Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Discovery of Personal Topics to Organize Email

We present in this paper a procedure to automatically discover a user s personal topics by clustering their emails. Unlike previous work, we automatically label topics using appropriate keywords. We show that, in order to get appropriate keywords, we must apply strong filters that use domain knowledge about e-mail and the workplace of the user. We demonstrate these keywords by creating an email/ document browser which makes use of these keywords as standing queries to create virtual folders that help organize, index and retrieve email efficiently. We present subjective user studies to show the usefulness of the strong filtering.
Momordica charantia (Bitter Melon) Reduces Obesity-Associated Macrophage and Mast Cell Infiltration as well as Inflammatory Cytokine Expression in Adipose Tissues

Obesity is a world-wide epidemic disease that correlates closely with type 2 diabetes and cardiovascular diseases. Obesity-induced chronic adipose tissue inflammation is now considered as a critical contributor to the above complications. Momordica charantia (bitter melon, BM) is a traditional Chinese food and well known for its function of reducing body weight gain and insulin resistance. However, it is unclear whether BM could alleviate adipose tissue inflammation caused by obesity. In this study, C57BL/6 mice were fed high fat diet (HFD) with or without BM for 12 weeks. BM-contained diets ameliorated HFD-induced obesity and insulin resistance. Histological and real-time PCR analysis demonstrated BM not only reduced macrophage infiltration into epididymal adipose tissues (EAT) and brown adipose tissues (BAT). Flow cytometry show that BM could modify the M1/M2 phenotype ratio of macrophages in EAT. Further study showed that BM lowered mast cell recruitments in EAT, and depressed pro-inflammatory cytokine monocyte chemotactic protein-1 (MCP-1) expression in EAT and BAT as well as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) expression in EAT. Finally, ELISA analysis showed BM-contained diets also normalized serum levels of the cytokines. In summary, in concert with ameliorated insulin resistance and fat deposition, BM reduced adipose tissue inflammation in diet-induced obese (DIO) mice.
The Heavy-Element Enrichment of Lyα Clouds in the Virgo Supercluster

Using high signal-to-noise ratio echelle spectra of 3C 273 obtained with the Space Telescope Imaging Spectrograph (resolution of 7 km s-1 FWHM), we constrain the metallicities of two Lyα clouds in the vicinity of the Virgo Cluster. We detect C II, Si II, and Si III absorption lines in the Lyα absorber at zabs = 0.00530. Previous observations with the Far Ultraviolet Spectroscopic Explorer have revealed Lyβ-Lyθ absorption lines at the same redshift, thereby accurately constraining the H I column density. We model the ionization of the gas and derive [C/H] = -1.2, [Si/C] = 0.2 ± 0.1, and log nH = -2.8 ± 0.3. The model implies a small absorber thickness, ~70 pc, and thermal pressure p/k ≈ 40 cm-3 K. It is most likely that the absorber is pressure confined by an external medium because gravitational confinement would require a very high ratio of dark matter to baryonic matter. Based on a sample of Milky Way sight lines in which carbon and silicon abundances have been reliably measured in the same interstellar cloud (including new measurements presented herein), we argue that it is unlikely that the overabundance of Si relative to C is due to depletion onto dust grains. Instead, this probably indicates that the gas has been predominately enriched by ejecta from Type II supernovae. Such enrichment is most plausibly provided by an unbound galactic wind, given the absence of known galaxies within a projected distance of 100 kpc and the presence of galaxies capable of driving a wind at larger distances (e.g., H I 1225+01). Such processes have been invoked to explain the observed abundances in the hot, X-ray-emitting gas in Virgo. However, the sight line to 3C 273 is more than 10° away from the X-ray emission region. We also constrain the metallicity and physical conditions of the Virgo absorber at zabs = 0.00337 in the spectrum of 3C 273 based on detections of O VI and H I and an upper limit on C IV. If this absorber is collisionally ionized, the O /C limit requires T ≳ 105.3 K in the O VI-bearing gas. For either collisional ionization or photoionization, we find that [O/H] ≳ -2.0 at zabs = 0.00337.
Efficient Recursive Estimators for a Linear, Time-Varying Gaussian Model with General Constraints

The adaptive estimation of a time-varying parameter vector in a linear Gaussian model is considered where we a priori know that the parameter vector belongs to a known arbitrary subset. We consider a family of efficient recursive estimators for this problem: the recursive constrained maximum likelihood (ML) estimator, the recursive affine minimax, and the recursive minimum mean square error (MMSE) estimator. We show that all three estimators can be substantially simplified by using the recursive weighted least squares (RWLS) algorithm in a first step as the RWLS computes the sufficient statistic for this estimation problem. The recursive constrained ML needs to solve an optimization problem in the second step for the case that the RWLS solution does not fulfill the constraint. In case of affine minimax, we have to solve an optimization problem and to perform an affine transform. The MMSE estimator needs to calculate the mean of a truncated Gaussian density in the second step which is done by Monte Carlo integration. A simple rejection scheme is used to take general constraints for the parameter vector into account. An example shows the superior performance of our proposed estimators in comparison to many other estimators.
The Effects of Objects-First and Objects-Late Methods on Achievements of OOP Learners

