Miracle-dz/UniFAR-FTUs huggingface dataset:
Consistent Nonparametric Regression for Functional Data Under the Stone–Besicovitch Conditions

In this paper, we address the problem of nonparametric regression estimation in the infinite-dimensional setting. We start by extending the Stone's seminal result to the case of metric spaces when the probability measure of the explanatory variables is tight. Then, under slight variations on the hypotheses, we state and prove the theorem for general metric measure spaces. From this result, we derive the mean square consistency of the k-NN and kernel estimators if the regression function is bounded and the Besicovitch condition holds. We also prove that, for the uniform kernel estimate, the Besicovitch condition is also necessary in order to attain L1 consistency for almost every x.
Silent information.

People communicate with each other by means of speech, countenance, miens, deeds; but they also read each other's thoughts and feelings. This silent interpersonal communication helps me, for example, perceive the presence of another man inmy vicinity, capture the attention of the audience when delivering a speech or performing an act, convey my contempt or admiration, pretend, exert authority, guess what I am expected to say or do, ingratiate myself into someone's favour, lend my ear to the argument, etc. The connection of the commuinicating parties is very accurate and instantaneous, and has a peculiar flair of mutual consent (I know he knows that I know he knows what I have in mind). Although evident, silent communication eludes dissection and resists straight verbalisation as much as the darkness escapes close examination in full light. If I were eccentric or impertinent enough as to ask people I come in contact with whether a moment ago, before I have questioned them, they felt my contempt, admiration, pretension, authority, sycophancy, alienation, or whatever, I would not learn much. Firstly, I cannot question everyone, but only those people I may expect to be likely to cooperate hence, my sample would miss enemies, crooks, high authorities, ephemeral acquaintances, and many others. Secondly, why should I trust a verbal or written account of a recent mutual experience rendered by another man, rather than rely on my immediate perception of the encounter in its statu nascendi? Should I accept at face value what she or he says, simply because the spoken or written statement is 'objectively verifiable' and available for subsequent study by a third party ?
Spatial Evaluation and Modeling of Dengue Seroprevalence and Vector Density in Rio de Janeiro, Brazil

Background Rio de Janeiro, Brazil, experienced a severe dengue fever epidemic in 2008. This was the worst epidemic ever, characterized by a sharp increase in case-fatality rate, mainly among younger individuals. A combination of factors, such as climate, mosquito abundance, buildup of the susceptible population, or viral evolution, could explain the severity of this epidemic. The main objective of this study is to model the spatial patterns of dengue seroprevalence in three neighborhoods with different socioeconomic profiles in Rio de Janeiro. As blood sampling coincided with the peak of dengue transmission, we were also able to identify recent dengue infections and visually relate them to Aedes aegypti spatial distribution abundance. We analyzed individual and spatial factors associated with seroprevalence using Generalized Additive Model (GAM). Methodology/Principal Findings Three neighborhoods were investigated: a central urban neighborhood, and two isolated areas characterized as a slum and a suburban area. Weekly mosquito collections started in September 2006 and continued until March 2008. In each study area, 40 adult traps and 40 egg traps were installed in a random sample of premises, and two infestation indexes calculated: mean adult density and mean egg density. Sera from individuals living in the three neighborhoods were collected before the 2008 epidemic (July through November 2007) and during the epidemic (February through April 2008). Sera were tested for DENV-reactive IgM, IgG, Nested RT-PCR, and Real Time RT-PCR. From the before–after epidemics paired data, we described seroprevalence, recent dengue infections (asymptomatic or not), and seroconversion. Recent dengue infection varied from 1.3% to 14.1% among study areas. The highest IgM seropositivity occurred in the slum, where mosquito abundance was the lowest, but household conditions were the best for promoting contact between hosts and vectors. By fitting spatial GAM we found dengue seroprevalence hotspots located at the entrances of the two isolated communities, which are commercial activity areas with high human movement. No association between recent dengue infection and household's high mosquito abundance was observed in this sample. Conclusions/Significance This study contributes to better understanding the dynamics of dengue in Rio de Janeiro by assessing the relationship between dengue seroprevalence, recent dengue infection, and vector density. In conclusion, the variation in spatial seroprevalence patterns inside the neighborhoods, with significantly higher risk patches close to the areas with large human movement, suggests that humans may be responsible for virus inflow to small neighborhoods in Rio de Janeiro. Surveillance guidelines should be further discussed, considering these findings, particularly the spatial patterns for both human and mosquito populations.
GNGS: An Artificial Intelligent Tool for Generating and Analyzing Gene Networks from Microarray Data

