nils-herrmann/clean_scirepeval huggingface dataset:
Nasal base reduction by alar release: a laboratory evaluation.

When reducing the broad nasal base, there is a limit to the amount of soft tissue that can be resected, beyond which the anatomy distorts and the nostrils become stenotic (if resection enters the nostril). Alar mobilization by freeing soft-tissue attachments helps. This study purported to examine the nature of those attachments and the extent of medialization.



The supporting tissues of the ala were sequentially divided in 16 fresh hemifacial cadavers. Key structures included the following: (1) the soft tissues and pyriform ligament of the anterior maxilla, (2) the periosteum posterior to the pyriform rim (in the bony nasal vault), and (3) the soft tissues along the horizontal pyriform rim. After release of each tethering region, the ala-pyriform distance was measured.



After releasing the anterior maxillary periosteum and pyriform ligament along the vertical pyriform rim, the ala-pyriform distance was reduced by a mean of 1.9 mm. After releasing the periosteum posterior to the pyriform rim (in the nasal vault), it was reduced by a mean of 1.7 mm. Releasing the soft tissues (which were thick medially) of the horizontal pyriform rim reduced the mean distance 1.0 mm for a total of 4.6 mm. Medialization resulting from anterior and posterior releases was significantly greater than that from the horizontal pyriform rim (p < 0.0006 and p < 0.015, respectively), but they were not significantly different from one another.



This cadaver study confirmed the role of the stabilizing effect of the pyriform ligament and the periosteum lateral and posterior to the pyriform rim. The total release was substantial, suggesting a clinical means of achieving tension-free alar medialization.
A Collaborative Approach to In-Place Sensor Calibration

Numerous factors contribute to errors in sensor measurements. In order to be useful, any sensor device must be calibrated to adjust its accuracy against the expected measurement scale. In largescale sensor networks, calibration will be an exceptionally difficult task since sensor nodes are often not easily accessible and manual device-bydevice calibration is intractable. In this paper, we present a two-phase post-deployment calibration technique for large-scale, dense sensor deployments. In its first phase, the algorithm derives relative calibration relationships between pairs of co-located sensors, while in the second phase, it maximizes the consistency of the pair-wise calibration functions among groups of sensor nodes. The key idea in the first phase is to use temporal correlation of signals received at neighboring sensors when the signals are highly correlated (i.e. sensors are observing the same phenomenon) to derive the function relating their bias in amplitude. We formulate the second phase as an optimization problem and present an algorithm suitable for localized implementation. We evaluate the performance of the first phase of the algorithm using empirical and simulated data.
Ear-EEG allows extraction of neural responses in challenging listening scenarios — A future technology for hearing aids?

Advances in brain-computer interface research have recently empowered the development of wearable sensors to record mobile electroencephalography (EEG) as an unobtrusive and easy-to-use alternative to conventional scalp EEG. One such mobile solution is to record EEG from the ear canal, which has been validated for auditory steady state responses and discrete event related potentials (ERPs). However, it is still under discussion where to place recording and reference electrodes to capture best responses to auditory stimuli. Furthermore, the technology has not yet been tested and validated for ecologically relevant auditory stimuli such as speech. In this study, Ear-EEG and conventional scalp EEG were recorded simultaneously in a discrete-tone as well as a continuous-speech design. The discrete stimuli were applied in a dichotic oddball paradigm, while continuous stimuli were presented diotically as two simultaneous talkers. Cross-correlation of stimulus envelope and Ear-EEG was assessed as a measure of ongoing neural tracking. The extracted ERPs from Ear-EEG revealed typical auditory components yet depended critically on the reference electrode chosen. Reliable neural-tracking responses were extracted from the Ear-EEG for both paradigms, albeit weaker in amplitude than from scalp EEG. In conclusion, this study shows the feasibility of extracting relevant neural features from ear-canal-recorded “Ear-EEG”, which might augment future hearing technology.
Design and Implementation of Energy Management System With Fuzzy Control for DC Microgrid Systems

