Miracle-dz/UniFAR-FTUs huggingface dataset:
Guiding Cnf-sat Search by Analyzing Constraint-variable Dependencies and Clause Lengths

The type of decision strategies employed for CNF-SAT have a profound effect on the efficiency and performance of SAT engines. Over the years, a variety of decision heuristics have been proposed; each has its own achievements and limitations. This paper re-visits the issue of decision heuristics and engineers a new approach that takes an integrated view of the overall problem structure. Our approach qualitatively analyzes clause-variable dependencies by accounting for vari-able/literal activity, clause connectivity, distribution of variables among clauses of different lengths, and correlation among variables, to derive an initial static ordering for SAT search. To account for conflict clauses and their resolution, a corresponding dynamic variable order update strategy is also presented. Quantitative metrics are proposed that are used to devise an algorithmic approach to guide overall SAT search. Experimental results demonstrate that our strategy significantly outperforms conventional approaches.
Fuzzy Method for Online Learning of Bayesian Network Parameters

In learning problems, there are situations where training data is not fully available at the learning time. They are incrementally generated by time, defining a type of domain called online that has among its characteristics the possibility of data failure or even missing data. In Bayesian networks, learning is divided into two categories: structure (related to the graph of conditional relations) and parameters (related to the strength of conditional relations). In this work we present an online parameter learning method that quickly adapts to changes in the environment aiming not only the reproduction of the probability distribution (generative learning) but also the increase of accuracy in the network (discriminatory learning). Our approach is compared with the Adaptative Voting EM method considering two simulation conditions: when distributions are unknown and when distributions undergo abrupt changes. The proposed method achieves good results in both situations by adjusting to environment changes more quickly and by simplifying the parameterization of the traditional approach.
Onboard Visual Control of a Quadcopter MAV Performing a Landing Task on an Unknown Platform

Vision based control allows Micro Air Vehicles (MAV) to move autonomously in GPS-denied environments, for example in indoor applications. An open issue in this field is landing on an unknown platform. The difficulty in visual control with respect to such an unknown platform, is a lack of scale. Without knowledge of the scale of offsets and object sizes (without height knowledge from GPS) it is difficult to determine an appropriate response from the controller. A control algorithm is designed to fit these requirements using an adaptation of a constant optical flow divergence based landing scheme, combined with an Image Based Visual Servoing approach applied to features in the Virtual Camera. The approach leads to satisfactory behavior in Gazebo simulations; it results in a robust controller for a range of starting heights and divergence settings.
D* mesons from beauty in photoproduction at HERA

The creation of heavy quarks in photoproduction (Q2 1GeV 2) in the ZEUS detector at HERA has been studied. We analyzed them through D∗ mesons, identi ed from the decay channel D∗ → Dπs with D0 → Kπ, and tried to evaluate the amount of this mesons coming from B0 meson decay, and so from beauty quarks. Statistics are not enough, and we would need at least few times more of them for a further study. But the results here are unambiguous, the amount is very small.
Prepared for submission to JHEP Covariant BSSN formulation in bimetric relativity

Abstract: Numerical integration of the field equations in bimetric relativity is necessary to obtain solutions describing realistic systems. Thus, it is crucial to recast the equations into a well-posed form. In general relativity, under certain assumptions, the covariant BSSN formulation is a well-posed formulation of the Einstein equations, due to its strong hyperbolicity. In this paper, we establish the covariant BSSN formulation of the bimetric field equations. It shares many features with the corresponding formulation in general relativity, but there are a few fundamental differences between them. Some of these differences depend on the gauge choice and alter the hyperbolic structure of the system of partial differential equations compared to general relativity. Accordingly, the strong hyperbolicity of the system cannot be claimed yet, under the same assumptions as in general relativity. In the paper, we stress the differences compared with general relativity and state the main issues that should be tackled next, to draw a road map towards numerical bimetric relativity.
P o S ( S S C 2 0 1 5 ) 0 1 2 Star Formation Histories of S 0 Galaxies

On the Hubble sequence of galaxies, S0s occupy a conspicious position between elliptical and spiral galaxies implying that all S0s are alike and a transition class of objects. However, using photometric analysis, in our past work [19, 20, 2, 4, 3, 5] we have shown that S0s represent a diverse variety of objects with different formation histories and mechanisms. As a part of this long term study of S0s and their overall place in galaxy formation theory, we are carrying out a detailed spectroscopic study of a select sample of S0 galaxies using SALT/RSS. This article describes the ongoing work, the challenges and proposed solutions.
SIOTOME: An Edge-ISP Collaborative Architecture for IoT Security

