nils-herrmann/clean_scirepeval huggingface dataset:
An alginate-antacid formulation (Gaviscon Double Action Liquid) can eliminate or displace the postprandial 'acid pocket' in symptomatic GERD patients.

Recently, an 'acid pocket' has been described in the proximal stomach, particularly evident postprandially in GERD patients, when heartburn is common. By creating a low density gel 'raft' that floats on top of gastric contents, alginate-antacid formulations may neutralise the 'acid pocket'.



To assess the ability of a commercial high-concentration alginate-antacid formulation to neutralize and/or displace the acid pocket in GERD patients.



The 'acid pocket' was studied in ten symptomatic GERD patients. Measurements were made using concurrent stepwise pH pull-throughs, high resolution manometry and fluoroscopy in a semi-recumbent posture. Each subject was studied in three conditions: fasted, 20 min after consuming a high-fat meal and 20 min later after a 20 mL oral dose of an alginate-antacid formulation (Gaviscon Double Action Liquid, Reckitt Benckiser Healthcare, Hull, UK). The relative position of pH transition points (pH >4) to the EGJ high-pressure zone was analysed.



Most patients (8/10) exhibited an acidified segment extending from the proximal stomach into the EGJ when fasted that persisted postprandially. Gaviscon neutralised the acidified segment in six of the eight subjects shifting the pH transition point significantly away from the EGJ. The length and pressure of the EGJ high-pressure zone were minimally affected.



Gaviscon can eliminate or displace the 'acid pocket' in GERD patients. Considering that EGJ length was unchanged throughout, this effect was likely attributable to the alginate 'raft' displacing gastric contents away from the EGJ. These findings suggest the alginate-antacid formulation to be an appropriately targeted postprandial GERD therapy.
Automatic and unsupervised methods in natural language processing

Natural language processing (NLP) means the computer-aided processing of language produced by a human. But human language is inherently irregular and the most reliable results are obtained when a human is involved in at least some part of the processing. However, manual work is time-consuming and expensive. This thesis focuses on what can be accomplished in NLP when manual work is kept to a minimum. We describe the construction of two tools that greatly simplify the implementation of automatic evaluation. They are used to implement several supervised, semi-supervised and unsupervised evaluations by introducing artificial spelling errors. We also describe the design of a rule-based shallow parser for Swedish called GTA and a detection algorithm for context-sensitive spelling errors based on semi-supervised learning, called ProbCheck. In the second part of the thesis, we first implement a supervised evaluation scheme that uses an error-free treebank to determine the robustness of a parser when faced with noisy input such as spelling errors. We evaluate the GTA parser and determine the robustness of the individual components of the parser as well as the robustness for different phrase types. Second, we create an unsupervised evaluation procedure for parser robustness. The procedure allows us to evaluate the robustness of parsers using different parser formalisms on the same text and compare their performance. Five parsers and one tagger are evaluated. For four of these, we have access to annotated material and can verify the estimations given by the unsupervised evaluation procedure. The results turned out to be very accurate with few exceptions and thus, we can reliably establish the robustness of an NLP system without any need of manual work. Third, we implement an unsupervised evaluation scheme for spell checkers. Using this, we perform a very detailed analysis of three spell checkers for Swedish. Last, we evaluate the ProbCheck algorithm. Two methods are included for comparison: a full parser and a method using tagger transition probabilities. The algorithm obtains results superior to the comparison methods. The algorithm is also evaluated on authentic data in combination with a grammar and spell checker.
Data Change Exploration Using Time Series Clustering

Analysis of static data is one of the best studied research areas. However, data changes over time. These changes may reveal patterns or groups of similar values, properties, and entities. We study changes in large, publicly available data repositories by modelling them as time series and clustering these series by their similarity. In order to perform change exploration on real-world data we use the publicly available revision data of Wikipedia Infoboxes and weekly snapshots of IMDB. The changes to the data are captured as events, which we call change records. In order to extract temporal behavior we count changes in time periods and propose a general transformation framework that aggregates groups of changes to numerical time series of different resolutions. We use these time series to study different application scenarios of unsupervised clustering. Our explorative results show that changes made to collaboratively edited data sources can help find characteristic behavior, distinguish entities or properties and provide insight into the respective domains.
Polarization-Insensitive Dual-Band Switchable Absorber With Independent Switching

