LLukas22/scidocs huggingface dataset:
Testing Finite State Machines (Extended Abstract)

We study the complexity of several fundamental problems in the testing of finite state machines (FSMl Our main results are as follows. Distinguishing sequences (State Identification): (1) We show that it is PSPACE-complete to determine whether an FSM has a preset distinguishing sequence. There are machines that have distinguishing sequences, but only of exponential length. (2) We give a polynomial time algorithm that determines whether an FSM has an adupfive distinguishing sequence. (The previous classical algorithms take exponential time.) Furthermore, if there is an adaptive distinguishing sequence, then we give an efficient algorithm that constructs such a sequence of length O (n2 ) (which is the best possible), where n is the number of states. Unique Input Output sequences (State Verification): It is PSPACE-complete to determine whether a state of an FSM has a Unique Input Output (UIO) sequence. There am machines whose states have UIO sequences, but only of exponential length. Checking sequences (Fault Detection): We present a simple randomized polynomial time algorithm that constructs with high probability a checking sequence of length 0(pn4 logn), where p is the size of the input alphaket. (There is a lower bound of pn3 on the length of checking sequences; again, the classical algorithms for this problem are exponential in general or work only for special cases.) Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and Its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. @ 1991 ACM 089791-397-3/91/000410476 $1.50
Effectiveness of occupational performance coaching in improving children's and mothers' performance and mothers' self-competence.

OBJECTIVE
This study examined the effectiveness of occupational performance coaching in improving children's and mothers' occupational performance and mothers' parenting self-competence.


METHOD
A one-group time-series design was used to evaluate changes in children's (n = 29) and mothers' (n = 8) occupational performance at four time points: (1) pre-wait list, (2) preintervention, (3) postintervention, and (4) follow-up.


RESULTS
Significant improvements in occupational performance occurred postintervention for children, F(1, 78) = 153.72, p < .001, η² = .86, and mothers, F(1, 78) = 153.72, p < .001, η² = .86, that were maintained 6 wk after intervention. Mothers' self-competence in parenting also improved, F(1, 72) = 17.36, p < .001, η² = .42.


CONCLUSION
Findings provide preliminary evidence supporting the effectiveness of occupational performance coaching in improving children's and mothers' occupational performance and mothers' parenting self-competence. Improvements were sustained and appeared to generalize to other areas of performance.
Simultaneous segmentation and reconstruction: a level set method approach for limited view computed tomography.

PURPOSE
An iterative tomographic reconstruction algorithm that simultaneously segments and reconstructs the reconstruction domain is proposed and applied to tomographic reconstructions from a sparse number of projection images.


METHODS
The proposed algorithm uses a two-phase level set method segmentation in conjunction with an iterative tomographic reconstruction to achieve simultaneous segmentation and reconstruction. The simultaneous segmentation and reconstruction is achieved by alternating between level set function evolutions and per-region intensity value updates. To deal with the limited number of projections, a priori information about the reconstruction is enforced via penalized likelihood function. Specifically, smooth function within each region (piecewise smooth function) and bounded function intensity values for each region are assumed. Such a priori information is formulated into a quadratic objective function with linear bound constraints. The level set function evolutions are achieved by artificially time evolving the level set function in the negative gradient direction; the intensity value updates are achieved by using the gradient projection conjugate gradient algorithm.


RESULTS
The proposed simultaneous segmentation and reconstruction results were compared to "conventional" iterative reconstruction (with no segmentation), iterative reconstruction followed by segmentation, and filtered backprojection. Improvements of 6%-13% in the normalized root mean square error were observed when the proposed algorithm was applied to simulated projections of a numerical phantom and to real fan-beam projections of the Catphan phantom, both of which did not satisfy the a priori assumptions.


CONCLUSIONS
The proposed simultaneous segmentation and reconstruction resulted in improved reconstruction image quality. The algorithm correctly segments the reconstruction space into regions, preserves sharp edges between different regions, and smoothes the noise within each region. The proposed algorithm framework has the flexibility to be adapted to different a priori constraints while maintaining the benefits achieved by the simultaneous segmentation and reconstruction.
Efficacy of corticosteroids in community-acquired pneumonia: a randomized double-blinded clinical trial.

RATIONALE
Some studies have shown a beneficial effect of corticosteroids in patients with community-acquired pneumonia (CAP), possibly by diminishing local and systemic antiinflammatory host response.


OBJECTIVES
To assess the efficacy of adjunctive prednisolone treatment in patients hospitalized with CAP.


