Miracle-dz/UniFAR-FTUs huggingface dataset:
SOME CLASSES OF ANALYTIC FUNCTIONS RELATED WITH FUNCTIONS OF BOUNDED RADIUS ROTATION WITH RESPECT TO SYMMETRICAL POINTS

In this paper, we introduce a class R s(γ) of analytic functions of bounded radius rotation with respect to symmetrical points and study some of its basic properties. Using this concept, two other classes Ts k (δ ,γ), K s k (δ ,γ) are also defined. We study coefficient results, arc-length and radius problems for these classes.
Automatically summarising Web sites: is there a way around it?

The challenge of automatically summarising Web pages and sites is a great one. However, currently there is no solution which offers an easy way to produce unbiased, coherent , and contentfull summaries of Web sites. In this work we suggest a new approach, which relies on the structure of hypertext and the way people describe information in it. As a proof-of-concept, we applied our approach to the problem of tailoring coherent snippets for search results. In this paper we describe the approach as it is implemented in our system, InCommonSense, and present results from a large scale evaluation of the snippets produced. We conclude by suggesting other applications that could make use of this technique.
Privacy preserving mapping schemes supporting comparison

To cater to the privacy requirements in cloud computing, we introduce a new primitive, namely Privacy Preserving Mapping (PPM) schemes supporting comparison. An PPM scheme enables a user to map data items into images in such a way that, with a set of images, any entity can determine the <, =, > relationships among the corresponding data items. We propose three privacy notions, namely ideal privacy, level-1 privacy, and level-2 privacy, and three constructions satisfying these privacy notions respectively.
Virtual Reality for Obsessive-Compulsive Disorder: Past and the Future

The use of computers, especially for virtual reality (VR), to understand, assess, and treat various mental health problems has been developed for the last decade, including application for phobia, post-traumatic stress disorder, attention deficits, and schizophrenia. However, the number of VR tools addressing obsessive-compulsive disorder (OCD) is still lacking due to the heterogeneous symptoms of OCD and poor understanding of the relationship between VR and OCD. This article reviews the empirical literatures for VR tools in the future, which involve applications for both clinical work and experimental research in this area, including examining symptoms using VR according to OCD patients' individual symptoms, extending OCD research in the VR setting to also study behavioral and physiological correlations of the symptoms, and expanding the use of VR for OCD to cognitive-behavioral intervention.
Actively Learning to Attract Followers on Twitter

Abstract Twitter, a popular social network, presents great opportunities for on-line machine learning research. However, previous research hasfocused almost entirely on learning from passively collected data. We study the problem of learning to acquire followers throughnormative user behavior, as opposed to the mass following policies applied by many bots. We formalize the problem as a contextualbandit problem, in which we consider retweeting content to be the action chosen and each tweet (content) is accompanied by context.We design reward signals based on the change in followers. The result of our month long experiment with 60 agents suggests that (1)aggregating experience across agents can adversely impact prediction accuracy and (2) the Twitter community’s response to differentactions is non-stationary. Our ﬁndings suggest that actively learning on-line can provide deeper insights about how to attract followersthan machine learning over passively collected data alone.Keywords: Reinforcement Learning, On-line Learning, Contextual Bandits, TwitterAcknowledgementsThe research leading to these results has received funding from the European Research Council under the European Unions Seventh
Progressive forest split compression

In this paper we introduce the Progressive Forest Split (PFS) representation, a new adaptive refinement scheme for storing and transmitting manifold triangular meshes in progressive and highly compressed form. As in the Progressive Mesh (PM) method of Hoppe, a triangular mesh is represented as a low resolution polygonal model followed by a sequence of refinement operations, each one specifying how to add triangles and vertices to the previous level of detail to obtain a new level. The PFS format shares with PM and other refinement schemes the ability to smoothly interpolate between consecutive levels of detail. However, it achieves much higher compression ratios than PM by using a more complex refinement operation which can, at the expense of reduced granularity, be encoded more efficiently. A forest split operation doubling the number n of triangles of a mesh requires a maximum of approximately 3:5n bits to represent the connectivity changes, as opposed to approximately (5 + log2(n))n bits in PM. We describe algorithms to efficiently encode and decode the PFS format. We also show how any surface simplification algorithm based on edge collapses can be modified to convert single resolution triangular meshes to the PFS format. The modifications are simple and only require two additional topological tests on each candidate edge collapse. We show results obtained by applying these modifications to the Variable Tolerance method of Gueziec. CR
Comparative evaluation of porous silica based carriers for lipids and liquid drug formulations

