nils-herrmann/clean_scirepeval huggingface dataset:
EMR Coding with Semi-Parametric Multi-Head Matching Networks

Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and mis-interpretation of a patient's well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. In this paper, we present a new neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models. Our evaluations are conducted using a well known deidentified EMR dataset (MIMIC) with a variety of multi-label performance measures.
A unifying framework for robot control with redundant DOFs

Recently, (Udwadia, 2003) suggested to derive tracking controllers for mechanical systems with redundant degrees-of-freedom (DOFs) using a generalization of Gauss’ principle of least constraint. This method allows reformulating control problems as a special class of optimal controllers. In this paper, we take this line of reasoning one step further and demonstrate that several well-known and also novel nonlinear robot control laws can be derived from this generic methodology. We show experimental verifications on a Sarcos Master Arm robot for some of the the derived controllers. The suggested approach offers a promising unification and simplification of nonlinear control law design for robots obeying rigid body dynamics equations, both with or without external constraints, with over-actuation or underactuation, as well as open-chain and closed-chain kinematics.
MiR-26b suppresses tumor cell proliferation, migration and invasion by directly targeting COX-2 in lung cancer.

Lung cancer, including non-small cell lung cancer (NSCLC), is the leading cause of cancer-related mortality worldwide. Despite recent advances in clinical and experimental oncology, the prognosis of patients with NSCLC still remains poor and the average survival time of patients suffer from lung cancer is low. Therefore, the potential mechanism accounting for the tumorigenesis of NSCLC is still needed to be explored.



A lentiviral vector over-expressing miR-26b in A549 lung cancer cells was constructed. Cell proliferation, migration and invasion analysis were measured by cell counting kit (MTT), would healing assay and Transwell assay. Direct target of miR-26b in A549 cells was examined using bioinformatics and Luciferase assay.



Herein, we found that over-expression of miR-26b significantly inhibited the proliferation, migration and invasion of A549 lung cancer cell in vitro and suppressed the growth of established tumors in vivo. By using bioinformatics, we found that COX-2 (Cyclooxygenase-2) is one of the potential targets of miR-26b. Moreover, miR-26b was found to negatively regulate COX-2 protein level by directly targeting its 3'UTR. In addition, depletion of endogenous COX-2 by the specific siRNA could mimic the function of miR-26b overexpression.



Taken together, our results demonstrate that miR-26b could suppress lung cancer cells proliferation, migration and invasion by directly negative regulation of COX-2. MiR-26b could serve as a novel potential marker for NSCLC therapy.
TGM5 mutations impact epidermal differentiation in acral peeling skin syndrome.

Acral peeling skin syndrome (APSS) is an autosomal recessive skin disorder characterized by acral blistering and peeling of the outermost layers of the epidermis. It is caused by mutations in the gene for transglutaminase 5, TGM5. Here, we report on clinical and molecular findings in 11 patients and extend the TGM5 mutation database by four, to our knowledge, previously unreported mutations: p.M1T, p.L41P, p.L214CfsX15, and p.S604IfsX9. The recurrent mutation p.G113C was found in 9 patients, but also in 3 of 100 control individuals in a heterozygous state, indicating that APSS might be more widespread than hitherto expected. Using quantitative real-time PCR, immunoblotting, and immunofluorescence analysis, we demonstrate that expression and distribution of several epidermal differentiation markers and corneodesmosin (CDSN) is altered in APSS keratinocytes and skin. Although the expression of transglutaminases 1 and 3 was not changed, we found an upregulation of keratin 1, keratin 10, involucrin, loricrin, and CDSN, probably as compensatory mechanisms for stabilization of the epidermal barrier. Our results give insights into the consequences of TGM5 mutations on terminal epidermal differentiation.
Fetal heart rate and motor activity associations with maternal organochlorine levels: results of an exploratory study

Contemporaneous associations between circulating maternal organochlorines (OCs) and measures of fetal heart rate and motor activity were evaluated. A panel of 47 OCs, including pesticides and polychlorinated biphenyls (PCBs), was analyzed from serum of 50 pregnant women at 36 weeks gestation. Data were empirically reduced into four factors and six individual compounds. All participants had detectable concentrations of at least one-quarter of the assayed OCs and, in general, higher socioeconomic level was associated with higher OC concentrations. Fetal heart rate measures were not consistently associated with maternal OCs. In contrast, one or more indicators of greater fetal motor activity were significantly associated with higher levels of the DDT and low chlorinated OC factors and five of the six individual compounds (heptachlor epoxide, trans nonachlor, oxychlordane, and PCBs 18 and 52). This preliminary demonstration of associations between fetal motor activity and maternal concentrations of persistent and pervasive environmental contaminants suggests that fetal assessment may be useful in ascertaining the potential early effects of these compounds on development.
MRF-Based Deformable Registration and Ventilation Estimation of Lung CT