The completion of the Human Genome Project has been recognized as a great achievement in the study of biomedicine; the project not only provides information regarding human genes but also provides new ways to study human diseases such as cancers. Highthroughput techniques, such as microarray experiments, have emerged as a method of study that measures the level of gene expression in gene networks. Since microarray experiments can produce thousands of datasets under various experimental conditions simultaneously, it is now feasible to study gene interactions and regulatory networks. How to analyze and interpret the results of these analyses, however, has become an important research area in bioinformatics. In the study of biological cellular behavior, understanding how biological activities are governed by the relationships among genes, RNA, and proteins is a common challenge. Gene networks represent such connectivity. A gene network consists of a group of genes that interact among themselves in order to synthesize proteins. Recently, genome-wide gene expression microarray data relevant to the yeast cell cycle has been collected (Spellman et al., 1998; Cho et al., 1998; Zhu et al., 2000). Since the gene expression profile data is a record of the network interactions between the regulators and the target genes, it is possible to use this information to trace these complex relationships. A variety of computer clustering methods have been developed in order to group together genes with similar patterns of expression (Eisen et al., 1998; Tamayo et al., 1999; Tavazoie et al, 1999). Previous efforts at modeling gene networks from high dimensional datasets have generally fallen into one of three classes, either employing Boolean networks (D’haeseleer et al., 1999; Husmeier et al., 2005), which are restricted to logical relationships between variables, or using systems of differential equations (Chen et al., 1999; Sakamoto & Iba, 2001; Thomas, 1990) to model the continuous dynamics of coupled biological reactions. The work of Friedman et al. (2000) uses Bayesian networks to analyze expression data. The statistical framework of Bayesian learning, since it deals with uncertainly, is designed for domains
Functional Implications of Cd34 Expression in Human Adipose–derived Stem/progenitor Cells

CD34 is frequently used as a marker of adipose-derived stem/stromal/progenitor cells (ASCs). However, CD34 expression in human ASCs (hASCs) decreases over time in culture, and the implications of this remain unclear. In this study, we sorted shortly-cultured hASCs into CD34+ and CD34− fractions and compared their biological functions. Results indicated that CD34+ hASCs were more proliferative and had a greater ability to form colonies. In contrast, CD34− cells showed a greater ability for differentiation into adipogenic and osteogenic lin-eages. Both CD34+ and CD34− cells showed similar abilities in migration and capillary formation in response to growth factors. Expression levels of endothelial progenitor markers (Flk-1, FLT1, and Tie-2) were higher in CD34+ cells, whereas those of pericyte markers (CD146, NG2, and α-smooth muscle actin) were higher in CD34− cells. Both CD34+ and CD34− cells showed similar expression levels of vascular endothelial growth factor and hepatocyte growth factor, although hypoxia affected the expression levels. Together these results suggest that CD34 expression in hASCs may correlate with replicative capacity, differentiation potentials, expression profi les of angiogenesis-related genes, and immaturity or stemness of the cells. Loss of CD34 expression may be related to the physiological process of commitment and/or differentiation from immature status into specifi c lineages such as adipose, bone, or smooth muscle.
Impact of changing US cigarette smoking patterns on incident cancer: risks of 20 smoking-related cancers among the women and men of the NIH-AARP cohort.

