nils-herrmann/clean_scirepeval huggingface dataset:
A mixed integer convex optimization framework for robust multilegged robot locomotion planning over challenging terrain
Venue: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2017

This paper introduces an optimization based framework for robust multilegged walking motion planning. Previous approaches use fixed gait sequences, and rely on Zero Moment Point (ZMP) to guarantee dynamic stability. While this combination works well on flat ground, it does not generalize to uneven terrain requiring aggressive gait or gait transition. To overcome such difficulties, in this paper, we present an optimization framework, that can plan both the contact location and gait sequence simultaneously in a mixed integer convex optimization program. Moreover, we rely on the Contact Wrench Cone (CWC) stability criterion, which generalizes the ZMP criterion to uneven terrain with friction cone constraints, and we plan the walking motion together with the angular momentum through a convex optimization program. Our approach is successfully tested on a LittleDog quadruped over simulated scenarios. We show that on the flat ground, our planner generates a periodic gait, same as Central Pattern Generator ZMP planner; while on uneven terrain, our planner can successfully generate a motion containing different gaits, with a center of mass motion that respects the friction cone constraints, which are violated by ZMP planners. This improvement clearly demonstrates the advantage of our approach over traditional planning strategies.
Physics reach of the XENON1T dark matter experiment
Year: 2016

The XENON1T experiment is currently in the commissioning phase at the Laboratori Nazionali del Gran Sasso, Italy. In this article we study the experiment's expected sensitivity to the spin independent WIMP nucleon interaction cross section, based on Monte Carlo predictions of the electronic and nuclear recoil backgrounds. The total electronic recoil background in 1 tonne fiducial volume and (1, 12) keV electronic recoil equivalent energy region, before applying any selection to discriminate between electronic and nuclear recoils, is (1.80 0.15) 10( (4) (kg*day*keV) (1) mainly due to the decay of (222)Rn daughters inside the xenon target. The nuclear recoil background in the corresponding nuclear recoil equivalent energy region (4, 50) keV, is composed of (0.6 0.1) (t*y) (1) from radiogenic neutrons, (1.8 0.3) 10( (2) (t*y) (1) from coherent scattering of neutrinos, and less than 0.01 (t*y) (1) from muon induced neutrons. The sensitivity of XENON1T is calculated with the Profile Likelihood Ratio method, after converting the deposited energy of electronic and nuclear recoils into the scintillation and ionization signals seen in the detector. We take into account the systematic uncertainties on the photon and electron emission model, and on the estimation of the backgrounds, treated as nuisance parameters. The main contribution comes from the relative scintillation efficiency Script L(eff) which affects both the signal from WIMPs and the nuclear recoil backgrounds. After a 2 y measurement in 1 t fiducial volume, the sensitivity reaches a minimum cross section of 1.6 10( (47) cm(2) at m(kh) 50 GeV/c(2)
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Year: 2015

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New upper bounds and exact methods for the knapsack sharing problem
Venue: Appl. Math. Comput.
Year: 2014

A new upper bound is proposed for the knapsack sharing problem.A new dichotomous search based exact algorithm is proposed.The new algorithm outperforms a most recent exact algorithm of the literature and the Cplex solver V 12. In this paper, we study the knapsack sharing problem (KSP) which is a variant of the well known NP Hard knapsack problem. We investigate the use of a dichotomous search based exact method for solving the KSP. Such a method is based on decomposing the original problem into a series of minimizing and maximizing knapsack problems, where each of them is embedded into a dichotomous search bounded by both lower and upper bounds. Throughout the interval search, we introduce new upper bounds and incremental valid lower bounds. One of these upper bounds can be viewed as an extended of Dantzig upper bound whereas a series of valid lower bounds are obtained when solving a subset of knapsack problems. Finally, the proposed algorithm is evaluated on a set of problem instances of the literature and other new harder ones. The provided results are compared to those reached by the Cplex solver and a more recent exact algorithm of the literature. The computational part shows the dominance of the new exact method.
Musculoskeletal application and validation of speckle tracking ultrasonography
Venue: BMC Musculoskeletal Disorders
Year: 2019

