Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Backdoor Sets with Respect to Horn and Binary Clauses

We study the parameterized complexity of detecting backdoor sets for instances of the propositional satisfiability problem (SAT) with respect to the polynomially solvable classes horn and 2-cnf. A backdoor set is a subset of variables; for a strong backdoor set, the simplified formulas resulting from any setting of these variables is in a polynomially solvable class, and for a weak backdoor set, there exists one setting which puts the satisfiable simplified formula in the class. We show that with respect to both horn and 2-cnf classes, the detection of a strong backdoor set is fixed-parameter tractable (the existence of a set of size k for a formula of length N can be decided in time f(k)NO(1)), but that the detection of a weak backdoor set is W[2]-hard, implying that this problem is not fixed-parameter tractable.
Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information

Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.
Reckon the Parameter of DBSCAN for Multi-density Data Sets with Constraints

DBSCAN is a typical density-based clustering algorithm, but it is time-consuming to ascertain the parameter Eps and it does not perform well on multi-density datasets because of the global parameter Eps. In this paper, we use must-link constraints to ascertain the parameter Eps for each density distribution effectively and automatically, which will be used to deal with multi-density data sets for traditional DBSCAN algorithm. Experimental results reveal that our algorithm can reckon the parameter of DBSCAN for multi-density data sets with constraints effectively.
1 1 C 4 anatomy can evolve via a single developmental change

3 Authors: Marjorie R. Lundgren (m.lundgren@lancaster.ac.uk), Luke T. Dunning 4 (l.dunning@sheffield.ac.uk), Jill K. Olofsson (j.k.olofsson@sheffield.ac.uk), Jose J. Moreno5 Villena (jjmorenovillena1@sheffield.ac.uk), Jacques W. Bouvier (jbouvier1@sheffield.ac.uk), 6 Tammy Sage (tammy.sage@utoronto.ca), Roxana Khoshravesh (r.khoshravesh@utoronto.ca), 7 Stefanie Sultmanis (stefanie.sultmanis@mail.utoronto.ca), Matt Stata 8 (matt.stata@mail.utoronto.ca), Brad Ripley (b.ripley@ru.ac.za), Maria S. Vorontsova 9 (M.Vorontsova@kew.org), Guillaume Besnard (guillaume.besnard@univ-tlse3.fr), Claire Adams 10 (claire3889@gmail.com), Nicholas Cuff (Nicholas.Cuff@nt.gov.au), Anthony Mapaura 11 (mapaura@yahoo.com), Matheus Bianconi (mebianconi1@sheffield.ac.uk), Christine M. Long 12 (christine.long@nt.gov.au), Pascal-Antoine Christin (p.christin@sheffield.ac.uk), Colin P. 13 Osborne* (c.p.osborne@sheffield.ac.uk) 14
Identifying metastasis in bone scans with Stochastic Diffusion Search

This paper introduces a novel approach in using a swarm intelligence algorithm - Stochastic Diffusion Search - as a tool to identify metastasis in bone scans. This algorithm is adapted for this particular purpose and its performance is investigated by running the agents of the swarm intelligence algorithm on sample bone scans whose status have been determined by the experts. The result of a statistical analysis is also reported, highlighting the behaviour of the algorithm when presented with different samples.
Simplex Representations Displaying Patterns of Multiparty Competition: Nagayama Diagrams and Comparing and Contrasting the Uses of Two Graphical Tools for

We compare two tools for displaying, in graphical form, information about vote outcomes in multiparty elections at the constituency level. One was recently proposed by Nagayama and introduced to the English-speaking world by Reed, who applied this method to Japanese and Italian election data. Reed labels the method Nagayama diagrams. Recently, Taagepera has shown how the domain of potential uses of Nagayama diagrams can be expanded significantly. A second graphical device has been used by a number of authors for various types of election analyses, but is not that well known in the comparative parties literature. This method, which uses barycentric coordinates (i.e. triangular) rather than the more familiar rectangular coordinates, has gone under a variety of names (e.g. trilinear plot, toroidal diagram and simplex representation), but we have chosen to use the last of these labels. We make use of both methods to visually present election data (by constituency) for the Italian national elections of 1994, 1996 and 2001. We show how different types of information may be readily gleaned from the two types of graph, and, perhaps most importantly, illustrate how we may improve the ready intuitive interpretability of each type of graph by specifying boundary constraints to define particular regions of the graph – a technique we call ‘segmentation’.
Running head : MULTIPLE MODELS OF LEARNING Modeling Users at Multiple Levels of Expertise While They Learn