Our research explored the effects of objects-first and objects-late methods on achievements of object-oriented pro-gramming (OOP) learners during a graduate course. The course’s scope was virtually identical for two groups, but the structure of the contents differed in sequence. The objects-first method emphasized the design and discussion of the object-oriented concepts from the very beginning while the objects-late deferred these concepts to the late lectures. The objects-first learners used all visual functionalities of BlueJ IDE. However, the objects-late learners started with only the text-based interfaces of BlueJ and they benefited its visual support in the last lectures. At the end of the study, we found that there was a statistically significant difference between OOP learner groups.
Allocation of Prizes in Contests with Participation Constraints

We study all-pay contests with an exogenous minimal effort constraint where a player can participate in a contest only if his effort (output) is equal to or higher than the minimal effort constraint. Contestants are privately informed about a parameter (ability) that affects their cost of effort. The designer decides about the size and the number of prizes. We analyze the optimal prize allocation for the contest designer who wishes to maximize either the total effort or the highest effort. It is shown that if the minimal effort constraint is relatively high, the winner-take-all contest in which the contestant with the highest effort wins the entire prize sum does not necessarily maximize the expected total effort nor the expected highest effort. In that case, the random contest in which the entire prize sum is equally allocated to all the participants is a legitimate alternative to the winner-take-all contest.
The Drug Derived Complexity Index (DDCI) Predicts Mortality, Unplanned Hospitalization and Hospital Readmissions at the Population Level

Objective to develop and validate the Drug Derived Complexity Index (DDCI), a predictive model derived from drug prescriptions able to stratify the general population according to the risk of death, unplanned hospital admission, and readmission, and to compare the new predictive index with the Charlson Comorbidity Index (CCI). Design Population-based cohort study, using a record-linkage analysis of prescription databases, hospital discharge records, and the civil registry. The predictive model was developed based on prescription patterns indicative of chronic diseases, using a random sample of 50% of the population. Multivariate Cox proportional hazards regression was used to assess weights of different prescription patterns and drug classes. The predictive properties of the DDCI were confirmed in the validation cohort, represented by the other half of the population. The performance of DDCI was compared to the CCI in terms of calibration, discrimination and reclassification. Setting 6 local health authorities with 2.0 million citizens aged 40 years or above. Results One year and overall mortality rates, unplanned hospitalization rates and hospital readmission rates progressively increased with increasing DDCI score. In the overall population, the model including age, gender and DDCI showed a high performance. DDCI predicted 1-year mortality, overall mortality and unplanned hospitalization with an accuracy of 0.851, 0.835, and 0.584, respectively. If compared to CCI, DDCI showed discrimination and reclassification properties very similar to the CCI, and improved prediction when used in combination with the CCI. Conclusions and Relevance DDCI is a reliable prognostic index, able to stratify the entire population into homogeneous risk groups. DDCI can represent an useful tool for risk-adjustment, policy planning, and the identification of patients needing a focused approach in everyday practice.
Upper Confidence Trees and Billiards for Optimal Active Learning

: This paper focuses on Active Learning (AL) with bounded computational resources. AL is formalized as a ﬁnite horizon Reinforcement Learning problem, and tackled as a single-player game. An approximate optimal AL strategy based on tree-structured multi-armed bandit algorithms and billiard-based sampling is presented together with a proof of principle of the approach. Mots-cl´es : Apprentissage actif, Fouille d’arbre Monte-Carlo, Bandits
Expressiveness of guarded existential rule languages

The so-called existential rules have recently gained attention, mainly due to their adequate expressiveness for ontological query answering. Several decidable fragments of such rules have been introduced, employing restriction such as various forms of guardedness to ensure decidability. Some of the more well-known languages in this arena are (weakly) guarded and (weakly) frontier-guarded fragments of existential rules. In this paper, we explore their relative and absolute expressiveness. In particular, we provide a new proof that queries expressed via frontier-guarded and guarded rules can be translated into plain Datalog queries. Since the converse translations are impossible, we develop generalizations of frontier-guarded and guarded rules to nearly frontier-guarded and nearly guarded rules, respectively, which have exactly the expressive power of Datalog. We further show that weakly frontier-guarded rules can be translated into weakly guarded rules, and thus, weakly frontier-guarded and weakly guarded rules have exactly the same expressive power. Such rules cannot be translated into Datalog since their query answering problem is ExpTime-complete in data complexity. We strengthen this result by showing that on ordered databases and with input negation available, weakly guarded rules capture all queries decidable in exponential time. We then show that weakly guarded rules extended with stratified negation are expressive enough to capture all database queries decidable in exponential time, without any assumptions about input databases. Finally, we note that the translations of this paper are, in general, exponential in size, but lead to worst-case optimal algorithms for query answering with considered languages.
Activation of Nuclear Receptors RAR, RXR, and LXR Does Not Reduce Cuprizone-Induced Demyelination in Mice

