nils-herrmann/clean_scirepeval huggingface dataset:
Cry based infant pathology classification using GMMs
Venue: Speech Commun.

Highlights We characterize the distributions of the acoustic features of infant cry signals with GMMs as a universal background model. An adapted BML method is presented to derive either healthy or pathology subclass models from the GMM UBM. A score level fusion of obtained log likelihood ratio scores from both expiratory and inspiratory sounds is performed. The proposed cry based diagnostic system is used to classify healthy and sick infants. Subjective results indicated that the proposed method can perform better than the Bayesian adaptation.
The Frankenstein's Monster of Extraterritoriality Law
Venue: AJIL Unbound

The judge made presumption against extraterritoriality has recently become a motley patchwork of eccentric and sometimes contradictory doctrines seemingly stitched together for one, and only one, mission: to deprive plaintiffs the right to sue in U.S. courts for harms suffered abroad. It lumbers along, blithely squashing precedent, principle, statutory text, and legislative intent all to heed its abiding and single minded obsession. The Supreme Court has so far mangled the scope of the Securities Exchange Act and the Alien Tort Statute (ATS) and, in RJR Nabisco v. European Community, has placed another statute The Racketeer Influenced and Corrupt Organizations Act (RICO) on the chopping block. The major surgery performed was amputating RICO's private right of action for extraterritorial offenses and replacing it with a much stubbier appendage limited to injuries suffered on U.S. territory.
Linearly convergent decentralized consensus optimization over directed networks
Venue: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Recently, there have been growing interests in solving distributed consensus optimization problems over directed networks that consist of multiple agents. In this paper, we develop a first order (gradient based) algorithm, referred to as Push DIGing, for this class of problems. To run Push DIGing, each agent in the network only needs to know its own out degree and employs a fixed step size. Under the strong convexity assumption, we prove that the introduced algorithm converges to the global minimizer at some R linear (geometric) rate as long as the nonnegative step size is no greater than some explicit bound.
Selective connectivity of dopamine neurons projecting to the posterior striatum
Venue: Movement disorders official journal of the Movement Disorder Society

Midbrain dopamine (DA) neurons play important roles in various brain functions, including motor control, motivation, and reinforcement learning. In Parkinson's disease (PD) midbrain DA neurons are progressively lost as the disease advances. Specifically, there is a prominent decay of dopaminergic axons in the posterior putamen and a corresponding loss of dopamine neurons in the ventrolateral SNpc. Here, Menegas and coworkers used a very complementary set of methods: rabies virus tracing, CLARITY, and light sheet microscopy to label subpopulations of DA neurons according to their projection sites and their monosynaptic inputs in mice. They found that almost all DA neurons received "clusters" of inputs originating within the ventral striatum. However, DA neurons whose target is the posterior striatum receive far fewer inputs from this area. Rather, they receive projections from other regions, such as the globus pallidus (GP) STN, and zona incerta. In addition, the inputs to DA neurons that project to the posterior striatum are shifted dorsolaterally compared to inputs to other DA subpopulations. This shift may be caused by a relatively low number of inputs from the ventral striatum and the large number of inputs from other regions (i.e. GP, entopeduncular nucleus, and STN) The investigators suggest that their findings may have practical implications regarding DBS of the STN in PD because this and other sites found to be effective in the treatment of PD send monosynaptic projections onto DA neurons projecting to the posterior putamen, thus having a more specific effect on the motor circuit. More important, however, these results support evidence that neurons projecting to the posterior striatum are unique and regulated by different inputs. Unlike other DA neurons that receive a prominent inhibitory expectation signal from the ventral striatum, those that project to the posterior striatum may facilitate encoding of more "stable values," compatible with a role for habitual, rather than goal directed, learning. This may provide a first approach to the still unanswered question of how DA subpopulations could be related to the variety of manifestations and the selective vulnerability of the SNpc ventrolateral tier in PD. Additionally, the methodology used by the investigators will facilitate similar anatomical studies in other brain areas in an efficient and quantitative manner.
Bearing Capacity Effect of Soft Clay with Sand Compaction Piles

An expansive soil is to be source of trouble to civil engineering as its strength, volume and compressibility changes tremendously on wetting. Sand compaction piles are one of the most widely used ground improvement techniques in case of soft clays. They increase the bearing capacity and reduce the settlements of soft clays. This paper is an attempt to investigate the load carrying capacity of soft clay with and without sand compaction piles through laboratory model tests conducted on single and group sand compaction piles by varying diameter, length and spacing of sand compaction piles.
SUSTAINABLE TRANSPORTATION: AIRSHIPS VERSUS JET AIRPLANES

