Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling Linguistic Data Structures

Linguistic corpora have been annotated by means of SGML-based markup languages for almost 20 years. We can, very roughly, differentiate between three distinct evolutionary stages of markup technologies. (1) Originally, single SGML tree-based document instances were deemed sufficient for the representation of linguistic structures. (2) Linguists began to realize that alternatives and extensions to the traditional model are needed. Formalisms such as, for example, NITE were proposed: the NITE Object Model (NOM) consists of multi-rooted trees. (3) We are now on the threshold of the third evolutionary stage: even NITE's very flexible approach is not suited for all linguistic purposes. As some structures, such as these, cannot be modeled by multi-rooted trees, an even more flexible approach is needed in order to provide a generic annotation format that is able to represent genuinely arbitrary linguistic data structures.
Neurocomputational mechanisms underlying subjective valuation of effort costs

In everyday life, we have to decide whether it is worth exerting effort to obtain rewards. Effort can be experienced in different domains, with some tasks requiring significant cognitive demand and others being more physically effortful. The motivation to exert effort for reward is highly subjective and varies considerably across the different domains of behaviour. However, very little is known about the computational or neural basis of how different effort costs are subjectively weighed against rewards. Is there a common, domain-general system of brain areas that evaluates all costs and benefits? Here, we used computational modelling and functional magnetic resonance imaging (fMRI) to examine the mechanisms underlying value processing in both the cognitive and physical domains. Participants were trained on two novel tasks that parametrically varied either cognitive or physical effort. During fMRI, participants indicated their preferences between a fixed low-effort/low-reward option and a variable higher-effort/higher-reward offer for each effort domain. Critically, reward devaluation by both cognitive and physical effort was subserved by a common network of areas, including the dorsomedial and dorsolateral prefrontal cortex, the intraparietal sulcus, and the anterior insula. Activity within these domain-general areas also covaried negatively with reward and positively with effort, suggesting an integration of these parameters within these areas. Additionally, the amygdala appeared to play a unique, domain-specific role in processing the value of rewards associated with cognitive effort. These results are the first to reveal the neurocomputational mechanisms underlying subjective cost–benefit valuation across different domains of effort and provide insight into the multidimensional nature of motivation.
The Implementation of Cluster-Based Traffic Simulation System

Self-similarity is one of the most important characteristics of real network traffic. Constructing an accurate model can reflect the real network environment precisely. It is also an effective measure to predict the upcoming network traffic, thus ensuring the quality and reliability of network service. In this paper, we present a cluster-based distributed system, which is based on FGN(Fraction Gaussian Noise) algorithm. With the purpose to generate large-scale traffic and simulate the real network environment. We offer a detailed description for this system in the following three aspects: algorithm, architecture and implementation. Experiments have proved that the cluster-based system can generate large-scale similar network traffic with designated Hurst parameter
Improvements on IPD Algorithm for Triangular Mesh Reconstruction from 3D Point Cloud

A new algorithm is proposed for triangular mesh reconstruction from 3D scattered points based on the existing intrinsic property driven (IPD) method. The improvements include a new approach to determine the seed triangle, a new approach to define the influence region for active edge, and a new approach to select the best active point. The new triangle is also tested with the constraint of geometric integrity. Our algorithm has been tested on some unorganized 3D point clouds. From the experimental results it can be found that our approach has the ability to generate more accurate details in the recovered surfaces.
Research Statement 2.1 Random Walks on Graphs

