Miracle-dz/UniFAR-FTUs huggingface dataset:
SemRPer - A Rule based Personalization System for Semantic Web

Personalization is the adaptation of information according to user needs. Personalization when applied to Semantic Web offers many advantages when compared to traditional Web as Semantic Web is the next generation Web. Ontologies play a crucial role in bringing the Semantic Web to its full potential. Ontological relationship can be utilized in inferring additional information in Semantic Web. In this paper, a SemRPer rule based personalization system for Semantic Web is proposed. SemRPer system generates personalized recommendations using a generic rule reasoner. Generic rule reasoner is built on the rule set which consist of a set of rules written to carry out the personalization task. Performance of SemRPer system is demonstrated by using two applications as examples.
An intelligent system for renal segmentation

Scintigraphic images are often characterized with much noise and a badly contrasted resolution which makes the perception of regions of interest very difficult. The renal quantification is how to define the regions of interests whose activities informs on the status of the renal function. In this context, the current study presents an intelligent system for the segmentation of renal regions in order to facilitate the process of quantification. The use of a multi-agent system based on the HOG3D descriptor combined with Fast Marching Method, has made our System of segmentation faster and more accurate. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. Experiments and tests were developed on a database including 1800 images from 15 patients selected to obtain a variety of images. The results of the application of our method on several dynamic images are presented and discussed.
Integer low-density lattices based on construction A

We describe a new family of integer lattices built from construction A and non-binary LDPC codes. An iterative message-passing algorithm suitable for decoding in high dimensions is proposed. This family of lattices, referred to as LDA lattices, follows the recent transition of Euclidean codes from their classical theory to their modern approach as announced by the pioneering work of Loeliger (1997), Erez, Litsyn, and Zamir (2004-2005). Besides their excellent performance near the capacity limit, LDA lattice construction is conceptually simpler than previously proposed lattices based on multiple nested binary codes and LDA decoding is less complex than real-valued message passing.
The role of tumor lymphangiogenesis in metastatic spread

The high mortality rates associated with cancer can be attributed to the metastatic spread of tumor cells from the site of their origin. Tumor cells invade either the blood or lymphatic vessels to access the general circulation and then establish themselves in other tissues. Clinicopathological data suggest that the lymphatics are an initial route for the spread of solid tumors. Detection of sentinel lymph nodes by biopsy provides significant information for staging and design‐ing therapeutic regimens. The role of angiogenesis in facilitating the growth of solid tumors has been well established, but the presence of lymphatic vessels and the relevance of lymphangiogenesis to tumor spread are less clear. Recently, the molecular pathway that signals for lymphangiogenesis and relatively specific markers for lymphatic endothelium have been described allowing analyses of tumor lymphangiogenesis to be performed in animal models. These studies demonstrate that tumor lymphangiogenesis is a major component of the metastatic process and implicate members of the VEGF family of growth factors as key mediators of lymphangiogenesis in both normal biology and tumors. —Stacker, S. A. , Baldwin, M. E. , Achen, M. G. The role of tumor lymphangiogenesis in metastatic spread. FASEB J. 16, 922–934 (2002)
The New Turkish Foreign Policy and the Future of Turkey-EU Relations

The article refutes the often-heard argument that Ankara’s recent ‘activism’ in the Middle East indicates that Turkey is ‘drifting away from the West’. Turkey’s improving relations with its neighbors (not only in the Middle East), are mainly a result of the end of the Cold War and of domestic developments which have ‘unlocked’ Turkey, transforming it into a more open and democratic country with an even greater stake in EU membership. At the same time, the many international and domestic changes that have occurred since Turkey was granted candidate status call for a ‘re-foundation’ of the Turkey-EU relationship. Lacking the latter, the future of TurkeyEU relations will indeed remain uncertain.
HDR light probe sequence resampling for realtime incident light field rendering

This paper presents a method for resampling a sequence of high dynamic range light probe images into a representation of Incident Light Field (ILF) illumination which enables realtime rendering. The light probe sequences are captured at varying positions in a real world environment using a high dynamic range video camera pointed at a mirror sphere. The sequences are then resampled to a set of radiance maps in a regular three dimensional grid before projection onto spherical harmonics. The capture locations and amount of samples in the original data make it inconvenient for direct use in rendering and resampling is necessary to produce an efficient data structure. Each light probe represents a large set of incident radiance samples from different directions around the capture location. Under the assumption that the spatial volume in which the capture was performed has no internal occlusion, the radiance samples are projected through the volume along their corresponding direction in order to build a new set of radiance maps at selected locations, in this case a three dimensional grid. The resampled data is projected onto a spherical harmonic basis to allow for realtime lighting of synthetic objects inside the incident light field.
Influences of Urban Expansion on Urban Heat Island in Beijing during 1989-2010