This paper presents the design and implementation of an energy management system (EMS) with fuzzy control for a dc microgrid system. Modeling, analysis, and control of distributed power sources and energy storage devices with MATLAB/Simulink are proposed, and the integrated monitoring EMS is implemented with LabVIEW. To improve the life cycle of the battery, fuzzy control manages the desired state of charge. The RS-485/ZigBee network has been designed to control the operating mode and to monitor the values of all subsystems in the dc microgrid system.
Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention

The fusion of tracked ultrasound with CT has benefits for a variety of clinical applications, however extensive manual effort is usually required for correct registration. We developed new methods that allow one to simulate medical ultrasound from CT in real-time, reproducing the majority of ultrasonic imaging effects. They are combined with a robust similarity measure that assesses the correlation of a combination of signals extracted from CT with ultrasound, without knowing the influence of each signal. This serves as the foundation of a fully automatic registration, that aligns a 3D ultrasound sweep with the corresponding tomographic modality using a rigid or an affine transformation model, without any manual interaction. These techniques were evaluated in a study involving 25 patients with indeterminate lesions in liver and kidney. The clinical setup, acquisition and registration workflow is described, along with the evaluation of the registration accuracy with respect to physician-defined Ground Truth. Our new algorithm correctly registers without any manual interaction in 76% of the cases, the average RMS TRE over multiple target lesions throughout the liver is 8.1mm.
Robust multivariate autoregression for anomaly detection in dynamic product ratings

User provided rating data about products and services is one key feature of websites such as Amazon, TripAdvisor, or Yelp. Since these ratings are rather static but might change over time, a temporal analysis of rating distributions provides deeper insights into the evolution of a products' quality. Given a time-series of rating distributions, in this work, we answer the following questions: (1) How to detect the base behavior of users regarding a product's evaluation over time? (2) How to detect points in time where the rating distribution differs from this base behavior, e.g., due to attacks or spontaneous changes in the product's quality? To achieve these goals, we model the base behavior of users regarding a product as a latent multivariate autoregressive process. This latent behavior is mixed with a sparse anomaly signal finally leading to the observed data. We propose an efficient algorithm solving our objective and we present interesting findings on various real world datasets.
Determining Image Origin and Integrity Using Sensor Noise

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware

Malware is constantly adapting in order to avoid detection. Model-based malware detectors, such as SVM and neural networks, are vulnerable to so-called adversarial examples which are modest changes to detectable malware that allows the resulting malware to evade detection. Continuous-valued methods that are robust to adversarial examples of images have been developed using saddle-point optimization formulations. We are inspired by them to develop similar methods for the discrete, e.g. binary, domain which characterizes the features of malware. A specific extra challenge of malware is that the adversarial examples must be generated in a way that preserves their malicious functionality. We introduce methods capable of generating functionally preserved adversarial malware examples in the binary domain. Using the saddle-point formulation, we incorporate the adversarial examples into the training of models that are robust to them. We evaluate the effectiveness of the methods and others in the literature on a set of Portable Execution (PE) files. Comparison prompts our introduction of an online measure computed during training to assess general expectation of robustness.
Who Invented the Reverse Mode of Differentiation ?

Nick Trefethen [13] listed automatic differentiation as one of the 30 great numerical algorithms of the last century. He kindly credited the present author with facilitating the rebirth of the key idea, namely the reverse mode. In fact, there have been many incarnations of this reversal technique, which has been suggested by several people from various fields since the late 1960s, if not earlier. Seppo Linnainmaa (Lin76) of Helsinki says the idea came to him on a sunny afternoon in a Copenhagen park in 1970. He used it as a tool for estimating the effects of arithmetic rounding errors on the results of complex expressions. Gerardi Ostrowski (OVB71) discovered and used it some five years earlier in the context of certain process models in chemical engineering. Here and throughout references that are not listed in the present bibliography are noted in parentheses and can be found in the book [7]. Also in the sixties Hachtel et al.  considered the optimization of electronic circuits using the costate equation of initial value problems and its discretizations to compute gradients in the reverse mode for explicitly time-dependent problems. Here we see, possibly for the first time, the close connection between the reverse mode of discrete evaluation procedures and continuous adjoints of differential equations. In the 1970s Iri analyzed the properties of dual and adjoint networks. In the 1980s he became one of the key researchers on the reverse mode. From a memory and numerical stability point of view the most difficult aspect of the reverse mode is the reversal of a program. This problem was discussed in the context of Turing Machines by Benett (Ben73), who foreshadowed the use of checkpointing as a tradeoff between numerical computational effort and memory requirement.
Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments

We present an extended analysis of our previous work on the HydroSense technology, which is a low-cost and easily installed single-point sensor of pressure for automatically disaggregating water usage activities in the home (Froehlich et al., 2009 ). We expand upon this work by providing a survey of existing and emerging water disaggregation techniques, a more comprehensive description of the theory of operation behind our approach, and an expanded analysis section that includes hot versus coldwater valve usage classification and a comparisonbetween two classification approaches: the template-based matching scheme used in Froehlich et al. (2009)  and a new stochastic approach using a Hidden Markov Model. We show that both are successful in identifying valveand fixturelevel water events with greater than 90% accuracies. We conclude with a discussion of the limitations in our experimental methodology and open problems going forward.
Combined software-defined network (SDN) and Internet of Things (IoT)

The Internet of Things (IoT) is an impending technology that captured the industries and research interests in a brief period. Presently, the number of Internet-connected devices are estimated at billions and by 2020 many studies expect to have around 50 billion devices that are connected to the Internet. The IoT devices have produced a significant amount of data that could be threatening to the security and directing the flow of data in the network. One of the alternative solutions for problems caused by IoT is to have some kind of solutions that are based on programming and primary control. Software Define Network (SDN) offers a programmable and primary control for the basic network without variability of the current architecture of the network. This paper discusses some solutions for various domains for the combined SDN based on IoT and current directions in studies. The relative analysis of these solutions prepares a simple and brief view of those directions.
A review of vision-based motion analysis in sport.

Efforts at player motion tracking have traditionally involved a range of data collection techniques from live observation to post-event video analysis where player movement patterns are manually recorded and categorized to determine performance effectiveness. Due to the considerable time required to manually collect and analyse such data, research has tended to focus only on small numbers of players within predefined playing areas. Whilst notational analysis is a convenient, practical and typically inexpensive technique, the validity and reliability of the process can vary depending on a number of factors, including how many observers are used, their experience, and the quality of their viewing perspective. Undoubtedly the application of automated tracking technology to team sports has been hampered because of inadequate video and computational facilities available at sports venues. However, the complex nature of movement inherent to many physical activities also represents a significant hurdle to overcome. Athletes tend to exhibit quick and agile movements, with many unpredictable changes in direction and also frequent collisions with other players. Each of these characteristics of player behaviour violate the assumptions of smooth movement on which computer tracking algorithms are typically based. Systems such as TRAKUS, SoccerMan, TRAKPERFORMANCE, Pfinder and Prozone all provide extrinsic feedback information to coaches and athletes. However, commercial tracking systems still require a fair amount of operator intervention to process the data after capture and are often limited by the restricted capture environments that can be used and the necessity for individuals to wear tracking devices. Whilst some online tracking systems alleviate the requirements of manual tracking, to our knowledge a completely automated system suitable for sports performance is not yet commercially available. Automatic motion tracking has been used successfully in other domains outside of elite sport performance, notably for surveillance in the military and security industry where automatic recognition of moving objects is achievable because identification of the objects is not necessary. The current challenge is to obtain appropriate video sequences that can robustly identify and label people over time, in a cluttered environment containing multiple interacting people. This problem is often compounded by the quality of video capture, the relative size and occlusion frequency of people, and also changes in illumination. Potential applications of an automated motion detection system are offered, such as: planning tactics and strategies; measuring team organisation; providing meaningful kinematic feedback; and objective measures of intervention effectiveness in team sports, which could benefit coaches, players, and sports scientists.
Screening for trisomy 18 by maternal age, fetal nuchal translucency, free beta-human chorionic gonadotropin and pregnancy-associated plasma protein-A.

To derive a model and examine the performance of first-trimester screening for trisomy 18 by maternal age, fetal nuchal translucency (NT) thickness, and maternal serum free beta-human chorionic gonadotropin (beta-hCG) and pregnancy-associated plasma protein-A (PAPP-A).



