Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending Tree Kernels Towards Paragraphs

We extend parse tree kernels from the level of individual sentences towards the level of paragraph to build a framework for learning short texts such as search results and social profile postings. We build a set of extended trees for a paragraph of text from the individual parse trees for sentences. It is performed based on coreferences and Rhetoric Structure relations between the phrases in different sentences. Tree kernel learning is applied to extended trees to take advantage of additional discourse-re-lated information. We evaluate our approach, tracking relevance improvement for multi-sentence search, comparing performances of individual sentence kernels with the ones for extended parse trees. The search problem is formulated as classification of search results into the classes of relevant and irrelevant, learning from the Bing search results, used as a baseline and as a training dataset.
The Unintegrated gluon density in the photon and heavy quark production

The production cross section of heavy quarks in real and virtual photon-photon collisions has been studied. The unintegrated gluon density in the photon was obtained using the full CCFM evolution equation for the first time. The gluon density was implemented in the Monte Carlo generator CASCADE, and cross sections for heavy quark production in $e^+e^-$ collisions were calculated and compared to LEP data. Also, predictions for heavy quark cross sections in $e^+e^-$ and $\gamma\gamma$ collisions at TESLA energies are given.
A Fully Coreference-annotated Corpus of Scholarly Papers from the ACL Anthology

We describe a large coreference annotation task performed on a corpus of 266 papers from the ACL Anthology, a publicly, electronically available collection of scientific papers in the domain of computational linguistics and language technology. The annotation comprises mainly noun phrase coreference of the full textual content of each paper in the Anthology subset. It has been performed carefully and at least twice for each paper (initial annotation and secondary correction phase). The purpose of this paper is to summarize the comprehensive annotation schema and release the corpus publicly, along with this paper. The corpus is by far larger than the ACE coreference corpora. It can be used to train coreference resolution systems in the Computational Linguistics and Language Technology domain for semantic search, taxonomy extraction, question answering, citation analysis, scientific discourse analysis, etc.
The GARF Library of DSM Consistency Models Extended

GARF is an object-oriented system intended to support high level and modular programming of reliable distributed applications. GARF provides a distributed shared memory abstraction, but does not enforce any particular consistency criterion. GARF rather ooers an extensible library of consistency criteria. The programmer can bind a speciic criterion to each object, according to its use and semantics. This exible approach enables to increase availability of objects of which behaviors do not require strong consistency guarantees.
Extended concepts for association rule discovery

The aim of data mining is the discovery of patterns within data stored in databases. Mining for association rules is a data mining method that lends itself to formulating conditional statements such as 'if customers buy product A then they also buy product B and C with a probability of 90 percent. ' We consider different extended concepts of basic association rules. One of these concepts, quantitative association rules, is discussed in detail. Quantitative association rules allow statements like '20 percent of customers who buy at least three units of product A also buy between five and ten units of B and two units of C. ' We show that the quantitative nature of data can be hidden from the algorithm that mines for association rules. Thus, any standard algorithm that solves the basic problem can be used to cope with quantitative association rules. The presentation of our approach includes the design of the database, algorithms and data structures, as well as experiments with a prototype implementation.
Management of bone metastases in refractory prostate cancer – role of denosumab

This article reviews the problem of bone disease in prostate cancer and the evolving role of the novel agent denosumab, a fully human monoclonal antibody that inhibits the receptor activator of nuclear factor-κB ligand, in suppressing bone resorption and offering bone protection in this disease. Prostate cancer frequently metastasizes to bone, and additionally its treatment with androgen deprivation leads to accelerated bone loss resulting in clinically relevant skeletal complications associated with disabling symptoms. Among the bone-targeting therapeutic strategies investigated for the prevention of bone complications, the potent bisphosphonate zoledronic acid has been the most widely used agent for bone protection in the past decade. Denosumab is the first among a new class of osteoclast-targeting agents to show superior efficacy in several clinical scenarios in both prostate and breast cancer, as well as in osteoporosis, but the focus of this review will be on its role in prostate cancer. The safety and efficacy of denosumab versus zoledronic acid was established in a randomized trial, demonstrating a delay in skeletal-related events in metastatic castration-resistant prostate cancer patients. This study led to the approval of denosumab in the US. The chief risks of denosumab were hypocalcemia and osteonecrosis of the jaw. Denosumab was also approved for fracture risk reduction in patients on androgen-deprivation therapy for nonmetastatic prostate cancer. Although denosumab extended bone metastasis-free survival in a Phase III trial in men with castration-resistant nonmetastatic prostate cancer to a statistically significant degree, a Food and Drug Administration committee found that the effect was not sufficiently clinically meaningful for regulatory approval, and the Food and Drug Administration issued a letter concurring with the committee’s recommendation. The role of denosumab in prostate cancer will continue to evolve either as monotherapy or in combination with other bone-targeting strategies.
Wnt/TLR Dialog in Neuroinflammation, Relevance in Alzheimer’s Disease

