Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-automatic construction of thyroid cancer intervention corpus from biomedical abstracts

Thyroid cancer is a common endocrine tumor that is experiencing a steady increase in incidence worldwide. The latest discoveries on disease and its treatment are mostly propagated in the form of biomedical publications such as those in PubMed. Unfortunately, this information is distributed in unstructured text with over two thousand articles being added annually. Text mining technology plays an important role in information extraction, since it can be used to uncover hidden value from the vast amount of text in reasonable time. In general, a preliminary task of text mining is Named Entity Recognition (NER). In this case, a gold standard corpus is needed, since the capability of NER depends on a trustworthy corpus. However the construction of gold standard corpus is a laborious and time-consuming process. In order to obtain a reasonably practical corpus in a limited time, this paper consequently proposes a semiautomatic approach to construct a thyroid cancer interventions corpus. The experimental results demonstrate that the proposed method can be used to construct a thyroid cancer intervention corpus reasonably in terms of both performance and overfitting avoidance.
Combining domain-specific heuristics for author name disambiguation

Author name disambiguation has been one of the hardest problems faced by digital libraries since their early days. Historically, supervised solutions have empirically outperformed those based on heuristics, but with the burden of having to rely on manually labelled training sets for the learning process. Moreover, most supervised solutions just apply some type of generic machine learning solution and do not exploit specific knowledge about the problem. In this paper, we follow a similar reasoning, but in the opposite direction. Instead of extending an existing supervised solution, we propose a set of carefully designed heuristics and similarity functions and apply supervision only to optimize such parameters for each particular dataset. As our experiments show, the result is a very effective, efficient and practical author name disambiguation method that can be used in many different scenarios.
Direct sequence modified time hopping PPM over ultra wideband S-V channel

A three position Pulse Position Modulation (PPM) scheme is proposed for a Direct Sequence Time Hopping Ultra Wideband wireless communication system. The channel is based on the Saleh-Valenzuela model. We show that by adapting the Space Time Spreading technique to use three pulse positions in a single input single output system representing -2,0,2 an improvement in Bit Error Rate is achieved compared to sending the same bits using two pulse positions only. In addition, the bit access rate is doubled as two bits can be successfully sent in the same time using our proposed system.
Moment sets and the unitary dual of a nilpotent Lie group.

Let G be a connected and simply connected nilpotent Lie group with Lie algebra g and unitary dual Ĝ. The moment map for π ∈ Ĝ sends smooth vectors in the representation space of π to g∗. The closure of the image of the moment map for π is called its moment set. N. Wildberger has proved that the moment set for π coincides with the closure of the convex hull of the corresponding coadjoint orbit. We say that Ĝ is moment separable when the moment sets differ for any pair of distinct irreducible unitary representations. Our main results provide sufficient and necessary conditions for moment separability in a restricted class of nilpotent groups.
Concepts and challenges

Volume I: Concepts and Challenges Part 1. The concepts of Freedom and Moral Responsibility Part 2. Skeptical Worries: The Worry that We Might Not Be Free and Morally Responsible A. Fatalism B. Divine Omniscience Volume II: Determinism Part 1. Causal Determinism A. Causal Determinism and Alternative Possibilities B. Causal Determinism and Sourcehood Part 2. Compatibilist Accounts of Free Will Volume III: Libertarianism, Alternative Possibilites and Moral Responsibility Part 1. Incompatibilist Accounts of Free Will A. Event-Causal Approaches B. Agent-Causal Approaches Part 2. Free Will and Moral Responsibility Volume IV: Free Agency, Moral Responsibility and Skepticism Part 1. Accounts of Acting Freely Part 2. Worries About the Coherence of Free Will and Moral Responsibility
Positive-Operator Valued Measure (POVM) Quantization

Department of Applied Mathematics, Illinois Institute of Technology, Chicago, IL 60616, USA;E-Mail: heller@iit.edu* Authors to whom correspondence should be addressed; E-Mail: gazeau@apc.univ-paris7.fr;Tel. : +331-5727-6049; Fax: +331-5727-6071.Academic Editor: James D. MalleyReceived: 3 September 2014 / Accepted: 18 December 2014 / Published: 25 December 2014Abstract: We present a general formalism for giving a measure space paired with a separableHilbert space a quantum version based on a normalized positive operator-valued measure.The latter are built from families of density operators labeled by points of the measurespace. We especially focus on various probabilistic aspects of these constructions. Simple ormore elaborate examples illustrate the procedure: circle, two-sphere, plane and half-plane.Links with Positive-Operator Valued Measure (POVM) quantum measurement and quantumstatistical inference are sketched.Keywords: POVM; quantization; covariance; density operators; quantum measurement1. IntroductionIn this paper, we propose a quantum analysis, generally non-commutative, of a measurespace based on a (normalized) positive-operator valued measure ((N)POVM) (in order not tospoil the text with too many acronyms, we will keep “POVM” in our paper to designate anormalized positive operator-valued measure) built from a density matrix or operator (in thequantum mechanics terminology) acting on some separable Hilbert space. One key aspect of
Ozone and short-term mortality in 95 US urban communities, 1987-2000.

CONTEXT Ozone has been associated with various adverse health effects, including increased rates of hospital admissions and exacerbation of respiratory illnesses. Although numerous time-series studies have estimated associations between day-to-day variation in ozone levels and mortality counts, results have been inconclusive. OBJECTIVE To investigate whether short-term (daily and weekly) exposure to ambient ozone is associated with mortality in the United States. DESIGN AND SETTING Using analytical methods and databases developed for the National Morbidity, Mortality, and Air Pollution Study, we estimated a national average relative rate of mortality associated with short-term exposure to ambient ozone for 95 large US urban communities from 1987-2000. We used distributed-lag models for estimating community-specific relative rates of mortality adjusted for time-varying confounders (particulate matter, weather, seasonality, and long-term trends) and hierarchical models for combining relative rates across communities to estimate a national average relative rate, taking into account spatial heterogeneity. MAIN OUTCOME MEASURE Daily counts of total non-injury-related mortality and cardiovascular and respiratory mortality in 95 large US communities during a 14-year period. RESULTS A 10-ppb increase in the previous week's ozone was associated with a 0.52% increase in daily mortality (95% posterior interval [PI], 0.27%-0.77%) and a 0.64% increase in cardiovascular and respiratory mortality (95% PI, 0.31%-0.98%). Effect estimates for aggregate ozone during the previous week were larger than for models considering only a single day's exposure. Results were robust to adjustment for particulate matter, weather, seasonality, and long-term trends. CONCLUSIONS These results indicate a statistically significant association between short-term changes in ozone and mortality on average for 95 large US urban communities, which include about 40% of the total US population. The findings indicate that this widespread pollutant adversely affects public health.
Growth performance, fatty-acid composition, lipid deposition and hepatic-lipid metabolism-related gene expression in juvenile pond loach Misgurnus anguillicaudatus fed diets with different dietary soybean oil levels.

A 10 week feeding trial was conducted to evaluate the effects of different dietary soybean oil (SO) levels on growth performance, fatty-acid composition and lipid deposition in viscera, histology and histochemistry of liver, intestine and hepatic-lipid metabolism-related gene expressions in pond loach Misgurnus anguillicaudatus juveniles. Misgurnus anguillicaudatus (mean ± s.d. mass 0·40 ± 0·01 g) were fed five experimental diets containing SO at different concentrations: 0, 20, 32, 56 and 100% SO and a diet containing 100% fish oil (100% FO). The mass gains and specific growth rates of M. anguillicaudatus fed 20% SO and 100% FO diets were significantly higher than those of the other groups (P < 0·05). The lipid content of viscera and the amount of cytoplasmic vacuolation in the liver increased with incremental dietary SO level. Meanwhile, increasing dietary SO levels up-regulated the messenger (m)RNA levels of lipogenic genes (such as Δ6fad, scd, pparγ, fas and srebp-1) and down-regulated the mRNA levels of the lipolytic genes (such as pparα, cpt1, atgl and hsl) in the liver. The percentage of 20:4n-6 significantly (P < 0·05) increased with increasing dietary SO level, which might be correlated with the up-regulation of the mRNA level of Δ6fad. The highest levels of dietary SO, however, had a negative effect on growth performance, lipid deposition of viscera and histology and histochemstry of liver and intestine. The increased lipid accumulation induced by incremental dietary SO level probably occurred through different strategies for lipid metabolism as a result of competition between lipolysis and lipogenesis and between export and import of lipids in this species.
Design and evaluation of the tightly coupled perceptual-cognitive tasks in knowledge domain visualization

Knowledge Domain Visualization (KDViz) aims to reveal the dynamics of how knowledge domains evolve over time. It is the ultimate role for information visualization to facilitate a wide variety of user tasks associated with the study of such dynamic, large-scale, transient, and self-organized phenomena. Since the evolution of knowledge domain is necessarily influenced by an array of factors, the design and empirical evaluation of a KDViz system is particularly challenging. In addition, KDViz typically involves a complex and lengthy sequence of data transformation and computational modeling that must be simplified for effective handling by the user interface design. In this article we present a perceptual-cognitive task taxonomy for designing and evaluating KDViz systems. The design implications are illustrated by the development and some refinements of CiteSpace, a Java application that enables users to study how networks of research fronts develop over time. Cognitive tasks such as finding pivotal intellectual points in an evolving knowledge domain are translated into tasks for visual search of objects with visually salient features. The design rationale is explained in terms of the taxonomy, including the use of Gestalt principles, graph drawing aesthetics, and pre-attentive visual search. The implications of the taxonomy on conducting empirical evaluations are illustrated by a case study using evaluation techniques focusing on usability issues. Three levels of user tasks are considered by the taxonomy: 1) the lowest level: manipulating the user interface; 2) the medium level: manipulating the visualization; and 3) the highest level: tracking the growth of a knowledge domain.
Analysis of Peer-to-Peer Network Security using Gnutella

Peer-to-peer (P2P) networks have emerged over the past several years as new and effective ways for distributed resources to communicate and cooperate. "Peer-to-peer computing is the sharing of computer resources and services by direct exchange between systems. These resources and services include the exchange of information, processing cycles, cache storage, and disk storage for files. " P2P networking has the potential to greatly expand the usefulness of the network be it for sharing music and video, privately contracting for services or for coordinating the use of expensive scientific instruments and computers. Some of the networks, such as Napster and Gnutella are created in an ad hoc manner with little or no centralized control. Other P2P networks such as computational and data grids are being designed and implemented in a very structured manner. P2P networks are presenting new challenges to computer security and privacy in a number of ways. This project will explore the security issues raised by P2P networks by studying an example on an extreme point on the design spectrum: Gnutella [Gnu]. Our primary focus will be the Gnutella network, which is the de facto standard for large, loosely structured, P2P networks. The Grid is a very large-scale , heterogeneous, formally structured P2P network that spans many organizations to make “virtual systems” of resources. [OGSA] It is quickly becoming the standard for distributed resource allocation for high-end computer and instrumentation systems. This paper demonstrates that for P2P networks with ad hoc structure, significant security concerns persist. What are Peer-to-peer
The Episode File System

We describe the design of Episode, TM a highly portable POSIX-compliant le system. Episode is designed to utilize the disk bandwidth e ciently, and to scale well with improvements in disk capacity and speed. It utilizes logging of meta-data to obtain good performance, and to restart quickly after a crash. Episode uses a layered architecture and a generalization of les called containers to implement lesets. A leset is a logical le system representing a connected subtree. Filesets are the unit of administration, replication, and backup in Episode. The system works well, both as a standalone le system and as a distributed le system integrated with the OSF's Distributed Computing Environment (DCE). Episode will be shipped with the DCE as the Local File System component, and is also exportable by NFS. As for performance, Episode meta-data operations are signi cantly faster than typical UNIX Berkeley Fast File System implementations due to Episode's use of logging, while normal I/O operations run near disk capacity.
Converted-wave common azimuth migration: Real data results

The converted-wave common-azimuth migration operator (PS-CAM) and the single mode common-azimuth migration operator share the advantage that they need a 3-D prestack cube of data with four dimensions (t, cmp x , cmp y , h x ), instead of the entire ve dimensions (t, cmp x , cmp y , h x , h y). The ve dimensions can be reduced to four dimensions through different processes. This paper compares two images of the PS data set from the OBS acquisition on the Alba oil eld in the North sea. The two images correspond to the following processes: 1. The common-azimuth migration of the data regularized using Normal Moveout. 2. The common-azimuth migration of the data regularized using PS Azimuth Moveout. The nal results show that the image from the data regularized using PS Azimuth Moveout is signicantly better than the images obtained the image from the data regularized using Normal Moveout.
A Summary of Team MIT's Approach to the Amazon Picking Challenge 2015

The Amazon Picking Challenge (APC), held alongside the International Conference on Robotics and Automation in May 2015 in Seattle, challenged roboticists from academia and industry to demonstrate fully automated solutions to the problem of picking objects from shelves in a warehouse fulfillment scenario. Packing density, object variability, speed, and reliability are the main complexities of the task. The picking challenge serves both as a motivation and an instrument to focus research efforts on a specific manipulation problem. In this document, we describe Team MIT's approach to the competition, including design considerations, contributions, and performance, and we compile the lessons learned. We also describe what we think are the main remaining challenges.
The Role of Phosphatidylinositol-3-Kinase and AMP-Activated Kinase in the Rapid Estrogenic Attenuation of Cannabinoid-Induced Changes in Energy Homeostasis

We sought to determine the involvement of phosphatidyl inositol 3-kinase (PI3K) and AMP-activated protein kinase (AMPK) in the estrogenic antagonism of the cannabinoid regulation of energy homeostasis. Food intake and body weight were evaluated in ovariectomized female guinea pigs treated s.c. with estradiol benzoate (EB) or its sesame oil vehicle, or the CB1 receptor antagonist AM251 or its cremephor/ethanol/0.9% saline vehicle. AMPK catalytic subunit, PI3K p85α regulatory subunit and proopiomelanocortin (POMC) gene expression was assessed via quantitative RT-PCR in microdissected hypothalamic tissue. Whole-cell patch clamp recordings were performed in hypothalamic slices. Both EB and AM251 decreased food intake and weight gain, and increased AMPKα1, AMPKα2 and PI3K p85α gene expression in the mediobasal hypothalamus. 17β-Estradiol rapidly and markedly attenuated the decreases in glutamatergic miniature excitatory postsynaptic current (mEPSC) frequency caused by the cannabinoid receptor agonist WIN 55,212-2 in POMC neurons. This rapid estrogenic diminution of cannabinoid-induced decreases in mEPSC frequency was blocked by the estrogen receptor (ER) antagonist ICI 182,780 and the PI3K inhibitor PI 828, the latter of which also prevented the AM251-induced increase in mEPSC frequency. In addition, the AMPK activator metformin reversed the EB-induced decreases in food intake and weight gain and restored the ability of WIN 55,212-2 to reduce mEPSC frequency. These data reveal that estrogens physiologically antagonize cannabinoid-induced changes in appetite and POMC neuronal activity by activating PI3K and inhibiting AMPK. As such, they provide insight into the neuroanatomical substrates and signal transduction mechanisms upon which these counter-regulatory factors converge in the control of energy homeostasis.
Topological augmentation to infer hidden processes in biological systems

Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb. ethz.ch/tools/index. Contact: mikael.sunnaker@bsse.ethz.ch; andreas.wagner@ieu.uzh. ch Supplementary information: Supplementary data are available at Bioinformatics online.
1H NMR Spectroscopy Profiling of Metabolic Reprogramming of Chinese Hamster Ovary Cells upon a Temperature Shift during Culture

We report an NMR based approach to determine the metabolic reprogramming of Chinese hamster ovary cells upon a temperature shift during culture by investigating the extracellular cell culture media and intracellular metabolome of CHOK1 and CHO-S cells during culture and in response to cold-shock and subsequent recovery from hypothermic culturing. A total of 24 components were identified for CHOK1 and 29 components identified for CHO-S cell systems including the observation that CHO-S media contains 5.6 times the level of glucose of CHOK1 media at time zero. We confirm that an NMR metabolic approach provides quantitative analysis of components such as glucose and alanine with both cell lines responding in a similar manner and comparable to previously reported data. However, analysis of lactate confirms a differentiation between CHOK1 and CHO-S and that reprogramming of metabolism in response to temperature was cell line specific. The significance of our results is presented using principal component analysis (PCA) that confirms changes in metabolite profile in response to temperature and recovery. Ultimately, our approach demonstrates the capability of NMR providing real-time analysis to detect reprogramming of metabolism upon cellular perception of cold-shock/sub-physiological temperatures. This has the potential to allow manipulation of metabolites in culture supernatant to improve growth or productivity.
Belief-Evidence Fusion in a Hybrid Intelligent System

A hybrid intelligent system that is able to successively refine knowledge stored in its rulebase is developed. The existing knowledge (referred to as belief rules), which may initially be defined by experts in a particular domain, is stored in the form of rules in the rulebase and is refined by comparing it with new knowledge (referred to as evidence rules) extracted from data sets trained under a neural network. Based on measurement, assessment, and interpretation of rule similarity, belief rules existing in the rulebase may be found to be confirmed, contradicted, or left unsupported by new training data. New evidence rules may also be discovered from the training data set. This rule comparison is unique in the sense that rules are viewed and compared in a geometric manner. As rules evolve in existence in the rulebase during the belief-evidence fusion process, their bounds, strengths, and certainties are also revised. The hybrid intelligent system is tested with different data sets, including hypothetical data sets and actual data sets.
Phosphene Evaluation in a Visual Prosthesis with Artificial Neural Networks

The electrical stimulation of the optic nerve is investigated, as an approach to the development of a microsystems-based visual prosthesis. Non-linear prediction models, i.e. artificial neural networks, for phosphene localisation are used and show promising results. The prediction error is reduced up to 20% in comparison with linear statistical models and the error standard deviation is restrained such that the prediction accuracy is similar to the measurements accuracy. In addition, it is shown that linear methods do not suit this kind of problems.
ECG Arrhythmias Recognition System Based on Independent Component Analysis Feature Extraction

Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalities. This paper presents a new approach to classification ECG signals based on feature extraction to diagnose heartbeat irregularities. We introduce the independent component analysis (ICA) feature extraction method and propose an over-complete feature extraction method combining ICA basis function's coefficients and wavelet transform coefficients. A set of relevant features are selected from the entire overcomplete features using a relevant feature selection method. The selected features are used to trained a support vector machine classifier to recognize different heartbeat arrhythmias. From computer simulations, the proposed method yields a more satisfactory classification results on the MIT-BIH arrhythmia database than the other existing methods, reaching an overall accuracy of 98.65%