Prospective combined screening for trisomy 21 was performed at 11 + 0 to 13 + 6 weeks in 56 893 singleton pregnancies, including 56 376 cases of euploid fetuses, 395 with trisomy 21 and 122 with trisomy 18. The measured free beta-hCG and PAPP-A were converted into a multiple of the median (MoM) and then into likelihood ratios (LR). Similarly, the measured NT was transformed into LRs using the mixture model of NT distributions. In each case the LRs for NT and the biochemical markers were multiplied by the age and gestation-related risk to derive the risk for trisomy 21 and trisomy 18. Detection rates (DRs) and false-positive rates (FPRs) were calculated by taking the proportions with risks above a given risk threshold.



In screening with the algorithm for trisomy 21, at a FPR of 3%, the estimated DRs of trisomies 21 and 18 were 89% and 82%, respectively. The use of an algorithm for trisomy 18 identified 93% of affected fetuses at a FPR of 0.2%. When the algorithm for trisomy 21 was used and screen positivity was fixed at a FPR of 3%, and in addition the algorithm for trisomy 18 was used and screen positivity was fixed at a FPR of 0.2%, the overall FPR was 3.1% and the DRs of trisomies 21 and 18 were 90% and 97%, respectively.



A beneficial side effect of first-trimester combined screening for trisomy 21 is the detection of a high proportion of fetuses with trisomy 18. If an algorithm for trisomy 18 in addition to the one for trisomy 21 is used, more than 95% of trisomy 18 fetuses can be detected with a minor increase of 0.1% in the overall FPR.
Modelling and Simulation for Optimal Control of Nonlinear Inverted Pendulum Dynamical System Using PID Controller and LQR

This paper presents the modelling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using Proportional-Integral-Derivative (PID) controller and Linear Quadratic Regulator (LQR). LQR, an optimal control technique, and PID control method, both of which are generally used for control of the linear dynamical systems have been used in this paper to control the nonlinear dynamical system. The nonlinear system states are fed to LQR which is designed using linear state-space model. Inverted pendulum, a highly nonlinear unstable system is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches at a desired position and the inverted pendulum stabilizes in upright position. The MATLAB-SIMULINK models have been developed for simulation of control schemes. The simulation results justify the comparative advantages of LQR control methods.
Search-based Neural Structured Learning for Sequential Question Answering

Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semistructured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.
Multiword Expressions in Child Language

The goal of this work is to introduce CHILDES-MWE, which contains English CHILDES corpora automatically annotated with Multiword Expressions (MWEs) information. The result is a resource with almost 350,000 sentences annotated with more than 70,000 distinct MWEs of various types from both longitudinal and latitudinal corpora. This resource can be used for large scale language acquisition studies of how MWEs feature in child language. Focusing on compound nouns (CN), we then verify in a longitudinal study if there are differences in the distribution and compositionality of CNs in child-directed and child-produced sentences across ages. Moreover, using additional latitudinal data, we investigate if there are further differences in CN usage and in compositionality preferences. The results obtained for the child-produced sentences reflect CN distribution and compositionality in child-directed
On visual gaze tracking based on a single low cost camera

Gaze tracking technologies provide an unconventional way of human–computer interaction, envisaged to advance practical applications and industrial products in a multitude of fields. The success of such systems depends on selecting the best calibration setup and image features that correspond to a person's line of sight. The purpose of this study is to estimate eye gaze from a single, low cost web-cam, under natural lighting. Facial traits are extracted from the sensory data, fromwhich distance vectors related to gaze are derived. Different experimental setups are studied to evaluate the robustness of the proposed method with respect to various calibration setups, camera position and head movements. The use of new additional features improves the modeling of the subtle eye movements in the vertical direction, while a new calibration setup is proposed that further enhances the performance. The results demonstrate that the proposed framework is able to track gaze with good accuracy, consolidating the use of inexpensive equipment and techniques towards an ever-expanding range of gaze tracking applications. & 2015
Word Spotting and Recognition with Embedded Attributes

This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.