A dual-band switchable absorber is presented with independent switching of absorption frequencies. The unit cell of the proposed structure is composed of an annular ring and a cross-dipole, where switchable p-i-n diodes are embedded. By controlling the bias voltage of the diodes, both the absorption frequencies can be switched between two frequency bands independently, thereby resulting in a dual-band/bandwidth-enhanced absorber. Moreover, the proposed design is polarization-independent due to fourfold symmetry unlike the earlier reported switchable absorbers. A novel biasing network has also been developed for measuring the fabricated structure, which is experimentally verified under normal incidence.
Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform

In this paper, we developed a new building extraction system applied on high resolution remote sensing imagery based on multi-scale object oriented classification and probabilistic Hough transform. This can be divided into two different phases: building roof extraction, and shape reconfiguration. For the first phase, the multispectral and panchromatic high resolution satellite imageries are firstly fused for spatial resolution improvement and color information enhancement. The multiresolution image segmentation is applied on the fused image, resulting in the formation of the different level of polygon primitives at different space scale, providing different view of the scene at different resolution. In addition to the spectral information, the tone, texture, shape, context information is evaluated in an object oriented manner. The classification is based on a fuzzy rule decision tree classifier. By fuzzy evaluating of the shape, texture, context and spectral information, building roofs are extracted by reconstruction and classification from an appropriate space scale of roof polygon primitives. For the shape reconfiguration phase, we adopt the probabilistic Hough transform to delineate the roof dominant line which shows the major orientation of the specific building roof. According to the dominant line, a building squaring algorithm is applied based on rectilinear fitting of the building boundary. It is shown by our experiment that most rectangular building roofs can be correctly detected, extracted and reconfigured, demonstrating the potential application of the method.
Establishing rigour in qualitative research: the decision trail.

The aim of this paper is to show the way in which the decision trail of a qualitative research process can be maintained. It is argued that the trustworthiness (rigour) of a study may be established if the reader is able to audit the events, influences and actions of the researcher. The actual study containing the recording of this decision trail aimed to express the concerns of older patients who were admitted to the acute care sector. The study took place in two care of the elderly wards in a 1000-bed National Health Service hospital in the UK, in 1991. Eventually, 14 patients were interviewed, each on several occasions, and their concerns are expressed in themes, namely: routine geriatric style of care, depersonalization, care deprivation and geriatric segregation. I describe the preparations that were undertaken before patient interviews could commence. The literature recording the process of the interviewer's experience as data in qualitative research is scarce. I show the researcher's participation in making the data as part of an existential phenomenological research process. Existential phenomenology relies on recording influences while generating data such as significant literature, media reports, my value position and journal data.
Business & IT Alignment in Theory and Practice

A key success factor for a successful company in a dynamic environment is effective and efficient information technology (IT) supporting business strategies and processes. In recent surveys however it is concluded that in most companies IT is not aligned with business strategy. The alignment between business needs and IT capabilities is therefore still a prominent area of concern. This paper reports the first stages of a research program exploring the differences of business & IT alignment (BIA) in theory and in practice. The paper presents an overview of the development of theory on BIA and reports the issues with aligning IT to business in practice based on a number of focus-group discussions with CIOs and IT managers. In line with the practical approach to BIA that the CIOs and IT managers in the focus-groups took, the last part of the paper builds upon Luftman's BIA maturity model and reports the application of the model to 12 Dutch firms
Design and implementation of NN5 for Hong Kong stock price forecasting

A number of published techniques have emerged in the trading community for stock prediction tasks. Among them is neural network (NN). In this paper, the theoretical background of NNs and the backpropagation algorithm is reviewed. Subsequently, an attempt to build a stock buying/selling alert system using a backpropagation NN, NN5, is presented. The system is tested with data from one Hong Kong stock, The Hong Kong and Shanghai Banking Corporation (HSBC) Holdings. The system is shown to achieve an overall hit rate of over 70%. A number of trading strategies are discussed. A best strategy for trading non-volatile stock like HSBC is recommended.
Real-Time Adaptive Image Compression

We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time. Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG 2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of generic images across all quality levels. At the same time, our codec is designed to be lightweight and deployable: for example, it can encode or decode the Kodak dataset in around 10ms per image on GPU. Our architecture is an autoencoder featuring pyramidal analysis, an adaptive coding module, and regularization of the expected codelength. We also supplement our approach with adversarial training specialized towards use in a compression setting: this enables us to produce visually pleasing reconstructions for very low bitrates.
An Improvement to Feature Selection of Random Forests on Spark

