Miracle-dz/UniFAR-FTUs huggingface dataset:
A Modiﬁcation of Ripley’s K Function to Measure Aggregation About a Mass

We present a methodology that detects spatial patterns of 3-dimensional point processes that include a mass within the study region. Spatial patterns such as clustering, randomness, or repulsion are considered in respect to the mass surface. Our method closely resembles Ripley’s K Function but is modiﬁed to discern the pattern about the mass surface. We walk though the deﬁnition and derivation of Ripley’s K Function and then follow this process to deﬁne the Modiﬁed K function. We develop this novel function according to the deﬁnition: the Modiﬁed K function times the intensity is the expected number of events within a distance h of a mass. Special consideration of edge eﬀects is taken in order to make the function invariant to the location of the mass within the study region. Signiﬁcance of spatial patterns is determined using Monte Carlo conﬁdence envelopes similar to Ripley’s K Function. Simulations are performed to inform researchers how the Modiﬁed K function performs under diﬀerent types of aggregation. Finally, we apply the Modiﬁed K function to neuroscience as a novel analysis tool by examining the spatial pattern of neurotransmitter release sites as events about a neuron. Supplemental materials for this article are available online.
BLIND TURBO DECODING OF SIDE-INFORMED DATA HIDING USING ITERATIVE CHANNEL ESTIMATION

Distortion-Compensated Dither Modulation (DC-DM) has been theoretically shown to be a near-capacity achieving data hiding method, thanks to its use of side information at the encoder. In practice, channel coding is needed to approach its achievable rate limit. However, the most powerful coding methods, such as turbo coding, require knowledge of the channel model. We investigate here the possibility of undertaking blind iterative decoding of DCDM. To this end, we undertake maximum likelihood estimation of the channel model, intertwining the Expectation-Maximization algorithm within the decoding procedure.
Core processes in acceptance and commitment therapy

Acceptance and commitment therapy (ACT; Hayes, Strosahl, and Wilson, 1999) has received empirical support in well over 20 randomized controlled trials as a psychotherapeutic treatment for psychological problems as diverse as depression (Zettle and Hayes, 1986; Zettle and Raines, 1989), anxiety (Block, 2002), substance abuse (Hayes, Wilson, Gifford, Bissett, Piasecki, Batten, Byrd, and Gregg, 2004), psychosis (Bach and Hayes, 2002) and the treatment of chronic pain (Dahl, Wilson, and Nilsson, 2004; Robinson, Wicksell, and Olson, 2005). ACT is offered as a modern but thoroughly behavioral approach to the treatment of complex psychological problems - one that honors the strengths of more conventional behavioral treatment strategies, but incorporates a new behavioral conceptualization of language and cognition (Relational Frame Theory, or RFT; Hayes, Barnes-Holmes, and Roche, 2001).
Mapping of p140Cap Phosphorylation Sites: The EPLYA and EGLYA Motifs Have a Key Role in Tyrosine Phosphorylation and Csk Binding, and Are Substrates of the Abl Kinase

Protein phosphorylation tightly regulates specific binding of effector proteins that control many diverse biological functions of cells (e. g. signaling, migration and proliferation). p140Cap is an adaptor protein, specifically expressed in brain, testis and epithelial cells, that undergoes phosphorylation and tunes its interactions with other regulatory molecules via post-translation modification. In this work, using mass spectrometry, we found that p140Cap is in vivo phosphorylated on tyrosine (Y) within the peptide GEGLpYADPYGLLHEGR (from now on referred to as EGLYA) as well as on three serine residues. Consistently, EGLYA has the highest score of in silico prediction of p140Cap phosphorylation. To further investigate the p140Cap function, we performed site specific mutagenesis on tyrosines inserted in EGLYA and EPLYA, a second sequence with the same highest score of phosphorylation. The mutant protein, in which both EPLYA/EGLYA tyrosines were converted to phenylalanine, was no longer tyrosine phosphorylated, despite the presence of other tyrosine residues in p140Cap sequence. Moreover, this mutant lost its ability to bind the C-terminal Src kinase (Csk), previously shown to interact with p140Cap by Far Western analysis. In addition, we found that in vitro and in HEK-293 cells, the Abelson kinase is the major kinase involved in p140Cap tyrosine phosphorylation on the EPLYA and EGLYA sequences. Overall, these data represent an original attempt to in vivo characterise phosphorylated residues of p140Cap. Elucidating the function of p140Cap will provide novel insights into its biological activity not only in normal cells, but also in tumors.
Effects of a bleaching agent with calcium on bovine enamel

