nils-herrmann/clean_scirepeval huggingface dataset:
Effective distant supervision for end-to-end knowledge base population systems

The growing amounts of textual data require automatic methods for structuring relevant information so that it can be further processed by computers and systematically accessed by humans. The scenario dealt with in this dissertation is known as Knowledge Base Population (KBP), where relational information about entities is retrieved from a large text collection and stored in a database, structured according to a prespecified schema. Most of the research in this dissertation is placed in the context of the KBP benchmark of the Text Analysis Conference (TAC KBP), which provides a test-bed to examine all steps in a complex end-to-end relation extraction setting. In this dissertation a new state of the art for the TAC KBP benchmark was achieved by focussing on the following research problems: (1) The KBP task was broken down into a modular pipeline of sub-problems, and the most pressing issues were identified and quantified at all steps. (2) The quality of semi-automatically generated training data was increased by developing noise-reduction methods, decreasing the influence of false-positive training examples. (3) A focus was laid on fine-grained entity type modelling, entity expansion, entity matching and tagging, to maintain as much recall as possible on the relational argument level. (4) A new set of effective methods for generating training data, encoding features and training relational classifiers was developed and compared with previous state-of-the-art methods.
Amorphous Oxide Semiconductor TFTs for Displays and Imaging

This paper reviews the mechanisms underlying visible light detection based on phototransistors fabricated using amorphous oxide semiconductor technology. Although this family of materials is perceived to be optically transparent, the presence of oxygen deficiency defects, such as vacancies, located at subgap states, and their ionization under illumination, gives rise to absorption of blue and green photons. At higher energies, we have the usual band-to-band absorption. In particular, the oxygen defects remain ionized even after illumination ceases, leading to persistent photoconductivity, which can limit the frame-rate of active matrix imaging arrays. However, the persistence in photoconductivity can be overcome through deployment of a gate pulsing scheme enabling realistic frame rates for advanced applications such as sensor-embedded display for touch-free interaction.
Investigating User Comprehension and Risk Perception of Apple's Touch ID Technology

Apple's Touch ID serves as an alternative to PIN/password for unlocking Apple devices, signing into third party iOS applications, and authorizing purchases on the iTunes Store by simply tapping a registered finger on the home button.
 This paper investigates the user comprehension and risk perception of Apple's Touch ID technology. We conducted two user studies to assess user perceptions in three domains: 1) Touch ID authentication process for third party applications; 2) fingerprint access and storage; and 3) ease of circumventing Touch ID. We first conducted an in-person study with 30 participants and then validated our findings through an online study over Amazon Turk on a larger sample of 125 participants. Our findings show that Touch ID users are unaware of the Touch ID authentication process for signing into applications, and have incorrect perceptions regarding the storage/access of their registered fingerprint before and after Touch ID authentication.
Detection and differentiation of serologically cross-reacting tobamoviruses of economical importance by RT-PCR and RT-PCR-RFLP.

A procedure involving reverse transcription followed by the polymerase chain reaction (RT-PCR) using a single primer pair was developed for the detection of five tobamovirus species which are related serologically. Either with a subsequent restriction enzyme analysis (RT-PCR-RFLP) or with a RT-PCR using species specific primers the five species can be differentiated. To differentiate those species by serological means is time consuming and might give ambiguous results. With the example of the isolate OHIO V, which is known to break the resistance in a selection of Lycopersicon peruvianum, the suitability of the RT-PCR-RFLP technique to detect variability at the species level was shown. In sequence analysis 47 codons of the coat protein gene of this isolate were found to be mutated compared to a tobacco mosaic virus (TMV) coat protein gene sequence. Forty of these mutations were silent and did not change the amino acid sequence. Both procedures are suitable to detect mixed infections. In addition, the RT-PCR-RFLP give information on the relative amounts of the viruses that are present in a doubly infected plant. The RT-PCR-RFLP using general primers as well as the RT-PCR using species specific primers were proven to be useful for the diagnosis and control of the disease and will be helpful for resistance breeding, epidemiological investigations and plant virus collections.
TaoStore: Overcoming Asynchronicity in Oblivious Data Storage