Deformable image registration is an important tool in medical image analysis. In the case of lung computed tomography (CT) registration there are three major challenges: large motion of small features, sliding motions between organs, and changing image contrast due to compression. Recently, Markov random field (MRF)-based discrete optimization strategies have been proposed to overcome problems involved with continuous optimization for registration, in particular its susceptibility to local minima. However, to date the simplifications made to obtain tractable computational complexity reduced the registration accuracy. We address these challenges and preserve the potentially higher quality of discrete approaches with three novel contributions. First, we use an image-derived minimum spanning tree as a simplified graph structure, which copes well with the complex sliding motion and allows us to find the global optimum very efficiently. Second, a stochastic sampling approach for the similarity cost between images is introduced within a symmetric, diffeomorphic B-spline transformation model with diffusion regularization. The complexity is reduced by orders of magnitude and enables the minimization of much larger label spaces. In addition to the geometric transform labels, hyper-labels are introduced, which represent local intensity variations in this task, and allow for the direct estimation of lung ventilation. We validate the improvements in accuracy and performance on exhale-inhale CT volume pairs using a large number of expert landmarks.
Pancreatic panniculitis complicated by infection with Corynebacterium tuberculostearicum: A case report

We present a case of pancreatic panniculitis in a patient with alcohol abuse where Corynebacterium tuberculostearicum was isolated from a pannicular nodule on the crus. The patient was started on linezolid treatment leading to regression of the patient's symptoms. Upon discontinuation of linezolid treatment progression of the skin symptoms progressed.
The Iseult/Inumac Whole Body 11.7 T MRI Magnet Design

A neuroscience research center with very high field MRI equipments has been opened in November 2006 by the CEA life science division. One of the imaging systems will require a 11.75 T magnet with a 900 mm warm bore. Regarding the large aperture and field strength, this magnet is a real challenge as compared to the largest MRI systems ever built, and is then developed within an ambitious R&D program, Iseult, focus on high field MRI. The conservative MRI magnet design principles are not readily applicable and other concepts taken from high energy physics or fusion experiments, namely the Tore Supra tokamak magnet system, will be used. The coil will thus be made of a niobium-titanium conductor cooled by a He II bath at 1.8 K, permanently connected to a cryoplant. Due to the high level of stored energy, about 340 MJ, and a relatively high nominal current, about 1500 A, the magnet will be operated in a non-persistent mode with a conveniently stabilized power supply. In order to take advantage of superfluid helium properties and regarding the high electromagnetic stresses on the conductors, the winding will be made of wetted double pancakes meeting the Stekly criterion for cryostability. The magnet will be actively shielded to fulfill the specifications regarding the stray field.
Face recognition with liveness detection using eye and mouth movement

The recent literature on face recognition technology discusses the issue of face spoofing which can bypass the authentication system by placing a photo/video/mask of the enrolled person in front of the camera. This problem could be minimized by detecting the liveness of the person. Therefore, in this paper, we propose a robust liveness detection scheme based on challenge and response method. The liveness module is added as extra layer of security before the face recognition module. The liveness module utilizes face macro features, especially eye and mouth movements in order to generate random challenges and observing the user's response on account of this. The reliability of liveness module is tested by placing different types of spoofing attacks with various means, like using photograph, videos, etc. In all, five types of attacks have been taken care of and prevented by our system. Experimental results show that system is able to detect the liveness when subjected to all these attacks except the eye & mouth imposter attack. This attack is able to bypass the liveness test but it creates massive changes in face structure. Therefore resultant unrecognized or misclassified by the face recognition module. An experimental test conducted on 65 persons on university of Essex face database confirms that removal of eye and nose components results 75% misclassification.
Public Intimacy : Disclosure Interpretation and Social Judgments on Facebook