Experiments with animal models of multiple sclerosis have shown that the expression of retinoid X receptors (RXR) increases during demyelination and that RXR is involved in the regulation of remyelination. After ligand binding RXRs form heterodimeric transcription factors with other nuclear receptor (NR) families including the retinoic acid receptors (RAR) and liver X receptors (LXR). We tested whether activation of these nuclear receptor complexes reduces pathological demyelination using the cuprizone mouse model. Cuprizone, which causes oligodendrocyte degeneration, was given for three weeks as a food additive. For the activation of nuclear receptors mice were treated with daily i.p. injections of agonists for RXR (9-cis RA), RAR (all-trans RA), and LXR (T0901317). Myelin status, oligodendrocyte survival, astrogliosis, microglial activation, and axon density were monitored with immunohistochemistry and evaluated quantitatively. Three weeks of cuprizone feeding caused severe demyelination and significantly raised the number of Iba1 immunoreactive microglia cells in the caudal corpus callosum. This increase of microglia activity was reduced with 9-cis RA treatment but was enhanced with all-trans RA and was not affected by T0901317. Nuclear receptor activation did not influence the degree of demyelination, oligodendrocyte survival, astrogliosis, or axonal preservation. We conclude that RXR activation, although affecting Iba1-positive microglia, does not protect oligodendrocytes from cuprizone toxicity and does not induce compensatory mechanisms in the initial phase of demyelination.
MRM: an adaptive framework for XML searching

In order to deal with the diversified nature of XML documents as well as individual user preferences, we propose a novel Multi-Ranker Model (MRM), which is able to abstract a spectrum of important XML properties and adapt the features to different XML search needs. The model consists of a novel three-level ranking structure and a training module called Ranking Support Vector Machine in a voting Spy Na¨1ve Bayes Framework (RSSF). RSSF is effective in learning search preference and then ranks the returned results adaptively. In this demonstration, we present our prototype developed from the model, which we call it the MRM XML search engine. The MRM engine employs only a list of simple XML tagged keywords as a user query for searching XML fragments from a collection of real XML documents. The demonstration presents an indepth analyses of the effectiveness of adaptive rankers, tailored XML rankers and a spectrum of low level ranking features.
PL-ranking: A Novel Ranking Method for Cross-Modal Retrieval

This paper proposes a novel method for cross-modal retrieval named Pairwise-Listwise \textbf{ranking} (PL-ranking) based on the low-rank optimization framework. Motivated by the fact that optimizing the top of ranking is more applicable in practice, we focus on improving the precision at the top of ranked list for a given sample and learning a low-dimensional common subspace for multi-modal data. Concretely, there are three constraints in PL-ranking. First, we use a pairwise ranking loss constraint to optimize the top of ranking. Then, considering that the pairwise ranking loss constraint ignores class information, we further adopt a listwise constraint to minimize the intra-neighbors variance and maximize the inter-neighbors separability. By this way, class information is preserved while the number of iterations is reduced. Finally, low-rank based regularization is applied to exploit the correlations between features and labels so that the relevance between the different modalities can be enhanced after mapping them into the common subspace. We design an efficient low-rank stochastic subgradient descent method to solve the proposed optimization problem. The experimental results show that the average MAP scores of PL-ranking are improved 5.1%, 9.2%, 4.7% and 4.8% than those of the state-of-the-art methods on the Wiki, Flickr, Pascal and NUS-WIDE datasets, respectively.
Image Stitching based on Particle Swarm and Maximum Mutual Information Algorithm

As a key link in image stitching, image registration based on maximum mutual information does not need any preprocess and has a high degree of automation and high registration accuracy, which thus attracted widespread attention. Optimization search in image registration process is easy to fall into local minima leading to the wrong registration parameters. This paper presents a method to build image pyramid based on wavelet transformation and uses swarm intelligence classical particle swarm optimization method to adjust its parameters with the mutual information and multi-resolution series. Experiments show that this method can effectively avoid local minima and can find the optimal registration transformation by finite optimization iterations. Performance of this proposed algorithm is better than the traditional multi-resolution method and its computational efficiency is higher than that of simulated annealing optimization method.
Hierarchical discriminant features for audio-visual LVCSR