The innate immune system (IIS) represents the first line of defense against exogenous and endogenous harmful stimuli. Different types of pathogens and diverse molecules can activate the IIS via a ligand–receptor mechanism. Cytokine release, recruitment of immunocompetent cells, and inflammation constitute the initial steps in an IIS-mediated response. While balanced IIS activity can resolve a harmful event, an altered response, such as deficient or persistent IIS activity, will have a critical effect on organism homeostasis. In this regard, chronic IIS activation has been associated with a wide range of diseases, including chronic inflammatory disorders (inflammatory bowel disease, arthritis, chronic obstructive pulmonary disease, among others), cancer and, more recently, neurodegenerative disorders. The relevance of the immune response, particularly inflammation, in the context of neurodegeneration has motivated rigorous research focused on unveiling the mechanisms underlying this response. Knowledge regarding the molecular hallmarks of the innate immune response and understanding signaling pathway cross talk are critical for developing new therapeutic strategies aimed at modulating the neuroinflammatory response within the brain. In the present review, we discuss the IIS in the central nervous system, particularly the cross talk between the toll-like receptor-signaling cascade and the wingless-related MMTV integration site (Wnt) signaling pathway and its relevance in neurodegenerative disorders such as Alzheimer’s disease.
Design space exploration in system level synthesis under memory constraints

This paper addresses the problem of component selection, task assignment and task scheduling for distributed embedded computer systems. Such systems have a large number of constraints of different nature, such as cost, execution time, memory capacity and limitations on resource usage. Previous approaches have concentrated on a specific class of requirements and thus they limit number of constraints which can be handled in the design process. This results very often in non-feasible or too expensive solutions. The system presented in this paper CLASS (Constraint Logic bAsed System Synthesis) makes it possible to impose different design constraints and thus model the design more realistically. It is also efficient in finding good solutions or in some cases, optimal solutions for even nontrivial problems.
Smartcam Design Framework.

The last few years have seen the advent of Smart Cameras, surveillance-camera sized devices with onboard programmable logic. Usually, Digital Signal Processors (DSPs) or general-purpose (GP) microprocessors are used, but obviously the specific field of image processing allows for many architectural optimizations, such as the use of single-instruction multiple-data (SIMD) processors for filtering. Furthermore, intuitive mappings of algorithms on architecture components are used, after the architecture has been determined. It will be clear that this is far from optimal. Finally we foresee a further integration, making a combination of on-chip vision sensor, pixel processing, control, and feature / object processing possible. The result is a low-cost one-chip smart camera (so-called SmartCam) solution. This means that research is needed to explore the new architectural opportunities and consequences. The SmartCam project investigates these new opportunities and contributes to a better and more quantitatively guided design trajectory. This paper describes the design flow as seen by the application developer: the use of algorithmic skeletons to exploit data-parallelism, and design space exploration to find a suitable boardand processor architecture. We explain how it can lead to smaller, cheaper, and more efficient devices, tailored to a specific application or set of applications.
Simulated Restriction of Coverage Criteria on UML State Machines

Model-based testing is a promising quality assurance technique. Automatic test generation from behavioral models is state of the art. Coverage criteria at model level are often used to measure test quality and to steer automatic test generation. While tests are focused on behavior, however, most coverage criteria are focused on the structure of test models. Thus, semantic-preserving model transformations can be used to alter the effect of the applied coverage criteria. In previous work, we presented the notion of simulated satisfaction to enhance the effect of applicable coverage criteria. In this paper, we present model transformations to restrict the effect of coverage criteria. The aim is to show the relative strength of coverage criteria and their dependence on the model structure. The long-term objective is to define lower boundaries for coverage criteria.
PID control using wireless measurements

This paper presents a wireless PID controller, known as PIDPlus. The same quality control as wired PID can be provided by PIDPlus using a wireless measurement, despite slower wireless measurement update and communication interruptions. The PIDPlus novel algorithm is based on a modification of the PID reset and rate calculation to account for non-periodic measurement updates. The paper presents and evaluates an alternate approach of PID control accounting for non-periodic measurements with a Kalman filter observer that has been modified for use with a wireless measurement. A second alternative shows how the Smith Predictor may be modified to work with a wireless measurement. Test results are presented that compare the PIDPlus performance to that achieved using these alternate approaches for a variety of operating conditions that may be encountered when using wireless transmitters.
Increasing the Efficiency of Genome-wide Association Mapping via Hidden Markov Models