The cloud that now surrounds the silver lining of jet airplanes is the smoke from their contrails. As the 21 st century progresses, burning fossil fuels with abandon is coming to an end. This paper offers a definition of a sustainable transport system while outlining the need, presents information on jet airplane Greenhouse Gas (GHG) emissions and mitigation. This is followed by highlighting transport airships that could replace the most polluting older passenger jetliners that have been converted to cargo carriage. The paper concludes with some thoughts on the impact of transport airships in international trade.
What's the Point: Semantic Segmentation with Point Supervision
Venue: ECCV

The semantic image segmentation task presents a trade off between test time accuracy and training time annotation cost. Detailed per pixel annotations enable training accurate models but are very time consuming to obtain; image level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point level supervision and objectness potential yields an improvement of (12.9\ mIOU over image level supervision. Further, we demonstrate that models trained with point level supervision are more accurate than models trained with image level, squiggle level or full supervision given a fixed annotation budget.
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts
Venue: ECML/PKDD

In machine learning, hyperparameter optimization is a challenging but necessary task that is usually approached in a computationally expensive manner such as grid search. Out of this reason, surrogate based black box optimization techniques such as sequential model based optimization have been proposed which allow for a faster hyperparameter optimization. Recent research proposes to also integrate hyperparameter performances on past data sets to allow for a faster and more efficient hyperparameter optimization. In this paper, we use products of Gaussian process experts as surrogate models for hyperparameter optimization. Naturally, Gaussian processes are a decent choice as they offer good prediction accuracy as well as estimations about their uncertainty. Additionally, their hyperparameters can be tuned very effectively. However, in the light of large meta data sets, learning a single Gaussian process is not feasible as it involves inversion of a large kernel matrix. This directly limits their usefulness for hyperparameter optimization if large scale hyperparameter performances on past data sets are given. By using products of Gaussian process experts the scalability issues can be circumvened, however, this usually comes with the price of having less predictive accuracy. In our experiments, we show empirically that products of experts nevertheless perform very well compared to a variety of published surrogate models. Thus, we propose a surrogate model that performs as well as the current state of the art, is scalable to large scale meta knowledge, does not include hyperparameters itself and finally is even very easy to parallelize. The software related to this paper is available at https:/github.com/nicoschilling/ECML2016.
A Neumann series of Bessel functions representation for solutions of perturbed Bessel equations

A new representation for a regular solution of the perturbed Bessel equation of the form is obtained. The solution is represented as a Neumann series of Bessel functions uniformly convergent with respect to For the coefficients of the series, explicit direct formulas are obtained in terms of the systems of recursive integrals arising in the spectral parameter power series (SPPS) method, as well as convenient for numerical computation recurrent integration formulas. The result is based on application of several ideas from the classical transmutation (transformation) operator theory, recently discovered mapping properties of the transmutation operators involved and a Fourier Legendre series expansion of the transmutation kernel. For convergence rate estimates, asymptotic formulas, a Paley Wiener theorem, and some results from constructive approximation theory were used. We show that the analytical representation obtained among other possible applications offers a simple and efficient numerical method able to compute large sets of eigendata with a nondeteriorating accuracy.
A Neumann series of Bessel functions representation for solutions of Sturm Liouville equations

A Neumann series of Bessel functions (NSBF) representation for solutions of Sturm Liouville equations and for their derivatives is obtained. The representation possesses an attractive feature for applications: for all real values of the spectral parameter \omega $o the estimate of the difference between the exact solution and the approximate one (the truncated NSBF) depends on N (the truncation parameter) and the coefficients of the equation and does not depend on \omega $o. A similar result is valid when \omega \in \mathbb {C}$oC belongs to a strip \left| \hbox {Im \omega \right| <C$$Imo<C. This feature makes the NSBF representation especially useful for applications requiring computation of solutions for large intervals of \omega $o. Error and decay rate estimates are obtained. An algorithm for solving initial value, boundary value or spectral problems for the Sturm Liouville equation is developed and illustrated on a test problem.
Hepatocellular carcinoma
Venue: Nature Reviews Disease Primers

Liver cancer is the second leading cause of cancer related deaths globally and has an incidence of approximately 850,000 new cases per year. Hepatocellular carcinoma (HCC) represents approximately 90% of all cases of primary liver cancer. The main risk factors for developing HCC are well known and include hepatitis B and C virus infection, alcohol intake and ingestion of the fungal metabolite aflatoxin B1. Additional risk factors such as non alcoholic steatohepatitis are also emerging. Advances in the understanding of the molecular pathogenesis of HCC have led to identification of critical driver mutations; however, the most prevalent of these are not yet druggable targets. The molecular classification of HCC is not established, and the Barcelona Clinic Liver Cancer staging classification is the main clinical algorithm for the stratification of patients according to prognosis and treatment allocation. Surveillance programmes enable the detection of early stage tumours that are amenable to curative therapies resection, liver transplantation or local ablation. At more developed stages, only chemoembolization (for intermediate HCC) and sorafenib (for advanced HCC) have shown survival benefits. There are major unmet needs in HCC management that might be addressed through the discovery of new therapies and their combinations for use in the adjuvant setting and for intermediate and advanced stage disease. Moreover, biomarkers for therapy stratification, patient tailored strategies targeting driver mutations and/or activating signalling cascades, and validated measurements of quality of life are needed. Recent failures in the testing of systemic drugs for intermediate and advanced stages have indicated a need to refine trial designs and to define novel approaches.
Association between nucleos(t)ide analog and tumor recurrence in hepatitis B virus related hepatocellular carcinoma after radiofrequency ablation
Venue: Hepatology

