nils-herrmann/clean_scirepeval huggingface dataset:
Hyperectasis: the hyperinflated tympanic membrane: the middle ear as an actively controlled system.

To describe and analyze a middle ear condition in which the steady state of the middle ear pressure is elevated above the atmospheric pressure.  This is a long-term survey of 59 patients from a private clinic who were observed on routine examination to have a ballooned out (hyperinflated) tympanic membrane.



All patients underwent hearing tests, tympanometry, and Shullers (lateral) mastoid radiography.



A hyperinflated tympanic membrane indicates a middle ear pressure that is higher than atmospheric pressure. The ballooned tympanic membrane returns to its physiological level after being punctured. This pressure situation is the reverse or opposite of atelectasis and is therefore termed hyperectasis. Hyperectasis, like atelectasis, is associated with a poorly pneumatized mastoid.



Fifty-nine hyperectatic ears persisted in their hyperinflated state for weeks, months, or even years. The hyperectasis was preceded by atelectasis, and both conditions occasionally changed one into the other. The ballooned part of the tympanic membrane is usually thin and "scarred." Hyperectasis is not a rare situation and, once recognized, can be readily encountered in an otologic clinic.



Like most biologic systems (e.g., blood pressure, temperature), the middle ear's central feature, i.e., pressure, also has a dynamic character vacillating up and down. It is conceivable that middle ear pressure is also actively regulated and controlled with the aid of a feedback mechanism. Passage of gas through the eustachian tube or absorption by diffusion-perfusion is also at least partly an active process. The up and down middle ear pressure vacillations are usually clinically benign and do not lead to any pathologic features as long as they are buffered by an accompanying normal mastoid pneumatization. It is the ear with a nonpneumatized mastoid that has limited ability to buffer pressure changes and that will present as an atelectasis, a retraction pocket, or (eventually a cholesteatoma) or their reverse, a hyperectatic tympanic membrane.
Compositionality Principle in Recognition of Fine-Grained Emotions from Text

The recognition of personal emotional state or sentiment conveyed through text is the main task we address in our research. The communication of emotions through text messaging and posts of personal blogs poses the ‘informal style of writing’ challenge for researchers expecting grammatically correct input. Our Affect Analysis Model was designed to handle the informal messages written in an abbreviated or expressive manner. While constructing our rule-based approach to affect recognition from text, we followed the compositionality principle. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses), and complex-compound sentences. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize affective information in text from an existing corpus of personal blog posts.
Automated Analysis of Privacy Requirements for Mobile Apps

Mobile apps have to satisfy various privacy requirements. Notably, app publishers are often obligated to provide a privacy policy and notify users of their apps’ privacy practices. But how can a user tell whether an app behaves as its policy promises? In this study we introduce a scalable system to help analyze and predict Android apps’ compliance with privacy requirements. We discuss how we customized our system in a collaboration with the California Office of the Attorney General. Beyond its use by regulators and activists our system is also meant to assist app publishers and app store owners in their internal assessments of privacy requirement compliance. Our analysis of 17,991 free Android apps shows the viability of combining machine learning-based privacy policy analysis with static code analysis of apps. Results suggest that 71% of apps tha lack a privacy policy should have one. Also, for 9,050 apps that have a policy, we find many instances of potential inconsistencies between what the app policy seems to state and what the code of the app appears to do. In particular, as many as 41% of these apps could be collecting location information and 17% could be sharing such with third parties without disclosing so in their policies. Overall, each app exhibits a mean of 1.83 potential privacy requirement inconsistencies.
Staying sticky: contact self-cleaning of gecko-inspired adhesives.

The exceptionally adhesive foot of the gecko remains clean in dirty environments by shedding contaminants with each step. Synthetic gecko-inspired adhesives have achieved similar attachment strengths to the gecko on smooth surfaces, but the process of contact self-cleaning has yet to be effectively demonstrated. Here, we present the first gecko-inspired adhesive that has matched both the attachment strength and the contact self-cleaning performance of the gecko's foot on a smooth surface. Contact self-cleaning experiments were performed with three different sizes of mushroom-shaped elastomer microfibres and five different sizes of spherical silica contaminants. Using a load-drag-unload dry contact cleaning process similar to the loads acting on the gecko foot during locomotion, our fully contaminated synthetic gecko adhesives could recover lost adhesion at a rate comparable to that of the gecko. We observed that the relative size of contaminants to the characteristic size of the microfibres in the synthetic adhesive strongly determined how and to what degree the adhesive recovered from contamination. Our approximate model and experimental results show that the dominant mechanism of contact self-cleaning is particle rolling during the drag process. Embedding of particles between adjacent fibres was observed for particles with diameter smaller than the fibre tips, and further studied as a temporary cleaning mechanism. By incorporating contact self-cleaning capabilities, real-world applications of synthetic gecko adhesives, such as reusable tapes, clothing closures and medical adhesives, would become feasible.
Beaver: Towards a Declarative Schema Mapping