1 Overview A broad range of graph-based real world applications, collaborative filtering in recommender networks, link prediction (e.g. predicting future links from the current snapshot of a graph) in social networks, fraud detection, personalized graph search techniques and graph visualization over time requires a deep understanding of the underlying graph structure. These networks can consist of millions of entities, and hence scalability is a critical concern for designing algorithms in these settings. My research focus is on understanding and analyzing the dynamics of entity relationships in large networks. This consists of two parts. The first involves answering questions about a given graph: which graph-theoretic measure is best suited for a given learning task? How can we compute these measures efficiently on massive networks? What are the properties of the graph that we can exploit for designing efficient algorithms? Since a lot of existing algorithms use random walks in one form or the other, I have worked on accelerating random walk based approaches, in particular for computing proximity measures in graphs. Currently I am working towards developing new algorithms with formally proven properties for ranking problems on graphs of up to a billion nodes on a single CPU machine. The second question is how to model the friendships drifting over time? I have also worked on tractable generative models which capture the evolution of large social networks.
On sets of integers containing k elements in arithmetic progression

In 1926 van der Waerden [13] proved the following startling theorem : If the set of integers is arbitrarily partitioned into two classes then at least one class contains arbitrarily long arithmetic progressions. It is well known and obvious that neither class must contain an infinite arithmetic progression. In fact, it is easy to see that for any sequence an there is another sequence bn9 with bn > an9 which contains no arithmetic progression of three terms, but which intersects every infinite arithmetic progression. The finite form of van der Waerden's theorem goes as follows: For each positive integer n9 there exists a least integer f{n) with the property that if the integers from 1 to /(/? ) are arbitrarily partitioned into two classes, then at least one class contains an arithmetic progression of « terms. (For a short proof, see the note of Graham and Rothschild [5].) However, the best upper bound on f{n) known at present is extremely poor. The best lower bound known, due to Berlekamp [3], asserts that/(«) < nl9 for n prime, which improves previous results of Erdös, Rado and W. Schmidt. More than 40 years ago, Erdös and Turân [4] considered the quantity rk{n)9 defined to be the greatest integer / for which there is a sequence of integers 0 < a\ < a2 < ••• < a; ^ n which does not contain an arithmetic progression of k terms. They were led to the investigation of rk{n) by several things. First of all the problem of estimating rk{n) is clearly interesting in itself. Secondly, rk{n) < n/2 would imply f{k) < 77, i.e., they hoped to improve the poor upper bound on f{k) by investigating rk{n). Finally, an old question in number theory asks if there are arbitrarily long arithmetic progressions of prime numbers. From rk{n) < %{rì) this would follow immediately. The hope was that this problem on primes could be attacked not by
Monitoring unstable parts in the ice-covered Weissmies northwest face

1 Laboratory of Hydraulics, Hydrology and Glaciology, ETH Zürich, Switzerland, fwalter@vaw.baug. ethz.ch 2 Laboratory of Hydraulics, Hydrology and Glaciology, ETH Zürich, Switzerland 3 Department of Geosciences and Earth and Environmental Systems Institute, Pennsylvania State University, USA 4 Department of Earth Sciences, University of Firenze, Italy 5 Computer Engineering and Networks Laboratory, ETH Zürich, Switzerland 6 Institute of Geodesy and Photogrammetry, ETH Zürich, Switzerland 7 GEOPRAEVENT AG, Zürich, Switzerland
Genotoxicity and antigenotoxicity study of aqueous and hydro-methanol extracts of Spondias mombin L., Nymphaea lotus L. and Luffa cylindrical L. using animal bioassays