Radiofrequency ablation (RFA) is the best choice for curative treatment of hepatocellular carcinoma (HCC) cases not suitable for surgical intervention, but efforts should be made to reduce the risk of tumor recurrence. We aimed to investigate the association between nucleos(t)ide analog (NA) therapy for hepatitis B virus (HBV) and the risk of HCC recurrence post RFA. Using the Taiwan National Health Insurance Research Database between July 1, 2004 and December 31, 2012, we screened 48,807 patients with newly diagnosed HBV related HCC. We identified 850 patients (200 patients who used NAs for more than 90 days and 650 who never used NA post RFA) who received RFA as a potentially curative treatment for HCC. Patients in the NA treated cohort were randomly matched 1:2 with patients in the untreated cohort by age, sex, cirrhosis, and the time period between RFA and initiation of NA therapy. Finally, 133 patients were recruited in the NA treated group and 266 in the untreated group for analysis. Cumulative incidences of and hazard ratios (HRs) for HCC recurrence were analyzed after adjusting for competing mortality. The HCC recurrence rate of the NA treated group was significantly lower than that of the untreated group (2 year recurrence rate: 41.8% 95% confidence interval [CI] 32.9 50.6 vs. 54.3% 95% CI: 48.0 60.6; modified log rank test: P 0.05) In modified Cox's regression analysis, NA therapy was independently associated with a decreased risk of HCC recurrence (HR, 0.69; 95% CI: 0.50 0.95; P 0.05) Multivariate stratified analyses verified the association of NA therapy and decreased HCC recurrence in almost all patient subgroups.  NA therapy was associated with a decreased risk of HCC recurrence among patients with HBV related HCC post RFA.
LiteBIRD: lite satellite for the study of B mode polarization and inflation from cosmic microwave background radiation detection
Venue: Astronomical Telescopes Instrumentation

LiteBIRD is a next generation satellite aiming for the detection of the Cosmic Microwave Background (CMB) B mode polarization imprinted by the primordial gravitational waves generated in the era of the inflationary universe. The science goal of LiteBIRD is to measure the tensor to scaler ratio r with a precision of dr 10 3, offering us a crucial test of the major large single field slow roll inflation models. LiteBIRD is planned to conduct an all sky survey at the sun earth second Lagrange point (L2) with an angular resolution of about 0.5 degrees to cover the multipole moment range of 2 l 200. We use focal plane detector arrays consisting of 2276 superconducting detectors to measure the frequency range from 40 to 400 GHz with the sensitivity of 3.2 mK*arcmin. including the ongoing studies.
The Simons Array CMB polarization experiment
Venue: Astronomical Telescopes Instrumentation

The Simons Array is a next generation cosmic microwave background (CMB) polarization experiment whose science target is a precision measurement of the B mode polarization pattern produced both by inflation and by gravitational lensing. As a continuation and extension of the successful POLARBEAR experimental program, the Simons Array will consist of three cryogenic receivers each featuring multichroic bolometer arrays mounted onto separate 3.5m telescopes. The first of these, also called POLARBEAR 2A, will be the first to deploy in late 2016 and has a large diameter focal plane consisting of dual polarization dichroic pixels sensitive at 95 GHz and 150 GHz. The POLARBEAR 2A focal plane will utilize 7,588 antenna coupled superconducting transition edge sensor (TES) bolometers read out with SQUID amplifiers using frequency domain multiplexing techniques. The next two receivers that will make up the Simons Array will be nearly identical in overall design but will feature extended frequency capability. The combination of high sensitivity, multichroic frequency coverage and large sky area available from our mid latitude Chilean observatory will allow Simons Array to produce high quality polarization sky maps over a wide range of angular scales and to separate out the CMB B modes from other astrophysical sources with high fidelity. After accounting for galactic foreground separation, the Simons Array will detect the primordial gravitational wave B mode signal to r 0.01 with a significance of 5s and will constrain the sum of neutrino masses to 40 meV (1s) when cross correlated with galaxy surveys. We present the current status of this funded experiment, its future, and discuss its projected science return.
Methodological Issues in Conducting Pilot Trials in Chronic Pain as Randomized, Double blind, Placebo controlled Studies.
Venue: Drug research