Schema mapping is used to transform data to a desired schema from data sources with different schemas. Manually writing complete schema mapping specifications requires a deep understanding of the source and target schemas, which can be burdensome for the user. Programming By Example (PBE) schema mapping methods allow the user to describe the schema mapping using data records. However, real data records are still harder to specify compared to other useful insights about the desired schema mapping the user might have. In this project, we develop a new schema mapping technique, Beaver, that enables an interaction model that gives the user more flexibility in describing the desired schema mapping. The end user is not limited to providing exact and complete target schema data examples but may also provide incomplete or ambiguous examples. Moreover, the user can provide other types of descriptions, like data type or value range, about the target schema. We design an explore-and-verify search-based algorithm to efficiently discover all satisfying schema mapping specifications. We implemented a prototype of our schema mapping technique and experimentally evaluated the efficiency of the system in handling traditional PBE schema mapping test cases, as well as our newly-proposed declarative schema mapping test cases. The experiment results show that the declarative queries, which we believe are easier for non-expert user to input, often cost around zero to five seconds more than the traditional PBE queries. This suggests we retain a system efficiency comparable to traditional PBE schema mapping systems.
Embedded Hypervisor Xvisor: A Comparative Analysis

Virtualization technology has shown immense popularity within embedded systems due to its direct relationship with cost reduction, better resource utilization, and higher performance measures. Efficient hypervisors are required to achieve such high performance measures in virtualized environments, while taking into consideration the low memory footprints as well as the stringent timing constraints of embedded systems. Although there are a number of open-source hypervisors available such as Xen, Linux KVM and OKL4 Micro visor, this is the first paper to present the open-source embedded hypervisor Extensible Versatile hyper Visor (Xvisor) and compare it against two of the commonly used hypervisors KVM and Xen in-terms of comparison factors that affect the whole system performance. Experimental results on ARM architecture prove Xvisor's lower CPU overhead, higher memory bandwidth, lower lock synchronization latency and lower virtual timer interrupt overhead and thus overall enhanced virtualized embedded system performance.
Microesthetic dental analysis in parents of children with oral clefts

ii ACKNOWLEDGEMENTS I wish to thank the members of my thesis committee, Drs. Marazita and her staff at the University of Pittsburgh who made this project possible with the use of the sample population. A special thank you goes to my thesis advisor, Dr. Lina Moreno, for all of her guidance in research, school, and life in general throughout my dental training and orthodontic residency. I also want to extend an enormous thank you to Dr. Steve Miller, who has always been ready to provide his help at the drop of a hat and has approached all of my questions with willingness and patience. I also wish to thank the faculty members of the University Of Iowa Department Of Orthodontics who have given me the tools necessary to become a great orthodontist. Thank you to my family, especially my parents, Brad and Jane Hoppens. Without their support and encouragement throughout my life and education, I would never have been able to make it this far. Finally, thank you to my husband, David Meier, whose love and support has kept me on track and helped me to realize just how blessed I truly am.
Vehicle parking system implementation using CPLD

The vehicle parking system is designed to prevent usual problems associated with the parking. This system is designed to solve the problem of locating empty parking slot, congestion and indiscriminate parking. The system has been designed using VHDL and successfully implemented on CPLD (family-MAX V,Device-5M1270ZT144C5, Board-Krypton v1.2). A complete design and layout was drawn out and suitably implemented. The different modules were separately tested and then combined together as a working model of intelligent vehicle parking system. The results discussed in this paper shows that very less hardware is utilized (21%) on CPLD board, thus proving the system to be cost effective.
Accurate WiFi packet delivery rate estimation and applications

The signal-to-noise ratio (SNR) is the gold standard metric for capturing wireless link quality, but offers limited predictability. Recent work shows that frequency diversity causes limited predictability in SNR, and proposes effective SNR. Owing to its significant improvement over SNR, effective SNR has become a widely adopted metric for measuring wireless channel quality and served as the basis for many recent rate adaptation schemes. In this paper, we first conduct trace driven evaluation, and find that the accuracy of effective SNR is still inadequate due to frequency diversity and bursty errors. While common wisdom says that interleaving should remove the bursty errors, bursty errors still persist under the WiFi interleaver. Therefore, we develop two complementary methods for computing frame delivery rate to capture the bursty errors under the WiFi interleaver. We then design a new interleaver to reduce the burstiness of errors, and improve the frame delivery rate. We further design a rate adaptation scheme based on our delivery rate estimation. It can support both WiFi and our interleaver. Using extensive evaluation, we show our delivery rate estimation is accurate and significantly out-performs effective SNR; our interleaver improves the delivery rate over the WiFi interleaver; and our rate adaptation improves both throughput and energy.
Selecting CNN features for online learning of 3D objects