Modern households are deploying Internet of Things (IoT) devices at a fast pace. The heterogeneity of these devices, which range from low-end sensors to smart TVs, make securing home IoT particularly challenging. To make matters worse, many consumer-IoT devices are hard or impossible to secure because device manufacturers fail to adopt security best practices (e.g., regular software patches). In this paper we propose a novel, cooperative system between the home gateway and the Internet Service Provider (ISP) to provide data driven security solutions for detecting and isolating IoT security attacks. Our approach is based on a combination of a large-scale view from the ISP (using powerful machine learning techniques on traffic traces), and the fine-grained view of the per-device activity from the home (using edge processing techniques) to provide efficient, yet privacy-aware IoT security services.
Sigmod Officers, Committees, and Awardees Chair Vice-chair Secretary/treasurer Sigmod Executive Com Mittee: Advisory Board: Infor Mation Director: Associate Infor Mation Directors: Sigmod Record Associate Editors: Sigmod Disc and Sigmod Anthology Editor: Awards Com Mittee: Jim Gray Doctoral Disserta

Ontology Matching aims to find the semantic correspondences between ontologies that belong to a single domain but that have been developed separately. However, there are still some problem areas to be solved, because experts are still needed to supervise the matching processes and an efficient way to reuse the alignments has not yet been found. We propose a novel technique named Reverse Ontology Matching, which aims to find the matching functions that were used in the original process. The use of these functions is very useful for aspects such as modeling behavior from experts, performing matching-by-example, reverse engineering existing ontology matching tools or compressing ontology alignment repositories. Moreover, the results obtained from a widely used benchmark dataset provide evidence of the effectiveness of this approach.
Sigmod Officers, Committees, and Awardees Chair Vice-chair Secretary/treasurer Sigmod Executive Committee: Advisory Board: Information Director, Sigmod Disc and Sigmod Anthology Editor: Associate Information Directors: Sigmod Record Associate Editors: Sigmod Conference Coordinator: Awards Committee:

Unlike harddisks, flash memory SSDs have very fast latency in random reads and thus the relative bandwidth gap between sequential and random read is quite small, though not negligible. For this reason, it has been believed that index scan would become more attractive access method in flash memory storage devices. In reality, however, the existing index scan can outperform the full table scan only in very selective predicates. In this paper, we investigate how to optimize the index scan on flash memory SSDs. First, we empirically show that the index scan underperforms the full table scan even when the selectivity of selection predicate is less than 5% and explain its reason. Second, we revisit the idea of sorted index scan and demonstrate that it can outperform the full table scan even when the selectivity is larger than 30%. However, one drawback of the sorted index scan is that it loses the sortedness of the retrieved records. Third, in order to efficiently resort the result from the sorted index scan, we propose a new external index-based sort algorithm, partitioned sort, which exploits the information of key value distribution in the index leaf nodes. It can sort data in one pass regardless of the available sort memory size. Keywords-flash memory SSDs; sorted index scan; partitioned sort
SIGMOD Officers , Committees , and Awardees ( continued )

For innovative and highly significant contributions of enduring value to the development, understanding, or use of database systems and databases. Until 2003, this award was known as the "SIGMOD Innovations Award. " In 2004, SIGMOD, with the unanimous approval of ACM Council, decided to rename the award to honor Dr. E. F. (Ted) Codd (1923-2003) who invented the relational data model and was responsible for the significant development of the database field as a scientific discipline. Recipients of the award are the following: SIGMOD Contributions Award For significant contributions to the field of database systems through research funding, education, and professional services. Recipients of the award are the following: SIGMOD has established the annual SIGMOD Jim Gray Doctoral Dissertation Award to recognize excellent research by doctoral candidates in the database field. Recipients of the award are the following: The issue opens with the Database Principles column, where Karvounarakis and Green survey the recent literature on querying semiring-annotated data. Annotated data models have been proposed for various applications, including provenance, probabilistic and incomplete information. An earlier work (PODS 2007) by Karvounarakis, Green and Tannen had proposed the general framework of K-relations, which captures the querying of such annotated relations, where K is a commutative semiring. The present paper surveys the literature on querying semiring-annotated data and presents new development of the ideas, based on semiring of polynomials, applying in particular to provenance representations and to XQuery views over annotated, unordered XML. The survey by Manica, Dorneles and Galante focuses on the support for the time dimension within Web search engines. Users may either issue search queries with explicit time constraints, or query data related to the " recent past " (what is the latest version of this information? ") or forecast data (what are the predictions for tomorrow's weather? ) Successive generations of Web search engines have tackled the time dimension from different angles, from providing the freshest results first, to allowing the user to specify a time window for their results, and finally to extract and manipulate the temporal information comprised in Web pages. The authors end with a set of currently open problems and perspectives on further features, and more efficiency, that could be achieved when answering search queries with a temporal dimension. Ryan Johnson is the recipient of the SIGMOD 2012 Doctoral Dissertation award (advised by Anastasia Ailamaki). In the Distinguished Database Profiles column, he talks about the importance of …
Poster: Studying Vehicle Movements on Highways and their Impact on Ad-Hoc Connectivity

