Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Where Your Classifier Does ( Not ) Work — the SCaPE Model Class for Exceptional Model Mining Te ch ni ca lR ep or t

FACT, the First G-APD Cherenkov Telescope, detects air showers induced by high-energetic cosmic particles. It is desirable to classify a shower as being induced by a gamma ray or a background particle. Generally, it is nontrivial to get any feedback on the real-life training task, but we can attempt to understand how our classifier works by investigating its performance on Monte Carlo simulated data. To this end, in this paper we develop the SCaPE (Soft Classifier Performance Evaluation) model class for Exceptional Model Mining, which is a Local Pattern Mining framework devoted to highlighting unusual interplay between multiple targets. In our Monte Carlo simulated data, we take as targets the computed classifier probabilities and the binary column containing the ground truth: which kind of particle induced the corresponding shower. Using a newly developed quality measure based on ranking loss, the SCaPE model class highlights subspaces of the search space where the classifier performs particularly well or poorly. These subspaces arrive in terms of conditions on attributes of the data, hence they come in a language a domain expert understands, which should aid him in understanding where his/her classifier does (not) work. Additional experiments are carried out on nine UCI datasets. Found subgroups highlight subspaces whose difficulty for classification is corroborated by astrophysical interpretation, as well as subspaces that warrant further investigation. Keywords—Astrophysics, Exceptional Model Mining, Cherenkov radiation, soft classifier. N.B.: A condensed version of this paper will appear as a short paper at the 14th IEEE International Conference on Data Mining [1].
PTRF/Cavin-1 and MIF Proteins Are Identified as Non-Small Cell Lung Cancer Biomarkers by Label-Free Proteomics

With the completion of the human genome sequence, biomedical sciences have entered in the “omics” era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer.
Robust multilinear principal component analysis

We propose two methods for robustifying multilinear principal component analysis (MPCA) which is an extension of the conventional PCA for reducing the dimensions of vectors to higher-order tensors. For two kinds of outliers, i.e., sample outliers and intra-sample outliers, we derive iterative algorithms on the basis of the Lagrange multipliers. We also demonstrate that the proposed methods outperform the original MPCA when datasets contain such outliers experimentally.
Data fusion and artificial neural networks for biomass estimation

The ability of artificial neural networks (ANNs) to learn from experience rather than from mechanistic descriptions makes them the preferred choice to model processes with intricate variable interrelations. Some of these processes can be found in the area of biotechnology. The authors aim to use ANNs and data fusion to provide better instrumentation for a fermentation process and eventually optimise its performance. Of particular interest is the robust estimation of biomass in the production of an antibiotic. Several feed-forward backpropagation neural networks (BPNs) have been chosen for the experiments using the Levenberg-Marquardt learning algorithm. Work has been carried out to test the generalisation capabilities and performance in the presence of noise and sensor failure. It has been observed that, given the appropriate training, data fusion and ANN methodology lead to estimation of these parameters with an accuracy comparable to instrumentation errors.
Effect of icodextrin dialysis solution on body weight and fat accumulation over time in CAPD patients.

BACKGROUND The purpose of this study was to analyse the changes of body composition and the effects of icodextrin dialysis solution over time on peritoneal dialysis (PD) in continuous ambulatory peritoneal dialysis (CAPD) patients. METHODS Among 183 incident patients, 75 patients finished a complete 36-month protocol. Clinical indices including daily glucose absorption and body composition, by bioelectrical impedance analysis (BIA), were measured in both groups (icodextrin group: 36 patients, non-icodextrin group: 39 patients) at the 1st (baseline), 12th, 24th and 36th months. RESULTS There were significant increases in body weight and fat mass during the 36 months after initiation of CAPD. It was found that 78% of 3 years of weight gain occurred during the first year and 88% of weight gain at the end of the first year was fat mass gain. The icodextrin group showed a significantly lower percent of fat mass during the first 36 months (P < 0.05) and also less changes in body weight, fat mass, percent (%) fat mass, visceral fat area and waist/hip ratio at 1, 2 and 3 years than the non-icodextrin group. There were no significant changes in total body water (TBW), extra cellular fluid (ECF), oedema index and lean body mass (LBM) through comparable daily and ultrafiltration volume (UFV) between the two groups during the initial 3 years. Factors associated with the higher percent of fat mass gain over time on peritoneal dialysis were age, diabetes, gender (female) and non-icodextrin group (all, P < 0.01, generalized estimating equation). CONCLUSION The application of icodextrin solution may be a better option to alleviate excessive fat gain over time for patients on PD.
Multiset signatures for transactional memory