BackgroundDiseased, injured, or dysfunctional skeletal muscles may demonstrate abnormal function and contractility. Currently, only few in vivo imaging techniques are able to characterize the contractile properties of muscle tissue. This study aimed to test the hypothesis that muscle strain can be tracked in two upper extremity skeletal muscles by speckle tracking ultrasonography (STU) and correlates with isometric muscle contractions.MethodsA convenience sample of 10 healthy, adult volunteers with normal shoulder function were tested. The 5 women and 5 men had a mean age of 45 years (range: 39 59 years) and BMI 30. STU was applied to the supraspinatus (SS) and biceps brachii (BB) muscles using a M11 L MHz linear transducer (frequency 8 15 MHz) hooked to a Vivid E 9TM ultrasound machine. Strain validation was performed by correlating peak strain against standardized sub maximal, isometric load conditions of the two muscles (20 80% of maximal voluntary contraction) using a custom built muscle dynamometer based on strain gauge technique. Data were analyzed offline using the EchoPac speckle tracking software and were blinded to the examiner.ResultsIntramuscular strain measured by STU in the SS and BB muscles showed moderate to strong correlations with external muscle load (SS: r 0.76, p 0.0001 and BB: r 0.60, p 0.0001) We found strain to vary from approximately 10 20% during increasing submaximal, isometric conditions.ConclusionsWe demonstrate that STU can be applied on healthy skeletal musculature (SS and BB muscles) The observed correlations between strain and isometric contractions suggest a valid technique. However, the concept of measuring muscle strain non invasively needs further investigation for validity, accuracy, responsiveness, and reliability before its therapeutic and research potential can be realized.
Compact Microwave and Millimeter Wave Bandpass Filters Using LTCC Based Hybrid Lumped and Distributed Resonators
Venue: IEEE Access
Year: 2019

A novel class of low temperature cofired ceramic (LTCC) bandpass filters (BPFs) is proposed in this paper, featuring of compact size and good filtering responses in both microwave and millimeter wave (mm wave) bands. By effectively merging the lumped elements and distributed effects, a modified LTCC based hybrid lumped and distributed resonator (HLDR) is introduced. In detail, the lumped part of a LTCC interdigital capacitor and a high impedance line is used to reduce circuit size, loss, and cross talks. The distributed part, saying, the mutual couplings between adjacent HLDRs and to the ground, is used to improve the design flexibility and filtering performance for HLDR filters. With mutual couplings, the coupling matrix method can be applied. Furthermore, the frequency variant mixed couplings between HLDRs are investigated, and coupling matrix method is improved by introducing a new coupling mechanism. Multiple transmission zeroes (TZs) are firstly realized with second order in line topology, but without adding cross couplings or bandstop structures. To validate above design strategies, one $S$ band and one $Ka$ band second order HLDR BPFs are simulated and measured, respectively. Very compact size, low loss, high selectivity, and wide upper stopband are observed, especially in mm wave range.
A deep learning based multi agent system for intrusion detection
Year: 2020

Intrusion detection systems play an important role in preventing attacks which have been increased rapidly due to the dependence on network and Internet connectivity. Deep learning algorithms are promising techniques, which have been used in many classification problems. In the same way, multi agent systems become a new useful approach in intrusion detection field. In this paper, we propose a deep learning based multi agent system for intrusion detection which combines the desired features of multi agent system approach with the precision of deep learning algorithms. Therefore, we created a number of autonomous, intelligent and adaptive agents that implanted three algorithms, namely autoencoder, multilayer perceptron and k nearest neighbors. Autoencoder is used as features reduction tool, and multilayer perceptron and k nearest neighbors are used as classifiers. The performance of our model is compared against traditional machine learning approaches and other multi agent system based systems. The experiments have shown that our hybrid distributed intrusion detection system achieves the detection with better accuracy rate and it reduces considerably the time of detection.
The Impact of Web Search Engines on Subject Searching in OPAC
Year: 2017

This paper analyzes the results of transaction logs at California State University, Los Angeles (CSULA) and studies the effects of implementing a Web based OPAC along with interface changes. The authors find that user success in subject searching remains problematic. A major increase in the frequency of searches that would have been more successful in resources other than the library catalog is noted over the time period 2000 2002. The authors attribute this increase to the prevalence of Web search engines and suggest that metasearching, relevance ranked results, and relevance feedback "more like this" are now expected in user searching and should be integrated into online catalogs as search options.
Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data
Venue: NeuroImage
Year: 2020

Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer's Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV IWAS) suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV IWAS to incorporate variant specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual level or summary data, we demonstrate the well controlled Type I errors and superior power of MV IWAS over UV IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV IWAS detected numerous IDPs as possible false positives by UV IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.
Adherence to Medication, Physical Activity and Diet in Older Adults With Diabetes: Its Association With Cognition, Anxiety and Depression
Venue: Journal of clinical medicine research
Year: 2019