BACKGROUND Historically, US women started smoking at a later age than men and had lower relative risks for smoking-related cancers. However, more recent birth cohorts of women and men have similar smoking histories and have now reached the high-risk age for cancer. The impact of these changes on cancer incidence has not been systematically examined. METHODS Relative risks (RR), 95% confidence intervals (CI) and attributable fractions were calculated for cigarette smoking and incidence of 20 smoking-related cancers in 186 057 women and 266 074 men of the National Institutes of Health-AARP cohort, aged 50 to 71 years in 1995 and followed for 11 years. RESULTS In the cohort, which included participants born between 1924 and 1945, most women and men started smoking as teenagers. RRs for current vs never smoking were similar in women and men for the following cancers: lung squamous-cell (RR women: 121.4, 95% CI: 57.3-257.4; RR men:114.6, 95% CI: 61.2-214.4), lung adenocarcinoma (RR women: 11.7, 95% CI: 9.8-14.0; RR men: 15.6, 95% CI: 12.5-19.6), laryngeal (RR women: 37.0, 95% CI: 14.9-92.3; RR men: 13.8, 95% CI: 9.3-20.2), oral cavity-pharyngeal (RR women:4.4, 95% CI: 3.3-6.0; RR men: 3.8, 95% CI: 3.0-4.7), oesophageal squamous cell (RR women: 7.3, 95% CI: 3.5-15.5; RR men: 6.2, 95% CI: 2.8-13.7), bladder (RR women: 4.7, 95% CI: 3.7-5.8; RR men: 4.0, 95% CI: 3.5-4.5), colon (RR women: 1.3, 95% CI: 1.2-1.5; RR men: 1.3, 95% CI: 1.1-1.4), and at other sites, with similar attributable fractions. CONCLUSIONS RRs for current smoking and incidence of many smoking-related cancers are now similar in US women and men, likely reflecting converging smoking patterns.
Pebbles: Fine-Grained Data Management Abstractions for Modern Operating Systems

Support for fine-grained data management has all but disappeared from modern operating systems such as Android and iOS. Instead, we must rely on each individual application to manage our data properly - e.g., to delete our emails, documents, and photos in full upon request; to not collect more data than required for its function; and to back up our data to reliable backends. Yet, research studies and media articles constantly remind us of the poor data management practices applied by our applications. We have developed Pebbles, a fine-grained data management system that enables management at a powerful new level of abstraction: application-level data objects, such as emails, documents, notes, notebooks, bank accounts, etc. The key contribution is Pebbles's ability to discover such high-level objects in arbitrary applications without requiring any input from or modifications to these applications. Intuitively, it seems impossible for an OS-level service to understand object structures in unmodified applications, however we observe that the high-level storage abstractions embedded in modern OSes - relational databases and object-relational mappers - bear significant structural information that makes object recognition possible and accurate.
High-Radix Floating-Point Division Algorithms for Embedded VLIW Integer Processors

This paper presents ﬂoating-point division algorithms and implementations for embedded VLIW integer processors. On those processors, there is no hardware ﬂoating-point unit, for cost reasons. But, for portability and/or accuracy reasons, a software FP emulation layer is sometime useful. Here, we focus on high-radix digit-recurrence algorithms for FP division on integer VLIW processors such as the ST200 from STMicroelectronics.
Co-Synthesis of Dynamically Reconfigurable SOPCs Specified by Conditional Task Graphs

In this work the co-synthesis method, that optimizes dynamically reconfigurable multiprocessor SOPC system architectures, is presented. The algorithm maximizes speed of a SOPC system, taking into consideration the space con- straints of the FPGA. The algorithm starts with the initial solution, where all tasks are assigned to only one general pur- pose processor module. Next, it produces new solutions using iterative improvement methods. To the best of authors' knowledge, it is the first algorithm that takes into consideration mutually exclusive tasks in optimization of dynamically reconfigurable systems. Such tasks are specified using conditional task graphs. Partially reconfigurable FPGAs enable re- use of the same hardware resources for mutually exclusive tasks. In this way, the area occupied by an embedded system can be decreased and free space may be used for other purposes. It was shown that the presented approach can also in- crease the performance of a SOPC system.
Adaptive Multiscale Optical Flowestimation