Objective: This in vitro study analyzed the effects of a bleaching treatment containing 35% hydrogen peroxide (HP) with or without calcium on bovine enamel, using the Knoop hardness number (KHN), tristimulus colorimetry (TC), and scanning electron microscopy. Materials and Methods: Forty-five specimens were randomly divided into groups (n = 5), which included artificial saliva (negative control [NC]), 35% HP (positive control [PC]), and 35% HP Blue Calcium (HP Blue). The specimens were subjected to three bleaching sessions. During the sessions, the specimens were immersed in artificial saliva at 37°C. Before and after bleaching, KHN tests were conducted using a force of 25 gf for 5 s. TC was performed using the CIE-LFNx01aFNx01bFNx01 system and readouts were obtained at the following 4 time points: Before the bleaching treatment; after the first session, the second session, and the third session. The specimens were dehydrated and coated with gold, and the photomicrographs were analyzed in a double-blind manner with a LEO microscope. Results: Using one-way analysis of variance and Tukey′s test (P < 0.05), a statistically significant difference was identified between the initial and final mean KHNs of the NC and PC groups, while the initial and final mean KHNs were not significantly different in the HP Blue group. The final mean values of ∆E, ∆L, and ∆b of the PC and HP Blue groups were significantly higher than the initial values (P < 0.01 for both). The photomicrographs revealed no differences among the groups. Conclusions: Therefore, treatment with HP Blue prevented changes in the KHN without reducing the efficacy of bleaching.
Replication in Distributed Storage Systems: State of the Art, Possible Directions, and Open Issues

Large-scale distributed storage systems have gained increasing popularity for providing highly available and scalable services. Most of these systems have the advantages of high performance, tolerant to failures, and elasticity. These desired properties are achieved mainly by means of the proper adaptation of replication techniques. We discuss the state-of-art in replication techniques for distributed storage systems. We present and compare four representative systems in this realm. We define a design space for replication techniques, identify current limitations, challenges and open future trends.
Teaching Criminological Theory: Using a Piaget Platform to Support Higher Level Learning

This paper proposes that higher level learning of criminological theory can be accomplished by having students express a theory creatively through an art form (i.e. a drawing, a dance, a poem, or role play). This type of constructivist assignment would empower undergraduate students to be theory co-creators and allow them to be actively engaged in building knowledge off of a Piagetian platform. While Piaget’s theory was formulated to explain the cognitive development in children, neuroscience has revealed its potential for expanding cognitive capacity in adults. When students are presented with a novel task requiring fuller focus, such as being required to build new knowledge onto existing schema in a uniquely creative way, deeper levels of learning are more likely to occur and be retained.
TREE LAYER SPATIAL STRUCTURE CAN AFFECT SAVANNA PRODUCTION AND WATER BUDGET: RESULTS OF A 3‐D MODEL

The spatially explicit and mechanistic model TREEGRASS was used to test the effects of tree density, tree spatial distribution, and tree size distribution on radiation absorption, net primary production (NPP), and water fluxes for a West African savanna. The model has previously been shown to adequately predict radiation absorption, production, and soil water balance in humid savannas. Using field measurements quantifying the spatial variations in grass NPP, the model was found to predict correctly the effect of tree abundance on grass NPP. Model simulations showed that annual photosynthetically active radiation (PAR), absorption efficiency, NPP, and transpiration of the tree layer increased with increasing tree density and decreased with increasing tree aggregation; the grass layer showed the opposite trends. Total NPP (tree plus grass) remained stable for all tree densities and decreased slightly for aggregated tree distributions. Total transpiration increased with tree density and was not affected by t.
Speaker based clustering using the differential energy

A new approach of speaker clustering is presented and discussed in this paper. The main technique consists in grouping all the homogeneous speech segments obtained at the end of the segmentation process, by using the spatial information provided by the stereophonic speech. The proposed system is suitable for debates or multi-conferences for which the speakers are located at fixed positions. The new method uses the differential energy of the two stereophonic signals collected by two cardioid microphones, in order to group all the speech segments that are uttered by the same speaker. The total number of clusters obtained at the end should be equal to the real number of speakers present in the meeting room and each cluster should contain the global intervention of only one speaker. The new proposed approach (which we called Energy Differential based Spatial Clustering or EDSC) has been experimented comparatively with a classic statistical approach called "Mono-Gaussian Sequential Clustering". Experiments of speaker clustering are done on a stereophonic speech corpus called DB15, composed of 15 stereophonic scenarios of about 3.5 minutes each. Every scenario corresponds to a free discussion between several speakers seated at fixed positions in the meeting room. Results show the strong performances of the new approach in terms of precision and speed, especially for short speech segments.
CREOLE: a Universal Language for Creating, Requesting, Updating and Deleting Resources