We propose the use of a hierarchical, two-stage discriminant transformation for obtaining audio-visual features that improve automatic speech recognition. Linear discriminant analysis (LDA), followed by a maximum likelihood linear transform (MLLT) is first applied to MFCC based audio-only features, as well as on visual only features, obtained by a discrete cosine transform of the video region of interest. Subsequently, a second stage of LDA and MLLT is applied to the concatenation of the resulting single modality features. The obtained audio-visual features are used to train a traditional HMM based speech recognizer. Experiments on the IBM ViaVoice/sup TM/ audio-visual database demonstrate that the proposed feature fusion method improves speaker-independent, large vocabulary, continuous speech recognition (LVCSR) for both clean and noisy audio conditions considered. A 24% relative word error rate reduction over an audio-only system is achieved in the latter case.
Workshop on Abduction and Induction in AI and Scientiﬁc Modelling

Abduction and Induction are forms of logical reasoning with incomplete information that have many applications in AI. Abduction reasons from eﬀects to possible causes and has been used in tasks such as planning and diagnosis. Induction learns general rules for observed data and is typically used for classiﬁcation and knowledge acquisition. This Workshop investigates the claim that Abduction and Induction play fundamental roles in the process of scientiﬁc knowledge development and that these these complementary forms of reasoning can be proﬁtably integrated in an automated cycle of theory reﬁnement. The contributed papers examine these issues from philosophical, cognitive and practical points of view
NUMERICAL SOLUTION OF TWO-REGION ADVECTION-DISPERSION TRANSPORT AND COMPARISON WITH ANALYTICAL SOLUTION ON EXAMPLE PROBLEMS

We deal with a two-region porous media transport, which represent either solute transport in a medium with immobile pore zone (double or dual porosity) or solute transport with nonequilibrium sorption. The model consist of two coupled PDEs for mobile and immobile zone. Numerical solution is based on the nite volume method, with explicit upstream scheme for advection term and analytical solution of ODE representing the exchange between mobile and immobile zone. For suitable 1D problem, with exactly solved plain advection-dispersion problem, we show that after involving the eect of mobile-immobile exchange, the numerical solution is very close to the analytical solution. The results in example cases give a groundwork information for a use of the model for realistic problems.
Towards Optimal Resource Provisioning for Economical and Green MapReduce Computing in the Cloud

Running MapReduce programs in the cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge or job finish time for a specific job? An important step towards this ultimate goal is modeling the cost of MapReduce program. In this chapter, we study the whole process of MapReduce processing and build 1
The Economic Impact of Oil on Industry Portfolios

We build an equilibrium model to disentangle industry-specific from business cycle effects of oil on stock returns. In our model oil is considered as an input factor for production and also as a macro variable. We estimate the model for 13 industries, including the oil industry. Our results suggest that the value of all non-oil industries decreases with an oil price shock. This result is explained by the effect of oil on the price-dividend ratios of the industries, in particular, by the significant negative effect of oil on their growth opportunities. The high persistence of the real oil price shocks makes these effects to be long-lived. The effect of oil on the current cash-flows is negative but small. This explains why the oil price shocks can produce such a significant effects on the US financial market despite the low US economy's oil intensity. The conditional expected portfolio returns decrease with the oil price because of the negative effect of oil on the market price of risk and interest rates. Moreover, industries with higher systematic risk have expected returns that are more affected by the oil price. We find that most of the systematic risk of the firms is explained by their output rather than by effect of oil on the cash-flows.
Production and characterization of astrocyte-derived human apolipoprotein E isoforms from immortalized astrocytes and their interactions with amyloid-B $

Department of Neurology, Washington University, St. Louis, MO 63130, USA Neurology, Bryan ADRC, Duke University, Durham, NC 27706, USA Neuroscience Discovery Research, Eli Lilly and Co., Indianapolis, IN 46285, USA Center for Neurologic Diseases, Brigham and Woman’s Hospital and Department of Neurology, Harvard Medical School, Boston, MA 02115, USA Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA Molecular Neurobiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan Department of Molecular Biology and Pharmacology, Hope Center for Neurological Disorders, Alzheimer’s Disease Research Center, Washington University, St. Louis, MO 63110, USA
Towards an efficient generation of linguistic summaries of time series using a degree of focus

We present an approach to a more efficient generation of linguistic summaries using the traditional degree of truth and a degree of focus based mechanism for discarding nonpromising summaries. We use the approach to derive linguistic data summaries to subsume the past performance of an investment (mutual) fund, and then present numerical results on the efficiency of the truncation process proposed. It turns out that the mechanism proposed is efficient and makes possible to disregards between 75% to 99% possible summaries.