We consider oblivious storage systems hiding both the contents of the data as well as access patterns from an untrusted cloud provider. We target a scenario where multiple users from a trusted group (e.g., corporate employees) asynchronously access and edit potentially overlapping data sets through a trusted proxy mediating client-cloud communication. The main contribution of our paper is twofold. Foremost, we initiate the first formal study of asynchronicity in oblivious storage systems. We provide security definitions for scenarios where both client requests and network communication are asynchronous (and in fact, even adversarially scheduled). While security issues in ObliviStore (Stefanov and Shi, S&P 2013) have recently been surfaced, our treatment shows that also CURIOUS (Bindschaedler at al., CCS 2015), proposed with the exact goal of preventing these attacks, is insecure under asynchronous scheduling of network communication. Second, we develop and evaluate a new oblivious storage system, called Tree-based Asynchronous Oblivious Store, or TaoStore for short, which we prove secure in asynchronous environments. TaoStore is built on top of a new tree-based ORAM scheme that processes client requests concurrently and asynchronously in a non-blocking fashion. This results in a substantial gain in throughput, simplicity, and flexibility over previous systems.
SEMIOSIS AS AN EMERGENT PROCESS

In this paper, we intend to discuss if and in what sense semiosis (meaning process, cf. C.S. Peirce) can be regarded as an “emergent” process in semiotic systems. It is not our problem here to answer when or how semiosis emerged in nature. As a prerequisite for the very formulation of these problems, we are rather interested in discussing the conditions which should be fulfilled for semiosis to be characterized as an emergent process. The first step in this work is to summarize a systematic analysis of the variety of emergence theories and concepts, elaborated by Achim Stephan. Along the summary of this analysis, we pose fundamental questions that have to be answered in order to ascribe a precise meaning to the term “emergence” in the context of an understanding of semiosis. After discussing a model for explaining emergence based on Salthe’s hierarchical structuralism, which considers three levels at a time in a semiotic system, we present some tentative answers to those questions.
The Influence of Travel Experience on Mature Travelers' Quality of Life

The purpose of this study was to examine the influence of past travel experience (i.e., number of trips and number of days away from home in last year), and on mature travelers’ quality of life (i.e., self-perceived health and global life satisfaction). A total number of 217 respondents (50+) in a southern state were used in this study. Path analysis (PROC CALIS in SAS) was performed to test the proposed model. An estimation of the proposed theoretical model revealed that the model fit the data. However, the model should be further examined and applied with caution.
Development of Biotechnology Products in Pre-filled Syringes: Technical Considerations and Approaches

A monoclonal antibody (mAb) product development case study is presented to address some of the issues faced during developing a pre-filled syringe (PFS) product for a biotherapeutic. In particular, issues involving incompatibility with silicone oil and a stability-based approach for selection of PFS barrel and tip cap components have been discussed. Silicone spiking studies followed by exposure to agitation stress or accelerated temperature conditions were used to check for incompatibilities of the mAb with silicone oil, a necessary product contact material in PFS. In addition, screening studies to compare various closure materials as well as syringe barrel processing methods were used to select the optimum closure materials as well as the correct syringe processing method. Results indicate that the model mAb formulation used was sensitive to high levels of silicone oil especially under accelerated temperature conditions resulting in formation of protein–silicone particles in the solution for samples that were spiked with the silicone oil. Agitation stress did not have any significant impact on the quality attributes tested. Samples stored in syringe barrels that were processed with sprayed-on silicone had higher levels of subvisible particles as compared to those that were processed with the baked-on process. The tip cap comparability study resulted in one tip cap material having superior compatibility among the three that were tested. The quality attribute that was most impacted by the tip cap materials was mAb oxidation. An approach for evaluation of primary packaging components during the development of pre-filled syringe presentations for biotechnology-based compounds has been highlighted.
Learning Bayesian networks for clinical time series analysis

Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status.