We report a way to build a series of GOMS-like cognitive user models representing a range of performance at different stages of learning. We use a spreadsheet task across multiple sessions as an example task; it takes about 20~30 min. to perform. The models were created in ACT-R using a compiler. The novice model has 29 rules and 1,152 declarative memory task elements (chunks)—it learns to create procedural knowledge to perform the task. The expert model has 617 rules and 614 task chunks (that it does not use) and 538 command string chunks—it gets slightly faster through limited declarative learning of the command strings and some further production compilation; there are a range of intermediate models. These models were tested against aggregate and individual human learning data, confirming the models' predictions. This work suggests that user models can be created that learn like users while doing the task.
Multipitch tracking with continuous correlation feature and hybrid DBNS/HMM model

This paper proposed a new approach used for tracking multi-pith within one mixture speech signal. In this method, we employed a novel continuous correlation feature for calculating pitch model. This feature not only represents the harmonicity but also includes the information of spectral continuity, and hence improving the accuracy of the multi-pitch estimate. A DBNs and HMM hybrid model was further utilized to construct pitch models for determining pitch states and search for the best pitch state sequence. The new approach has been evaluated on mixture speech data and the results demonstrated its efficiency.
Deregulation of SOCS5 suppresses dendritic cell function in chronic lymphocytic leukemia

One cause of morbidity and mortality in chronic lymphocytic leukemia (CLL) is infection, which results from defects in a number of components of the immune system. In particular, dendritic cells (DCs) are functionally defective in patients with CLL. To understand the molecular mechanism for this abnormality, we focused on signal transduction pathways that regulate the function of monocyte-derived dendritic cells (Mo-DCs). Monocytes from CLL patients exhibit high IL-4Rα expression due to the enhanced activation of STAT3. However, IL-4R signaling is decoupled from activation of its downstream mediator STAT6 by enhanced levels of the negative regulator SOCS5. This impairs differentiation of functionally mature DCs leading to decreased expression of HLA-DR and costimulatory molecules, and reduced secretion of pro-inflammatory cytokines in LPS-activated DCs. Moreover, Mo-DCs from CLL patients display a decreased ability to induce pro-inflammatory T-cell responses. IL-10-treatment of monocytes from healthy donors mimics the alteration in signaling observed in CLL patients, through enhanced STAT3-dependent expression of SOCS5. The higher level of SOCS5 inhibits STAT6 activation and leads to defective DC differentiation. These findings indicate that SOCS5 mediates the impaired function of DCs in CLL patients, and has the potential to be a new therapeutic target for reversing cancer-associated immune suppression.
GRT: Program-Analysis-Guided Random Testing (T)

We propose Guided Random Testing (GRT), which uses static and dynamic analysis to include information on program types, data, and dependencies in various stages of automated test generation. Static analysis extracts knowledge from the system under test. Test coverage is further improved through state fuzzing and continuous coverage analysis. We evaluated GRT on 32 real-world projects and found that GRT outperforms major peer techniques in terms of code coverage (by 13 %) and mutation score (by 9 %). On the four studied benchmarks of Defects4J, which contain 224 real faults, GRT also shows better fault detection capability than peer techniques, finding 147 faults (66 %). Furthermore, in an in-depth evaluation on the latest versions of ten popular real-world projects, GRT successfully detects over 20 unknown defects that were confirmed by developers.
Zigbee Based WSN with IP Connectivity

Wireless sensor network (WSN) consists of distributed sensor nodes in the remote locations and are used to measure the sensor data in remote locations. Each node of WSN consists of a wireless micro controller interfaced with sensors. The user is confronted with independently working and diverse electronic devices like television, PDAs, laptops etc. Middleware is required to glue all these heterogeneous devices. This also integrates WSN with IP to cater the real world requirements and enables the electronic device of the IP-based WSN to access the other device of the same or different network. TCP / IP stack is not suitable to be ported into the memory of WSN node. It occupies more memory and causes more overhead to the WSN. There are two basic approaches towards the realization of IP based WSN viz., gateway based approach and virtual gateway approach. A new network based on wireless micro controllers to integrate IP and WSN is proposed. The hardware is employed with one coordinator, three routers and one handheld device that integrate IP and WSN. New addressing mechanisms are proposed to create virtual IP and WSN addresses as part of this integration.
Opportunities or Risks to Reduce Labor in Crowdsourcing Translation? Characterizing Cost versus Quality via a PageRank-HITS Hybrid Model

Crowdsourcing machine translation shows advantages of lower expense in money to collect the translated data. Yet, when compared with translation by trained professionals, results collected from non-professional translators might yield low-quality outputs. A general solution for crowdsourcing practitioners is to employ a large amount of labor force to gather enough redundant data and then solicit from it. Actually we can further save money by avoid collecting bad translations. We propose to score Turkers by their authorities during observation, and then stop hiring the unqualified Turkers. In this way, we bring both opportunities and risks in crowdsourced translation: we can make it cheaper than cheaper while we might suffer from quality loss. In this paper, we propose a graphbased PageRank-HITS Hybrid model to distinguish authoritative workers from unreliable ones. The algorithm captures the intuition that good translation and good workers are mutually reinforced iteratively in the proposed frame. We demonstrate the algorithm will keep the performance while reduce work force and hence cut cost. We run experiments on the NIST 2009 Urdu-to-English evaluation set with Mechanical Turk, and quantitatively evaluate the performance in terms of BLEU score, Pearson correlation and real money.
Histogram-based Image Quality Checking