The current paper presents a novel adaptive multiscale scheme to estimate optical flow from image sequences. The scheme models estimation uncertainties which are used to reduce the influence of unreliable intermediate estimates on accuracy. The experimental results show that the proposed method provides more accurate estimates for both small and large motions than a standard multiscale scheme in which an increment is added to an intermediate estimate regardless of estimation certainty.
Acceleration for MPI derived datatypes using an enhancer of memory and network

This paper presents a support function for MPI derived datatypes on an enhancer of memory and network named DIMMnet-3. It is a network interface with vector access functions and multi-banked extended memory, which is under development. Semi-hardwired derived datatype communication based on RDMA with hardwired scatter and gather is proposed. This mechanism and MPI using it are implemented and validated on DIMMnet-2 which is a former prototype operating on DDR DIMM slot. The performance of scatter and gather transfer of 8byte elements with large interval by using vector commands of DIMMnet-2 is 6.8 compared with software on a host. Proprietary benchmark of MPI derived datatype communication for transferring a submatrix corresponding to a narrow HALO area is executed. Observed bandwidth on DIMMnet-2 is far higher than that for similar condition with VAPI based MPI implementation on InfniBand, even though very old generation FPGA, poorer CPU and motherboard are used. This function will avoid cache pollution and save CPU time for processing with local data which can be overlapped with communication. A new commercial machine with vector scatter/gather functions in NIC named SGI Altix UV is launched recently. It may be able to adopt our proposed concept partially, even though the capacity and fine grain access throughput of main memory attached with CPU are not enhanced on it.
Role of Leukotriene B4 Receptors in the Development of Atherosclerosis: Potential Mechanisms

Objective—Leukotriene B4 (LTB4), a potent leukocyte chemoattractant, is known to promote several inflammatory diseases, including atherosclerosis. We sought to determine mechanisms through which LTB4 modulates atherosclerosis in cell lines expressing LTB4 receptors, BLT-1, and in mice deficient in BLT-1 as well as macrophage cell lines derived from BLT-1+/+ and BLT-1−/− mice. Methods and Results—Analysis of global changes in gene expression induced by LTB4 in rat basophilic leukemia cells (RBL-2H3) expressing the human BLT-1 showed highest-fold increase in expression of fatty acid translocase/CD36 and the chemokine MCP1/JE/CCL2, which are critical in atherogenesis. To determine the importance of BLT-1 in atherogenesis, we crossed BLT-1-null mice with apolipoprotein (apo)-E-deficient mice, which develop severe atherosclerosis. Deletion of BLT-1 significantly reduced the lesion formation in apo-E−/− mice only during initiating stages (4 and 8 weeks) but had no effect on the lesion size in mice fed atherogenic diet for 19 weeks. Macrophage cell lines from BLT-1-deficient mice expressed the low-affinity LTB4 receptor, BLT-2, and exhibited chemotaxis to LTB4. Conclusions—The effects of LTB4 in atherosclerosis are likely mediated through the high-affinity BLT-1 and the low-affinity BLT-2 receptors. LTB4 promotes atherosclerosis by chemo-attracting monocytes, by providing an amplification loop of monocyte chemotaxis via CCL2 production, and by converting monocytes to foam cells by enhanced expression of CD36 and fatty acid accumulation.
Difference of Convex Functions Programming Applied to Control with Expert Data

This paper reports applications of Difference of Convex functions (DC) programming to Learning from Demonstrations (LfD) and Reinforcement Learning (RL) with expert data. This is made possible because the norm of the Optimal Bellman Residual (OBR), which is at the heart of many RL and LfD algorithms, is DC. Improvement in performance is demonstrated on two specific algorithms, namely Reward-regularized Classification for Apprenticeship Learning (RCAL) and Reinforcement Learning with Expert Demonstrations (RLED), through experiments on generic Markov Decision Processes (MDP), called Garnets.
Robust algorithms for direction-finding in the presence of model errors