Beijing has experienced rapid urbanization and associated urban heat island (UHI) effects. This study aimed at analyzing the impact of urban form on UHI in Beijing using TM/ETM images between 1989 and 2010. Spatial analysis was proposed to explore the relationships between area, compactness ratio, the gravity centers of urban land, and UHI. The UHI in Beijing spatially represented a “NE-SW” spindle. The land surface temperature (LST) was higher in south than in north. Urban Heat Island Ratio Index (URI) was well interrelated with urban land area in different zones. Under the similar urban land area condition, UHI and compactness ratio of urban land were in positive correlation. The moving direction of the UHI gravity center was basically in agreement with urban land sprawl. The encroachment of urban land on suburban land is the leading source of UHI effect. The results suggest that urban design based on urban form would be effective for regulating the thermal environment.
Entangled histories: Climate science and nuclear weapons research

Climate science and nuclear weapons testing have a long and surprisingly intimate relationship. The global networks that monitored the Fukushima radiation plume and forecasted its movement are the direct descendants of systems and computer models developed to trace fallout from weapons tests. Tracing radioactive carbon as it cycles through the atmosphere, the oceans, and the biosphere has been crucial to understanding anthropogenic climate change. The earliest global climate models relied on numerical methods very similar to those developed by nuclear weapons designers for solving the fluid dynamics equations needed to analyze shock waves produced in nuclear explosions. The climatic consequences of nuclear war also represent a major historical intersection between climate science and nuclear affairs. Without the work done by nuclear weapons designers and testers, scientists would know much less than they now do about the atmosphere. In particular, this research has contributed enormously to knowledge about both carbon dioxide, which raises Earth’s temperature, and aerosols, which lower it. Without climate models, scientists and political leaders would not have understood the full extent of nuclear weapons’ power to annihilate not only human beings, but other species as well. In the post-Cold War era, US national laboratories built to create the most fearsome arsenal in history are now using their powerful supercomputers, their expertise in modeling, and their skills in managing very large data sets to address the threat of catastrophic climate change.
Apricoxib, a Novel Inhibitor of COX-2, Markedly Improves Standard Therapy Response in Molecularly Defined Models of Pancreatic Cancer

Purpose: COX-2 is expressed highly in pancreatic cancer and implicated in tumor progression. COX-2 inhibition can reduce tumor growth and augment therapy. The precise function of COX-2 in tumors remains poorly understood, but it is implicated in tumor angiogenesis, evasion of apoptosis, and induction of epithelial-to-mesenchymal transition (EMT). Current therapeutic regimens for pancreatic cancer are minimally effective, highlighting the need for novel treatment strategies. Here, we report that apricoxib, a novel COX-2 inhibitor in phase II clinical trials, significantly enhances the efficacy of gemcitabine/erlotinib in preclinical models of pancreatic cancer. Experimental Design: Human pancreatic cell lines were evaluated in vitro and in vivo for response to apricoxib ± standard-of-care therapy (gemcitabine + erlotinib). Tumor tissue underwent posttreatment analysis for cell proliferation, viability, and EMT phenotype. Vascular parameters were also determined. Results: COX-2 inhibition reduced the IC50 of gemcitabine ± erlotinib in six pancreatic cancer cell lines tested in vitro. Furthermore, apricoxib increased the antitumor efficacy of standard combination therapy in several orthotopic xenograft models. In vivo apricoxib combination therapy was only effective at reducing tumor growth and metastasis in tumors with elevated COX-2 activity. In each model examined, treatment with apricoxib resulted in vascular normalization without a decrease in microvessel density and promotion of an epithelial phenotype by tumor cells regardless of basal COX-2 expression. Conclusions: Apricoxib robustly reverses EMT and augments standard therapy without reducing microvessel density and warrants further clinical evaluation in patients with pancreatic cancer. Clin Cancer Res; 18(18); 5031–42. ©2012 AACR.
An overview of multivariate gamma distributions as seen from a (multivariate) matrix exponential perspective

Numerous definitions of multivariate exponential and gamma distributions can be retrieved from the literature [4]. These distribtuions belong to the class of Multivariate Matrix-- Exponetial Distributions (MVME) whenever their joint Laplace transform is a rational function. The majority of these distributions further belongs to an important subclass of MVME distributions [5, 1] where the multivariate random vector can be interpreted as a number of simultaneously collected rewards during sojourns in a the states of a Markov chain with one absorbing state, the rest of the states being transient. We present the corresponding representations for all such distributions. In this way we obtain a unification of the variety of existing distributions as well as a deeper understanding of their probabilistic nature and a clarification of their similarities and differences. In particular one may easily generalize or combine any of the known distributions by modifying the generators adequately. Also, it is straightforward to simulate from this class. Thus, by identifying distributions as belonging to this subclass it becomes apparent how to simulate from most previously discussed distributions with rational Laplace transform. In a longer perspective stochastic and statistical analysis for MVME will in particular apply to any of the previously defined distributions. Multivariate gamma distributions have been used in a variety of fields like hydrology, [11], [10], [6], space (wind modeling) [9] reliability [3], [7], traffic modeling [8], and, finance [2]. It is our hope that our the paper will assist practitioners in formulating and analyzing models in a much more transparent and easily accessible way.
Multiple Document Summarization Using Principal Component Analysis Incorporating Semantic Vector Space Model