Abstract Spondias mombin (Linn), Nymphaea lotus (Linn) and Luffa cylindrica (Linn) (syn Luffa aegyptiaca Mill) are plants traditionally used as food ingredients and in the management of diseases, including cancer, in Nigeria. Despite the therapeutic potentials attributed to these plants, reports on their genotoxicity are scanty. In this study, the genotoxicity of the aqueous and hydro-methanol extract of these plants was evaluated using mouse bone marrow micronucleus and sperm morphology assays. Antigenotoxicity was assessed by the bone marrow micronucleus test. The highest attainable dose of 5 000 mg/kg according to OECD guidelines was first used to assess acute toxicity of the aqueous and hydro-methanol extracts in Swiss albino mice. For each extract, there were five groups of mice (n=4/group) treated with different concentrations of the extract as against the negative and positive control group for the genotoxicity study. In the antigenotoxicity study, five groups of mice were exposed to five different concentrations of the extracts along with 60 mg/kg of methyl methane sulfonate (MMS), which was used to induce genotoxicity. The mice were administered 0.2 mL of extract per day for 10 days in the genotoxicity and antigenotoxicity groups. Administration of each of the extracts at the concentration of 5 000 mg/kg did not induce acute toxicity in mice. At the concentrations tested, all the extracts, except aqueous S. mombin, increased micronucleated polychromatic erythrocytes. The aqueous and hydro-methanol extracts of N. lotus increased the frequency of aberrant sperm cells. All the extracts were also able to ameliorate MMS induced genotoxicity in bone marrow cells of the exposed mice. The results showed the potential of the extracts to induce somatic and germ cell mutation in male mice. The extracts also ameliorated the genotoxic effect of MMS.
Software Defect Prediction Tool based on Neural Network

There has been a tremendous growth in the demand for software fault prediction during recent years. In this paper, Levenberg-Marquardt (LM) algorithm based neural network tool is used for the prediction of software defects at an early stage of the software development life cycle. It helps to minimize the cost of testing which minimizes the cost of the project. The methods, metrics and datasets are used to find the fault proneness of the software. The study used data collected from the PROMISE repository of empirical software engineering data. This dataset uses the CK (Chidamber and Kemerer) OO (object-oriented) metrics. The accuracy of Levenberg-Marquardt (LM) algorithm based neural network are comparing with the polynomial function-based neural network predictors for detection of software defects. Our results indicate that the prediction model has a high accuracy.
A branch-and-cut SDP-based algorithm for minimum sum-of-squares clustering

Minimum sum-of-squares clustering (MSSC) consists in partitioning a given set of n points into k clusters in order to minimize the sum of squared distances from the points to the centroid of their cluster. Recently, Peng & Xia (2005) established the equivalence between 0-1 semidefinite programming (SDP) and MSSC. In this paper, we propose a branch-and-cut algorithm for the underlying 0-1 SDP model. The algorithm obtains exact solutions for fairly large data sets with computing times comparable with those of the best exact method found in the literature.
Combining Funnels: A Dynamic Approach to Software Combining

We enhance the well-established software combining synchronization technique to create combining funnels. Previous software combining methods used a statically assigned tree whose depth was logarithmic in the total number of processors in the system. On shared memory multiprocessors the new method allows one to dynamically build combining trees with depth logarithmic in the actual number of processors concurrently accessing the data structure. The structure is comprised from a series of combining layers through which processors' requests are funneled. These layers use randomization instead of a rigid tree structure to allow processors to find partners for combining. By using an adaptive scheme the funnel can change width and depth to accommodate different access frequencies without requiring global agreement as to its size. Rather, processors choose parameters of the protocol privately, making this scheme very simple to implement and tune. When we add an “elimination” mechanism to the funnel structure, the randomly constructed “tree” is transformed into a “forest” of disjoint (and on average shallower) trees of requests, thus enhancing the level of parallelism and decreasing latency. We present two new linearizable combining funnel based data structures: a fetch-and-add object and a stack. We study the performance of these structures by benchmarking them against the most efficient software implementations of fetch-and-add and stacks known to date, combining trees and elimination trees, on a simulated shared memory multiprocessor using Proteus. Our empirical data shows that combining funnel-based fetch-and-add outperforms combining trees of fixed height by as much as 70%. In fact, even compared to combining trees optimized for a given load, funnel performance is the same or better. Elimination trees, which are not linearizable, are 10% faster than funnels under highest load, but as load drops, combining funnels adapt their size, giving them a 34% lead in latency.
A Saddle Point Approach to Structured Low-rank Matrix Learning in Large-scale Applications