The application of high-resolution, subspace-based methods to narrowband direction-of-arrival (DOA) estimation relies on several critical assumptions. Two of these are that the response of the antenna array is known in all directions of interest, and that the spatial covariance of the background noise is known. Neither of these assumptions is satisfied in practice, often resulting in a serious degradation of algorithm performance. A model error sensitivity analysis is carried out for some popular high-resolution subspace-based algorithms. Theoretical expressions for the covariance of the DOA estimation error are developed and compared with that obtained by simulation. The analysis is also used to develop optimally weighted versions of the algorithms that are robust to the types of model errors considered. <<ETX>>
Efficient Pattern Matching in Python

Pattern matching is a powerful tool for symbolic computations. Applications include term rewriting systems, as well as the manipulation of symbolic expressions, abstract syntax trees, and XML and JSON data. It also allows for an intuitive description of algorithms in the form of rewrite rules. We present the open source Python module MatchPy, which offers functionality and expressiveness similar to the pattern matching in Mathematica. In particular, it includes syntactic pattern matching, as well as matching for commutative and/or associative functions, sequence variables, and matching with constraints. MatchPy uses new and improved algorithms to efficiently find matches for large pattern sets by exploiting similarities between patterns. The performance of MatchPy is investigated on several real-world problems.
An Experimental Study of a Biosequence Big Data Analysis Service

With the development of next-generation sequencing (NGS), DNA/RNA sequencing has become cheaper and more efficient. Today, a whole human genome can be sequenced under $1,000, providing opportunities for large-scale bioinformatic analysis on big datasets. However, most of existing bioinformatic analysis tools are programmed for single server based computing platform and not suitable to process such big datasets. As Hadoop MapReduce and Spark are gaining popularity as cluster computing based big data processing platform, more and more bioinformatic applications start to explore cluster computing platform for large scale data analysis. In this paper we present an in-depth experimental study on deploying Spark clusters for high performance bioinformatic short sequence reconstruction. Our experimental results enable us to answer a number of challenging and yet most frequently asked questions regarding efficient management of bioinformatic data analysis services on Spark systems. Example questions include how to best split big dataset into multiple partitions, and how to distribute data partitions and bioinformatic analysis tasks on a Spark cluster for carrying out a high performance distributed analysis job? What types of memory models are effective for bioinformatic data analysis services on a Spark cluster? Why do different bioinformatic data analysis operations exhibit different throughput performance on the same Spark cluster? We conjecture that this experimental study not only demonstrates the feasibility of high performance bioinformatic data analysis on Spark platform, but also will help bioinformatic application developers to make more informed decisions on both design and configuration of Spark Cluster, managing and tuning parameters of Spark runtime system for enhancing the performance of large scale big data analytics.
New Constructions of Revocable Identity-Based Encryption From Multilinear Maps

A revocable identity-based encryption (RIBE) provides an efficient revocation method in IBE that a trusted authority periodically broadcasts an update key for nonrevoked users and a user can decrypt a ciphertext if he is not revoked in the update key. Boldyreva, Goyal, and Kumar (CCS 2008) defined RIBE and proposed an RIBE scheme that uses a tree-based revocation encryption scheme to revoke users' private keys. In this paper, we devise a new technique for RIBE and propose RIBE schemes with a constant number of private key elements. We achieve the following results. We first devise a new technique for RIBE that combines a hierarchical IBE (HIBE) scheme and a public-key broadcast encryption (PKBE) scheme using multilinear maps. In contrast to the previous technique for RIBE, our technique uses a PKBE scheme in bilinear maps for revocation to achieve short private keys and update keys. Following our new technique for RIBE, we propose an RIBE scheme in three-leveled multilinear maps that combines the HIBE scheme of Boneh and Boyen (EUROCRYPT 2004) and the PKBE scheme of Boneh, Gentry, and Waters (CRYPTO 2005). The private key and update key of our scheme possess a constant number of group elements. Next, we propose another RIBE scheme with reduced public parameters and short keys by combining the HIBE scheme of Boneh and Boyen and the PKBE scheme of Boneh, Waters, and Zhandry (CRYPTO 2014), which uses multilinear maps. Compared with our first RIBE scheme, our second RIBE scheme requires high-leveled multilinear maps.