Text Summarization is very effective in relevant assessment tasks. The Multiple Document Summarizer presents a novel approach to select sentences from documents according to several heuristic features. Summaries are generated modeling the set of documents as Semantic Vector Space Model (SVSM) and applying Principal Component Analysis (PCA) to extract topic features. Pure Statistical VSM assumes terms to be independent of each other and may result in inconsistent results. Vector space is enhanced semantically by modifying the weight of the word vector governed by Appearance and Disappearance (Action class) words. The knowledge base for Action words is maintained by classifying the words as Appearance or Disappearance with the help of Wordnet. The weights of the action words are modified in accordance with the Object list prepared by the collection of nouns corresponding to the action words. Summary thus generated provides more informative content as semantics of natural language has been taken into consideration.
Molecular Electrostatic Potential Analysis for Enzymatic Substrates, Competitive Inhibitors, and Transition-State Inhibitors

Recent advances in the application of kinetic isotope effects to enzyme-catalyzed reactions have provided reliable information for enzymatic transition state structures. A method is presented for quantifying the similarity of substrates and inhibitors with their enzyme-stabilized transition states. On the basis of transition-state stabilization theory for enzymatic reactions, molecules most similar to the transition state structure bind with greatest affinity. Molecular similarity measures are applied to compare substrates, competitive inhibitors, and transition state inhibitors with the transition state structures stabilized by the enzymes AMP deaminase, adenosine deaminase, and AMP nucleosidase. (R)- and (S)-Coformycin 5‘-phosphate are inhibitors for AMP deaminase, with the R-species superior to its enantiomer. Formycin 5‘-phosphate 4-aminopyrazolo[3,4-d]pyrimidine-1-ribonucleotide, and tubercidin 5‘-phosphate inhibit AMP nucleosidase. The transition state for adenosine deaminase is analogous to that fo.
3 D reconstruction from incomplete Fourier spectra : An extrapolation approachEtienne

3D reconstruction from an incomplete data set is an ill-posed problem. To overcome this drawback, an approach based on constrained optimization is introduced. This approach provides a powerful mathematical framework for selecting a speciic solution from the set of feasible solutions; this is done by minimizing some criteria depending on prior densitometric information. We propose a global optimization scheme using a deterministic relaxation algorithm based on Bregman's algorithm associated with half-quadratic minimization techniques. When used for 3D vascular reconstruction from 2D Digital subtracted Angiography (DSA) data, such an approach allows reconstructing well-contrasted 3D vascular network in comparison with results obtained by using standard algorithms.
Extraction of relief information on textured and coloured rough surfaces for the optimisation of a propagation model for wireless transmission

This study is carried out within the framework of the development of simulation systems of high frequency radio wave propagation (60 GHz) for wireless local area networks in an indoor environment. Thus the corresponding wavelength is in millimeters. At this frequency, in order to optimise models of radioelectric wave propagation, it is important to have information related to the 3D roughness of the main reflective surfaces encountered during the transmission. But the estimation of the relief of 3D textured surface is generally made on grey level images. This supposes that variations in grey levels are representative of local variations in the relief. This assumption is justified in the case of uniformly coloured surfaces, but is no longer valid when these surfaces present variations of colour or aspect. The corresponding image will then present variations in grey levels which can be related to colour variations or relief variations or both. It becomes difficult in this case to evaluate relief based on image analysis. Before any study of roughness, it is therefore necessary to devise a method for separating the information linked to colour variation from the information linked to relief variation. In this paper, we propose to carry out this separation through a photometric stereovision system. The method we have developed is based first on the acquisition of three images of the studied surface, obtained under different light conditions, and second on the photometric model of the surface. So we have established the relationship between the local relief of the surface, its colour aspect and the corresponding grey level image. Then, for the studied surface, we have extracted an image representative only of its colour aspect and an image representative only of its local relief. Finally, we have extended the proposed method to the case of coloured images.
Hierarchical segmentation approach to detection of defects on welding radiographic images

