Miracle-dz/UniFAR-FTUs huggingface dataset:
Light transport simulation with vertex connection and merging

Developing robust light transport simulation algorithms that are capable of dealing with arbitrary input scenes remains an elusive challenge. Although efficient global illumination algorithms exist, an acceptable approximation error in a reasonable amount of time is usually only achieved for specific types of input scenes. To address this problem, we present a reformulation of photon mapping as a bidirectional path sampling technique for Monte Carlo light transport simulation. The benefit of our new formulation is twofold. First, it makes it possible, for the first time, to explain in a formal manner the relative efficiency of photon mapping and bidirectional path tracing, which have so far been considered conceptually incompatible solutions to the light transport problem. Second, it allows for a seamless integration of the two methods into a more robust combined rendering algorithm via multiple importance sampling. A progressive version of this algorithm is consistent and efficiently handles a wide variety of lighting conditions, ranging from direct illumination, diffuse and glossy inter-reflections, to specular-diffuse-specular light transport. Our analysis shows that this algorithm inherits the high asymptotic performance from bidirectional path tracing for most light path types, while benefiting from the efficiency of photon mapping for specular-diffuse-specular lighting effects.
Send Orders of Reprints at Reprints@benthamscience.org Galectin-1 and Galectin-3 Mediate Protocadherin-24-dependent Membrane Localization of Β-catenin in Colon Cancer Cell Line Hct116

Protocadherin-24 (PCDH24) is linked to the suppression of tumor growth and the inhibition of cell proliferation in the colon cancer cell line HCT116. We previously observed that β-catenin is localized to the plasma membrane when PCDH24 is expressed in these cells, but the molecular mechanisms by which PCDH24 induces the membrane local-ization of β-catenin remain largely unknown. To clarify these mechanisms, we identified molecules that interact with ec-topically expressed PCDH24 in HCT116 cells using a HaloTag ® pull-down assay. We found that galectin-1 and galectin-3 physically interact with PCDH24 and are retained at the plasma membrane in association with PCDH24 expression. A lu-ciferase-based pull-down assay using HaloTag-fused galectins revealed that an intracellular region of PCDH24 (amino acids 1186–1280) is essential for this interaction. Furthermore, the over-expression of galectin-1 or-3, or the depletion of endogenous galectins by small interfering RNA modulates β-catenin translocation. We also revealed that the retention of galectin-1 and-3 at the plasma membrane results in the inactivation of PI3K activity. From these findings, we propose a model in which the galectin-anchoring activity of PCDH24 leads to the suppression of β-catenin signaling by the local-ization of β-catenin at the plasma membrane in PCDH24-expressing HCT116 colon cancer cells. INTRODUCTION Protocadherin-24 (PCDH24, also known as CDHR2 and protocadherin LKC) was originally identified as a novel member of the human non-classical cadherin family. PCDH24 expression is markedly reduced in cancers derived from liver and colon tissues, while the over-expression of PCDH24 protein restores the contact inhibition of cell proliferation in the colon cancer cell line HCT116, and suppresses the growth of these cells in a nude mouse model [1, 2]. We previously demonstrated that stably expressed PCDH24 proteins accumulate at cell-to-cell adhesion sites and regulate the localization of β-catenin to the cell membrane in the HCT116 cell line. These previous results suggested that PCDH24 is important for the contact inhibition of epithelial cells, and that the absence of PCDH24 induces tumor growth; more specifically, PCDH24 acts as a tumor suppressor protein, at least in the HCT116 cell line [1, 2]. Although we proposed a novel model for the suppression of β-catenin signaling by PCDH24, the molecular mechanism by which
Brain Nerve Bundles Estimation by Restoring and Filtering IntrañVoxel Information in Diffusion Tensor MRI

We present a regularized method for solving an inverse problem in Diffusion Tensor Magnetic Resonance Imaging (DTn MRI) data. In the case of brain images, DTnMR obtains a tensor eld that indicates the local orientation of nerve bundles. Now days, the spatial resolution of this technique is limited by the partial volume effect; i.e. the measured tensors at voxels that contain ber crossings or bifurcations results from the addition of several tensors, each one oriented with its corresponding nerve bundle. In this paper, we proposed a method for recovering the original intranvoxel information. We assume that the observed tensors are a linear combination of a given tensor base, therefore, the aim of our approach is the computation of the unknown coefcients of this linear combination. By regularizing the problem, we introduce the prior information about the piecewise smoothness of nerve bundles orientation. Such regularization process performs an anisotropic ltering of the coefcients. As a result, we recover a multintensor eld. Moreover, we propose to estimate the nerve bundles trajectory by performing stochastic walks of particles in the computed multintensor eld. In order to demonstrate the performance of the method, we ran several experiments using both synthetic and real data.
Physical Hypermedia: a Context-Aware Approach