The mobility of the nodes in a Mobile Ad-Hoc Network (MANET) is a crucial factor in the performance studies of communication protocols for these kind of networks. For this reason, researchers usually use a randomized node movement model, such as the Random Way-Point Model [6], in the process of designing or analyzing the behavior of their protocols. Since movement is not very predictable in these scenarios, they serve as a worst case assumption of node mobility concerning communication protocol performance in the sense that a positive correlation between performance in an RWP scenario and an arbitrary scenario exists. Additionally, RWP is an analytically well-understood mobility scheme and the movements can be generated very easily with tools complementary to most of the common network simulators. Vehicular Ad-Hoc Networks (VANETs) are one type of MANETs that is recently attracting the attention of both industry and academia. VANETs are characterized by theusuallystreetbound scenarios due to the special kind of their nodes, i.e., vehicles. Recent research [3] though, shows that protocol performance in VANETs is quite different from performance in RWP scenarios and specializing protocols in these scenarios can be both challenging but also beneficial. In this work, we present a set of real scenarios of one of the common situations in vehicular environments, i.e., highways. Additionally, an analysis from a connectivity perspective has been performed intending to support the design and/or study of communication protocols tailored to this specific environments. Starting from reality-audited highway movement data generated for the FleetNet project [5], we have created a set of node movement traces especially for the use with network simulators like ns-2 [7]. In addition, we provide a tool, called HWGui, for the visualization and computation of the set of statistics dealing (a) with the movements itself and (b) with the communication consequences assuming a Unit Disk Graph [2] radio connectivity model. A screen shot of HWGui can be found in Figure 1 showing a highway scenario with nodes moving from left to right. The yellow lines depicts the being in each others range probability of two cars. For further information on the software and for downloading the movement traces and the software, please refer to [1, 4]. The next section will briefly outline statistical examples.
Data Mining with R: Learning with Case Studies, Second Edition

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the books web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Lus Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.
The Implementation of Internet Interventions for Depression: A Scoping Review

Background Depression is one of the most common mental health problems among adults, but effective treatments are not widely accessible. The Internet holds promise as a cost-effective and convenient delivery platform of interventions for depression. However, studies suggest that Internet interventions are not widely available in routine settings. Objective The aim of this study was to review the literature and examine whether there are systematic differences in reporting of the various implementation components on Internet interventions for depression, and then to examine what is known about and is characteristic of the implementation of these Internet interventions in regular care settings. Methods We performed a scoping review, drawing upon a broad range of the literature on Internet interventions for depression in regular care, and used the active implementation framework to extract data. Results Overall, the results suggested that knowledge about the implementation of Internet interventions for depression in regular care is limited. However, guided support from health professionals emphasizing program adherence and recruitment of end users to the interventions emerged as 2 main themes. We identified 3 additional themes among practitioners, including their qualifications, training, and supervision, but these were scarcely described in the literature. The competency drivers (ie, staff and user selection, training, and supervision) have received the most attention, while little attention has been given to organizational (ie, decision support, administration, and system intervention) and leadership drivers. Conclusions Research has placed little emphasis on reporting on the implementation of interventions in practice. Leadership and organizational drivers, in particular, have been largely neglected. The results of this scoping review have implications for future research and efforts to successfully implement Internet interventions for depression in regular care.
Causal effects of challenge and threat appraisals on pain self-efficacy, pain coping, and tolerance for laboratory pain: An experimental path analysis study