In the context of Service-Oriented Computing, applications can be developed following the REST (Representation State Transfer) architectural style. This style corresponds to a resource oriented model, where resources are manipulated via CRUD (Create, Request, Update, Delete) interfaces. The diversity of CRUD languages due to the absence of a standard leads to composition problems related to adaptation, integration and coordination of services. To overcome these problems, we propose a pivot architecture built around a universal language to manipulate resources, called CREOLE, a CRUD Language for Resource Edition. In this architecture, scripts written in existing CRUD languages, like SQL, are compiled into CREOLE and then executed over different CRUD interfaces. After stating the requirements for a universal language for manipulating resources, we formally describe the language and informally motivate its definition with respect to the requirements. We then concretely show how the architecture solves adaptation, integration and coordination problems in the case of photo management in Flickr and Picasa, two well-known service-oriented applications. Finally, we propose a roadmap for future work.
Spatial distribution of stars and brown dwarfs in σ Orionis

I have re-visited the spatial distribution of stars and high-mass brown dwarfs in the a Orionis (a Ori) cluster (∼3 Ma, ∼360pc). The input was a catalogue of 340 cluster members and candidates at separations less than 30 arcmin to σ Ori AB. Of them, 70 per cent have features of extreme youth. I fitted the normalized cumulative number of objects counting from the cluster centre to several power-law, exponential and King radial distributions. The cluster seems to have two components: a dense core that extends from the centre to r ≈ 20 arcmin and a rarified halo at larger separations. The radial distribution in the core follows a power law proportional to r 1 , which corresponds to a volume density proportional to r -2 . This is consistent with the collapse of an isothermal spherical molecular cloud. The stars more massive than 3.7 M ⊙ concentrate, however, towards the cluster centre, where there is also an apparent deficit of very low mass objects (M < 0.16 M ⊙ ). ). Last, I demonstrated through Monte Carlo simulations that the cluster is azimuthally asymmetric, with a filamentary overdensity of objects that runs from the cluster centre to the Horsehead Nebula.
Satellite Image Resolution Enhancement Using Dual-Tree Complex Wavelet Transform and Nonlocal Means

Resolution enhancement (RE) schemes (which are not based on wavelets) suffer from the drawback of losing high-frequency contents (which results in blurring). The discrete-wavelet-transform-based (DWT) RE scheme generates artifacts (due to a DWT shift-variant property). A wavelet-domain approach based on dual-tree complex wavelet transform (DT-CWT) and nonlocal means (NLM) is proposed for RE of the satellite images. A satellite input image is decomposed by DT-CWT (which is nearly shift invariant) to obtain high-frequency subbands. The high-frequency subbands and the low-resolution (LR) input image are interpolated using the Lanczos interpolator. The high-frequency subbands are passed through an NLM filter to cater for the artifacts generated by DT-CWT (despite of its nearly shift invariance). The filtered high-frequency subbands and the LR input image are combined using inverse DT-CWT to obtain a resolution-enhanced image. Objective and subjective analyses reveal superiority of the proposed technique over the conventional and state-of-the-art RE techniques.
Robust speech recognition using DNN-HMM acoustic model combining noise-aware training with spectral subtraction

Recently, acoustic models based on deep neural notworks (DNNs) have been introduced and showed dramatic improvements over acoustic models based on GMM in a variety of tasks. In this paper, we considered the improvement of noise robustness of DNN. Inspired by Missing Feature Theory and static noise aware training, we proposed an approach that uses a noise-suppressed acoustic feature and estimated noise information as input of DNN. We used simple Spectral Subtraction as noise-suppression. As noise estimation, we used estimation per utterance or frame. In noisy speech recognition experiments, we compared the proposed method with other methods and the proposed method showed the superior performance than the other approaches. For noise estimation per utterance with log Mel Filterbank, we obtained 28.6% word error rate reduction compared with multi condition training, 5.9% reduction compared with noise adaptive training. Index Terms: speech recognition, deep neural network, noise robustness, adaptive training, noise aware training, Spectral Subtraction, Missing Feature Theory
Geographic Data Base Enriching from ERS SAR Scenes: Examples in French Guiana

Geographic information system accessibility and popularity play an important role in spatial and temporal analysis of anthropic and natural activities. Optical images are not efficient for geographic data base on the French Guiana because of its characteristics (cloud cover, dense forest cover). A series of applications have been integrated in a G.I. S. on French Guiana. SAR ERS data are used as cartographic resource when available ones are obsolete, as environmental data source, and also to enrich and bring up to date available vector layers (by classification process or photo-interpretation). We have a global ERS SAR PRI cover of the territory at our disposal and multi-temporal sequences on the coastal plain. Because of the effect of speckle, isolated scenes are, in general cases, only useful for small scale map (about 1 : 200 000). But this scale is not proportional with available vector layers. SAR sequences are filtered with a multi temporal filter so as to increase the image quality by reducing the variance of the speckle. The average scenes are used for applications that require larger scale map (1 : 50 000). The capacity of ERS SAR sensor to enrich geographic data bases is valorized by some application examples : developments, health (malaria zones), natural cover and deforestation , coastal cartography.
Cyberbullying Among Chilean Students and the Protective Effects of Positive School Communities

