Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonsteroidal anti-inflammatory drug use and breast cancer risk by stage and hormone receptor status.

BACKGROUND Epidemiologic studies of the association between nonsteroidal anti-inflammatory drugs (NSAIDs), such as aspirin and ibuprofen, and breast cancer risk have yielded inconsistent results. We investigated the association of NSAID use with risk of breast cancer in the California Teachers Study cohort, with special attention to risk of specific breast cancer subtypes and to type of NSAID used. METHODS We analyzed data on 114 460 women in the California Teachers Study cohort who were aged 22 to 85 years and free of breast cancer at baseline in 1995 to 1996. Information on frequency and duration of NSAID use was collected through a self-administered questionnaire. A total of 2391 women were diagnosed with breast cancer during the follow-up period from 1995 to 2001. We used Cox proportional hazards regression to estimate relative risks (RR) and 95% confidence intervals (CI) of breast cancer subtypes with NSAID use. RESULTS Neither regular use (more than once a week) of any NSAID (aspirin and ibuprofen combined) nor regular use of aspirin was associated with breast cancer risk (RR = 1.09, 95% CI = 0.97 to 1.21 for daily versus no regular use of NSAIDs and RR = 0.98, 95% CI = 0.86 to 1.13 for daily versus no regular use of aspirin). However, long-term (> or = 5 years) daily aspirin users had a non-statistically significant decreased risk of estrogen receptor and progesterone receptor (ER/PR)-positive breast cancer (RR = 0.80, 95% CI = 0.62 to 1.03). In contrast, we observed a statistically significantly increased risk of ER/PR-negative breast cancer with long-term daily use of aspirin (RR = 1.81, 95% CI = 1.12 to 2.92). In this population, 11 fewer ER/PR-positive breast cancer cases and seven excess ER/PR-negative breast cancer cases may be due to daily long-term aspirin use among 2391 breast cancer cases observed over 6 years if the association were proven to be causal. Long-term daily use of ibuprofen was also associated with an increased risk of breast cancer (RR = 1.51, 95% CI = 1.17 to 1.95), particularly of nonlocalized tumors (RR = 1.92, 95% CI = 1.24 to 2.97). If causality were subsequently proven, 16 of the observed 2391 breast cancer cases and 8 of the 713 non-localized breast cancer cases would be attributable to long-term daily use of ibuprofen. CONCLUSIONS Long-term daily use of NSAIDs was not associated with breast cancer risk overall. Ibuprofen use was associated with an increased risk of breast cancer, and long-term daily aspirin use was associated with an increased risk of ER/PR-negative breast cancer. However, it is not clear if the observed association is causal.
Comprehensive Network Analysis of Anther-Expressed Genes in Rice by the Combination of 33 Laser Microdissection and 143 Spatiotemporal Microarrays

Co-expression networks systematically constructed from large-scale transcriptome data reflect the interactions and functions of genes with similar expression patterns and are a powerful tool for the comprehensive understanding of biological events and mining of novel genes. In Arabidopsis (a model dicot plant), high-resolution co-expression networks have been constructed from very large microarray datasets and these are publicly available as online information resources. However, the available transcriptome data of rice (a model monocot plant) have been limited so far, making it difficult for rice researchers to achieve reliable co-expression analysis. In this study, we performed co-expression network analysis by using combined 44 K agilent microarray datasets of rice, which consisted of 33 laser microdissection (LM)-microarray datasets of anthers, and 143 spatiotemporal transcriptome datasets deposited in RicexPro. The entire data of the rice co-expression network, which was generated from the 176 microarray datasets by the Pearson correlation coefficient (PCC) method with the mutual rank (MR)-based cut-off, contained 24,258 genes and 60,441 genes pairs. Using these datasets, we constructed high-resolution co-expression subnetworks of two specific biological events in the anther, “meiosis” and “pollen wall synthesis”. The meiosis network contained many known or putative meiotic genes, including genes related to meiosis initiation and recombination. In the pollen wall synthesis network, several candidate genes involved in the sporopollenin biosynthesis pathway were efficiently identified. Hence, these two subnetworks are important demonstrations of the efficiency of co-expression network analysis in rice. Our co-expression analysis included the separated transcriptomes of pollen and tapetum cells in the anther, which are able to provide precise information on transcriptional regulation during male gametophyte development in rice. The co-expression network data presented here is a useful resource for rice researchers to elucidate important and complex biological events.
Integrating Models Derived from non-Parametric Bayesian Co-segmentation into a Statistical Machine Transliteration System