Background Primary appraisals of pain as a potential threat influence pain perception and coping but comparatively less is known about related effects of challenge appraisals or causal effects of primary appraisals on secondary appraisals of perceived pain coping capacities (e.g., pain self-efficacy). Methods To address these gaps, young Chinese women (N = 147) and men (N = 88) were randomly assigned to one of three appraisal conditions prior to engaging in a cold pressor test (CPT): (1) a higher threat appraisal condition featuring task orienting information describing symptoms and consequences of frostbite, (2) a lower threat appraisal condition featuring orienting information about safety of the CPT, or (3) a challenge appraisal condition featuring orienting information describing benefits of persistence despite discomfort for future life satisfaction. Results Compared to peers in the higher threat condition, challenge condition cohorts experienced smaller decreases in task-related self-efficacy, more cognitive coping, and less catastrophizing as well as more overall pain tolerance. A path analysis indicated that self-efficacy changes, cognitive coping and catastrophizing fully mediated links of subjective challenge and threat appraisals with pain tolerance. Conclusions Initial appraisals about the nature of pain influence, not only pain tolerance and coping, but also perceived coping capacities. Perspective Through examining particular theory-based pain appraisal and coping processes, this experiment is the first to identify both perceived coping capacities (pain self-efficacy) and coping responses as factors that explain why appraisals of pain as a challenge or threat have differential effects on pain tolerance.
Isolation and Functional Characterization of MsFTa, a FLOWERING LOCUS T Homolog from Alfalfa (Medicago sativa)

The production of hay and seeds of alfalfa, an important legume forage for the diary industry worldwide, is highly related to flowering time, which has been widely reported to be integrated by FLOWERING LOCUS T (FT). However, the function of FT(s) in alfalfa is largely unknown. Here, we identified MsFTa, an FT ortholog in alfalfa, and characterized its role in flowering regulation. MsFTa shares the conserved exon/intron structure of FTs, and the deduced MsFTa is 98% identical to MtFTa1 in Medicago trucatula. MsFTa was diurnally regulated with a peak before the dark period, and was preferentially expressed in leaves and floral buds. Transient expression of MsFTa-GFP fusion protein demonstrated its localization in the nucleus and cytoplasm. When ectopically expressed, MsFTa rescued the late-flowering phenotype of ft mutants from Arabidopsis and M. trucatula. MsFTa over-expression plants of both Arabidopsis and M. truncatula flowered significantly earlier than the non-transgenic controls under long day conditions, indicating that exogenous MsFTa strongly accelerated flowering. Hence, MsFTa functions positively in flowering promotion, suggesting that MsFTa may encode a florigen that acts as a key regulator in the flowering pathway. This study provides an effective candidate gene for optimizing alfalfa flowering time by genetically manipulating the expression of MsFTa.
Enhanced CO2 Adsorption on Nitrogen-Doped Carbon Materials by Salt and Base Co-Activation Method

Nitrogen-doped carbon materials with enhanced CO2 adsorption were prepared by the salt and base co-activation method. First, resorcinol-formaldehyde resin was synthesized with a certain salt as an additive and used as a precursor. Next, the resulting precursor was mixed with KOH and subsequently carbonized under ammonia flow to finally obtain the nitrogen-doped carbon materials. A series of samples, with and without the addition of different salts, were prepared, characterized by XRD (X-ray powder diffraction), elemental analysis, BET (N2-adsorption-desorption analysis), XPS (X-ray photoelectron spectroscopy) and SEM (Scanning electron microscopy) and tested for CO2 adsorption. The results showed that the salt and base co-activation method has a remarkable enhancing effect on the CO2 capture capacity. The combination of KCl and KOH was proved to be the best combination, and 167.15 mg CO2 could be adsorbed with 1 g nitrogen-doped carbon at 30 °C under 1 atm pressure. The materials characterizations revealed that the introduction of the base and salt could greatly increase the content of doped nitrogen, the surface area and the amount of formed micropore, which led to enhanced CO2 absorption of the carbon materials.
Candida albicans Interactions with Mucosal Surfaces during Health and Disease

Flexible adaptation to the host environment is a critical trait that underpins the success of numerous microbes. The polymorphic fungus Candida albicans has evolved to persist in the numerous challenging niches of the human body. The interaction of C. albicans with a mucosal surface is an essential prerequisite for fungal colonisation and epitomises the complex interface between microbe and host. C. albicans exhibits numerous adaptations to a healthy host that permit commensal colonisation of mucosal surfaces without provoking an overt immune response that may lead to clearance. Conversely, fungal adaptation to impaired immune fitness at mucosal surfaces enables pathogenic infiltration into underlying tissues, often with devastating consequences. This review will summarise our current understanding of the complex interactions that occur between C. albicans and the mucosal surfaces of the human body.