This research examines how sociotechnical affordances shape interpretation of disclosure and social judgments on social networking sites. Drawing on the disclosure personalism framework, Study 1 revealed that information unavailability and relational basis underlay personalistic judgments about Facebook disclosures: Perceivers inferred greater message and relational intimacy from disclosures made privately than from those made publicly. Study 2 revealed that perceivers judged intimate disclosures shared publicly as less appropriate than intimate disclosures shared privately, and that perceived disclosure appropriateness accounted for the effects of public versus private contexts on reduced liking for a discloser. Taken together, the results show how sociotechnical affordances shape perceptions of disclosure and relationships, which has implications for understanding relational development and maintenance on SNS.
Predicting Decision in Human-Agent Negotiation using functional MRI

The importance of human-agent negotiation, and the role of emotion in such negotiations, have been emphasized in humanagent interaction research. Thus far, studies have focused on behavioral effects, rather than examining the neural underpinnings of different behaviors shown in human-agent interactions. Here, we used a multi-round negotiation platform, instead of the more common single-shot negotiation, and were able to find distinct brain patterns in emotion-related regions of the brain during different types of offers. Using multi-voxel pattern analysis to analyze brain imaging data acquired during functional MRI scanning, we show that it is possible to predict whether the negotiator concedes, does not change, or asks for more during the negotiation. Most importantly, we demonstrate that left dorsal anterior insula, which is known to be an emotion-related brain region, shows a different pattern of activity for each of the three offer types.
Sentic Avatar: Multimodal Affective Conversational Agent with Common Sense

The capability of perceiving and expressing emotions through different modalities is a key issue for the enhancement of human-computer interaction. In this paper we present a novel architecture for the development of intelligent multimodal affective interfaces. It is based on the integration of Sentic Computing, a new opinion mining and sentiment analysis paradigm based on AI and Semantic Web techniques, with a facial emotional classifier and Maxine, a powerful multimodal animation engine for managing virtual agents and 3D scenarios. One of the main distinguishing features of the system is that it does not simply perform emotional classification in terms of a set of discrete emotional labels but it operates in a continuous 2D emotional space, enabling the integration of the different affective extraction modules in a simple and scalable way.
5G City: A Novel 5G-Enabled Architecture for Ultra-High Definition and Immersive Media on City Infrastructure

The rapid adoption of cutting edge media services such as ultra-high definition (UHD/4K) video and immersive media i.e, virtual and augmented reality (VR/AR), will demand large investments in a scalable, ubiquitous, and robust communication infrastructure and services. The H2020 5GCity project aims to solve such issues and beyond while designing, developing, and deploying a sliceable, distributed cloud & edge and radio 5G-platform with neutral hosting capability to support the sharing between information technology (IT) infrastructure owners and media service providers i.e., vertical media actors. In this paper we discuss how 5G technology will facilitate the rise of highly demanding media use cases and its implication on how service providers typically operate (in terms of business model). After a brief introduction of the 5GCity's architecture we present how three media-related use cases will be deployed in the cities of Barcelona, Bristol and Lucca by considering the project's architecture and the underlying neutral host parading to understand the benefits of such model for infrastructure owners and media service providers.
Courteous Autonomous Cars

Typically, autonomous cars optimize for a combination of safety, efficiency, and driving quality. But as we get better at this optimization, we start seeing behavior go from too conservative to too aggressive. The car's behavior exposes the incentives we provide in its cost function. In this work, we argue for cars that are not optimizing a purely selfish cost, but also try to be courteous to other interactive drivers. We formalize courtesy as a term in the objective that measures the increase in another driver's cost induced by the autonomous car's behavior. Such a courtesy term enables the robot car to be aware of possible irrationality of the human behavior, and plan accordingly. We analyze the effect of courtesy in a variety of scenarios. We find, for example, that courteous robot cars leave more space when merging in front of a human driver. Moreover, we find that such a courtesy term can help explain real human driver behavior on the NGSIM dataset.
Duckneglect: video-games based neglect rehabilitation.

Video-games are becoming a common tool to guide patients through rehabilitation because of their power of motivating and engaging their users. Video-games may also be integrated into an infrastructure that allows patients, discharged from the hospital, to continue intensive rehabilitation at home under remote monitoring by the hospital itself, as suggested by the recently funded Rewire project.



Goal of this work is to describe a novel low cost platform, based on video-games, targeted to neglect rehabilitation.



The patient is guided to explore his neglected hemispace by a set of specifically designed games that ask him to reach targets, with an increasing level of difficulties. Visual and auditory cues helped the patient in the task and are progressively removed. A controlled randomization of scenarios, targets and distractors, a balanced reward system and music played in the background, all contribute to make rehabilitation more attractive, thus enabling intensive prolonged treatment.