The Random Forests algorithm belongs to the class of ensemble learning methods, which are common used in classification problem. In this paper, we studied the problem of adopting the Random Forests algorithm to learn raw data from real usage scenario. An improvement, which is stable, strict, high efficient, data-driven, problem independent and has no impact on algorithm performance, is proposed to investigate 2 actual issues of feature selection of the Random Forests algorithm. The first one is to eliminate noisy features, which are irrelevant to the classification. And the second one is to eliminate redundant features, which are highly relevant with other features, but useless. We implemented our improvement approach on Spark. Experiments are performed to evaluate our improvement and the results show that our approach has an ideal performance.
Recognition of Pollen-Bearing Bees from Video Using Convolutional Neural Network

In this paper, the recognition of pollen bearing honey bees from videos of the entrance of the hive is presented. This computer vision task is a key component for the automatic monitoring of honeybees in order to obtain large scale data of their foraging behavior and task specialization. Several approaches are considered for this task, including baseline classifiers, shallow Convolutional Neural Networks, and deeper networks from the literature. The experimental comparison is based on a new dataset of images of honeybees that was manually annotated for the presence of pollen. The proposed approach, based on Convolutional Neural Networks is shown to outperform the other approaches in terms of accuracy. Detailed analysis of the results and the influence of the architectural parameters, such as the impact of dedicated color based data augmentation, provide insights into how to apply the approach to the target application.
Flying Swarm of Drones Over Circulant Digraph

Drones are critical to military collaborative engagement, surveillance, and monitoring of areas of interest. They also play an increasingly influential role in commercial applications. We describe a new innovative method for collisionless, survivable, and persistent flying of given <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> drones over <inline-formula><tex-math notation="LaTeX">$2n$</tex-math></inline-formula> waypoints that results in dispersed coverage of a given area of interest, where <inline-formula><tex-math notation="LaTeX">$n > k \geq 3$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> divides <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>. Our method is fundamentally different than any known to date since it does not need sensors to avoid collisions between drones. In addition, the flight of drones does not have to be coordinated.
Situationally Aware In-Car Information Presentation Using Incremental Speech Generation: Safer, and More Effective

Holding non-co-located conversations while driving is dangerous (Horrey and Wickens, 2006; Strayer et al., 2006), much more so than conversations with physically present, “situated” interlocutors (Drews et al., 2004). In-car dialogue systems typically resemble non-co-located conversations more, and share their negative impact (Strayer et al., 2013). We implemented and tested a simple strategy for making in-car dialogue systems aware of the driving situation, by giving them the capability to interrupt themselves when a dangerous situation is detected, and resume when over. We show that this improves both driving performance and recall of system-presented information, compared to a non-adaptive strategy.
The Matlab/Simulink modeling and numerical simulation of an analogue capacitive micro-accelerometer. Part 1: Open loop

The paper deal with an open loop analogue capacitive micro-accelerometer both from the point of view of mathematical modeling and through the achievement of certain numerical simulations which must confirm the mathematical models and in the same time help to establish the optimal architecture of the system. The capacitive transducer is one of differential type, with two fixed plates and one moving, which play the role of proof mass. Structure of the system contains the proper transducer and an electronic device (charge amplifier + phase-sensitive demodulator) to process the signal obtained from transducer. The accelerometer is mathematical modeled, the mathematical relation being expressed both in complex domain and in time. The mathematical modeling contains two distinct phases: the modeling of the proper accelerometer, which includes the proof mass and elastic element, and the modeling of the differential capacitive transducer for the proof mass displacements. Using these mathematical relations, the block schemas are realized for each block separately and for the full system. Starting from the numerical values of the accelerometer's parameters the numerical simulations with programming MATLAB/SIMULINK are made for each of the deduced block schemas.
Towards more variation in text generation: Developing and evaluating variation models for choice of referential form

In this study, we introduce a nondeterministic method for referring expression generation. We describe two models that account for individual variation in the choice of referential form in automatically generated text: a Naive Bayes model and a Recurrent Neural Network. Both are evaluated using the VaREG corpus. Then we select the best performing model to generate referential forms in texts from the GREC-2.0 corpus and conduct an evaluation experiment in which humans judge the coherence and comprehensibility of the generated texts, comparing them both with the original references and those produced by a random baseline model.
Visualising convolutional neural network decisions in automated sleep scoring

