Unscented Kalman filter (UKF) are implemented for joint state and parameter estimation of a novel multi-axial electromagnetically actuated punch. These observers are compared in terms of convergence and response time to erroneous parameter and state initialization, as well as parameter modifications during operation. The AHG is further analyzed proposing an adaptive gain, which reduces the observer's high sensibility with respect to noise. Simulation results show that AHG is more suitable compared to state-of-the-art EKF and UKF.
Adaptive Generation of Location Update in Cellular Mobile Computing Systems

An important issue in the design of a mobile computing system is how to manage the real-time locations of mobile clients. In the existing commercial cellular mobile computing systems, a two-tier architecture is used. However, the two-tier architecture is not scalable and is not suitable to the new mobile computing applications in which there are a very large of mobile users. In the literatures, a hierarchical database structure is proposed. The location databases of different cells are organized into a tree structure to facilitate the search of mobile clients. Although this architecture can distribute the update and searching workload amongst the location databases in the system, it has the problem of heavy location update overhead and long search delay. This paper studies how to generate location update based on the distance-based update method in the tree structure. A location update generation method is proposed to calculate the optimal distance threshold with the objective to minimize the total location management cost.
The Impact of Automatic Morphological Analysis & Disambiguation on Dependency Parsing of Turkish

The studies on dependency parsing of Turkish so far gave their results on the Turkish Dependency Treebank. This treebank consists of sentences where gold standard part-of-speech tags are manually assigned to each word and the words forming multi word expressions are also manually determined and combined into single units. For the first time, we investigate the results of parsing Turkish sentences from scratch and observe the accuracy drop at the end of processing raw data. We test one state-of-the art morphological analyzer together with two different morphological disambiguators. We both show separately the accuracy drop due to the automatic morphological processing and to the lack of multi word unit extraction. With this purpose, we use and present a new version of the Turkish Treebank where we detached the multi word expressions (MWEs) into multiple tokens and manually annotated the missing part-of-speech tags of these new tokens.
Cocoa

Although the Infrastructure-as-a-Service (IaaS) cloud offers diverse instance types to users, a significant portion of cloud users, especially those with small and short demands, cannot find an instance type that exactly fits their needs or fully utilize purchased instance-hours. In the meantime, cloud service providers are also faced with the challenge to consolidate small, short jobs, which exhibit strong dynamics, to effectively improve resource utilization. To handle such inefficiencies and improve cloud resource utilization, we propose Cocoa (COmputing in COntAiners), a novel group buying mechanism that organizes jobs with complementary resource demands into groups and allocates them to group buying deals predefined by cloud providers. Each group buying deal offers a resource pool for all the jobs in the deal, which can be implemented as either a virtual machine or a physical server. By running each user job on a virtualized container, our mechanism allows flexible resource sharing among different users in the same group buying deal, while improving resource utilization for cloud providers. To organize jobs with varied resource demands and durations into groups, we model the initial static group organization as a variable-sized vector bin packing problem, and the subsequent dynamic group organization problem as an online multidimensional knapsack problem. Through extensive simulations driven by a large amount of real usage traces from a Google cluster, we evaluate the potential cost reduction achieved by Cocoa. We show that through the effective combination and interaction of the proposed static and dynamic group organization strategies, Cocoa greatly outperforms the existing cloud workload consolidation mechanism, substantiating the feasibility of group buying in cloud computing.
Additive Decompositions with Interaction Effects

This paper proposes a comprehensive, path-independent decomposition formula of changes into ceteris paribus effects and interaction effects. The formula implies a reassessment of sequential decomposition methods that are widely used in the literature and that are restrictive in how they treat interaction effects. If counterfactual outcomes are correctly specified, it may also be viewed as a description of certain aspects of causality in the situation where more than one causal influence is present.
Analyses of Pixel-Value-Differencing Schemes with LSB Replacement in Stegonagraphy

This paper shows that a steganographic scheme based on Wu et al. 's pixel-value differencing (PVD) and least-significant-bit (LSB) replacement method is too conformable to the LSB approach. Their PVD+LSB method can embed secret data larger than the PVD method, however, it is detectable by Fridrich et al. 's method but PVD method is not. Some of the shortcomings of the PVD+LSB method are shown in this paper. At the same time, some techniques are proposed for improving the image quality of the PVD+LSB method without sacrificing the capacity. All proposed techniques are demonstrated by experimental results.
Filtrage bayésien de la récompense