In this paper we describe an original architecture for building and deploying physical hypermedia applications, i.e. those applications in which digital and real world objects are linked together using the well-known navigational metaphor of the World Wide Web. We also explain how our architecture evolved from a substrate for implementing location-based services to a powerful and open basis for supporting different navigation semantics and for building travel assistance services according to the current user’s context. After motivating our research and describing the architecture, we illustrate our ideas with some simple examples and compare our research with other related work in the subject.
The Oxford handbook of the economics of networks

The Oxford Handbook of the Economics of Networks represents the frontier of research into how and why networks form, how they influence behavior, how they help govern outcomes in an interactive world, and how they shape collective decision making, opinion formation, and diffusion dynamics. From a methodological perspective, the contributors to this volume devote attention to theory, field experiments, laboratory experiments, and econometrics. Theoretical work in network formation, games played on networks, repeated games, and the interaction between linking and behavior is synthesized. A number of chapters are devoted to studying social process mediated by networks. Topics here include opinion formation, diffusion of information and disease, and learning. There are also chapters devoted to financial contagion and systemic risk, motivated in part by the recent financial crises. Another section discusses communities, with applications including social trust, favor exchange, and social collateral; the importance of communities for migration patterns; and the role that networks and communities play in the labor market. A prominent role of networks, from an economic perspective, is that they mediate trade. Several chapters cover bilateral trade in networks, strategic intermediation, and the role of networks in international trade. Contributions discuss as well the role of networks for organizations. On the one hand, one chapter discusses the role of networks for the performance of organizations, while two other chapters discuss managing networks of consumers and pricing in the presence of network-based spillovers. Finally, the authors discuss the internet as a network with attention to the issue of net neutrality. Available in OSO: http://www.oxfordhandbooks. com/oso/public/content/oho_economics/9780199948277/toc.html Contributors to this volume - Daron Acemoglu Sinan Aral Lori Beaman Francis Bloch Vincent Boucher Yann Bramoulle Emily Breza Antonio Cabrales Arun Chandrasekhar Thomas Chaney Syngjoo Choi Daniele Condorelli Wouter Dessein Marcin Dziubinski Nick Economides Bernard Fortin Douglas Gale Andrea Galeotti Edoardo Gallo Ben Golub Piero Gottardi Sanjeev Goyal Matthew Jackson Shachar Kariv Alan Kirman Rachel Kranton PJ Lamberson Mihai Manea Ana Mauleon Dina Mayzlin Markus Mobius Kaivan Munshi Francesco Nava Asu Ozdaglar Paolo Pin Andrea Prat Brian Rogers Tanya Rosenblat Evan Sadler Alireza Tahbaz-Salehi Vincent Vannetelbosch Fernando Vega-Redondo Adrien Vigier Duncan Watts Yves Zenou
Imbalanced Classification Using Genetically Optimized Random Forests

Class imbalance is a problem that commonly affects 'real world' classification datasets, and has been shown to hinder the performance of classifiers. A dataset suffers from class imbalance when the number of instances belonging to one class outnumbers the number of instance belonging to another class. Two ways of dealing with class imbalance are modifying the dataset to reduce the number of instances belonging to the majority class(es) (known as resampling), or allowing the classifier to penalize misclassifying the minority class(es) more than the majority class(es), this can be done by implementing a cost matrix. This paper attempts to improve the classification performance of the Random Forest classifier on imbalanced datasets by exploiting these two techniques, to do this a genetic algorithm is employed to find optimal parameters. Results are compared to commonly used classification algorithms.
An effective tree metrics graph cut algorithm for MR brain image segmentation and tumor Identification

The proposed algorithm describes the problem of Magnetic Resonance (MR) brain image segmentation using the tree-metric graph cuts (TM) algorithm, a novel segmentation algorithm and introducing a “tree-cutting” method to interpret the labeling returned by the TM algorithm as tissue classification for the input brain MR brain image. The algorithm has three sequential steps: 1) pre-processing, which generates a tree of labels as key to the TM algorithm; 2) a sweep of the TM algorithm, which proceeds a globally optimal labeling with respect to the tree of labels; 3) post-processing, which involves running the “tree-cutting” method to generate a mapping from labels to brain tissues such as Grey Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF) producing a meaningful MR brain image segmentation. On comparison with the current approaches, the result obtained shows that the tree metrics graph cut algorithm is faster and the overall segmentation accuracy is better for segmenting both T1 and T2 weighted MR axial brain slice images.
Hypoxia‐Inducible Factor‐1 Is a Determinant of Lobular Structure and Oxygen Consumption in the Liver