 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set.



The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.
Fatal toxic epidermal necrolysis and severe granulocytopenia following therapy with cefuroxime.

Toxic epidermal necrolysis (TEN) is one of the most threatening adverse reactions to various drugs. No case of concomitant occurrence TEN and severe granulocytopenia following the treatment with cefuroxime has been reported to date. Herein we present a case of TEN that developed eighteen days of the initiation of cefuroxime axetil therapy for urinary tract infection in a 73-year-old woman with chronic renal failure and no previous history of allergic diathesis. The condition was associated with severe granulocytopenia and followed by gastrointestinal hemorrhage, severe sepsis and multiple organ failure syndrome development. Despite intensive medical treatment the patient died. The present report underlines the potential of cefuroxime to simultaneously induce life threatening adverse effects such as TEN and severe granulocytopenia. Further on, because the patient was also taking furosemide for chronic renal failure, the possible unfavorable interactions between the two drugs could be hypothesized. Therefore, awareness of the possible drug interaction is necessary, especially when given in conditions of their altered pharmacokinetics as in case of chronic renal failure.
Microbes and mental health: A review

There is a growing emphasis on the relationship between the microorganisms inhabiting the gut (gastrointestinal microbiota) and human health. The emergence of a microbiota-gut-brain axis to describe the complex networks and relationship between the gastrointestinal microbiota and host reflects the major influence this environment may have in brain health and disorders of the central nervous system (CNS). Bidirectional communication between the microbiota and the CNS occurs through autonomic, neuroendocrine, enteric, and immune system pathways. Potential neurobiological mechanisms through which disruptions in this network may impact health and disease include hypothalamic-pituitary-adrenal (HPA)-axis activation, and altered activity of neurotransmitter and immune systems. Perturbations of the gut microbial community have already been implicated in multiple host diseases such as obesity, diabetes, and inflammation, while recent evidence suggests a potential role of the microbiota-gut-brain axis in neuropsychiatric disorders, such as depression and anxiety. Here, we review the current literature related to the influence of the gut microbial community on central nervous system function, with a specific focus on anxiety and depressive symptoms. The role of stress and stress-mediated changes in autonomic, neuroendocrine, immune, and neurotransmitter systems are examined, followed by a discussion of the role of the microbiota in novel gastrointestinal-based treatment options for the prevention and treatment of brain-based disorders such as anxiety and depression.
α-lipoic acid protects rat cortical neurons against cell death induced by amyloid and hydrogen peroxide through the Akt signalling pathway

Substantial evidence suggests that the accumulation of beta-amyloid (Abeta)-derived peptides contributes to the aetiology of Alzheimer's disease (AD) by stimulating formation of free radicals. Thus, the antioxidant alpha-lipoate, which is able to cross the blood-brain barrier, would seem an ideal substance in the treatment of AD. We have investigated the potential effectiveness of alpha-lipoic acid (LA) against cytotoxicity induced by Abeta peptide (31-35) (30 microM) and hydrogen peroxide (H(2)O(2)) (100 microM) with the cellular 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) reduction and fluorescence dye propidium iodide assays in primary neurons of rat cerebral cortex. We found that treatment with LA protected cortical neurons against cytotoxicity induced by Abeta or H(2)O(2). In addition, LA-induced increase in the level of Akt in the neurons was observed by Western blot. The LA-induced neuroprotection and Akt increase were attenuated by pre-treatment with the phosphatidylinositol 3-kinase inhibitor, LY294002 (50 microM). Our data suggest that the neuroprotective effects of the antioxidant LA are partly mediated through activation of the PKB/Akt signaling pathway.
Review of Signaling Pathways Governing MSC Osteogenic and Adipogenic Differentiation