The system presented in this paper is based upon a phrase-based statistical machine transliteration (SMT) framework. The SMT system’s log-linear model is augmented with a set of features specifically suited to the task of transliteration. In particular our model utilizes a feature based on a joint source-channel model, and a feature based on a maximum entropy model that predicts target grapheme sequences using the local context of graphemes and grapheme sequences in both source and target languages. The segmentation for our approach was performed using a non-parametric Bayesian co-segmentation model, and in this paper we present experiments comparing the effectiveness of this segmentation relative to the publicly available state-of-the-art m2m alignment tool. In all our experiments we have taken a strictly language independent approach. Each of the language pairs were processed automatically with no special treatment.
Solving Large POMDPs using Real Time Dynamic Programming

Partially Observable Markov Decision Processes (pomdps) are general models of sequential decision problems in which both actions and observations may be noisy. Many problems of interest can be formulated as pomdps yet the use of pomdps has been limited by the lack of eeective algorithms: optimal algorithms don't scale up and heuristic algorithms often do poorly. In this paper, a new pomdp algorithm is introduced that combines the beneets of optimal and heuristic procedures producing good solutions quickly even in problems that are large. Like optimal procedures, the procedure rtdp-bel attempts to solve the information mdp, yet like heuristic procedures, it makes decisions in real time following a suitable heuristic function. rtdp-bel is a Real Time Dynamic Programming algorithm 1], namely, a greedy search algorithm that learns to solve mdps by repeatedly updating the heuristic values of the states that are visited. As shown by Barto et al. such updates eventually deliver an optimal behavior provided that the state space is nite and the initial heuristic values are admissible. Since information mdps have innnite state spaces we discretize probabilities and combine them with heuristic values obtained from the underlying mdp. Although the resulting algorithm is not guaranteed to be optimal, experiments over a number of benchmarks suggest that large pomdps are quickly and consistently solved, and that solutions, if not optimal, tend to be very good.
Asset Allocation and Pension Liabilities in the Presence of a Downside Constraint∗

We revisit the question of a pension sponsor’s optimal asset allocation in the presence of a downside constraint and the possibility for the pension sponsor to contribute money to the pension plan. Using detailed data on pension plans’ asset allocations and voluntary contributions, we find a U-shaped pattern of equity weights in terms of funding status. Especially, equity weights and voluntary contribution simultaneously increase when funding status deteriorates, which is hard to reconcile with a hypothesis of risky gambling. We build a model of a pension sponsor with disutility associated with contributions, that produces observed pattern of equity weights and contribution. Relative to a common risk management strategy that a pension sponsor should fully switch to the risk-free portfolio during underfunding, implementing the optimal asset allocation and contribution policy implies a welfare gain of XX%.
Why Firms Smooth Dividends: Empirical Evidence

While dividend smoothing is taken as an article of faith, little is known about the cross-sectional properties of smoothing policies. Why do some firms smooth more than others? We examine firms' dividend smoothing behavior across a wide spectrum of publicly traded firms in the U.S. We find that larger firms, firms with more tangible assets, and firms with lower price volatility and earnings volatility smooth more. The findings also indicate that firms with slower growth prospects and firms that are "cash cows" smooth more. Firms with a more significant presence of institutional investors and firms with higher payout ratios also smooth more. Taken together, the results suggest that agency considerations play an important role in firms' decision to smooth dividends. Asymmetric information based theories are largely unsupported by the data.
CONDITIONAL VOLATILITY OF COLOMBIAN GOVERNMENTAL FIXED INCOME SECURITIES AS A PREDICTOR OF SHORT-TERM RETURNS (VOLATILIDAD CONDICIONAL DE LOS TíTULOS DE RENTA FIjA DEL GOBIERNO COLOMBIANO COMO PREDICTOR DE LOS RETORNOS DE CORTO PLAZO)

According to literature, the long-maturity forward rates have information about the structure of the expected short-term returns. This paper finds that the conditional volatility factor also has information for predicting the term premium in the six-month expected returns with different maturities.That is, including conditional volatility allows capturing a risk factor consistent with the agent’s expectations. A slow mean-reverting processis also found across different maturities, which is the case of the governmental fixed income securities. In fact, the power of fore casting changes from a six-month to a three-year forward period, at six-month steps according to the mean-reverting tendency which also implied that its predictive power improves at longer forecasting horizons. On the other hand,it presents evidence about the Colombian markets crash in May 2006, which generated special conditions that impacted the market’s behavior and the agent’srisk tolerance. Resumen: De acuerdo con la literatura las tasas de interes futuras de largo plazo ofrecen informacion relevante que las convierte en un buen predictor de la estructura de retornos de corto plazo. Este articulo encuentra que el factor de volatilidad condicional posee informacion para predecir la prima a plazo con intervalos de seis meses para diferentes vencimientos. En otras palabras, incluirla volatilidad condicional como factor permite captarlas senales de riesgo en consonancia con las expectativas de los agentes. Se evidencia, ademas, un proceso lento de reversion a la media para diferentes vencimientos en los titulos gubernamentales, lo cual incide sobre la capacidad de prediccion, que en este caso muestra ser mayor para vencimientos mas tardios. Por otra parte, se presenta evidencia sobre la caida del mercado colombiano en mayo de 2006, hecho que genero condiciones especiales de operacion, ademas de impactar sobre la percepcion de riesgo de los agentes.
The Techniques for Software Pipelining Loops with Conditional Constructs