Hypoxia‐inducible factor is a hypoxia‐responsive transcriptional factor that controls the expression of proteins contributing to homeostatic responses to hypoxia. Spatial heterogeneity of tissue oxygenation has been postulated as a determinant of structure and function of hepatic lobules, although its molecular mechanisms remain unknown. This study aimed to examine the role of HIF‐1 expressed in hepatocytes in regulation of hepatic microcirculation.
Differences in Myoelectric and Body-Powered Upper-Limb Prostheses: Systematic Literature Review

ABSTRACT The choice of a myoelectric or body-powered upper-limb prosthesis can be determined using factors including control, function, feedback, cosmesis, and rejection. Although body-powered and myoelectric control strategies offer unique functions, many prosthesis users must choose one. A systematic review was conducted to determine differences between myoelectric and body-powered prostheses to inform evidence-based clinical practice regarding prescription of these devices and training of users. A search of 9 databases identified 462 unique publications. Ultimately, 31 of them were included and 11 empirical evidence statements were developed. Conflicting evidence has been found in terms of the relative functional performance of body-powered and myoelectric prostheses. Body-powered prostheses have been shown to have advantages in durability, training time, frequency of adjustment, maintenance, and feedback; however, they could still benefit from improvements of control. Myoelectric prostheses have been shown to improve cosmesis and phantom-limb pain and are more accepted for light-intensity work. Currently, evidence is insufficient to conclude that either system provides a significant general advantage. Prosthetic selection should be based on a patient’s individual needs and include personal preferences, prosthetic experience, and functional needs. This work demonstrates that there is a lack of empirical evidence regarding functional differences in upper-limb prostheses.
Webclass: Web Document Classiication Using Modiied Decision Trees

Searching for Web sites is one of the most common tasks performed on the Web. Web page classi cation is the rst step for Web search service construction. This paper proposes a system, named WebClass, for classifying Web documents by using a height-three modi ed decision tree which splits the root, depth-one nodes, and depth-two nodes on the keywords, descriptions, and hyperlinks, respectively. Start a URL at the root of the decision tree and trace paths downward to leaves, which give the categories the URL belongs to. A comparison of manual classi cation to WebClass shows the later achieves over 73% accuracy of human classi cation.
Still the tragic mulatto? Manufacturing multiracialization in magazine media, 1961–2011

ABSTRACT On the heels of the 2000 US Census allowance of multiracial categorization and with rising mainstream discussion of multiracial heritage, questions over the meanings of multiracialism are quite prevalent. Scholars have highlighted how mainstream-oriented and black-oriented media structure (multi)racial conflicts, concepts, and categories. However, sociological analysis has neither examined qualitative differences and nuance among multiracial-oriented media sources nor specified the precise qualitative themes, frames, and discourse of that representation across time and media format. A content analysis of mainstream-, black-, and multiracial-oriented magazine articles demonstrates how varied media sources differently drew upon, resisted, and reproduced distinct understandings of multiracialism to reproduce the dominance of the “Tragic Mulatto” trope. The implications for this study illuminate the import of multiracial self-esteem, the intersection of conservative political movements and black interest groups in the fight for and against a multiracial movement, and the paradoxical role of anti-black stereotypes in multiracial representations.
Time-dependent backgrounds in supergravity and string theory

Time-dependent solutions of supergravity and string theory are studied. The examples are obtained from de Sitter deformation of gauge-gravity dualities, analytical continuation of static solutions, and ``exactly solvable'' worldsheet models. Among other things, it is shown that turning on a Hubble parameter in the background of a confining gauge theory in four dimensions can restore chiral symmetry. Some of the solutions obtained from analytical continuation have the interpretation of a universe with a bounce separating a big bang from a big crunch singularity. In the worldsheet context, it is argued why string propagation close to a Milne-type cosmological singularity might be physically nonsingular.
dedupv1: Improving deduplication throughput using solid state drives (SSD)

Data deduplication systems discover and remove redundancies between data blocks. The search for redundant data blocks is often based on hashing the content of a block and comparing the resulting hash value with already stored entries inside an index. The limited random IO performance of hard disks limits the overall throughput of such systems, if the index does not fit into main memory. This paper presents the architecture of the dedupv1 dedupli-cation system that uses solid-state drives (SSDs) to improve its throughput compared to disk-based systems. dedupv1 is designed to use the sweet spots of SSD technology (random reads and sequential operations), while avoiding random writes inside the data path. This is achieved by using a hybrid deduplication design. It is an inline deduplication system as it performs chunking and fingerprinting online and only stores new data, but it is able to delay much of the processing as well as IO operations.
A multi-objective evolutionary approach to imbalanced classification problems