The efficacy of tapentadol extended release (ER) for managing chronic pain has been demonstrated in large scale, randomized, controlled, phase 3 studies (N=318 1,030) in patients with chronic osteoarthritis (OA) pain, low back pain (LBP) and pain related to diabetic peripheral neuropathy (DPN) which led to registration in many regions, including the United States and Europe. 2 pilot 12 week, randomized, double blind, placebo controlled phase 2 studies of tapentadol ER for chronic pain (OA knee pain or LBP, n=91; DPN or peripheral herpetic neuralgia [PHN] pain; n=91) were conducted in Japan. These small exploratory studies were substantially underpowered compared with the registration trials.  Patients in both studies were randomized (2:1) to tapentadol ER (25 250 mg) or placebo for 12 weeks =6 week titration plus maintenance periods) RESULTS For the primary efficacy endpoint (change in pain intensity from baseline to last week of treatment; last observation carried forward) both studies failed to differentiate between tapentadol ER and placebo; least squares mean differences (95% confidence intervals) for tapentadol ER vs. placebo were 0.1 1.04, 0.80) in the OA/LBP study and 0.1 1.10, 0.99) in the DPN/PHN study. More than 80% of patients took concomitant analgesics during these studies. Tapentadol was well tolerated.  Both studies were associated with methodological issues, including populations with different disease entities, small sample sizes, use of concomitant analgesics, and possible placebo effect that may have led to the failure to differentiate between tapentadol ER and placebo.
Over a third of children aged 10 11 in England are overweight or obese
Venue: British Medical Journal

More than a third of children aged 10 11 are overweight or obese, national figures in England have shown.1 Child health experts now describe the level of childhood obesity in the United Kingdom as a "state of emergency." The figures for 2015 16 come from the national child measurement programme, which measures height and weight in more than a million children in reception class (aged 4 5) and in year six (aged 10 11) in schools in England each year. The latest measurements showed that the prevalence of obesity in year six children had increased from 19.1%
Self Transfer Learning for Weakly Supervised Lesion Localization
Venue: MICCAI

Recent advances of deep learning have achieved remarkable performances in various computer vision tasks including weakly supervised object localization. Weakly supervised object localization is practically useful since it does not require fine grained annotations. Current approaches overcome the difficulties of weak supervision via transfer learning from pre trained models on large scale general images such as ImageNet. However, they cannot be utilized for medical image domain in which do not exist such priors. In this work, we present a novel weakly supervised learning framework for lesion localization named as self transfer learning (STL) STL jointly optimizes both classification and localization networks to help the localization network focus on correct lesions without any types of priors. We evaluate STL framework over chest X rays and mammograms, and achieve significantly better localization performance compared to previous weakly supervised localization approaches.
Combining multiple expert annotations using semi supervised learning and graph cuts for medical image segmentation
Venue: Comput. Vis. Image Underst.

Semisupervised learning (SSL) is used for predicting missing annotations.A novel self consistency score quantifies reliability of experts annotations.Graph cuts obtain the consensus segmentation through a globally accurate solution. Generating consensus ground truth segmentation from multiple experts is important in medical imaging applications such as segmentation. We propose a novel approach to combine multiple expert annotations using graph cuts (GC) and semi supervised learning (SSL) Current methods use iterative Expectation Maximization (EM) based approaches to estimate the final annotation and quantify annotator's performance. This poses the risk of getting trapped in local minimum and providing inaccurate estimates of annotator performance. A novel self consistency (SC) score quantifies annotator performance based on the consistency of their annotations in terms of low level image features. The missing annotations are predicted using SSL techniques that consider global features and local image consistency. The self consistency score also serves as the penalty cost in a second order Markov random field (MRF) cost function which is optimized using graph cuts to obtain the final consensus label. Graph cut optimization gives a global maximum and is non iterative, thus speeding up the process. Experimental results on synthetic images, real data of Crohn's disease patients and retinal images show our final segmentation to be accurate and more consistent than those obtained by competing methods. It also highlights the effectiveness of self consistency in quantifying expert reliability and accuracy of SSL in predicting missing labels.
Tables as Semi structured Knowledge for Question Answering
Venue: ACL

Question answering requires access to a knowledge base to check facts and reason about information. Knowledge in the form of natural language text is easy to acquire, but difficult for automated reasoning. Highly structured knowledge bases can facilitate reasoning, but are difficult to acquire. In this paper we explore tables as a semi structured formalism that provides a balanced compromise to this trade off. We first use the structure of tables to guide the construction of a dataset of over 9000 multiple choice questions with rich alignment annotations, easily and efficiently via crowd sourcing. We then use this annotated data to train a semi structured feature driven model for question answering that uses tables as a knowledge base. In benchmark evaluations, we significantly outperform both a strong unstructured retrieval baseline and a highly structured Markov Logic Network model.