METHODS
Hospitalized patients, clinically and radiologically diagnosed with CAP using standard clinical and radiological criteria, were randomized to receive 40 mg prednisolone for 7 days or placebo, along with antibiotics. Primary outcome was clinical cure at Day 7. Secondary outcomes were clinical cure at Day 30, length of stay, time to clinical stability, defervescence, and C-reactive protein. Disease severity was scored using CURB-65 (a severity index for community-acquired pneumonia evaluating Confusion, blood Urea nitrogen, Respiratory rate, Blood pressure, and age 65 or older) and Pneumonia Severity Index.


MEASUREMENTS AND MAIN RESULTS
We enrolled 213 patients. Fifty-four (25.4%) patients had a CURB-65 score greater than 2, and 93 (43.7%) patients were in Pneumonia Severity Index class IV-V. Clinical cure at Days 7 and 30 was 84/104 (80.8%) and 69/104 (66.3%) in the prednisolone group and 93/109 (85.3%) and 84/109 (77.1%) in the placebo group (P = 0.38 and P = 0.08). Patients on prednisolone had faster defervescence and faster decline in serum C-reactive protein levels compared with placebo. Subanalysis of patients with severe pneumonia did not show differences in clinical outcome. Late failure (>72 h after admittance) was more common in the prednisolone group (20 patients, 19.2%) than in the placebo group (10 patients, 6.4%; P = 0.04). Adverse events were few and not different between the two groups.


CONCLUSIONS
Prednisolone (at 40 mg) once daily for a week does not improve outcome in hospitalized patients with CAP. A benefit in more severely ill patients cannot be excluded. Because of its association with increased late failure and lack of efficacy prednisolone should not be recommended as routine adjunctive treatment in CAP.
Quantum Walks in artificial electric and gravitational Fields

The continuous limit of quantum walks (QWs) on the line is revisited through a new, recently developed method. In all cases but one, the limit coincides with the dynamics of a Dirac fermion coupled to an artificial electric and/or relativistic gravitational field. All results are carefully discussed and illustrated by numerical simulations. Possible experimental realizations are also addressed.
Health-related quality of life in outpatient women with COPD in daily practice: the MUVICE Spanish study.

AIMS
A cross-sectional multicenter study was designed to assess health-related quality of life (HRQL) in women with chronic obstructive pulmonary disease (COPD) who were attended in the outpatient setting in actual conditions of the daily practice.


METHODS
A total of 1786 women with COPD (mean age of 66.5 years) and 1661 pairs of men and women matched by age and COPD severity participated in a cross-sectional study. HRQL was measured with the Short Form 12 Health Survey Questionnaire (SF-12).


RESULTS
The mean PCS-12 and MCS-12 scores were 36.5+/-10.3 and 44.1+/-11.8, respectively. General health and physical functioning domains were those with the lowest scores, whereas role emotional and social functioning were those with the highest scores. The percentage of women with low HRQL increased according to age, whereas the percentage of women with high or normal HRQL decreased significantly. In relation to COPD severity, more women rated HRQL as low in the physical component than in the mental component. HRQL correlated significantly with FEV(1) in both PCS-12 and MCS-12 scales. As expected, an inverse significant correlation between HRQL and degree of dyspnea in the PCS-12 and the MCS-12 scales was observed. Women had also a significantly worse HRQL than men in all physical and mental domains.


CONCLUSIONS
In outpatient women with COPD, HRQL was impaired especially the physical component of the SF-12. For the same age and severity of COPD, women showed significantly lower scores in all physical and mental domains of the SF-12 than men.
Molecular kinetics of muscle adenosinetriphosphatase. III. Influence of hydrostatic pressure.

Abstract 1. 1. An experimental study has been made of the kinetics of the myosinadenosine triphosphate system over a range of hydrostatic pressures (up to 10,000 lb./sq. in.). The work was done over a range of substrate concentrations and at three potassium chloride concentrations, namely 0.05, 0.3, and 0.6 M . 2. 2. The data have been analyzed in terms of the Michaelis-Menten mechanism of enzyme action, and volumes of activation calculated for the bimolecular interaction of enzyme and substrate and for the breakdown of the complex into products; these results are summarized in Table II. Some indication has also been obtained of the effect of KCl concentration on the kinetics. 3. 3. The results are shown to be consistent with previous hypotheses as to the nature of the enzyme-substrate interaction and of the breakdown of the complex. The volumes associated with the enzyme-substrate interaction are positive, which is consistent with there being a charge neutralization as the enzyme and substrate come together. The volumes associated with the breakdown are negative, a fact which is attributed to changes in the conformation of the enzyme. 4. 4. The data obtained for this reaction are compared with volume and entropy data for other enzyme reactions, and are found to show some features in common.
Comparison of the effects of metoprolol or carvedilol on serum gamma-glutamyltransferase and uric acid levels among patients with acute coronary syndrome without ST segment elevation

OBJECTIVE
Serum gamma-glutamyltransferase (GGT) and uric acid levels measured in patients with acute coronary syndrome without ST segment elevation (NSTEMI) are important in diagnosis and in predicting the prognosis of the disease. There is a limited number of clinical studies investigating the effects of beta-blockers on GGT and uric acid levels in these patients. In our study, we aimed to investigate the effects of beta-blocker therapy on GGT and uric acid levels.