Abstract Conversion of liquid and semisolid lipids into free flowing powders is an advantageous technique, as the carriers display high surface area, strong adsorption capacity, ease of processing, and ability to generate lipid loaded free flowing powders which can be converted to solid dosage forms like tablets and capsules. A combination of density, adsorption capacity and desorption is found to be of importance in the selection of the right adsorbent. Adsorbents like magnesium aluminium silicates (MAS), granulated fumed silica (GFS) and mesoporous silica gel (MSG) were characterized by flow property measurements, particle size, scanning electron microscopy (SEM) and pore structure by mercury (Hg) intrusion study. SEM results reveal adsorbent morphology, whereas an intrusion-extrusion study reveal pore size distributions. Tablets and capsules of oil loaded adsorbents were prepared by conventional methods. Oil loaded adsorbents were evaluated for the ability to convert oil into powder, easy of processing into tablets and capsules, and release of the loaded oil (Vitamin E) or active (Glyburide). All adsorbents possess good flow property while MSG has higher density than GFS and MAS. This helps to deliver maximum active per unit volume. A wider pore size distribution of MAS was observed in comparison to MSG and GFS. MAS exhibited poor oil release from powder and its formulations, whereas GFS demonstrated closely similar release to MSG. Maximum 70% oil loaded MSG can be delivered in tablet dosage form andMSG can deliver the highest amount in limited volume capsules due to its high density. Hence, lower density and poor oil release from MAS limit its applications in solid oral drug delivery,while both MSG and GFS proved to be suitable.
A Novel Palmprint Segmentation Technique

Recent paradigm shift from the conventional contact based palmprint recognition to contactless based systems (CBS) has necessitated the development of a variety of these systems. A major challenge of these systems is it robustness to illumination variation in unconstrained environment, thus making segmentation difficult. In this paper, the acquired image undergoes color space conversion and the output is filtered using coefficients obtained from the training of an artificial neural network (ANN) based model coefficient determination technique. Performance analysis of the proposed technique shows better performance in term of mean square error, true positive rate and accuracy when compared with two other techniques. Furthermore, it has also been observed that the proposed method is illumination invariant hence its suitability for deployment in contactless palmprint recognition systems.
Skin color and feature-based face detection in complicated backgrounds

An efficient face detection scheme that can detect multiple faces in a color image with a complex environment and different illumination levels is proposed in this paper. The first stage adopts skin color-based segmentation to search potential face regions. The second stage detects the lip in potential face regions using a lip color model and searches the eyes using geometry textures. The last stage clips the face region using an optimization ellipse. Results of the study demonstrate that the proposed approach performs well in detecting various poses in different illumination conditions and complicated backgrounds.
NBER WORKING PAPER SERIES HUMAN CAPITAL AND REGIONAL DEVELOPMENT

We are grateful to Nicolas Ciarcia, Nicholas Coleman, Sonia Jaffe, Konstantin Kosenko, Francisco Queiro, and Nicolas Santoni for dedicated research assistance over the past 4 years. We thank Gary Becker, Edward Glaeser, Gita Gopinath, Josh Gottlieb, Elhanan Helpman, Chang-Tai Hsieh, Mathew Kahn, Pete Klenow, Casey Mulligan, Elias Papaioannou, Jacopo Ponticelli, Giacomo Ponzetto, Jesse Shapiro, Chad Syverson, and seminar participants at the UCLA Anderson School for extremely helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Artificial neural network based fault diagnostics of rolling element bearings using continuous wavelet transform

Any industry needs an efficient predictive plan in order to optimize the management of resources and improve the economy of the plant by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in productive processes are caused by bearings. This paper presents a methodology for fault diagnosis of ball bearings based on continuous wavelet transform (CWT) and artificial neural network (ANN). Three wavelet selection criteria Maximum Energy, Minimum Shannon Entropy, and Maximum Energy to Shannon Entropy ratio are used and compared to select an appropriate wavelet to extract statistical features. Total 15 feature set and 87 mother wavelet candidates were studied, and results show that complex morlet 1-1 has a best diagnosis performance based on minimum shannon entropy than the other mother wavelets and criteria. Also results show the potential application of proposed methodology with ANN for the development of on-line fault diagnosis systems for machine condition.
Evaluation of competing software reliability predictions

Different software reliability models can produce very different answers when called on to predict future reliability in a reliability growth context. Users need to know which, if any, of the competing predictions are trustworthy. Some techniques are presented which form the basis of a partial solution to this problem. Rather than attempting to decide which model is generally best, the approach adopted allows a user to decide on the most appropriate model for each application.
99designs: An Analysis of Creative Competition in Crowdsourced Design

We provide a study of crowdsourced design based on the popular service 99designs, a website where users create design contests for other users to submit their ideas for evaluation, competing for a monetary amount offered. By quantitatively analyzing a large number of contests, we report on the dynamics of the interactions between contests and designers. We show results on the effects of different financial incentives and contests' properties on the number and quality of submissions, as well as on how rewards are distributed across designers. We find that higher financial incentives do not translate to more effort by individual designers, but nonetheless have an impact on the quality outcome of contests by attracting a larger pool of designers. We also show that a majority of contests are dominated by a disproportionate few designers, which are both more active and effective in the service.
Mono(2-ethylhexyl) phthalate induces both pro- and anti-inflammatory responses in rat alveolar macrophages through crosstalk between p38, the lipoxygenase pathway and PPARα