Adherence to medication, physical activity (PA) and diet in diabetes mellitus (DM) patients is crucial for its good management, avoiding acute and chronic complications. There are several risk factors associated with non adherence, including cognitive impairment, depression and anxiety. Nevertheless, studies on therapeutic adherence in older patients with DM are scarce. In this context, the present study aimed to analyze whether adherence to medication, PA and diet are associated with cognitive impairment, anxiety and depression. It also aimed to identity predictors of medication non adherence.  A cross sectional study of older patients 65 years old) with DM was carried out in the Outpatient Department of Internal Medicine Service of CHUSJ Porto, Portugal. Those unable to communicate were excluded. Cognition (mini mental state examination) anxiety and depression (hospital anxiety and depression scale) were assessed. Adherence to medication, PA and diet was measured, based on self reporting patient/family, questionnaires, physician clinical opinion, hemoglobin test and pharmacy records. Patient groups were compared, using the Mann Whitney or the Kruskal Wallis test for continuous variables and the Chi square test for paired categorical variables (significance level of 0.05) The odds ratio (OR) was calculated to identify independent predictors of non adherence to medication.  The final sample (n 94) had a mean age of 75.2 years (standard deviation: 6.7) and mostly were female (53.2% married (63.8% and with a low education level (61.7% Also, 22.3% with cognitive impairment, 16% with depression and 23.4% with anxiety were found. Patients non adherent to medication had higher depression (P 0.048) and anxiety (P 0.010) compared to adherents/partial adherents. Patients non adherent to PA showed higher anxiety (P 0.035) and depression (P 0.004) compared to adherents. Non adherents to PA had more cognitive impairment than adherents (26.3% vs. 0% P 0.034) Patients who had insulin prescribed presented a higher risk of non adherence to medication (OR: 4.041, 95% confidence interval (CI) 1.404 11.628; P 0.010) Also, the risk of non adherence to medication is higher by an increase of one unit in anxiety (OR: 1.252, 95% CI: 1.046 1.499; P 0.014) Conclusions Higher anxiety and depression were associated with non adherence to medication and to PA. Insulin prescribed and high anxiety scores were predictors of medication non adherence. This study appears to contribute to the knowledge about the influence of cognitive and psychological factors in therapeutic adherence in these older diabetic patients.
"A Passage to India" Pre trained Word Embeddings for Indian Languages
Venue: SLTU
Year: 2020

Dense word vectors or 'word embeddings' which encode semantic properties of words, have now become integral to NLP tasks like Machine Translation (MT) Question Answering (QA) Word Sense Disambiguation (WSD) and Information Retrieval (IR) In this paper, we use various existing approaches to create multiple word embeddings for 14 Indian languages. We place these embeddings for all these languages, viz. Assamese, Bengali, Gujarati, Hindi, Kannada, Konkani, Malayalam, Marathi, Nepali, Odiya, Punjabi, Sanskrit, Tamil, and Telugu in a single repository. Relatively newer approaches that emphasize catering to context (BERT, ELMo, etc. have shown significant improvements, but require a large amount of resources to generate usable models. We release pre trained embeddings generated using both contextual and non contextual approaches. We also use MUSE and XLM to train cross lingual embeddings for all pairs of the aforementioned languages. To show the efficacy of our embeddings, we evaluate our embedding models on XPOS, UPOS and NER tasks for all these languages. We release a total of 436 models using 8 different approaches. We hope they are useful for the resource constrained Indian language NLP. The title of this paper refers to the famous novel "A Passage to India" by E.M. Forster, published initially in 1924.
Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint
Venue: Evol. Intell.
Year: 2020

The basic idea of Apriori algorithm is first introduced in this paper, which is to find all frequent sets in a transaction. The frequent requirements of these frequent sets are greater than or equal to the minimum support of the set. On this basis, the working principle of the traditional Apriori algorithm is analyzed, and the existing problems are pointed out. To solve these problems, an improved Apriori algorithm is proposed for time series of frequent itemsets. Finally, on the basis of analyzing the methods and processes of mining association rules for time series, this improved time series Apriori algorithm for frequent itemsets is applied to mining association rules based on time constraints. The experimental results show that the improved Apriori algorithm is better than the traditional one in storage space.
In Silico Modeling of Non Linear Drug Absorption for the P gp Substrate Talinolol and of Consequences for the Resulting Pharmacodynamic Effect
Venue: Pharmaceutical Research
Year: 2006