We present a novel method for classifying 3D objects that is particularly tailored for the requirements in robotic applications. The major challenges here are the comparably small amount of available training data and the fact that often data is perceived in streams and not in fixed-size pools. Traditional state-of-the-art learning methods, however, require a large amount of training data, and their online learning capabilities are usually limited. Therefore, we propose a modality-specific selection of convolutional neural networks (CNN), pre-trained or fine-tuned, in combination with a classifier that is designed particularly for online learning from data streams, namely the Mondrian Forest (MF). We show that this combination of trained features obtained from a CNN can be improved further if a feature selection algorithm is applied. In our experiments, we use the resulting features both with a MF and a linear Support Vector Machine (SVM). With SVM we beat the state of the art on an RGB-D dataset, while with MF a strong result for active learning is achieved.
The body-image questionnaire: an extension.

The 19-item Body-Image Questionnaire, developed by our team and first published in this journal in 1987 by Bruchon-Schweitzer, was administered to 1,222 male and female French subjects. A principal component analysis of their responses yielded an axis we interpreted as a general Body Satisfaction dimension. The four-factor structure observed in 1987 was not replicated. Body Satisfaction was associated with sex, health, and with current and future emotional adjustment.
IoT based traffic light control system using Raspberry Pi

Congestion in traffic is a serious issue. In existing system signal timings are fixed and they are independent of traffic density. Large red light delays leads to traffic congestion. In this paper, IoT based traffic control system is implemented in which signal timings are updated based on the vehicle counting. This system consists of WI-FI transceiver module it transmits the vehicle count of the current system to the next traffic signal. Based on traffic density of previous signal it controls the signals of the next signal. The system is based on raspberry-pi and Arduino. Image processing of traffic video is done in MATLAB with simulink support. The whole vehicle counting is performed by raspberry pi.
Gellan gum biosynthesis in Sphingomonas paucimobilis ATCC 31461: Genes, enzymes and exopolysaccharide production engineering

The commercial gelling agent, gellan, is an extracellular polysaccharide (EPS) produced by Sphingomonas paucimobilis ATCC 31461. In recent years, significant progress in understanding the relationship between gellan structure and properties and elucidation of the biosynthesis and engineering of this recent product of biotechnology has been made. This review focuses on recent advances in this field. Emphasis is given to identification and characterization of genes and enzymes involved, or predicted to be involved, in the gellan biosynthetic pathway, at the level of synthesis of sugar-activated precursors, of the repeat unit assembly and of gellan polymerization and export. Identification of several genes, biochemical characterization of the encoded enzymes and elucidation of crucial steps of the gellan pathway indicate that possibilities now exist for exerting control over gellan production at any of the three levels of its biosynthesis. However, a better knowledge of the poorly understood steps and of the bottlenecks and regulation of the pathway, the characterization of the composition, structure and functional properties of gellan-like polymers produced either by the industrial strain under different culture conditions or by mutants are still required for eventual success of the metabolic engineering of gellan production.
Towards Parallel Spatial Query Processing for Big Spatial Data

In recent years, spatial applications have become more and more important in both scientific research and industry. Spatial query processing is the fundamental functioning component to support spatial applications. However, the state-of-the-art techniques of spatial query processing are facing significant challenges as the data expand and user accesses increase. In this paper we propose and implement a novel scheme (named VegaGiStore) to provide efficient spatial query processing over big spatial data and numerous concurrent user queries. Firstly, a geography-aware approach is proposed to organize spatial data in terms of geographic proximity, and this approach can achieve high aggregate I/O throughput. Secondly, in order to improve data retrieval efficiency, we design a two-tier distributed spatial index for efficient pruning of the search space. Thirdly, we propose an "indexing + MapReduce'' data processing architecture to improve the computation capability of spatial query. Performance evaluations of the real-deployed VegaGiStore system confirm its effectiveness.
Factors influencing Internet shopping value and customer repurchase intention

a School of Business, Yeungnam University, South Korea, 241-1, Dae-dong, Gyeongsan-si, Gyeongsangbuk-do 712-749, South Korea b Department of Information and Process Management, Bentley University, Adamian Academic Center 242, Waltham, MA 02452, USA c Department of Information Technology, Seidenberg School of Computer Science and Information Systems, Pace University, 163 William Street, New York, NY 10038, USA d Division of International Studies, Hanyang University, Seongdonggu, Seoul, 133-791, South Korea e School of Business, University of Bridgeport, Bridgeport, CT 06604, USA
A Wireless Reflectance Pulse Oximeter With Digital Baseline Control for Unfiltered Photoplethysmograms