Results from our first patient, who underwent rehabilitation for half an hour, for five days a week for one month, showed on one side a very positive attitude of the patient towards the platform for the whole period, on the other side a significant improvement was obtained. Importantly, this amelioration was confirmed at a follow up evaluation five months after the last rehabilitation session and generalized to everyday life activities.



Such a system could well be integrated into a home based rehabilitation system.
Combining Data Mining and Machine Learning for Effective User Profiling

This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the de&,, ic nrrnm,-,li~h~rl ,,&,a n .am.L~ nf mn.-h;na lm..~:~~ e-. .. ..--..*.*yYYA’“.. UY.“b Y UISLUY “I III-Yllr IxuIY11~ methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rulelearning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.
Quantifying and optimizing visualization: An evolutionary computing-based approach

Advances in computing technology and computer graphics engulfed with huge collections of data have introduced new visualization techniques. This gives users many choices of visualization techniques to gain an insight about the dataset at hand. However, selecting the most suitable visualization for a given dataset and the task to be performed on the data is subjective. The work presented here introduces a set of visualization metrics to quantify visualization techniques. Based on a comprehensive literature survey, we propose effectiveness, expressiveness, readability, and interactivity as the visualization metrics. Using these metrics, a framework for optimizing the layout of a visualization technique is also presented. The framework is based on an evolutionary algorithm (EA) which uses treemaps as a case study. The EA starts with a randomly initialized population, where each chromosome of the population represents one complete treemap. Using the genetic operators and the proposed visualization metrics as an objective function, the EA finds the optimum visualization layout. The visualizations that evolved are compared with the state-of-the-art treemap visualization tool through a user study. The user study utilizes benchmark tasks for the evaluation. A comparison is also performed using direct assessment, where internal and external visualization metrics are used. Results are further verified using analysis of variance (ANOVA) test. The results suggest better performance of the proposed metrics and the EA-based framework for optimizing visualization layout. The proposed methodology can also be extended to other visualization techniques.
Bayesian Deep Learning

In previous chapters we reviewed Bayesian neural networks (BNNs) and historical techniques for approximate inference in these, as well as more recent approaches. We discussed the advantages and disadvantages of different techniques, examining their practicality. This, perhaps, is the most important aspect of modern techniques for approximate inference in BNNs. The field of deep learning is pushed forward by practitioners, working on real-world problems. Techniques which cannot scale to complex models with potentially millions of parameters, scale well with large amounts of data, need well studied models to be radically changed, or are not accessible to engineers, will simply perish. In this chapter we will develop on the strand of work of [Graves, 2011; Hinton and Van Camp, 1993], but will do so from the Bayesian perspective rather than the information theory one. Developing Bayesian approaches to deep learning, we will tie approximate BNN inference together with deep learning stochastic regularisation techniques (SRTs) such as dropout. These regularisation techniques are used in many modern deep learning tools, allowing us to offer a practical inference technique. We will start by reviewing in detail the tools used by [Graves, 2011]. We extend on these with recent research, commenting and analysing the variance of several stochastic estimators in variational inference (VI). Following that we will tie these derivations to SRTs, and propose practical techniques to obtain model uncertainty, even from existing models. We finish the chapter by developing specific examples for image based models (CNNs) and sequence based models (RNNs). These will be demonstrated in chapter 5, where we will survey recent research making use of the suggested tools in real-world problems.
Note on Attacking Object Detectors with Adversarial Stickers

Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks. However, deep learning algorithms are known to be vulnerable to adversarial examples. These adversarial inputs are created such that, when provided to a deep learning algorithm, they are very likely to be mislabeled. This can be problematic when deep learning is used to assist in safety critical decisions. Recent research has shown that classifiers can be attacked by physical adversarial examples under various physical conditions. Given the fact that stateof-the-art objection detection algorithms are harder to be fooled by the same set of adversarial examples, here we show that these detectors can also be attacked by physical adversarial examples. In this note, we briefly show both static and dynamic test results. We design an algorithm that produces physical adversarial inputs, which can fool the YOLO object detector and can also attack Faster-RCNN with relatively high success rate based on transferability. Furthermore, our algorithm can compress the size of the adversarial inputs to stickers that, when attached to the targeted object, result in the detector either mislabeling or not detecting the object a high percentage of the time. This note provides a small set of results. Our upcoming paper will contain a thorough evaluation on other object detectors, and will present the algorithm.