Classification problems for imbalanced data distribution pose many challenges to standard learning algorithms as at least one class is under-represented relative to others. In this paper, we present a new approach to deal with this kind of problems, in which a multi-objective evolutionary algorithm is engaged to detect the best cost matrix to be further used by the learning algorithm in the classification task. Two objectives are set for the evolutionary algorithm as follows: maximize the true positive rate and maximize precision on the minority class. A multi-objective search algorithm is used for this optimization problem and the detected optimal costs are then used in the classifier. Experiments are performed for several imbalanced datasets and the results obtained support a competitive performance of the proposed approach.
Single site-specific integration targeting coupled with embryonic stem cell differentiation provides a high-throughput alternative to in vivo enhancer analyses

Summary Comprehensive analysis of cis-regulatory elements is key to understanding the dynamic gene regulatory networks that control embryonic development. While transgenic animals represent the gold standard assay, their generation is costly, entails significant animal usage, and in utero development complicates time-course studies. As an alternative, embryonic stem (ES) cells can readily be differentiated in a process that correlates well with developing embryos. Here, we describe a highly effective platform for enhancer assays using an Hsp68/Venus reporter cassette that targets to the Hprt locus in mouse ES cells. This platform combines the flexibility of Gateway® cloning, live cell trackability of a fluorescent reporter, low background and the advantages of single copy insertion into a defined genomic locus. We demonstrate the successful recapitulation of tissue-specific enhancer activity for two cardiac and two haematopoietic enhancers. In addition, we used this assay to dissect the functionality of the highly conserved Ets/Ets/Gata motif in the Scl+19 enhancer, which revealed that the Gata motif is not required for initiation of enhancer activity. We further confirmed that Gata2 is not required for endothelial activity of the Scl+19 enhancer using Gata2−/− Scl+19 transgenic embryos. We have therefore established a valuable toolbox to study gene regulatory networks with broad applicability.
mmWave SVD-based beamformed MIMO communication systems

This paper provides concept of data transmission protocol for millimeter wave (mmWave) wireless systems operating in Non-Line-of-Sight environment. This concept is designed to provide an effective and practical functioning of Multiple-Input Multiple-Output (MIMO) transmission mode that exploits combination of Singular Value Decomposition (SVD) of channel matrix and non-adaptive beamforming. The proposed protocol reduces complexity of implementation of the beamformed MIMO system with SVD by transmitting the special signals, called by MIMO postambles. Using these MIMO postambles allows stations to obtain the channel state information and operate in SVD mode without any feedback and simplifies MIMO system architecture significantly.
COMP 4801 Final Year Project Modified R-CNN : Object Recognition by Deep Learning Neural Networks Interim

Deep Learning Neural Networks have been commonly used in the field of object recognition. This draft interim report intends to give a detailed overview on the final year project “Object Recognition by Deep Learning Neural Networks”. The ultimate objectives of this project are to: 1) reproduce R-CNN on Python; and 2) replace original classifier with Latent Dirichlet Allocation classifier to improve accuracy. In order to achieve the goal, project team will utilize public datasets to train and evaluate the algorithm. At current stage, Matlab version RCNN has been implemented with a 49.6% mAP and selective search has been reproduced in Python version R-CNN. No particular difficulties were encountered at this stage since the project is still in the early phase. It is expected that ultimate deliverable will be able to achieve higher accuracy rate than Python implemented R-CNN.
Association among Socioeconomic Status, Health and Function-related Variables, and Onset of Depression in the Case of Middle-aged and Older People in Japan

Although many studies show that people at low socioeconomic states (SES) are more likely to be depressed, longitudinal studies on SES and depression which take into account health and function-related variables in the case of middle-aged and older people are scarce, especially in Asian nations. By performing multivariate logistic regressions using longitudinal data from the Japanese Study of Aging and Retirement (JSTAR), we investigate the longitudinal association between SES plus health and function-related variables at baseline and an onset of depression two years later for people over the age of 50. We find that, out of the respondents who are not depressed at baseline, respondents with the lowest education levels are more likely to develop depression two years later. This result was maintained after adjusting for total family income and total wealth, but was attenuated and not significant after adjusting for health and function-related variables. We also find that those with any disability in the instrumental activities of daily living (IADL) and intellectual activities (IA) at baseline are more likely to develop depression two years later.