Pulse oximeters are central to the move toward wearable health monitoring devices and medical electronics either hosted by, e.g., smart phones or physically embedded in their design. This paper presents a small, low-cost pulse oximeter design appropriate for wearable and surface-based applications that also produces quality, unfiltered photo-plethysmograms (PPGs) ideal for emerging diagnostic algorithms. The design's “filter-free” embodiment, which employs only digital baseline subtraction as a signal compensation mechanism, distinguishes it from conventional pulse oximeters that incorporate filters for signal extraction and noise reduction. This results in high-fidelity PPGs with thousands of peak-to-peak digitization levels that are sampled at 240 Hz to avoid noise aliasing. Electronic feedback controls make these PPGs more resilient in the face of environmental changes (e.g., the device can operate in full room light), and data stream in real time across either a ZigBee wireless link or a wired USB connection to a host. On-board flash memory is available for store-and-forward applications. This sensor has demonstrated an ability to gather high-integrity data at fingertip, wrist, earlobe, palm, and temple locations from a group of 48 subjects (20 to 64 years old).
he revolution re-visited : Clinical and genetics research paradigms nd the productivity paradox in drug discovery

Breakthroughs in genetics and molecular biology in the 1970s and 1980s were heralded as a major technological revolution in medicine that would yield a wave of new drug discoveries. However, some forty years later the expected benefits have not materialized. I question the narrative of biotechnology as a Schumpeterian revolution by comparing it to the academic research paradigm that preceded it, clinical research in hospitals. I analyze these as distinct research paradigms that involve different epistemologies, practices, and institutional loci. I develop the claim that the complexity of biological systems means that clinical research was well adapted to medical innovation, and that the genetics/molecular biology paradigm imposed a predictive logic to search that was less effective at finding new drugs. The paper describes how drug discovery unfolds in each paradigm: in clinical research, discovery originates with observations of human subjects and proceeds through feedback-based learning, whereas in the genetics model, discovery originates with a precisely-defined molecular target; feedback from patients enters late in the process. The paper reviews the post-War institutional history that witnessed the relative decline of clinical research and the rise of genetics and molecular science in the United States bio-medical research landscape. The history provides a contextual narrative to illustrate that, in contrast to the framing of biotechnology as a Schumpeterian revolution, the adoption of biotechnology as a core drug discovery platform was propelled by institutional changes that were largely disconnected from processes of scientific or technological selection. Implications for current medical policy initiatives and translational science are discussed.
Dynamics of dynamics : following the formation of a line attractor

Trained recurrent neural networks are commonly used both in neuroscience and in machine learning. Recently, several attempts were made to understand the dynamics of such trained networks. Understanding how these dynamics arise through learning, however, is an uncharted territory. Here, we study this process in a simple setting – learning to memorize analog values, leading to the formation of a line attractor. We show that problems associated with sequential learning of different tasks also occur in this setting. Specifically, catastrophic forgetting dramatically hinders a local learning rule from converging. In contrast, we show that the FORCE algorithm avoids this issue, and analyze the underlying causes. Using these insights, we suggest steps towards a more biologically plausible version of FORCE, as well as an approach to design optimal training protocols.
CBTree: A Practical Concurrent Self-Adjusting Search Tree

We present the CBTree, a new counting-based self-adjusting search tree that like splay trees, moves more frequently accessed nodes closer to the root. After m operations on n items, q of which access some item v, an operation on v traverses a path of length O(log m q ) while performing few if any rotations. In contrast to the traditional self-adjusting splay tree in which each accessed item is moved to the root through a sequence of tree rotations, CBTree performs rotations infrequently (an amortized subconstant o(1) per operation if m n), mostly at the bottom of the tree, therefore CBTree scales with the concurrency level. We adapt CBTree to a multicore setting and show experimentally that it improves performance compared to existing concurrent search trees on non-uniform access sequences derived from real workloads. CBTree achieves the above by trading-o rotations for keeping history. Each node maintains a count of the number of accesses to it, and CBTree uses the counters to decide when to do a rotation. CBTree's decision rule is inspired by the analysis of splaying, which we draw upon to prove that CBTree achieves similar time bounds to the splay tree while performing only few if any tree rotations per operation. We present another new self-adjusting algorithm LazyCBTree. LazyCBTree is near optimal sequentially, however it improves the performance on multi-core machines in practice. LazyCBTree, the lazy counting-based tree, like CBTree counts accesses to nodes and moves frequently accessed subtrees towards the root, obtaining optimal tree structure in practice including in a sequential setting. Unlike CBTree, LazyCBTree lazily makes at most one local tree rotation on each lookup, usually requiring no restructuring at all. LazyCBTree thus avoids creating a sequential bottleneck. We evaluate CBTree and LazyCBTree on real non-uniform access patterns and show that they are signi cantly improve performance compared to existing self-adjusting trees as well as balanced (non-adjusting) trees.