Software pipelining, as an important parallel technique for loop structure, exploits the parallelism present among the iterations of a loop by overlapping the execution of successive iterations. The diiculty encountered when software pipelin-ing loops with conditional branches is that arbitrary control ow complicates the construction of an overlapping schedule since there are multiple possible execution paths. In this article, we present a number of techniques developed in the past ten years for attempting to alleviate this issue and suggest the possible direction for our future work.
Equivalence of Non-Iterative Algorithms for Simultaneous Low Rank Approximations of Matrices

Recently four non-iterative algorithms for simultaneous low rank approximations of matrices (SLRAM) have been presented by several researchers. In this paper, we show that those algorithms are equivalent to each other because they are reduced to the eigenvalue problems of row-row and column-column covariance matrices of given matrices. Also, we show a relationship between the non-iterative algorithms and another algorithm which is claimed to be an analytical algorithm for the SLRAM. Experimental results show that the analytical algorithm does not necessarily give the optimal solution of the SLRAM.
Rapid enumeration within a fraction of a single glance: The role of visible persistence in object individuation capacity

The number of items that can be individuated at a single glance is limited. Here, we investigate object individuation at a higher temporal resolution, in fractions of a single glance. In two experiments involving object individuation we manipulated the duration of visual persistence of the target items with a forward masking procedure. The number of items as well as their stimulus–onset asynchrony (SOA) to the mask was varied independently. The results showed main effects of numerosity and SOA, as well as an interaction. These effects were not caused by a generic reduction of item visibility by the mask. Instead, the SOA manipulation appeared to fractionate the time to access the sensory image. These findings suggest that the capacity limit of 3–4 items found in object individuation is, at least partially, the consequence of the temporal window of access to sensory information.
MSE uplink-downlink duality of MIMO systems with arbitrary noise covariance matrices

This paper establishes three kinds of mean-square-error (MSE) uplink-downlink duality for multiple-input multiple-output (MIMO) systems. Our duality is established for the practically relevant scenario where the noise vector of each mobile station (MS) is a zero-mean circularly symmetric complex Gaussian (ZMCSCG) random variable with arbitrary covariance matrix. As an application example of our duality, we examine the linear transceiver design for the weighted sum MSE minimization constrained with a total base station (BS) power problem for the downlink multiuser MIMO systems. To solve this problem, first we establish the MSE uplink-downlink duality. Then, we formulate the power allocation part of the equivalent problem in the uplink channel as a Geometric Programming (GP). Finally, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. The proposed duality maintains the easier-to-handle mathematical structure of MSE-based problems in the uplink channel and generalizes the existing MSE uplink-downlink duality. Furthermore, by utilizing our duality, we exploit the hidden convexity of the sum MSE minimization constrained with a total BS power problem in the downlink channel.
PageRank revisited

PageRank, one part of the search engine Google, is one of the most prominent link-based rankings of documents in the World Wide Web. Usually it is described as a Markov chain modeling a specific random surfer. In this article, an alternative representation as a power series is given. Nonetheless, it is possible to interpret the values as probabilities in a random surfer setting, differing from the usual one.Using the new description we restate and extend some results concerning the convergence of the standard iteration used for PageRank. Furthermore we take a closer look at sinks and sources, leading to some suggestions for faster implementations.
Size distributions of maternal and fetal DNA in maternal plasma.

BACKGROUND The discovery of fetal DNA in maternal plasma has opened up an approach for noninvasive prenatal diagnosis. Despite the rapid expansion in clinical applications, the molecular characteristics of plasma DNA in pregnant women remain unclear. METHODS We investigated the size distribution of plasma DNA in 34 nonpregnant women and 31 pregnant women, using a panel of quantitative PCR assays with different amplicon sizes targeting the leptin gene. We also determined the size distribution of fetal DNA in maternal plasma by targeting the SRY gene. RESULTS The median percentages of plasma DNA with size >201 bp were 57% and 14% for pregnant and nonpregnant women, respectively (P <0.001, Mann-Whitney test). The median percentages of fetal-derived DNA with sizes >193 bp and >313 bp were 20% and 0%, respectively, in maternal plasma. CONCLUSION Plasma DNA molecules are mainly short DNA fragments. The DNA fragments in the plasma of pregnant women are significantly longer than those in the plasma of nonpregnant women, and the maternal-derived DNA molecules are longer than the fetal-derived ones.
A Proteogenomic Approach to Understanding MYC Function in Metastatic Medulloblastoma Tumors