We propose a novel optimization approach for learning a low-rank matrix which is also constrained to be in a given linear subspace. Low-rank constraints are regularly employed in applications such as recommender systems and multi-task learning. In addition, several system identification problems require a learning matrix with both low-rank and linear subspace constraints. We model the classical nuclear norm regularized formulation as an equivalent saddle point minimax problem. This decouples the low-rank and the linear subspace constraints onto separate factors. Motivated by large-scale problems, we reformulate the minimax problem via a rank constrained non-convex surrogate. This translates into an optimization problem on the Riemannian spectrahedron manifold. We exploit the Riemannian structure to propose efficient first- and second-order algorithms. The duality theory allows to compute the duality gap for a candidate solution and our approach easily accommodates popular non-smooth loss functions, e.g., the absolute-value loss. We effortlessly scale on the Netflix data set on both matrix completion and robust matrix completion problems, obtaining state-of-the-art generalization performance. Additionally, we demonstrate the efficacy of our approach in Hankel matrix learning and multi-task learning problems.
Predicting the time to localized muscle fatigue using ANN and evolved sEMG feature

Surface Electromyography (sEMG) activity of the biceps muscle was recorded from nine subjects. Data were recorded while subjects performed dynamic contraction until fatigue. The signals were initially segmented into two parts (Non-Fatigue and Transition-to-Fatigue) to enable the evolutionary process. A novel feature was evolved by selecting then using a combination of the eleven sEMG muscle fatigue features and six mathematical operators. The evolutionary program used the DB index in its fitness function to derive the best feature that best separate the two segments (Non-Fatigue and Transition-to-Fatigue), for both Maximum Dynamic Strength (MDS) percentage of 40 and 70 MDS. Using the evolved feature we enabled an ANN to predict the time to fatigue by using only twenty percent of the total sEMG signal with an average prediction error of 9.22%.
Spectral Conformal Parameterization

We present a spectral approach to automatically and efficiently obtain discrete free‐boundary conformal parameterizations of triangle mesh patches, without the common artifacts due to positional constraints on vertices and without undue bias introduced by sampling irregularity. High‐quality parameterizations are computed through a constrained minimization of a discrete weighted conformal energy by finding the largest eigenvalue/eigenvector of a generalized eigenvalue problem involving sparse, symmetric matrices. We demonstrate that this novel and robust approach improves on previous linear techniques both quantitatively and qualitatively.
Identifying Key Challenges in Performance Issues in Cloud Computing

Cloud computing is a harbinger to a newer era in the field of computing where distributed and centralized services are used in a unique way. In cloud computing, the computational resources of different vendors and IT services providers are managed for providing an enormous and a scalable computing services platform that offers efficient data processing coupled with better QoS at a lower cost. The on-demand dynamic and scalable resource allocation is the main motif behind the development and deployment of cloud computing. The potential growth in this area and the presence of some dominant organizations with abundant resources (like Google, Amazon, Salesforce, Rackspace, Azure, GoGrid), make the field of cloud computing more fascinating. All the cloud computing processes need to be in unanimity to dole out better QoS i.e., to provide better software functionality, meet the tenant's requirements for their desired processing power and to exploit elevated bandwidth. However, several technical and functional e.g., pervasive access to resources, dynamic discovery, on the fly access and composition of resources pose serious challenges for cloud computing. In this study, the performance issues in cloud computing are discussed. A number of schemes pertaining to QoS issues are critically analyzed to point out their strengths and weaknesses. Some of the performance parameters at the three basic layers of the cloud — Infrastructure as a Service, Platform as a Service and Software as a Service — are also discussed in this paper.
Role of kit-ligand in proliferation and suppression of apoptosis in mast cells: basis for radiosensitivity of white spotting and steel mutant mice

The receptor tyrosine kinase Kit and its cognate ligand KL/steel factor are encoded at the white spotting (W) and Steel (Sl) loci of the mouse, respectively. Mutations at both the W and the Sl loci affect hematopoiesis including the stem cell hierarchy, erythropoiesis, and mast cells, as well as gametogenesis and melanogenesis. In addition, mutant mice display an increased sensitivity to lethal doses of irradiation. The role of KL/c-kit in cell proliferation and survival under conditions of growth factor-deprivation and gamma-irradiation was studied by using bone marrow-derived mast cells (BMMC) as a model. Whereas apoptosis induced by growth factor deprivation in BMMC is a stochastic process and follows zero order kinetics, gamma-irradiation- induced apoptosis is an inductive process and follows higher order kinetics. In agreement with these results, gamma-irradiation-induced apoptosis in BMMC was shown to be dependent on p53 whereas apoptosis induced by deprivation is partly dependent on p53, implying that there are other mechanisms mediating apoptosis in KL-deprived BMMC. In the presence and in the absence of serum, KL stimulated proliferation by promoting cell cycle progression. The presence of KL was required only during the early part of the G1 phase for entry into the S phase. At concentrations lower than those required for proliferation, KL suppressed apoptosis induced by both growth factor-deprivation and gamma-irradiation, and internucleosomal DNA fragmentation characteristic of apoptosis. The ability of KL to suppress apoptosis was independent of the phase of the cell cycle in which the cells were irradiated and suppression of apoptosis was a prerequisite for subsequent cell cycle progression. Moreover, addition of KL to gamma- irradiated and growth factor-deprived cells could be delayed for up to 1 h after irradiation or removal of growth factors when cells became irreversibly committed to apoptosis. KL and IL-3 induce suppression of apoptosis in mast cells by different mechanisms based on the observations of induction of bcl-2 gene expression by IL-3 but not by KL. It is proposed that the increased sensitivity of W and Sl mutant mice to lethal irradiation results from paucity of the apoptosis suppressing and proliferative effects of KL.
Depression and suicidality among bisexual youth: A nationally representative sample

ABSTRACT To address gaps in the literature on bisexual youth, we used the first nationally representative sample of high school students from the United States to determine profiles of behaviors related to depressive symptoms, a suicide attempt, and a medically-serious attempt. We examined the data from 922 bisexual students in grades 9–12 who completed the Centers for Disease Control and Prevention's 2015 National Youth Risk Behavior Survey. Latent class analysis derived three classes reflecting unique patterns of 10 risk and protective behaviors. For both the sexes: one group was high on all the risk behaviors and demonstrated the greatest risk of depression, a suicide attempt, and a medically-serious attempt; another was below average on all the risk and protective behaviors and demonstrated average/moderate risk of the outcomes; and the last demonstrated the highest levels of protective behaviors and the lowest risk of depression and suicidal behavior. Bisexual students are not homogenous with regard to the risk of mental health problems. Greater involvement in several risk behaviors and less involvement in protective behaviors, rather than merely a bisexual identity, might contribute to bisexual adolescents' increased risk of depression and suicidal behavior. Findings have implications for prevention programming, clinical interventions, and future research.
Integrating goals after prioritization and evaluation-A Goal-oriented requirements engineering method

Decision support system in Requirements engineering plays an important role in software development life cycle. The relationship between functional and non-functional requirements often plays a crucial role in resolving conflicts or arriving at decisions in requirements engineering phase. Goal-Oriented Requirements Engineering (GORE) methods make a good attempt of addressing these aspects which are helpful in decision support. We propose a GORE method - Integrating goals after prioritization and evaluation (IGAPE). The method is semi-formal in nature thereby ensuring active stakeholder participation. In this paper we elaborate the various steps of IGAPE method. The output of IGAPE is then given as input to a decision support system which makes use of Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Integration of IGAPE with AHP and TOPSIS will clearly provide a rationale for various decisions which are arrived at during the requirements engineering phase. The method is illustrated for an e-commerce application and is validated by expert analysis approach .