Adaptation to Drifting User's Interests

In recent years, many systems have been developed which aim at helping users to find pieces of information or other objects that are in accordance with their personal interests. In these systems, machine learning methods are often used to acquire the user interest profile. Frequently user interests drift with time. The ability to adapt fast to the current user's interests is an important feature for recommender systems. This paper presents a method for dealing with drifting interests by introducing the notion of gradual forgetting. Thus, the last observations should be more "important" for the learning algorithm than the old ones and the importance of an observation should decrease with time. The conducted experiments with a recommender system show that the gradual forgetting improves the ability to adapt to drifting user's interests. Experiments with the STAGGER problem provide additional evidences that gradual forgetting is able to improve the prediction accuracy on drifting concepts (incl. drifting user's interests)

Learning about Users from Observation

Many approaches and systems for recommending information, goods, or other kinds of objects have been developed in recent years. In these systems, machine learning methods are often used that need training input to acquire a user interest profile. Such methods typically need positive and negative evidence of the user’s interests. To obtain both kinds of evidence, many systems make users rate relevant objects explicitly. Others merely observe the user’s behavior, which yields positive evidence only; in order to be able to apply the standard learning methods, these systems mostly use heuristics to also find negative evidence in observed behavior. In this paper, we present an approach for learning interest profiles from positive evidence only, as it is contained in observed user behavior. Thus, both the problem of interrupting the user for ratings and the problem of somewhat artificially determining negative evidence are avoided. A methodology for learning explicit user profiles and recommending interesting objects has been developed. It is used in the context of ELFI – a Web-based information system. The evaluation results are briefly described in this paper. Our current efforts revolve around further improvements of the methodology and its implementation for recommending interesting web pages to users of a web browser

Learning to Recommend from Positive Evidence

In recent years, many systems and approaches for recommending information, goods, or other kinds of objects have been developed. In these systems, often machine learning methods are used that need training input to acquire a user interest profile. Such methods typically need positive and negative evidence of the user’s interests. To obtain both kinds of evidence, many systems make users rate relevant objects explicitly. Others merely observe the user’s behavior, which fairly obviously yields positive evidence; in order to be able to apply the standard learning methods, these systems mostly use heuristics that attempt to find also negative evidence in observed behavior. In this paper, we present several approaches to learning interest profiles from positive evidence only, as it is contained in observed user behavior. Thus, both the problem of interrupting the user for ratings and the problem of somewhat artificially determining negative evidence are avoided. The learning approaches were developed and tested in the context of the Web-based ELFI information system that is in real use by more than 1000 people. We give a brief sketch of ELFI and describe the experiments we made based on ELFI usage logs to evaluate the different proposed method

Pterygium and Associated Ocular Surface Squamous Neoplasia

Objective: To measure the rate of histopathologically identified ocular surface squamous neoplasia (OSSN) in pterygium specimens. Methods: All pterygium specimens collected from consecutive patients between April 8, 2003, and February 6, 2008, were submitted for histopathologic examination, and the rate of OSSN was calculated. Results: The rate of OSSN was 9.8% (52 of 533) in sequential pterygium specimens. Conclusions: This rate of unsuspected OSSN suggests that all specimens of pterygium should be submitted for histopathologic examination and that patients in whom OSSN is noted should be examined at more frequent intervals so any clinical OSSN that develops can be identified at an early stage

Ionic Components of Electric Current at Rat Corneal Wounds

Peer reviewedPublisher PD

Prognostic Factors in Dedifferentiated Chondrosarcoma: A Retrospective Analysis of a Large Series Treated at a Single Institution.

Background:Dedifferentiated chondrosarcomas (DDCSs) are highly malignant tumors with a dismal prognosis and present a significant challenge in clinical management. Methods:In an IRB approved retrospective protocol, we identified 72 patients with DDCS treated at our institution between 1993 and 2017 and reviewed clinicopathological characteristics, treatment modalities, and outcomes to analyze prognostic factors. Results:Femur (44.4%), pelvis (22.2%), and humerus (12.5%) were most commonly involved sites. Twenty-three patients (31.9%) presented with distant metastasis, and 3 (4.2%) of them also had regional lymph node involvement. The median overall survival (OS) was 13.9 months. On multivariate analysis, pathological fracture, larger tumor size, lymph node involvement, metastasis at diagnosis, extraosseous extension, and undifferentiated pleomorphic sarcoma component correlated with worse OS, whereas surgical resection and chemotherapy were associated with improved OS. For progression-free survival (PFS), pathological fracture and metastasis at diagnosis showed increased risk, while chemotherapy was associated with decreased risk. Among patients who received chemotherapy, doxorubicin and cisplatin were significantly associated with improved PFS but not OS. Among patients without metastasis at diagnosis, 17 (34.7%) developed local recurrence. Thirty-one (63.3%) developed distant metastases at a median interval of 18.1 months. On multivariate analysis, R1/R2 resection was related with local recurrence, while macroscopic dedifferentiated component was associated with distant metastasis. Conclusions:The prognosis of DDCS is poor. Complete resection remains a significant prognostic factor for local control. Chemotherapy with doxorubicin and cisplatin seems to have better PFS. More prognostic, multicenter trials are warranted to further explore the effectiveness of chemotherapy in selected DDCS patients