Many medical image analysis algorithms make assumptions concerning the image formation process, the structure of the intensity histogram, or other statistical properties of the input data. Application of such algorithms to image data that do not fit these assumptions will produce unreliable results. This paper describes a technique for the automatic identification of images that do not have histogram structure consistent with that expected. The approach is based upon a component analysis followed by statistical testing. Experiments validate its use in the identification of quantisation problems and unexpected image structure. It is intended that this test will form one component of a quality control assessment, to aid in the use of sophisticated statistical image analysis software by non-expert users.
Software Reliability Management

This entry identifies the factors that control software reliability and the approaches that are needed to achieve desired reliability targets. Common reliability measures are defined. The factors that impact defect density and defect finding rates are discussed and software reliability growth modeling is introduced. Both test-timeand test-coverage-based models are introduced. Modeling for security vulnerability discovery process is also presented. Reliability of multicomponent software systems is discussed followed by optimal allocation of test resources to achieve a target reliability level. Finally some of the applicable software tools available today are mentioned.
Facial Expression Recognition with Local Binary Patterns and Linear Programming 1

In this work, we propose a novel approach to recognize facial expressions from static images. First, the local binary patterns (LBP) are used to efficiently represent the facial images and then the linear program- ming (LP) technique is adopted to classify seven facial expressions—anger, disgust, fear, happiness, sadness, surprise, and neutral. Experimental results demonstrate an average recognition accuracy of 93.8% in the JAFFE database, which outperforms the rates of all other reported methods in the same database.
A Statistical Approach to Modelling Breast Tissue Appearance in Mammograms

In this paper, we present an approach to build models of breast tissue appearance in mammograms. Mammographic tissue is modelled based on a statistical analysis of local appearance. We investigate five strategies by using different types of local features, covering aspects of intensity, texture, and geometry. A visual dictionary is generated to summarise local tissue appearance with descriptive “words”. The global appearance of the breast is represented as an occurrence histogram over the dictionary. The resulting histogram models can be applied to breast density classification. The validity is qualitatively and quantitatively evaluated using the full MIAS database. The consensus of three experts according to the BIRADS criterion is used as the classification ground truth. We test the performance of each individual strategy and the combination of all strategies. The results indicate that our approach has potential for mammographic risk assessment.
Reachability: a constrained path propagator implemented as a multi-agent system

Reachability is a propagator that implements a generalized reachability constraint on a directed graph g. Given a source node source in g, we can identify three parts in the Reachability constraint: (1) the relation between each node of g and the set of nodes that it reaches, (2) the association of each pair of nodes 〈source, i〉 with its set of cut nodes, and (3) the association of each pair of nodes 〈source, i〉 with its set of bridges. The effectiveness of our Reachability propagator has been shown by applying it to the Hamiltonian Path problem. The experimental evaluations that we have done in [10] show that it provides strong pruning, obtaining solutions with very little search. Furthermore, the experiments show that Reachability is also useful for defining a good distribution strategy and dealing with ordering constraints among mandatory nodes. These experimental results give evidence that Reachability is a useful primitive for solving constrained path problems over graphs. In this paper we elaborate on the implementation of Reachability. Reachability has been implemented using a message passing approach on top of the multi-paradigm programming language Oz [7]. I.e., Reachability is a multi-agent system where agents send each other synchronous and asynchronous messages and their transition state functions rely on data flow and constraint programming primitives [14]. In the implementation, we profited from the Finite Integer Set module provided by the Mozart system, which implements Oz [5].
Review on tax research in accounting: Is the information given by U.S. GAAP income taxes also provided by IFRS?

In this paper, we present a review of tax research in accounting. We outline U.S. GAAP accounting rules for the following four income tax notes and survey the area of research literature dealing with the information content provided by U.S. GAAP: (1) unrecognized tax benefits, (2) valuation allowances, (3) foreign earnings designated as permanently reinvested, and (4) book-tax differences. Building on this, we present the accounting rules for comparable income tax notes following IFRS standards and offer which we believe are interesting avenues for future research on the information on tax notes provided by IFRS financial statements.
The Effect of Mandatory Disclosure on Market Inefficiencies: Evidence from Statement of Financial Accounting Standard Number 161