Brain tumors are the leading cause of cancer-related deaths in children, and medulloblastoma is the most prevalent malignant childhood/pediatric brain tumor. Providing effective treatment for these cancers, with minimal damage to the still-developing brain, remains one of the greatest challenges faced by clinicians. Understanding the diverse events driving tumor formation, maintenance, progression, and recurrence is necessary for identifying novel targeted therapeutics and improving survival of patients with this disease. Genomic copy number alteration data, together with clinical studies, identifies c-MYC amplification as an important risk factor associated with the most aggressive forms of medulloblastoma with marked metastatic potential. Yet despite this, very little is known regarding the impact of such genomic abnormalities upon the functional biology of the tumor cell. We discuss here how recent advances in quantitative proteomic techniques are now providing new insights into the functional biology of these aggressive tumors, as illustrated by the use of proteomics to bridge the gap between the genotype and phenotype in the case of c-MYC-amplified/associated medulloblastoma. These integrated proteogenomic approaches now provide a new platform for understanding cancer biology by providing a functional context to frame genomic abnormalities.
Mobile robot localization using an incremental eigenspace model

When using appearance-based recognition for self-localization of mobile robots, the images obtained during the exploration of the environment need to be efficiently stored in the memory. PCA offers means for representing the images in a low-dimensional subspace, which allows for efficient matching and recognition. For active exploration it is necessary to use an incremental method for the computation of the subspace. We propose to use an incremental PCA algorithm with the updating of partial image representations in a way that allows the robot to discard the acquired images immediately after the update. Such a model is open-ended, meaning that we can easily update it with new images. We show that the performance of the proposed method is comparable to the performance of the batch method in terms of compression, computational cost and the precision of localization. We also show that by applying the repetitive learning, the subspace converges to that constructed with the batch method.
A novel deep model for image recognition

In this paper we propose a hybrid deep network for image recognition. First we use the sparse autoencoder(SAE) which is a method to extract high-level feature representations of data in an unsupervised way, without any manual feature engineering, and then we perform the classification using the deep belief networks(DBNs), which consist of restricted Boltzmann machine(RBM). Finally, we implement some comparative experiments on image datasets, and the results show that our methods achieved better performance when compared with neural network and other deep learning techniques such as DBNs.
Motion detection and segmentation using image mosaics

Proposes a motion segmentation algorithm for extracting foreground objects with a pan-tilt camera. Segmentation is achieved by spatio-temporal filtering of the scene to model the background. Temporal filtering is done by a set of modified AR (autoregressive) filters which model the background statistics for a particular view of the scene. Backgrounds from different views of the pan-tilt camera are stitched together into a planar mosaic using a real-time image mosaicking strategy. Our algorithms work in real time, require no user intervention, and facilitate high-quality video transmission at low bandwidths.
Influence of Vacancy Defect on Surface Feature and Adsorption of Cs on GaN(0001) Surface

The effects of Ga and N vacancy defect on the change in surface feature, work function, and characteristic of Cs adsorption on a (2 × 2) GaN(0001) surface have been investigated using density functional theory with a plane-wave ultrasoft pseudopotential method based on first-principles calculations. The covalent bonds gain strength for Ga vacancy defect, whereas they grow weak for N vacancy defect. The lower work function is achieved for Ga and N vacancy defect surfaces than intact surface. The most stable position of Cs adatom on Ga vacancy defect surface is at T1 site, whereas it is at BGa site on N vacancy defect surface. The E ads of Cs on GaN(0001) vacancy defect surface increases compared with that of intact surface; this illustrates that the adsorption of Cs on intact surface is more stable.
Class Diagram Extraction from Textual Requirements Using Natural Language Processing (NLP) Techniques

The automation of class generation from natural language requirements is highly challenging. This paper proposes a method and a tool to facilitate requirements analysis process and class diagram extraction from textual requirements supporting natural language processing NLP and Domain Ontology techniques. Requirements engineers analyze requirements manually to come out with analysis artifacts such as class diagram. The time spent on the analysis and the low quality of human analysis proved the need of automated support. A "Requirements Analysis and Class Diagram Extraction (RACE)" is a desktop instrument to assist requirements analysts and SE students in analyzing textual requirements, finding core concepts and its relationships, and step by step extraction of the class diagram. The evaluation of RACE system is in the process and will be conducted using two forms of evaluation, experimental and expert evaluation.