ABSTRACT Cyberbullying is a type of bullying that involves the use of technology to harm other students. Most researchers have examined individual-level characteristics, but the perception of school climate and school characteristics such as type, size, and level of vulnerabilities have not been considered using multilevel methods. Using a sample of 8,237 Chilean seventh graders from 1,322 schools we examined individual- and school-level predictors of cyberbullying perpetration. Cyberbullying perpetration was predicted by prior victimization, interpersonal relationships, and gender. We found an interaction effect between interpersonal relationships and teacher support for victims. Our findings support the notion that school policies and culture can play a vital role in the prevention of cyberbullying, supporting a protective resilience model.
Cross-lingual Information Extraction from Web pages : the use of a general-purpose Text Engineering Platform

In this paper we present how the use of a general-purpose text engineering platform has facilitated the development of a cross-lingual information extraction system and its adaptation to new domains and languages. Our approach for crosslingual information extraction from the Web covers all the way from the identification of Web sites of interest, to the location of the domainspecific Web pages, to the extraction of specific information from the Web pages and its presentation to the end-user. This approach has been implemented in the context of the IST project CROSSMARC. The text engineering platform “Ellogon” offers functionalities that facilitated the development of core CROSSMARC components as well as their porting into new domains
EXACT STRONG LAWS FOR SIMULTANEOUS ST . PETERSBURG GAMES

Herein we show a simple way to make the St. Petersburg Game fair for both Peter and Paul. By just considering the minimum of two simultaneous games we establish a way that could easily have been understood three centuries ago. Similarly, we show how to play the St. Petersburg Game by observing the maximum of two concurrent games. After showing how to make this fair for both Peter and Paul by playing two games at a time we extend this so that they can play m simultaneous St. Petersburg Games.
Efficient Relationship Pattern Mining using Multi-relational Data Cubes

A large class of data mining applications involve data sets that pertain to multiple entities and relationship. This has led to the suggestion of multi-relational data mining(MRDM) that aims to incorporate and exploit the heterogeneous and semantically rich relationships that exist among entity types. Specifically, given a database consisting of multiple tables linked through foreign key joins, a target table (that typically represents a certain realworld entity type) and, optionally, a target attribute (e.g. a class label attribute), MRDM aims to discover patterns and models spanning all the tables and links that either describe or predict the target entity or attribute. In this paper, we study one class of MRDM task, namely multi-relational association rule mining. For this task, we distinguish two types of target tables, namely entity and relationship target tables. For the case of entity target tables, we cast the computation of frequent patterns as multi-relational iceberg cube computation and propose an efficient algorithm for it. Then, we study the application and peculiar requirements of target tables that are relationship tables. This study reveals a new mining task, dubbed linkage mining, where the mere instances of relationships are the objects of mining. We then show how our multi-relational iceberg computation algorithm is extended to do linkage mining. In the end, we present performance studies of our algorithms.
Modelling Project Trade-Off Using Bayesian Networks

This paper considers trade-offs that may be made during project among time, cost and quality. Most projects are unique one-of-a-kind activities. Hence, the project manager may find it extremely difficult to stay within the time-cost- quality triangle. Different project trade-off preferences exist in different industries. Based on this we propose set of axioms in project risk management which reflect relationships between key project variables. The conceptual model we develop satisfies this basic set of axioms. We use Bayesian Networks (BNs) to model project trade-off. The purpose is to provide a quantitative model for trade-off analysis in project risk management.
A Methodology for Information Quality Management in Self-Healing Web Services

Self-healing systems have attracted vast attention by the research community. Self-healing Web services have the ability to perceive that they are not operating correctly and, without human intervention, make the necessary adjustments to restore itself to normal operation. Information quality is a fundamental aspect of Web services since wrong or out-of-date information can cause the failure of the service. Measuring and improving information quality is a complex task. Several methodologies have been developed in the last years to provide a support for the design and implementation of assessment and improvement techniques in order to guarantee high data quality level. The existing approaches focus on the system design phases in which enterprise context is analyzed, quality dimensions are selected, assessment and improvement techniques and tools are modelled. In this paper, a methodology is instead proposed to be a support to self-healing environments and to solve run-time data quality problems. The analysis of the business processes and context in the design phase allows identifying critical points in the business tasks in which information quality might be worsened. In these points, information quality blocks have to be inserted in order to continuously monitor the information flows. Through suitable checks, failures due to information quality problems can be detected. Furthermore, failures and warnings in service execution may depend on a wide variety of causes. Along the causes, the methodology also produces a list of the suitable recovery actions for a timely intervention and quality improvement. In particular, the focus of the paper is on data quality problems introduced by a shared use of data in different processes or by periodically synchronized data sources in process based systems using Web services. The methodology is explained by means of a running example.