Airway inflammation is important in asthma pathogenesis. Recent epidemiological data have indicated an association between asthma symptoms in children and exposure to di(2-ethylhexyl) phthalate (DEHP). Thus, we have studied inflammatory responses in primary rat alveolar macrophages (AMs) after exposure to mono(2-ethylhexyl) phthalate (MEHP), the major primary metabolite of DEHP. First, we show that MEHP induces a dose-dependent release of the pro-inflammatory tumour necrosis factor-α (TNF-α) in AMs, giving a maximal (5-fold) increase at 0.7 mM. This concentration also induced some cell death. MEHP also induced phosphorylation of MAPK p38, while the p38 inhibitor SB 202190 reduced MEHP-induced TNF-α, suggesting a p38-dependent cytokine production. Next, we elucidated possible effects of MEHP on the 5-lipoxygenase (5-LO) pathway and found that MEHP caused increased leukotriene (LTB4) release. Further, we found that the 5-LO inhibitor nordihydrogualaretic acid (NDGA) significantly reduced both MEHP-induced TNF-α release and MEHP-induced formation of reactive oxygen species (ROS), supporting an involvement of the 5-LO pathway in MEHP induced inflammatory reactions. Last, we found that MK-886, a known inhibitor of peroxisome proliferator-activated receptor α (PPARα), increased the MEHP-induced TNF-α response. This indicates that MEPH-PPARα binding mediates an anti-inflammatory signal.
Making prophecies with decision predicates

We describe a new algorithm for proving temporal properties expressed in LTL of infinite-state programs. Our approach takes advantage of the fact that LTL properties can often be proved more efficiently using techniques usually associated with the branching-time logic CTL than they can with native LTL algorithms. The caveat is that, in certain instances, nondeterminism in the system's transition relation can cause CTL methods to report counter examples that are spurious with respect to the original LTL formula. To address this problem we describe an algorithm that, as it attempts to apply CTL proof methods, finds and then removes problematic nondeterminism via an analysis on the potentially spurious counterexamples. Problematic nondeterminism is characterized using decision predicates, and removed using a partial, symbolic determinization procedure which introduces new prophecy variables to predict the future outcome of these choices. We demonstrate---using examples taken from the PostgreSQL database server, Apache web server, and Windows OS kernel---that our method can yield enormous performance improvements in comparison to known tools, allowing us to automatically prove properties of programs where we could not prove them before.
Analyses of Nuclearly Encoded Mitochondrial Genes Suggest Gene Duplication as a Mechanism for Resolving Intralocus Sexually Antagonistic Conflict in Drosophila

Gene duplication is probably the most important mechanism for generating new gene functions. However, gene duplication has been overlooked as a potentially effective way to resolve genetic conflicts. Here, we analyze the entire set of Drosophila melanogaster nuclearly encoded mitochondrial duplicate genes and show that both RNA- and DNA-mediated mitochondrial gene duplications exhibit an unexpectedly high rate of relocation (change in location between parental and duplicated gene) as well as an extreme tendency to avoid the X chromosome. These trends are likely related to our observation that relocated genes tend to have testis-specific expression. We also infer that these trends hold across the entire Drosophila genus. Importantly, analyses of gene ontology and functional interaction networks show that there is an overrepresentation of energy production-related functions in these mitochondrial duplicates. We discuss different hypotheses to explain our results and conclude that our findings substantiate the hypothesis that gene duplication for male germline function is likely a mechanism to resolve intralocus sexually antagonistic conflicts that we propose are common in testis. In the case of nuclearly encoded mitochondrial duplicates, our hypothesis is that past sexually antagonistic conflict related to mitochondrial energy function in Drosophila was resolved by gene duplication.
Automatic Summarizer to aid a Q/A system

In this paper we couple a Q/A system with a summarizer and claim that an automatic summarizer coupled with a Q/A system will increase the efficiency of the Q/A system. Coupling a question-answering system with a text-summarization system may make the question-answering system more efficient without significantly altering the quality of results. This paper investigates methods to develop an extractive summarizer which aims to select the most important sentences which could be a possible answer to a given query.
Evaluation of OPEN Zinc Finger Nucleases for Direct Gene Targeting of the ROSA26 Locus in Mouse Embryos