Résumé : Une large variété de schémas d’approximation de la fonction de valeur a été appliquée à l’apprentissage par renforcement. Cependant, les approches par filtrage bayésien, qui se sont pourtant montrées efficaces dans d’autres domaines comme l’apprentissage de paramètres pour les réseaux neuronaux, ont été peu étudiées jusqu’à présent. Cette contribution introduit un cadre de travail général pour l’apprentissage par renforcement basé sur le filtrage bayésien, ainsi qu’une implémentation spécifique basée sur un filtre de Kalman à sigma-points et une paramétrisation par noyaux. Cela nous permet de proposer un algorithme de différences temporelles pour des espaces d’état et/ou d’action continus qui soit model-free et off-policy. Il sera illustré sur deux problèmes simples. Mots-clés : Apprentissage par renforcement, filtrage bayésien, méthodes à noyaux.
Supporting Information Retrieval via Matchmaking

The massive increase in information available via electronic networks is placing severe burdens on traditional methods of information sharing and retrieval. Matchmaking proposes an intelligent facilitation agent that accepts machine-readable requests and advertisements from information consumers and providers, and determines potential information sharing paths. Matchmaking permits large nmnbers of dynamic consumers and providers, operating on rapidly-changing data, to locate and share information effectively. This paper introduces matchmaking, as enabled by knowledge sharing standards like KQML, and gives a brief description of the the SHADE and COINS matchmakers. In addition, several applications are described to illustrate the utility of matchmaking for information retrieval.
GPU Acceleration of a Basket Option Pricing Engine

— One of the most important methods for pricing complex derivatives is Monte Carlo simulation. However, this method requires a large amount of computing resources for accurate estimates. Since Monte Carlo simulations used in derivatives pricing are often parallelisable, one way to reduce the computing time is to use GPUs, which allow many copies of the same process to be run in parallel with different data. This paper first presents a GPU implementation of a Basket Option pricing engine and an analysis of the timing and memory resources for different algorithm parameters. The results show that, on an NVidia GTX670 card using NVidia's proprietary CUDA programming platform, a speedup of over 250 can sometimes be achieved compared with a sequential C implementation. To produce more portable code which will run on a range of different parallel architectures, OpenCL is becoming popular. However, users can be reluctant to adopt OpenCL in case they lose performance compared with an architecture-specific programming platform such as NVidia's CUDA, particularly when high speed-ups are at stake. This paper secondly reports experiments which show that, all things being equal, the performance of OpenCL and CUDA implementations are within approximately 10% of each other, with the OpenCL implementation sometimes being the faster. This suggests that OpenCL is a viable programming platform for GPU acceleration of pricing engines, even when aiming for high speed-up factors of 100-300.
HUBUNGAN ANTARA SELF EFFICACYDAN DUKUNGAN SOSIAL ORANGTUA DENGAN SELF REGULATED LEARNING SERTA IMPLIKASINYA TERHADAP BIMBINGAN DAN KONSELING

This research was based on how important to increase the students self regulated learning. Self efficacy and parent social support were the factors assumed to affect the self regulated learning. The purpose of this research were to describe: (1) self efficacy, (2) parent social support, (3) self regulated learning, (4) the relationship between self efficacy with self regulated learning, (5) the relationship between parent social support with self regulated learning, and (6) the simultaneous relationship between self efficacy and parent social support with self regulated learning. This research used a quantitative method with correlative descriptive type. The population in this research was the students at SMP Negeri 29 Padang many as 522 students. The sample in this research was many as 213 students who selected with a proportional stratified random sampling technique. The instrument was a Likert scale model. The results of instrument reliability test showed that self efficacy was much as 0.851, parent social support 0.946 and self regulated learning 0.860. The data were analyzed by using descriptive statistic, simple regression, and multiple regression. This discover research show: (1) students self efficacy was at high category, (2) students parent social support was at high category, (3) students self regulated learning was at high category, (4) there was a positive and significant relationship between of self efficacy to self regulated learning, (5) there wasa positive and significant relationship between of parent social support to self regulated learning, and (6) there wasa positive and significant simultaneous relationship between of self efficacy and parent social support with self regulated learning. Implications of these results could be used as suggestion to arrange programs of guidance and counseling service at the school.
On the whiteness of high-resolution quantization errors

A common belief in quantization theory says that the quantization noise process resulting from uniform scalar quantization of a correlated discrete-time process tends to be white in the limit of small distortion ("high resolution"). A rule of thumb for this property to hold is that the source samples have a "smooth" joint distribution. We give a precise statement of this property, and generalize it to nonuniform quantization and to vector quantization. We show that the quantization errors resulting from independent quantizations of dependent real random variables become asymptotically uncorrelated (although not necessarily statistically independent) if the joint Fisher information (FI) under translation of the two variables is finite and the quantization cells shrink uniformly as the distortion tends to zero.