Detection of defects on welding radiographic images has been a challenging task since radiographic films normally have deficient contrast caused by the inspection techniques. In this paper, a region-based hierarchical detection approach is proposed to remedy this problem that is based on multi-scales segmentation strategies. The proposed method consists of two major stages: initial partition and iterative refinement until all defects are found out. In which energy-based evaluation models are proposed as stopping criteria for the controlling of the defect scales segmented such that only the desired defects in specific scale are detected to meet the requirements of different applications. The proposed algorithm has been demonstrated by several numerical experiment results.
Centre for Dynamic Macroeconomic Analysis Working Paper Series Unconditionally Optimal Monetary Policy

for helpful comments on this and related work. We also benefited greatly from comments from a referee and Robert King. The usual disclaimer, of course, applies. T. Damjanovic acknowledges the support of the May Wong Smith Foundation. V. Damjanovic acknowledges the support of a Royal Economic Society Research Fellowship. ABSTRACT We develop a simple and intuitive approach for analytically deriving unconditionally optimal (UO) policies, a topic of enduring interest in optimal monetary policy analysis. The approach can be employed to both general linear-quadratic problems and to the underlying non-linear environments. We provide a detailed example using a canonical New Keynesian framework.
Dynamic Biped Locomotion in Structured Environment

Stable biped locomotion with minimum power requirement has become a major research topic as a result of the recent advances in humanoid robots. The paper deals with the gait generation of 12 degrees of freedom biped robot for dynamic walking and stair climbing. The main objective of the project is to simulate the gait pattern for dynamically stable locomotion in a structured environment. The kinematic analysis is done with Denavit--Hartenberg formulation and the dynamic analysis using Newton Euler method. The stability of the system is analyzed with Zero Moment Point concept.
Microneedling dilates the follicular infundibulum and increases transfollicular absorption of liposomal sepia melanin

Encapsulation of chemicals in liposomes and microneedling are currently used techniques to enhance the penetration of several substances through skin and hair. In this study, we apply a liposomal melanin–fluorescein compound to an ex vivo model of human skin, using a new electrical microneedling device (Nanopore turbo roller). The product was applied by hand massage (A) or with the assistance of the electrical roller for 2 minutes (B). An additional test was performed free of product and with only the E-roller (C). Histological changes and product absorption were evaluated by optical and fluorescent microscopy 60 and 90 minutes after the treatment. Site B showed larger deposits of melanin–fluorescein at superficial and deep levels of hair structures in comparison to site A. Light, epidermal deposits of the melanin–fluorescein complex were also observed. Sites B and C showed a significant widening (47%) of the follicular infundibulum which could explain the increased penetration of the formulation. Microneedling also removed the scales and sebum residues in the neighborhood of the infundibulum. Targeting hair follicles with melanin may be useful to dye poorly pigmented hairs, improving laser hair removal. The procedure accelerates the delivery of melanin into hair structures allowing an even absorption, larger pigment deposits, and deeper penetration of the formulation into the hair.
Explaining Bankruptcy Using Option Theory

This study builds on, and extends, option-pricing theory to explain financial distress based on a sample of 420 distressed U.S. firms for the period 1986-2001. Our results indicate that the primary option variables, such as firm volatility, play an important role in explaining distress up to five years prior to bankruptcy filing. When the model is extended to account for the probability of default on interest and debt repayments due at intermediate times prior to debt maturity (due to voluntary equityholder default or due to cash flow problems), an option-motivated transformation of the cash flow coverage is shown to have incremental explanatory power, while the primary option variables remain statistically significant. Our theory-driven models have significant explanatory power in the years tested.
Incremental learning on a budget and its application to quick maximum power point tracking of photovoltaic systems

Maximum power point tracking(MPPT) is an essential technique to improving the efficiency of renewable energy systems. Although various techniques exist that can realize MPPT, there are fewer techniques that can realize quick control using conventional circuit design. In this paper, we propose a quick MPPT converter using a limited general regression neural network (LGRNN). The proposed LGRNN is an incremental learning method for regression on a budget [1]. Therefore, the LGRNN is able to work on small embedded systems, which allows the MPPT converter to be constructed at low cost using the normal combination of a chopper circuit and a controlling microcomputer. This means that the MPPT converter can be constructed in a low cost. The LGRNN learns the maximum power point (MPP) found by the perturb and observe (P&O) method, and sets the reference voltage of the converter immediately after a sudden irradiation change. By using this strategy, the MPPT quickly without predetermination of parameters. The experimental results suggests that after learning, the proposed converter controls the chopper circuit within about 20 [ms] after sudden irradiation changes. Moreover, the converter was designed to be attached to each solar panel to obtain the MPP of each panel. The proposed converter was tested with two series-connected solar panels. The results shows that the proposed system maintains a high efficiency even if one of the two panels is shadowed.
GFS: Evolution on Fast-forward

During the early stages of development at Google, the initial thinking did not include plans for building a new file system. While work was still being done on one of the earliest versions of the company’s crawl and indexing system, however, it became quite clear to the core engineers that they really had no other choice, and GFS (Google File System) was born.