Zinc finger nucleases (ZFNs) enable precise genome modification in a variety of organisms and cell types. Commercial ZFNs were reported to enhance gene targeting directly in mouse zygotes, whereas similar approaches using publicly available resources have not yet been described. Here we report precise targeted mutagenesis of the mouse genome using Oligomerized Pool Engineering (OPEN) ZFNs. OPEN ZFN can be constructed using publicly available resources and therefore provide an attractive alternative for academic researchers. Two ZFN pairs specific to the mouse genomic locus gt(ROSA26)Sor were generated by OPEN selections and used for gene disruption and homology-mediated gene replacement in single cell mouse embryos. One specific ZFN pair facilitated non-homologous end joining (NHEJ)-mediated gene disruption when expressed in mouse zygotes. We also observed a single homologous recombination (HR)-driven gene replacement event when this ZFN pair was co-injected with a targeting vector. Our experiments demonstrate the feasibility of achieving both gene ablation through NHEJ and gene replacement by HR by using the OPEN ZFN technology directly in mouse zygotes.
Broadcast Authentication Scheme Based on Tornado Code for Wireless Sensor Networks

Broadcast authentication is an essential security service in wireless sensor networks, as it allows a sender to broadcast messages to multiple nodes in an authenticated way. This paper propose a light-weight, scalable broadcast authentication scheme over a long period of time. Instead of using multi-level key chains as in multilevel µTESLA, the scheme uses multiple short key chains to replace one long key chain in µTESLA. In order to reduce the communication overhead, this scheme uses Tornado codes that encode/decode hash authentication packet. To evaluate the performance of our scheme, we compare our scheme with multilevel µTESLA.
News and financial intermediation in aggregate and sectoral fluctuations

We estimate a two-sector DSGEmodel with financial intermediaries—a-la Gertler and Karadi (2011) and Gertler and Kiyotaki (2010)—and quantify the importance of news shocks in accounting for aggregate and sectoral fluctuations. Our results indicate a significant role of financial market news as a predictive force behind fluctuations. Specifically, news about the value of assets held by financial intermediaries, reflected one to two years in advance in corporate bond markets, generate countercyclical corporate bond spreads, affect the supply of credit, and are estimated to be a significant source of aggregate fluctuations, accounting for approximately 31% of output, 22% of investment and 31% of hours worked variation in cyclical frequencies. Importantly, asset value news shocks generate both aggregate and sectoral co-movement with a standard preference specification. Financial intermediation is key for the importance and propagation of asset value news shocks.
miRror-Suite: decoding coordinated regulation by microRNAs

MicroRNAs (miRNAs) are short, non-coding RNAs that negatively regulate post-transcriptional mRNA levels. Recent data from cross-linking and immunoprecipitation technologies confirmed the combinatorial nature of the miRNA regulation. We present the miRror-Suite platform, developed to yield a robust and concise explanation for miRNA regulation from a large collection of differentially expressed transcripts and miRNAs. The miRror-Suite platform includes the miRror2.0 and Probability Supported Iterative miRror (PSI-miRror) tools. Researchers who performed large-scale transcriptomics or miRNA profiling experiments from cells and tissues will benefit from miRror-Suite. Our platform provides a concise, plausible explanation for the regulation of miRNAs in such complex settings. The input for miRror2.0 may include hundreds of differentially expressed genes or miRNAs. In the case of miRNAs as input, the algorithm seeks the statistically most likely set of genes regulated by this input. Alternatively, for a set of genes, the miRror algorithm seeks a collection of miRNAs that best explains their regulation. The miRror-Suite algorithm designates statistical criteria that were uniformly applied to a dozen miRNA-target prediction databases. Users select the preferred databases for predictions and numerous optional filters/parameters that restrict the search to the desired tissues, cell lines, level of expression and predictor scores. PSI-miRror is an advanced application for refining the input set by gradually enhancing the degree of pairing of the sets of miRNAs with the sets of targets. The iterations of PSI-miRror probe the interlinked nature of miRNAs and targets within cells. miRror-Suite serves experimentalists in facilitating the understanding of miRNA regulation through combinatorial– cooperative activity. The platform applies to human, mouse, rat, fly, worm and zebrafish. Database URL: http://www.mirrorsuite. cs.huji. ac.il.
A Hyperlink-Based Recommender System Written in Sqeal

1. ABSTRACT Human beings, not machines, are the ultimate experts for information retrieval tasks, including recommender systems. Consequently, computers are most useful when they combine information about people’s judgments. Collaborative filtering systems make use of this observation by having users explicitly rate items, such as Web pages, with the system making recommendations to other users based on overlapping areas of interest. A disadvantage of collaborative filtering, at least as currently implemented, is that it depends on users’ explicitly entering data, which can be inconvenient and time-consuming. We describe the design, implementation, and performance of a recommender system that works by mining publiclyavailable hyperlinks on the Web, producing results competitive with the best text-based system. We also demonstrate the utility of the Squeal language for structure-based Web queries.