Prior research finds that unrealized gains/losses on cash flow hedges are negatively associated with future earnings. However, equity investors and analysts fail to anticipate this association. These studies speculate that the mispricing is due to poor derivative disclosures. In this study, we examine whether the enhanced mandatory derivatives disclosures set forth in FAS 161 improve users’ understanding of firms’ hedging activities, and offer two main findings. First, we find no evidence of mispricing after FAS 161, suggesting that enhanced mandatory derivative disclosures helped correct investors’ understanding of the implication of unrealized cash flow hedge gains/losses for future firm performance. Second, we find that analysts’ forecasts exhibit less error related to cash flow hedges after FAS 161, suggesting that these enhanced disclosures improve the information environment for sophisticated information intermediaries. In additional analysis, we find that the reduction in mispricing holds regardless of a firm’s institutional ownership level, suggesting that the additional disclosures appear to have benefited all investors regardless of their sophistication. Overall, our results suggest that the enhanced mandatory derivative disclosures required by FAS 161 improved investors’ and analysts’ understanding of the effects of derivative and hedging activities on future firm performance and firm value.
Correcting Distorted Histology Slices for 3 D Reconstruction

Histology acquisition often introduces tissue distortions that make 3D reconstruction difficult. We present a framework to detect distorted slices, distorted structures and eventually correct the distortions by a reasonable estimation. The first detection specifies distorted slices by comparing similarity measures in a group of neighbouring slices. The distorted structures detection attempts to predict the type, size and position of the distortions in the absence of a priori information about expected distortions. The last algorithm uses undistorted tissue information in corresponding regions in neighbouring slices to correct distorted areas, facilitating pair-wise slice registration for 3D reconstruction.
Sensorless and constraint based peg-in-hole task execution with a dual-arm robot

Fast and sensorless peg-in-hole insertion is a challenging task for a robotic manipulator. In order to deal with the peg-in-hole insertion problem without any need of an external force/torque sensor, this paper proposes to actively accomplish compliance in the insertion task relying on an admittance based control. This is combined with a real-time trajectory generator, by means of constraint based optimization, where a model-based sensorless observer of interaction forces is exploited. Experiments have been performed on an ABB dual-arm 7-DOF lightweight prototype robot to validate the proposed approach, with an insertion speed comparable to human manual execution and in presence of geometric uncertainty.
Topic Classification using Latent Dirichlet Allocation at Multiple Levels

We propose a novel low-dimensional text representation method for topic classiﬁcation. Several Latent Dirichet Allocation (LDA) models are built on a large amount of unlabelled data, in order to extract potential topic clusters, at different levels of generalization. Each document is represented as a distribution over these topic clusters. We experiment with two datasets. We collected the ﬁrst dataset from the FriendFeed social network and we manually annotated part of it with 10 general classes [1]. The second dataset is a standard text classiﬁcation benchmark, Reuters 21578, the R8 subset (annotated with 8 classes). We show that classiﬁcation based on our multi-level LDA representation leads to improved results for both datasets. Our representation catches topic distributions from generic ones to more speciﬁc ones and allows the machine learning algorithm choose the appropriate level of generalization for the task. Another advantage is the dimensionality reduction, which permitting the use of machine learning algorithms that cannot run on high-dimensional feature spaces. Even for the algorithms that can deal with high-dimensional features spaces, it is often useful to speed up the training and testing time by using the lower dimensionality.
Points of Reference Finding Common Ground among Reference Level Approaches to Move REDD + Forward

With tropical deforestation and forest degradation now accounting for about 15 percent of global CO2 emissions, there has been renewed e! ort in recent years to include policies for “reducing emissions from deforestation and degradation plus related pro-forest activities” (REDD+) in programs designed to combat climate change. "e driving force behind REDD+ has been the United Nations Framework Convention on Climate Change (UNFCCC). REDD+ was #rst o$cially proposed at the UNFCCC Conference of Parties (COP) in Montreal in 2005. At the 2010 COP meeting in Cancun, REDD+ was o$cially accepted as an important tool.
Development of ATLAS Primary Vertex Reconstruction for LHC Run 3

Increasing luminosity at the Large Hadron Collider (LHC) poses a challenge for primary vertex reconstruction in the ATLAS experiment. A rate of 70 or more inelastic proton-proton collisions per beam crossing was observed during the recently-completed Run 2 and even higher vertex density, or pile-up, is expected in Run 3. To meet this challenge, ATLAS has developed new tools: a Gaussian track density seed finder and an adaptive multi-vertex finder. The former constructs a simple but powerful analytic model of the track density along the beam axis to locate candidate vertices, and the latter applies a global approach to vertex finding and fitting, allowing vertices to compete for nearby tracks. These proceedings document the strategy, optimization and preliminary performance of this new vertex reconstruction software, highlighting improvements in vertex finding efficiency, purity and spatial resolution under Run 3 pile-up conditions.