Mesenchymal stem cells (MSC) are multipotent cells, functioning as precursors to a variety of cell types including adipocytes, osteoblasts, and chondrocytes. Between osteogenic and adipogenic lineage commitment and differentiation, a theoretical inverse relationship exists, such that differentiation towards an osteoblast phenotype occurs at the expense of an adipocytic phenotype. This balance is regulated by numerous, intersecting signaling pathways that converge on the regulation of two main transcription factors: peroxisome proliferator-activated receptor- γ (PPAR γ ) and Runt-related transcription factor 2 (Runx2). These two transcription factors, PPAR γ and Runx2, are generally regarded as the master regulators of adipogenesis and osteogenesis. This review will summarize signaling pathways that govern MSC fate towards osteogenic or adipocytic differentiation. A number of signaling pathways follow the inverse balance between osteogenic and adipogenic differentiation and are generally proosteogenic/antiadipogenic stimuli. These include β -catenin dependent Wnt signaling, Hedgehog signaling, and NELL-1 signaling. However, other signaling pathways exhibit more context-dependent effects on adipogenic and osteogenic differentiation. These include bone morphogenic protein (BMP) signaling and insulin growth factor (IGF) signaling, which display both proosteogenic and proadipogenic effects. In summary, understanding those factors that govern osteogenic versus adipogenic MSC differentiation has significant implications in diverse areas of human health, from obesity to osteoporosis to regenerative medicine.
Stochastic Optimization with Bandit Sampling

Many stochastic optimization algorithms work by estimating the gradient of the cost function on the fly by sampling datapoints uniformly at random from a training set. However, the estimator might have a large variance, which inadvertantly slows down the convergence rate of the algorithms. One way to reduce this variance is to sample the datapoints from a carefully selected non-uniform distribution. In this work, we propose a novel non-uniform sampling approach that uses the multiarmed bandit framework. Theoretically, we show that our algorithm asymptotically approximates the optimal variance within a factor of 3. Empirically, we show that using this datapoint-selection technique results in a significant reduction of the convergence time and variance of several stochastic optimization algorithms such as SGD and SAGA. This approach for sampling datapoints is general, and can be used in conjunction with any algorithm that uses an unbiased gradient estimation – we expect it to have broad applicability beyond the specific examples explored in this work.
Food Recognition and Detection with Minimum Supervision

Food recognition can help people keep track of and analyze their eating habits conveniently by snapping a photo on their smartphones. A lot of existing work has been published in food recognition using computer vision and deep learning techniques. However, most previous work assumed that one food image contains only one food item, thus can not handle images which contain multiple food items. In real-life scenarios, it is more common for a food image having more than one food item. Existing methods for multiple-food detection have various limitations: they either require certain kinds of additional user operations (such as drawing bounding boxes on food items) or are still in primitive stage with low accuracy rate. Current state-of-theart object detection models usually utilize the power of convolutional neural networks (CNNs) which require a lot of training data with ground truth bounding boxes. Those limitations narrow down the usability of existing methods in real life. To solve these problems, we propose a novel method which jointly detects food item combinations and their locations in the image with minimal supervision. We create a food image dataset with 10 categories for our experiments. In our dataset, images with only one food item are used as the training set while images with multiple food items are used as the test dataset. Our method does not require any ground truth bounding boxes in the training set. At the training stage, we firstly use region proposal algorithms to generate candidate regions and extract the CNN features of all regions. Secondly, we perform region mining to select discriminative positive regions for each food category using maximum cover by submodular optimization. With these mined positive regions, we train a binary SVM classifier for each food category and further refine these classifiers with hard negative examples. At the testing stage, we firstly generate a set of candidate regions. For each region, a classification score is computed based on its extracted CNN features. Then we select the regions using non-maximum suppression and output the locations and predicted food names of the selected regions. Our experiments show very promising results. We obtain the average recall rate of 80.18% and precision rate of 83.78% on our test dataset. Our method achieve minimum supervision in the sense that it uses only “weakly labeled” images for training and does not require any ground truth bounding boxes for training dataset, thus can be easily extended to a larger dataset with more food categories.
A Performance Comparison of RAID-5 and Log-Structured Arrays

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