With the rapid production of high dimensional genetic data, one major challenge in genome-wide association studies is to develop effective and efficient statistical tools to resolve the low power problem of detecting causal SNPs with low to moderate susceptibility, whose effects are often obscured by substantial background noises. Here we present a novel method that serves as an optimal technique for reducing background noises and improving detection power in genome-wide association studies. The approach uses hidden Markov model and its derivate Markov hidden Markov model to estimate the posterior probabilities of a markers being in an associated state. We conducted extensive simulations based on the human whole genome genotype data from the GlaxoSmithKline-POPRES project to calibrate the sensitivity and specificity of our method and compared with many popular approaches for detecting positive signals including the χ2 test for association and the Cochran-Armitage trend test. Our simulation results suggested that at very low false positive rates (< 10−6), our method reaches the power of 0.9, and is more powerful than any other approaches, when the allelic effect of the causal variant is non-additive or unknown. Application of our method to the data set generated by Welcome Trust Case Control Consortium using 14,000 cases and 3,000 controls confirmed its powerfulness and efficiency under the context of the large-scale genome-wide association studies.
Subconductance States of a Mutant NMDA Receptor Channel Kinetics, Calcium, and Voltage Dependence

The kinetic properties of main and subconductance states of a mutant mouse N-methyl-d-aspartate (NMDA) receptor channel were examined. Recombinant receptors made of ζ-ε2 (NR1-NR2B) subunits having asparagine-to-glutamine mutations in the M2 segment (ζN598Q /ε2N589Q) were expressed in Xenopus oocytes. Single channel currents recorded from outside-out patches were analyzed using hidden Markov model techniques. In Ca2+-free solutions, an open receptor channel occupies a main conductance (93 pS) and a subconductance (62 pS) with about equal probability. There are both brief and long-lived subconductance states, but only a single main level state. At −80 mV, the lifetime of the main and the longer-lived sub level are both ∼3.3 ms. The gating of the pore and the transition between conductance levels are essentially independent processes. Surprisingly, hyperpolarization speeds both the sub-to-main and main-to-sub transition rate constants (∼120 mV/e-fold change), but does not alter the equilibrium occupancies. Extracellular Ca2+ does not influence the transition rate constants. We conclude that the subconductance levels arise from fluctuations in the energetics of ion permeation through a single pore, and that the voltage dependence of these fluctuations reflects the modulation by the membrane potential of the barrier between the main and subconductance conformations of the pore.
An ontology-based approach to Acquisition and Reconstruction

This paper aims at presenting the development phase of a novel ontology for modeling the knowledge underlying the pipeline of acquisition and reconstruction processes for shapes. We explain the urge to develop an ontology in this domain in a typical e-science scenario taking into account the approach and the technologies suggested by the current Semantic Web initiatives. We present the domain of our ontology, its possible applications and a list of informal desiderata, which eventually lead to the constitution of precise competency questions. Furthermore, a list of fundamental concepts for the acquisition and reconstruction pipeline are carried out. Finally, we present sketches of the ontology and the future work that should be done according to the users’ needs.
The Benefits of Social Influence in Optimized Cultural Markets

Social influence has been shown to create significant unpredictability in cultural markets, providing one potential explanation why experts routinely fail at predicting commercial success of cultural products. As a result, social influence is often presented in a negative light. Here, we show the benefits of social influence for cultural markets. We present a policy that uses product quality, appeal, position bias and social influence to maximize expected profits in the market. Our computational experiments show that our profit-maximizing policy leverages social influence to produce significant performance benefits for the market, while our theoretical analysis proves that our policy outperforms in expectation any policy not displaying social signals. Our results contrast with earlier work which focused on showing the unpredictability and inequalities created by social influence. Not only do we show for the first time that, under our policy, dynamically showing consumers positive social signals increases the expected profit of the seller in cultural markets. We also show that, in reasonable settings, our profit-maximizing policy does not introduce significant unpredictability and identifies “blockbusters”. Overall, these results shed new light on the nature of social influence and how it can be leveraged for the benefits of the market.
Modeling and algorithm of logistics channel selection based on the network utility of nodes and edges

As the logistics and supply chain network develops, it becomes increasingly important to make better decision of the logistics channel. In this paper we expand the concept of centralities of node degree and edge betweenness. Afterwards we establish the logistics channel selection model taking not only the travel time and infrastructure costs but the network utility of the channel into consideration. Subsequently, a combination algorithm of Dijkstra algorithm, Floyd algorithm and ith shortest path algorithm is developed to solve the problem of our model. At last, a numerical experiment is conducted to verify the validity of the model and the algorithm. The research offers a new approach to the strategic logistics decision-making.
Impact of News Articles on Stock Prices: An Analysis using Machine Learning