PurposeThe aim of the present work was to demonstrate P glycoprotein's involvement in the non linear talinolol pharmacokinetics using an advanced compartment and transit model (ACAT) and to compare the results predicted from the model to the finding of a phase I dose escalation study with oral talinolol doses increasing from 25 to 400 mg.Materials and MethodsBesides minimum input parameters for the compound (pKa(s) solubility at one or more pH's, Peff, doses, formulation, diffusivity) physiological and pharmacokinetic properties, transporter data are included in these predictions. The simulations assumed higher expression levels in lower gastrointestinal regions, in particular in the colon, which is in accordance with the results of intestinal rat perfusion studies and intestinal distribution data from rats, catfishes, micropigs and humans reported in the literature. Optimized values for P glycoprotein (P gp) Km and Vmax were used for the final simulation results and for a stochastic virtual trial with 12 patients.ResultsTalinolol, a P gp substrate, exhibits non linear dose AUC relationship after administration of 25, 50, 100 and 400 mg immediate release tablets. This dose dependency is due to a decrease of efflux transport caused by saturation of P gp by talinolol. It was found that oral bioavailability increases after administration of higher doses of talinolol. The predicted bioavailability of the p.o. 25, 50, 100 and 400 mg doses of talinolol was 64, 76, 85, 94% respectively. Pharmacokinetic parameters (AUC, Cmax) from in silico simulations are within acceptable range comparing with data, observed in vivo. However, the in vitro value of Km for talinolol's interactions with P gp could not be used in the simulation and still reproduce the observed non linear dose dependence. For each of the four doses, GastroPlus(r) was used to model pharmacodynamic (PD) response and to optimize the values of CLe, Emax, and EC50 with the effect compartment linked indirectly to the central compartment. For all simulations, EC50 was 114 nM and E0 was 83 bpm.ConclusionComparison between the results of the in vivo study and the in silico simulations determined the quality and reliability of the in silico predictions and demonstrate the simulation of dose dependent absorption. In contrast to previous simulation work for the non linear dose dependence of interaction with intestinal transporters or enterocyte metabolism, optimized Km and Vmax values were required to reproduce the clinically observed non linear dose dependence. The model developed may be useful in the prediction of absorption of other P gp substrates including pharmacodynamic consequences.
A hierarchical recurrent approach to predict scene graphs from a visual attention oriented perspective
Venue: Comput. Intell.
Year: 2019

A scene graph provides a powerful intermediate knowledge structure for various visual tasks, including semantic image retrieval, image captioning, and visual question answering. In this paper, the task of predicting a scene graph for an image is formulated as two connected problems, ie, recognizing the relationship triplets, structured as and constructing the scene graph from the recognized relationship triplets. For relationship triplet recognition, we develop a novel hierarchical recurrent neural network with visual attention mechanism. This model is composed of two attention based recurrent neural networks in a hierarchical organization. The first network generates a topic vector for each relationship triplet, whereas the second network predicts each word in that relationship triplet given the topic vector. This approach successfully captures the compositional structure and contextual dependency of an image and the relationship triplets describing its scene. For scene graph construction, an entity localization approach to determine the graph structure is presented with the assistance of available attention information. Then, the procedures for automatically converting the generated relationship triplets into a scene graph are clarified through an algorithm. Extensive experimental results on two widely used data sets verify the feasibility of the proposed approach.
Total Factor Productivity Spillovers from Trade Reforms in India
Year: 2018

It is often argued that the positive effects of policies or institutional reforms on the productivity of firms are amplified by positive spillovers to other firms. In this paper, we use a comprehensive dataset of Indian manufacturing firms and employ spatial econometric techniques to estimate the strength of inter firm total factor productivity (TFP) spillovers within India following trade reforms. We use externally imposed tariff reductions as an exogenous source of variation in TFP (following Topalova and Khandelwal 2011) and test for spillovers arising from observation, labour mobility and intermediate input use. We find no evidence of TFP spillovers between Indian manufacturing firms after trade liberalization on average. However, initially productive firms seem to benefit from TFP increases in other highly productive firms located in their vicinity.
Pulmonary vascular endothelium: the orchestra conductor in respiratory diseases
Venue: European Respiratory Journal
Year: 2018

The European Respiratory Society (ERS) Research Seminar entitled "Pulmonary vascular endothelium: orchestra conductor in respiratory diseases highlights from basic research to therapy" brought together international experts in dysfunctional pulmonary endothelium, from basic science to translational medicine, to discuss several important aspects in acute and chronic lung diseases. This review will briefly sum up the different topics of discussion from this meeting which was held in Paris, France on October 27 28, 2016. It is important to consider that this paper does not address all aspects of endothelial dysfunction but focuses on specific themes such as: 1) the complex role of the pulmonary endothelium in orchestrating the host response in both health and disease (acute lung injury, chronic obstructive pulmonary disease, high altitude pulmonary oedema and pulmonary hypertension) and 2) the potential value of dysfunctional pulmonary endothelium as a target for innovative therapies. The central role of pulmonary endothelial dysfunction in respiratory diseases http:/ow.ly/