Transactional Memory (TM) systems must record the memory locations read and written (read and write sets) by concurrent transactions in order to detect conflicts. Some TM implementations use signatures for this purpose, which summarize read and write sets in bounded hardware at the cost of false positives (detection of non-existing conflicts). Read/write signatures are usually implemented as two separate Bloom filters with the same size. In contrast, transactions usually exhibit read/write sets of uneven cardinality, where read sets use to be larger than write sets. Thus, the read filter populates earlier than the write one and, consequently the read signature false positive rate may be high while the write filter has still a low occupation. In this paper, a multiset signature design is proposed which records both the read and write sets in the same Bloom filter without adding significant hardware complexity. Several designs of multiset signatures are analyzed and evaluated. New problems arise related to hardware complexity and the existence of cross false positives, i.e. new false positives coming from the fact that both sets share the same filter. Additionally, multiset signatures are enhanced using locality-sensitive hashing, proposed by the authors in a previous work. Experimental results show that the multiset approach is able to reduce the false positive rate and improve the execution performance in most of the tested codes, without increasing the required hardware area in a noticeable amount.
Reluctance to Accept Alcohol Treatment by Alcoholic Liver Disease Transplant Patients: A Qualitative Study

Background Liver transplantation (LT) is the optimum treatment for patients with end-stage alcoholic liver disease (ALD). However, despite a recognized risk of relapse to harmful drinking, ALD transplant patients are reluctant to use speciality alcohol treatment to support their abstinence, even when offered within the LT context. This study aimed to understand and identify factors contributing to alcohol treatment reluctance by ALD patients undergoing transplantation. Methods We conducted an in-depth qualitative study of ALD transplant patients. Minimally structured face-to-face interviews explored participants' alcohol-related experiences and their reasons for not using alcohol treatment during the course of their transplantation. Thematic analysis was used to analyze and interpret interview data to understand treatment reluctance based on participants' experiences. Results Five major themes were identified among 3 subgroups of patients (pretransplant and posttransplant abstainers and posttransplant relapsers): (i) the “contract” of mandatory abstinence, (ii) the “gap in the program” involving the lack of candour between patient and staff about alcohol-related matters and the lack of addiction services, (iii) a preference by participants to self-manage their alcohol use disorder, (iv) social support as a facilitator of abstinence and the risk of relapse when social support is diminished, and (v) the fear of stigmatization. Each of these factors were dynamically interrelated and differed slightly for each subgroup. Conclusions The LT services may benefit from the inclusion of integrated specialist addiction services in their model of care. Such an approach may enhance the acceptability of alcohol treatment and reduce the risk of relapse among ALD transplant participants, especially for those whose social supports have diminished.
Separate brain regions code for salience vs. valence during reward prediction in humans

Predicting rewards and avoiding aversive conditions is essential for survival. Recent studies using computational models of reward prediction implicate the ventral striatum in appetitive rewards. Whether the same system mediates an organism's response to aversive conditions is unclear. We examined the question using fMRI blood oxygen level‐dependent measurements while healthy volunteers were conditioned using appetitive and aversive stimuli. The temporal difference learning algorithm was used to estimate reward prediction error. Activations in the ventral striatum were robustly correlated with prediction error, regardless of the valence of the stimuli, suggesting that the ventral striatum processes salience prediction error. In contrast, the orbitofrontal cortex and anterior insula coded for the differential valence of appetitive/aversive stimuli. Given its location at the interface of limbic and motor regions, the ventral striatum may be critical in learning about motivationally salient stimuli, regardless of valence, and using that information to bias selection of actions. Inc. Hum Brain Mapp, 2007. © 2006 Wiley‐Liss, Inc.
Interplay Between Visual and Spatial: The Effect of Landmark Descriptions on Comprehension of Route/Survey Spatial Descriptions

Successful wayfinding requires accurate encoding of two types of information: landmarks and the spatial relations between them (e.g., landmark X is left/north of Y). Although both types of information are crucial to wayfinding, behavioral and neurological evidence suggest that they have different substrates. In this paper, we consider the nature of the difference by examining comprehension times of spatial information (i.e., route and survey descriptions) and landmark descriptions. In two studies, participants learned simple environments by reading descriptions from route or survey perspectives, half with a single perspective switch. On half of the switch trials, a landmark description was introduced just prior to the perspective switch. In the first study, landmarks were embellished with descriptions of visual details, while in the second study, landmarks were embellished with descriptions of historic or other factual information. The presence of landmark descriptions did not increase the comprehension time of either route or survey descriptions, suggesting that landmark descriptions are perspective-neutral. Furthermore, visual landmark descriptions speeded comprehension time when the perspective was switched, whereas factual landmark descriptions had no effect on perspective switching costs. Taken together, the findings support separate processes for landmark and spatial information in construction of spatial mental models, and point to the importance of visual details of landmarks in facilitating mental model construction.
That is disgusting! Should I trust you?: The role of mindsets in brand trust after a negative incident

Purpose – The purpose of this investigation was to examine the influence of mindsets on consumers’ reactions to a negative event involving a corporate brand. Design/methodology/approach – We conducted two studies, one qualitative and one quantitative. In study 1, participants were shown a picture of the incident with a caption and personal interviews were conducted with consumers of the category involved in the event, i.e. green chilies. In study 2, participants were shown the same picture as in study 1 and completed a set of questionnaires assessing mindsets, brand trust, and purchase intention. Findings – Study 1 showed the presence of a theme involving the stability versus malleability of brands, used to explain the decision to either trust and purchase the brand again or not. Study 2 showed a positive relationship between a growth mindset and brand trust. Similarly, brand trust was positively related to purchase intention. The latent class model showed that the two classes with higher levels of a growth mindset were more likely to trust the brand and purchase it after the negative event. Originality/value – To our knowledge, only one investigation has examined the role of mindsets in consumers’ reactions after a negative incident involving a corporate brand. Hence, this investigation tried to answer a recent call from several consumer behavior scholars for more research on mindsets.
Family History, Diabetes, and Other Demographic and Risk Factors Among Participants of the National Health and Nutrition Examination Survey 1999–2002

Introduction Family history of diabetes has been recognized as an important risk factor of the disease. Family medical history represents valuable genomic information because it characterizes the combined interactions between environmental, behavioral, and genetic factors. This study examined the strength and effect of having a family history of diabetes on the prevalence of self-reported, previously diagnosed diabetes among adult participants of the National Health and Nutrition Examination Survey 1999–2002. Methods The study population included data from 10,283 participants aged 20 years and older. Gender, age, race/ethnicity, poverty income ratio, education level, body mass index, and family history of diabetes were examined in relation to diabetes status. Diabetes prevalence estimates and odds ratios of diabetes were calculated based on family history and other factors. Results The prevalence of diabetes among individuals who have a first-degree relative with diabetes (14.3%) was significantly higher than that of individuals without a family history (3.2%), corresponding to a crude odds ratio of five. Both prevalence and odds ratio estimates significantly increased with the number of relatives affected with diabetes. Family history was also associated with several demographic and risk factors. Conclusion Family history of diabetes was shown to be a significant predictor of diabetes prevalence in the adult U.S. population. We advocate the inclusion of family history assessment in public health prevention and screening programs as an inexpensive and valuable source of genomic information and measure of diabetes risk.
Towards a Linguistic Linked Open Data cloud: The Open Linguistics Working Group

The Open Linguistics Working Group (OWLG) is an initiative of experts from different fields concerned with linguistic data, including academic linguistics (e.g. typology, corpus linguistics), applied linguistics (e.g. computational linguistics, lexicography and language documentation), and NLP (e.g. from the Semantic Web community). The primary goals of the working group are 1) promoting the idea of open linguistic resources, 2) developing means for their representation, and 3) encouraging the exchange of ideas across different disciplines. To a certain extent, the activities of the Open Linguistics Working Group converge towards the creation of a Linguistic Linked Open Data cloud, which is a topic addressed from different angles by several members of the Working Group. In this article, some of these currently on-going activities are presented and described. RESUME. Le groupe OWLG est une initiative d’experts provenant de differents domaines linguistiques, comprenant la linguistique academique (typologie, corpus), la linguistique appliquee (linguistique computationelle, lexicographie, documentation de langues) et le traitement automatique des langues (p.ex. Web Semantique). Les objectifs principaux de ce groupe sont 1) la promotion de l’idee de ressources ouvertes et accessibles, 2) le developpement de moyens pour representer lesdites ressources et 3) la stimulation d’echanges entre les diverses disciplines et subdisciplines. Les activites de l’OWLG sont pour la plupart liees a la creation d’un nuage de donnees linguistiques Linked Open Data. Les membres du groupe abordent ce theme sous des aspects differents, dont nous presenterons quelques-uns ci-dessous.
Stochastic modeling of spectral adjustment for high quality pitch modification

We present a new algorithm for adjusting the magnitude spectrum when the fundamental frequency (F/sub 0/) of a speech signal is altered. The algorithm exploits the correlation between F/sub 0/ and the magnitude spectrum of speech as represented by line spectral frequencies (LSFs). This correlation is class-dependent, and thus a broad classification of the input is achieved by a Gaussian mixture model (GMM). The within-class dependencies of LSFs on F/sub 0/ values are captured by constructing their joint probability densities using a series of GMMs, one for each speech class. The proposed system is used for post-processing the pitch modified signal. Perceptual tests showed that the addition of this post-processing system improves the naturalness of the pitch modified signal for large pitch modification factors.
How Community Matters for User Innovation: The Open Source of Sports Innovation

the students of the "Empirical Methods" course at the University of Munich, and participants at the Harvard Business School "Open Source" Lunch for their time, advice, and stimulating discussions. Abstract This exploratory study analyzes the context within which end-users develop sports-related consumer product innovations. We find that end-user innovators who are members of voluntary special-interest user communities often receive assistance in developing their innovations from fellow community members. Within these user communities, we find that innovation-related information and assistance, as well as the innovations themselves, are freely shared. The processes by which these voluntary user innovation communities operate and the "institutional" structure supporting innovation and free sharing of iimovations may be of general interest both within and beyond the consumer products arena. Communities of open source software developers, for example, appear to operate in very similar ways.
California Center for Population Research On-Line Working Paper Series NBER WORKING PAPER SERIES BIOLOGY AS DESTINY? SHORT AND LONG-RUN DETERMINANTS OF INTERGENERATIONAL TRANSMISSION OF BIRTH WEIGHT

Little is known about the mechanisms underlying the transfer of economic status between generations. This paper addresses the question of whether inter-generational correlations in health contribute to the perpetuation of economic status. We examine inter-generational correlations in birth weight, a key indicator of the health of newborns that we link to future educational attainment and earnings using a unique data set based on California births from 1960s to the present. We use names and birth dates to link the records of mothers and children. We also identify mothers who are siblings. We show that there is a strong intergenerational correlation in the birth weight of mothers and children, but that a measure of household income at the time of the mother's birth is also predictive of low birth weight and that there is an interaction between maternal low birth weight and poverty in the production of low birth weight. Together these findings suggest that intergenerational correlations in health could play a role in the intergenerational transmission of income. Parent's income affects child health, and health at birth affects future income. Janet Currie Department of Economics Columbia University 1022 International Affairs Building MC 3308 420 W 118 Street New York, NY 10027 and NBER currie@simba.sscnet. ucla.edu Enrico Moretti Department of Economics 549 Evans Hall #3880 University of California Berkeley, CA 94720-3880 and NBER moretti@econ.berkeley. edu
Industry Returns, Single and Multifactor Asset Pricing Tests

Empirical tests of the capital asset pricing model (CAPM) have shown that movements in a single factor market index poorly explain returns on individual securities. Assuming that a possible reason is the existence of pricing anomalies, more recent research has extended the model by adding new factors to explain systematic variations in stock returns and achieved significant results. In particular, securities with high book to market ratios (book equity divided by market equity, BE/ME) appear to command a value premium. One suggestion is that the BE/ME premium compensates for the systematic risk associated with financial distress. It has been argued that industries experience periods of growth and financial distress and it has been reported in the US literature that the BE/ME premium is not stationary. To explore this proposition, this paper examines the behaviour of CAPM and three-factor models when tested using portfolios of different industries through time. The results show that the three-factor model provides a better explanation of industry returns than the CAPM. Loadings on the BE/ME premium are not stationary and appear to vary for the different industries through time. Monthly returns in excess of the one month t-bill tend to be generally higher for the Basic industrial sector through time.
Endoscopic Management of Anastomotic Strictures after Liver Transplantation

Endoscopic treatment of biliary strictures involving plastic stent placement has been used widely. The use of self-expandable metal stents (SEMSs) has been described for anastomotic strictures following liver transplantation (LT). This review aimed to assess and compare the efficacy of plastic stents with SEMS in LT patients. Information was retrieved regarding technical success, stricture resolution, the number of endoscopic retrograde cholangiography procedures, follow-up, immediate, and late complications. Eight studies involving plastic stents had a stricture resolution rate of 84.5%, with the rates ranging from 63% to 100%. These rates are comparable with the stricture resolution rate of 75% determined from six studies that involved 236 patients who received metal stents and the rates ranged from 53% to 81%. The observed success rate for metal stents used to manage post-LT anastomotic biliary strictures was below the reported rate for multiple plastic stents. Hence, the currently available metal stents should not be offered for the management of post-LT anastomotic biliary strictures.