Machine learning paradigms are increasingly being used along with text categorization for knowledge discovery in unstructured data. In this paper we use machine learning techniques to textually analyze online news articles. We try to establish whether, in addition to technical approaches, the information in news articles can influence stock prices. Indian elections, an exceptionally dynamic period for the Indian economy is chosen to form two corpuses: pre and post-election. These corpuses comprise online news articles collected over a period of 4 months from some leading newspapers of India. This facilitates comparison and helps us to train a model to demonstrate that the strong form of the Efficient Market Hypothesis is prevalent.
The Pathways Commission Report: Implications for Accounting Higher Education in Sub Sahara Africa

This article highlights the relevance of the Pathways Commission report (PCR) to accounting higher education in one of the developing regions of the world. Using qualitative research method and triangulation it examines the issues surrounding the provision of accounting higher education in the Sub Sahara Africa (SSA) region. The commentary is conducted against the backdrops of the growth in both the demand and supply of higher education, the globalization of accounting curricula and advances in information technology. The findings are three. First, despite the heterogeneity and the gap between rich and poor nations, many of the recommendations of the PCR are relevant to SSA countries. Second, consistent with theory, policies that lead to high quality accounting education and research have aspects of capability, duality, and endogeneity in moving a nation from one development stage to the next. Third, the commentary conjectures that the globalization of the content of professional education and the emergence of transnational-virtual universities are more than likely to continue to change the landscape of accounting higher education and the labor process in the region.
Multinational Firms ’ Dynamic Decisions on Offshoring and Clean Technology Adoptions ∗

This paper develops a dynamic general equilibrium model to analyze firms’ joint decisions on production offshoring and clean technology adoptions when facing different labor productivities and environmental regulations between home country and offshoring host country. During their decision processes, both workers’ wages and emission levels of related countries are endogenously determined. Later this model is calibrated to match the data on manufacturing workers’ wages in the U.S. and China’s foreign invested enterprises and both countries’ PM2.5 emissions over 1999-2013. This paper quantifies the offshoring levels which is measured by the share of total low-skilled labor-intensive manufacturing production processes shifted from U.S. to China. My paper also gives the long-run predictions on future offshoring level and environmental qualities in both countries. Besides, different counterfactual policy experiments are conducted and pollution haven effects on offshoring is estimated in this paper. (JEL F18, F23, D21, J31, Q52, Q56) ∗Please check the latest version at my web page http://www.ssc. wisc.edu/ xmeng/research †Department of Economics, University of Wisconsin-Madison. Email:xmeng5@wisc.edu. I am grateful to my advisers Bob Staiger, Enghin Atalay, Alan Sorensen and Charles Engel for their guidance and support at all stages of this project. I express my thanks to Jim Markusen, Keller Wolfgang, Kerry Smith, Laura Taylor, Ivan Kandilov and seminar participants at Wisconsin, 8th AERUS at Purdue and Camp Resources XXII at NC State.
3D model selection from an internet database for robotic vision

We propose a new method for automatically accessing an internet database of 3D models that are searchable only by their user-annotated labels, for using them for vision and robotic manipulation purposes. Instead of having only a local database containing already seen objects, we want to use shared databases available over the internet. This approach while having the potential to dramatically increase the visual recognition capability of robots, also poses certain problems, like wrong annotation due to the open nature of the database, or overwhelming amounts of data (many 3D models) or the lack of relevant data (no models matching a specified label). To solve those problems we propose the following: First, we present an outlier/inlier classification method for reducing the number of results and discarding invalid 3D models that do not match our query. Second, we utilize an approach from computer graphics, the so called ‘morphing’, to this application to specialize the models, in order to describe more objects. Third, we search for 3D models using a restricted search space, as obtained from our knowledge of the environment. We show our classification and matching results and finally show how we can recover the correct scaling with the stereo setup of our robot.
Physics : Structure and Dynamics in Complex Networks Opinion Dynamics on Networks with Community Structure

The study of networks has concentrated on their complex (i.e. topologically non-trivial) structure on the one hand and dynamics on networks on the other hand. Only a little is known about the actual interplay of complex network structure and dynamics. We will investigate this for the case of opinion dynamics on social networks with community structure. Therefore, we work with two opinion models, the well-known voter model and the new “travelling competitors model” and examine the opinion spreading in the communities. Results from computer simulations (for both models) and analytical calculations (for the voter model) are presented and interpreted in the context of social integration.