A Sublinear Variance Bound for Solutions of a Random Hamilton Jacobi Equation

We estimate the variance of the value function for a random optimal control problem. The value function is the solution $w^\epsilon$ of a Hamilton-Jacobi equation with random Hamiltonian $H(p,x,\omega) = K(p) - V(x/\epsilon,\omega)$ in dimension $d \geq 2$. It is known that homogenization occurs as $\epsilon \to 0$, but little is known about the statistical fluctuations of $w^\epsilon$. Our main result shows that the variance of the solution $w^\epsilon$ is bounded by $O(\epsilon/|\log \epsilon|)$. The proof relies on a modified Poincar\'e inequality of Talagrand

Hot new directions for quasi-Monte Carlo research in step with applications

This article provides an overview of some interfaces between the theory of quasi-Monte Carlo (QMC) methods and applications. We summarize three QMC theoretical settings: first order QMC methods in the unit cube $[0,1]^s$ and in $\mathbb{R}^s$, and higher order QMC methods in the unit cube. One important feature is that their error bounds can be independent of the dimension $s$ under appropriate conditions on the function spaces. Another important feature is that good parameters for these QMC methods can be obtained by fast efficient algorithms even when $s$ is large. We outline three different applications and explain how they can tap into the different QMC theory. We also discuss three cost saving strategies that can be combined with QMC in these applications. Many of these recent QMC theory and methods are developed not in isolation, but in close connection with applications

Cystic Fibrosis-Niche Adaptation of Pseudomonas aeruginosa Reduces Virulence in Multiple Infection Hosts

The opportunistic pathogen Pseudomonas aeruginosa is able to thrive in diverse ecological niches and to cause serious human infection. P. aeruginosa environmental strains are producing various virulence factors that are required for establishing acute infections in several host organisms; however, the P. aeruginosa phenotypic variants favour long-term persistence in the cystic fibrosis (CF) airways. Whether P. aeruginosa strains, which have adapted to the CF-niche, have lost their competitive fitness in the other environment remains to be investigated. In this paper, three P. aeruginosa clonal lineages, including early strains isolated at the onset of infection, and late strains, isolated after several years of chronic lung infection from patients with CF, were analysed in multi-host model systems of acute infection. P. aeruginosa early isolates caused lethality in the three non-mammalian hosts, namely Caenorhabditis elegans, Galleria mellonella, and Drosophila melanogaster, while late adapted clonal isolates were attenuated in acute virulence. When two different mouse genetic background strains, namely C57Bl/6NCrl and Balb/cAnNCrl, were used as acute infection models, early P. aeruginosa CF isolates were lethal, while late isolates exhibited reduced or abolished acute virulence. Severe histopathological lesions, including high leukocytes recruitment and bacterial load, were detected in the lungs of mice infected with P. aeruginosa CF early isolates, while late isolates were progressively cleared. In addition, systemic bacterial spread and invasion of epithelial cells, which were detected for P. aeruginosa CF early strains, were not observed with late strains. Our findings indicate that niche-specific selection in P. aeruginosa reduced its ability to cause acute infections across a broad range of hosts while maintaining the capacity for chronic infection in the CF host

Association study in the 5q31-32 linkage region for schizophrenia using pooled DNA genotyping

<p>Abstract</p> <p>Background</p> <p>Several linkage studies suggest that chromosome 5q31-32 might contain risk loci for schizophrenia (SZ). We wanted to identify susceptibility genes for schizophrenia within this region.</p> <p>Methods</p> <p>We saturated the interval between markers D5S666 and D5S436 with 90 polymorphic microsatellite markers and genotyped two sets of DNA pools consisting of 300 SZ patients of Bulgarian origin and their 600 parents. Positive associations were followed-up with SNP genotyping.</p> <p>Results</p> <p>Nominally significant evidence for association (p < 0.05) was found for seven markers (D5S0023i, IL9, RH60252, 5Q3133_33, D5S2017, D5S1481, D5S0711i) which were then individually genotyped in the trios. The predicted associations were confirmed for two of the markers: D5S2017, localised in the <it>SPRY4-FGF1 </it>locus (p = 0.004) and IL9, localized within the IL9 gene (p = 0.014). Fine mapping was performed using single nucleotide polymorphisms (SNPs) around D5S2017 and IL9. In each region four SNPs were chosen and individually genotyped in our full sample of 615 SZ trios. Two SNPs showed significant evidence for association: rs7715300 (p = 0.001) and rs6897690 (p = 0.032). Rs7715300 is localised between the <it>TGFBI </it>and <it>SMAD5 </it>genes and rs6897690 is within the <it>SPRY4 </it>gene.</p> <p>Conclusion</p> <p>Our screening of 5q31-32 implicates three potential candidate genes for SZ: <it>SMAD5</it>, <it>TGFBI </it>and <it>SPRY4</it>.</p