METHODS
We conducted a randomized, prospective clinical study. Hundred patients with NSTEMI were included in this study, and they were divided into two groups. Fifty patients were administered metoprolol succinate treatment (1 x 50 mg), whereas the remaining 50 patients were administered carvedilol treatment (2 x 12.5 mg). Thereafter, all of the patients underwent coronary angiography. Blood samples were taken at the time of admission, at the 1st month, and 3rd month to detect GGT and uric acid levels.


RESULTS
There was no statistically significant difference among the metoprolol or carvedilol groups in terms of the GGT levels measured at the baseline, 1st month, and 3rd month (p=0.904 and p=0.573, respectively). In addition, there was no statistically significant difference among the metoprolol or carvedilol groups in terms of uric acid levels measured at the baseline, 1st month, and 3rd month (p=0.601 and p=0.601, respectively).


CONCLUSION
We found that GGT and uric acid levels did not show any change compared to the baseline values, with metoprolol and carvedilol treatment initiated in the early period in patients with NSTEMI.
Automated test case generation for FBD programs implementing reactor protection system software

Automated and effective testing for function block diagram (FBD) programs has become an important issue, as FBD is increasingly used in implementing safety-critical systems. This work describes an automated test case generation technique for FBD programs and its associated tool—FBDTester. Given an FBD program and desired test coverage criteria, FBDTester generates test requirements and invokes the Satisfiability Modulo Theories solver iteratively to derive a set of test cases. An industrial case study using reactor protection system software shows that the automatically generated test suites detected at least 82% of the known faults, whereas manually generated test cases only detected approximately 35%. Mutation analysis revealed that the automatically generated test suites substantially outperformed manually generated ones. Although test sequence generation requires some manual effort in the current FBDTester, it is apparent that the proposed approach significantly improves the efficiency and the reliability of FBD testing. Copyright © 2014 John Wiley & Sons, Ltd.
Generating Patent Claims from Interactive Input

Patent claims are the subject of legal protection. They must be formulated according to a set of precise syntactic, lexical and stylistic guidelines. Composing patent claims is a complex task, even for experts. In this paper we report about an tmplemented system for supporting authoring claims for patents describing apparatuses. The system generates claim texts from the input specified partly by the stored conceptual text schemata and partly by the input from the user. The result of the interactive content acquisition stage is a shaUow-level representation which can be considered a draft to be automatically revised into the final text of the claim. Subjec t
A novel time optimal path following controller with bounded velocities for mobile robots with independently steerable wheels

Mobile robots with independently steerable wheels possess many high maneuverability features of omnidirectional robots while benefiting from better performance and capability of moving on rough terrains. However, motion control of such robots is a challenging task due to presence of singular configurations and unboundedly large steering velocities in the neighborhood of those singularities. Many proposed approaches rely on numerical solutions that keep the robot out of bulky regions around the singular points and hence lose some of the robot maneuverability. Based on a class of traditional path followers we design a new globally stable path following controller that exploits the high maneuverability of the platform. This design allows us to derive a set of closed-form analytical functions that describe the robot base velocity as a function of the wheels driving and steering velocities while abide to the robot non-holonomic constraints. Those functions are then utilized to find the maximum instantaneous velocity of the body that keeps the wheels velocities under the pre-specified bounds no matter how much the robot gets close or far from its singular configurations. The control algorithms developed in this paper have been evaluated on iMoro, a four wheel independently steered mobile manipulator designed and developed at IHA/TUT. Experimental data is also shown that show efficacy of the method.
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method