Current sleep medicine relies on the supervised analysis of polysomnographic recordings, which comprise amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN) provide an interesting framework for automated sleep classification, however, the lack of interpretability of its results has hampered CNN’s further use in medicine. In this study, we train a CNN using as input Continuous Wavelet transformed EEG, EOG and EMG recordings from a publicly available dataset. The network achieved a 10-fold cross-validation Cohen’s Kappa score of κ = 0.71±0.01. Further, we provide insights on how this network classifies individual epochs of sleep using Guided Gradient-weighted Class Activation Maps (Guided Grad-CAM). The proposed approach is able to produce fine-grained activation maps on time-frequency domain for each signal providing a useful tool for identifying relevant features in CNNs.
Multi-target threat assessment for automotive applications

Auto-brake systems have been on the market for a few years, but in order to continue improving their performance, multi-target threat assessment is a key component. The goal is to early be able to say that a collision can no longer be avoided by a steering maneuver, and thus braking can be started earlier. This is done by looking at all objects in the road scene, including other vehicles and barriers, and then looking for escape paths. The paper presents a computationally efficient way of determining whether an escape path can be found in a scenario with generally positioned objects, given the limited dynamics of the vehicle.
Color reduction based on ant colony

In this article a method for color reduction based on ant colony algorithm is presented. Generally color reduction involves two steps: choosing a proper palette and mapping colors to this palette. This article is about the first step. Using ant colony algorithm, pixel clusters are formed based on their colors and neighborhood information to make the final palette. A comparison between the results of the proposed method and some other methods is presented. There are some parameters in the proposed method which can be set based on user needs and priorities. This increases the flexibility of the method.
Suicidal decapitation using a tractor loader: a case report and review of the literature.

In forensic practice, decapitated bodies are predominantly associated with decapitation by wheels of trains or with postmortem dismemberment following homicide. In the suicidal context, decapitation accounts for less than 1% of total suicide. Apart from decapitation by trains, other encountered methods involve suicidal hanging and vehicle-assisted ligature suicide. Reported here is a unique case of suicidal decapitation in a 45-year-old man using a tractor loader at the foot of a silo, on his farm. The head was recovered in the loader and there were several impact spots from the loader as well as blood on the silo wall. The autopsy revealed a complete decapitation wound with the severance plane located between the third and fourth cervical vertebra. A 1.5 cm wide abrasion on the anterior part of the neck and abrasions under the chin were noted. This very unique case of intentional suicidal decapitation is the first reported case of a planned system intended to create decapitation outside the unique case of homemade guillotine and the more common decapitation by train.
Developing language technology tools and resources for a resource-poor language: Sindhi

Sindhi, an Indo-Aryan language with more than 75 million native speakers1 is a resourcepoor language in terms of the availability of language technology tools and resources. In this thesis, we discuss the approaches taken to develop resources and tools for a resourcepoor language with special focus on Sindhi. The major contributions of this work include raw and annotated datasets, a POS Tagger, a Morphological Analyser, a Transliteration and a Machine Translation System.
Natural Gradient Deep Q-learning

We present a novel algorithm to train a deep Q-learning agent using naturalgradient techniques. We compare the original deep Q-network (DQN) algorithm to its natural-gradient counterpart, which we refer to as NGDQN, on a collection of classic control domains. Without employing target networks, NGDQN significantly outperforms DQN without target networks, and performs no worse than DQN with target networks, suggesting that NGDQN stabilizes training and can help reduce the need for additional hyperparameter tuning. We also find that NGDQN is less sensitive to hyperparameter optimization relative to DQN. Together these results suggest that natural-gradient techniques can improve value-function optimization in deep reinforcement learning.
Standard cell library and automated design flow for circuits on flexible substrates

A-Si:H TFTs are traditionally used in backplane arrays for active matrix displays and occasionally in row or column drive electronics with current efforts focusing on flexible displays and drivers. This paper extends flexible electronics to complex digital circuitry by designing a standard cell library for a-Si:H TFTs on flexible stainless steel and plastic substrates. The standard cell library enables layout automation with a standard cell place and route tool, significantly speeding the layout of a-Si:H digital circuits on the backplane to enhance display functionality. Since only n-channel transistors are available, the gates are designed with a bootstrap pull-up network to ensure good output voltage swings. The library developed consists of 7 gates: 5 combinational gates (Inverter, NAND2, NOR2, NOR3, and MUX2) and 2 sequential gates (latch and ‘D’ flip-flop). Test structures have been fabricated to experimentally characterize the delay vs. fan-out of the standard cells. Automatic extraction of electrical interconnections from layout, enabling layout versus schematic (LVS) has also incorporated into the existing tool suite for bottom gate a-Si:H TFTs. A 3bit counter was designed, fabricated and tested to demonstrate the standard cell library is described.