Liver lesion segmentation is an important step for liver cancer diagnosis, treatment planning and treatment evaluation. LiTS (Liver Tumor Segmentation Challenge) provides a common testbed for comparing different automatic liver lesion segmentation methods. We participate in this challenge by developing a deep convolutional neural network (DCNN) method. The particular DCNN model works in 2.5D in that it takes a stack of adjacent slices as input and produces the segmentation map corresponding to the center slice. The model has 32 layers in total and makes use of both long range concatenation connections of U-Net [1] and short-range residual connections from ResNet [2]. The model was trained using the 130 LiTS training datasets and achieved an average Dice score of 0.67 when evaluated on the 70 test CT scans, which ranked first for the LiTS challenge at the time of the ISBI 2017 conference.
Effects of intermittent pneumatic foot compression on blood coagulability and fibrinolysis assessed by a whole blood viscometer Sonoclot

abnormal coagulopathy. They were randomly (by the envelope technique) divided into two groups according to the type of IPC device used: standard or rapid. The subjects were requested to lie in a supine position and to remain calm. Both soles of each subject in one group were compressed for 3 s (130mmHg) at 0.3-Hz intervals during rapid IPC. In the other group, both legs of each subject from the ankles to the thighs were compressed by the standard IPC device, which has a six-chambered cuff that applies compression of 45mmHg for 12s sequentially followed by a 60-s noncompression period. To measure whole blood coagulability using the Sonoclot viscometer, venous blood samples were collected from the left cephalic vein at 10min before compression (control) and at 25 and 60min after compression. The following Sonoclot variables were recorded: sonACT, i.e., Sonoclot-measured activated clotting time (liquid phase or onset of clot formation); clot rate, i.e., slope of the Sonoclot signature (fibrin gel formation stage); and time-to-peak, i.e., duration from the start of measurement to the peak of the Sonoclot signature (Fig. 1A). Data are expressed as means SD. All data were analyzed by one-way ANOVA, and Fisher’s test was used as a post hoc test. P 0.05 was considered to indicate statistical significance. Typical time courses of the Sonoclot signature before and 25 min after IPC by the use of the AV impulse are shown in Fig. 1. The AV impulse significantly shortened the time-to-peak without changing sonACT or clot rate. Both of the IPC devices significantly shortened the time-to-peak in a time-dependent manner (by more than 40% at 60 min after compression). The other parameters, sonACT and clot rate, were not changed during the study period by the use of either device (Table 1). There were no significant differences in these parameters between the IPC devices tested. The Sonoclot analyzer is used as a blood viscoelastic monitor. One of the benefits of its use is that the Sonoclot signature provides global measurement of
Interorganizational Collaboration and the Locus of Innovation : Networks of Learning in Biotechnology

http://www.jstor.org Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology Author(s): Walter W. Powell, Kenneth W. Koput and Laurel Smith-Doerr Source:  Administrative Science Quarterly, Vol. 41, No. 1 (Mar., 1996), pp. 116-145 Published by: on behalf of the Sage Publications, Inc. Johnson Graduate School of Management, Cornell University Stable URL: http://www.jstor.org/stable/2393988 Accessed: 18-12-2015 16:50 UTC
UGT1A1 genotype-guided phase I study of irinotecan, oxaliplatin, and capecitabine

Purpose We performed a UGT1A1 genotype-guided study to determine the maximum tolerated dose (MTD) and evaluate the toxicities and pharmacokinetics of the combination of capecitabine (CAP), oxaliplatin (OX), and irinotecan (IRIN). Experimental design Patients were screened for UGT1A1 *28 genotype prior to treatment. The starting dose (mg/m2) was IRIN (150), OX (85) and CAP (400), days 2–15. Doses were escalated or de-escalated within each genotype group (*28/*28, *1/*28 and *1/*1). IRIN pharmacokinetics was performed at the MTD. Results 50 patients were evaluable for toxicity [11 (*28/*28); 18 (*1/*28); 21 (*1/*1)]. UGT1A1 *28/*28 patients experienced hematologic dose limiting toxicity (DLT), requiring dose-de-escalation. The UGT1A1 *28/*28 recommended phase 2 dose (RP2D) was IRIN (75), OX (85), and CAP (400). In contrast, both UGT1A1 *1/*28 and *1/*1 tolerated higher doses of IRIN and non-hematologic toxicity was dose limiting for UGT1A1 *1/*1. The RP2D was IRIN (150), OX (85), and CAP (400) for UGT1A1*1/*28 and IRIN (150), OX (100), and CAP (1600) for UGT1A1 *1/*1. UGT1A1 *1/*28 and *1/*1 patients treated with IRIN (150) had similar AUCs for the active irinotecan metabolite, SN38 (366 +/− 278 and 350 +/− 159 ng/ml*hr, respectively). UGT1A1 *28/*28 patients (n = 3) treated with a lower IRIN dose (100) had non-significantly higher mean SN38 exposures (604 +/− 289 ng/ml*hr, p = 0.14). Antitumor activity was observed in all genotype groups. Conclusions UGT1A1 genotype affects the dose and pharmacokinetics of the CAPIRINOX regimen and UGT1A1 genotype-guided dosing of CAPIRINOX is ongoing in a phase II study of small bowel cancer (NCT00433550).
Self-Taught convolutional neural networks for short text clustering

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction method. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets.