Miracle-dz/UniFAR-FTUs huggingface dataset:
Which 'Lancaster' do you mean? Disambiguation challenges in extracting place names for Spatial Humanities

It is already possible to apply simple gazetteer-based named-entity recognition (NER) techniques to textual sources in order to extract candidate place names. Combining NER techniques with Geographical Information Systems (GIS) allows the digital humanities researcher to overlay the information on a map and visualise the result. This allows us to ask questions such as “what place is this corpus talking about? ”, “what is being said about different places? ” and “how has the way that places are represented in the corpus changed over time? ”. Together, these techniques have spawned the development of Spatial Humanities (Bodenhamer et al. 2010). However, as the application of these techniques widens and the scale of full-text datasets grows (e.g. EEBO-TCP), we need to refine the techniques to improve their accuracy with both identification of candidate place names and linking to the correct location on a map. For example, Lancaster (a city), needs to be extracted whereas Lancaster bomber (a plane or type of beer), Stuart Lancaster (the England rugby coach) and Duke of Lancaster (a nobleman or pub) do not. In addition, linking occurrences of Lancaster from a textual source to the correct location in the north-west of England rather than other locations in Australia, Canada and the United States would be vital. In this paper, we argue that techniques from corpus linguistics and natural language processing are essential when tackling both these issues, e.g. concordancing to examine the co-text, part-of-speech tagging to identify proper nouns, frequency analysis and keyness statistics to highlight significant differences in place names between texts. Techniques such as edit distance, letter-replacement rules and phonetic matching also permit spelling variation issues in place names to be overcome when extracting mentions (Baron and Rayson, 2008). Collocation between place names and semantic tags on the surrounding context has already proven useful in identifying topics associated with particular places (Gregory and Hardie, 2011). In addition, we argue that combining collocations between place names and distance information from GIS will provide strong evidence for the disambiguation of location.
A method to determine validity and reliability of activity sensors

METHOD Four sensors were securely fastened to a mechanical oscillator (Vibration Exciter, type 4809, Bruel & Kjaer) and moved at various frequencies (6.67Hz; 13.45Hz; 19.88Hz) within the range of human physical activity. For each of the three sensor axes, the sensors were simultaneously moved for five minutes per defined frequency. The acceleration of each movement was expressed by its RMS value (RMSinput). Raw sensor output (sample frequency 200 Hz) was converted to IMA counts per minute (cpm, in [ms-2]), according to Bouten et al. (1997) [3]. Linear regression analysis was used to examine the relationship between the RMS and IMA. Factors taken into account were the four sensors and the three axes. The device tested in this study was the ProMove2 (Inertia Technology), containing the 3D MEMS inertial sensor (type: LIS3LV02DL, ST Microsystems).
Neural Network Disturbance Observer-Based Adaptive Tracking Control for Robot Manipulator

A novel neural network disturbance observer-based adaptive tracking control approach for robot manipulator is presented in the paper. Using the concept of ‘dominant input’, the robot manipulator is transferred into several SISO systems with the unknown compound disturbance which consists of external disturbance, internal parameter uncertainty and cross-coupling of subsystems. In order to reduce the restrained conditions on the disturbance, the compound disturbance is monitored by the designed neural network observer. Then parameters update laws and control laws based on Lyapunov theory and neural network observer are proposed to guarantee that all the signals in the system are uniformly ultimately bounded and achieve given tracking performance index. Finally, the simulation results are presented to demonstrate the efficiency of the control scheme.
Marked sex differences on a fine motor skill task disappear when finger size is used as covariate

Purdue Pegboard performance of 16 male and 25 female right-handed college students were compared, and results were replicated with 25 male and 28 female subjects. In agreement with the literature, women performed significantly better than men. When measures of index finger and thumb thickness were used as covariate, all significant sex differences in performance disappeared. Negative correlations between performance and finger size were observed in both sexes. Sex differences in fine manual dexterity tasks may therefore be confounded by sex differences in finger size.
Exploration and Exploitation Strategies in SME Family Firms

The survival of family firms may lie in their ability in exploiting their current competitive advantage while concurrently exploring new opportunities present in their way. However, scantiness of resources makes it hard to employ both strategies simultaneously. As such, this paper examines on how family firms decide to pursue exploitation or exploration strategies. The unique culture of family firms, for instance, long-term orientation, risk aversion, and aspiration, suggests a variety of ways to implement strategies, which could be the exploration or exploitation strategy. Hence, the main objective of this study is to review both the exploration and exploitation strategies found in the literature as practiced in family firms. In addition, this study proposes a conceptual model to further comprehend the decisions made in undertaking exploration and exploitation strategies among family firms.
Predictors of presence in virtual reality

The subjective experience of presence is considered to be important in the treatment of anxiety disorders using virtual reality. Presence can be defined as a psychological phenomenon through which one's cognitive processes are oriented towards another world. Most of the research on presence has focused on the roles of technological factors influencing presence, while the number of studies focusing on the personality and physiological predictors are far fewer. Thus, the present study examined the relationship between various personality variables and presence, along with physiological correlates of presence when engaged in a virtual environment. The Presence Questionnaire, to determine their experience of presence, and a small battery of personality-related questionnaires were administered to 70 young adults who participated in 3 different virtual reality scenarios. Participants' physiological responses were recorded in the form of heart rate, galvanic skin levels, and galvanic skin responses were assessed as were urges to drink (craving). Data analysis showed that expectations, levels of craving, and drinking history played a significant role in the experience of presence.
A Proof-of-Trust Consensus Protocol for Enhancing Accountability in Crowdsourcing Services

Incorporating accountability mechanisms in online services requires effective trust management and immutable, traceable source of truth for transaction evidence. The emergence of the blockchain technology brings in high hopes for fulfilling most of those requirements. However, a major challenge is to find a proper consensus protocol that is applicable to the crowdsourcing services in particular and online services in general. Building upon the idea of using blockchain as the underlying technology to enable tracing transactions for service contracts and dispute arbitration, this paper proposes a novel consensus protocol that is suitable for the crowdsourcing as well as the general online service industry. The new consensus protocol is called “Proof-of-Trust” (PoT) consensus; it selects transaction validators based on the service participants’ trust values while leveraging RAFT leader election and Shamir's secret sharing algorithms. The PoT protocol avoids the low throughput and resource intensive pitfalls associated with Bitcoin’ s “Proof-of-Work” (PoW) mining, while addressing the scalability issue associated with the traditional Paxos-based and Byzantine Fault Tolerance (BFT)-based algorithms. In addition, it addresses the unfaithful behaviors that cannot be dealt with in the traditional BFT algorithms. The paper demonstrates that our approach can provide a viable accountability solution for the online service industry.
Will artificial intelligence replace ophthalmologist in diabetic retinopathy screening

We read a paper titled “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs” published in “The Journal of the American Medical Association” [2]. In this paper, the authors designed an artificial intelligence diagnostic tool using a deep learning algorithm for identification of fundus of normal and diabetic retinopathy patients. This diagnostic tool yielded satisfactory results, with sensitivity values of 97.5% and 96.1% and specificity values of 93.4% and 93.9% in two test systems [2]. We are encouraged by this finding, but are concerned about the public expectation of the findings whether the emergence of such a revolutionary tool would replace ophthalmologist in diabetic retinopathy screening in the future.
Managing Networks for School Improvement: Seven Lessons from the Field

In recent decades, new networks for school improvement (NSI) have proliferated across the country. These emerging organizational structures present education leaders with an opportunity to build dynamic infrastructures to engage schools in improvements to teaching and learning. NSI are diverse. Some NSI are part of school districts, while others are contracted by school districts to design blueprints for school improvement. What all NSI have in common is a central hub supporting a set of member schools, like the center of a wheel and its spokes. In this guidebook, we focus on common lessons for designing improvement infrastructures from the perspective of leaders across four different types of networks, including: • Local district superintendents who support schools in a particular geographic area; • Field support centers, which partner with district superintendents in the intermediary space between the central office and schools; • Affinity organizations, which are independent non-profit organizations that work under contract from the central district office to support a select group of district schools; and • Charter school management organizations that operate outside the district, supporting their affiliated
Boundedness in families with applications to arithmetic hyperbolicity

Motivated by conjectures of Demailly, Green–Griffiths, Lang and Vojta, we show that several notions related to hyperbolicity behave similarly in families. We apply our results to show the persistence of arithmetic hyperbolicity along field extensions for projective normal surfaces with non‐zero irregularity. These results rely on the mild boundedness of semi‐abelian varieties. We also introduce and study the notion of pseudo‐algebraic hyperbolicity which extends Demailly's notion of algebraic hyperbolicity for projective schemes.
Fast electro-optic switching for coherent laser ranging and velocimetry

The growth of 3D imaging across a range of sectors has driven a demand for high performance beam steering techniques. Fields as diverse as autonomous vehicles and medical imaging can benefit from a high speed, adaptable method of beam steering. We present a monolithic, sub-microsecond electro-optic switch as a solution satisfying the need for reliability, speed, dynamic addressability and compactness. Here we demonstrate a laboratory-scale, solid-state lidar pointing system, using the electro-optic switch to launch modulated coherent light into free space, and then to collect the reflected signal. We use coherent detection of the reflected light to simultaneously extract the range and axial velocity of targets at each of several electronically addressable output ports.
What kind of foreign policy actor can the EU be defined as in respect to their sanctions regime against Belarus

The aim of this paper is to assess the character of the European Union as a foreign policy actor through examining its restrictive and proactive measures vis-à-vis Belarus, a country often branded as ‘Europe’s last dictatorship’. For this purpose, documents of the European Parliament are examined throughout a ten-year period, allowing for a coherent policy assessment of European Union decision-making. By depicting the Parliament’s impetus behind each decision to impose or lift sanctions and behind introducing other foreign policy tools, this analysis will gain some insight on the principles guiding the European Union as a foreign policy actor. The comprehensive approach further allows us to draw conclusions on the coherence and structure of the European Union’s foreign policy towards Belarus, as well as on the type of actor the European Union can be defined as in light of their policies.
A multimodal machine-learning graph-based approach for segmenting glaucomatous optic nerve head structures from SD-OCT volumes and fundus photographs

Glaucoma is the second leading cause of blindness worldwide. The clinical standard for monitoring the functional deficits in the retina that are caused by glaucoma is the visual field test. In addition to monitoring the functional loss, evaluating disease-related structural changes in the human retina also helps with diagnosis and management of this progressive disease. The characteristic changes of retinal structures such as the optic nerve head (ONH) are monitored utilizing imaging modalities such as color (stereo) fundus photography and, more recently, spectral-domain optical coherence tomography (SD-OCT). With the inherent subjectivity and time required for manually segmenting retinal structures, there has been a great interest in automated approaches. Since both fundus and SD-OCT images are often acquired for the assessment of glaucoma, automated segmentation approaches can benefit from combining multimodal complementary information from both sources. The goal of the current work is to automatically segment the retinal structures and extract the proper parameters of the optic nerve head related to the diagnosis and management of glaucoma. The structural parameters include the cup-to-disc ratio (CDR) which is a 2D parameter and is obtainable from both fundus and SDOCT modalities. Bruch’s membrane opening-minimum rim width (BMO-MRW) is a recently introduced 3D structural parameter that is obtainable from the SD-OCT modality only. We propose to use the complementary information from both fundus and SD-OCT modalities in order to enhance the segmentation of structures of interest. In order to enable combining information from different modalities, a feature-based registration method is proposed for aligning the fundus and OCT images. In addition, our goal is to incorporate the machine-learning techniques into the graph-theoretic approach that is used for segmenting the structures of interest. Thus, the major contributions of this work include: 1) use of complementary information from SD-OCT and fundus images for segmenting the optic disc and cup boundaries in both modalities, 2) identifying the extent that accounting for the pres-
EXPLORING A MACHINE LEARNING BASED APPROACH FOR ANALYZING ANONYMIZED DATA

viii Glossary . ix Acknowledgements . x Chapter
Synthetic Aperture Radar Signal Processing using GPGPU

In this work an efficient parallel implementation of the Chirp Scaling Algorithm (CSA) for Synthetic Aperture Radar (SAR) processing is presented. The architecture selected for the implementation is General Purpose Graphic Processing Unit (GPGPU), as it is well suited for scientific applications and real time implementation of algorithms. The analysis of a first implementation led to several improvements which resulted in an important final speedup. Details of the issues found are explained, and the performance improvement of their correction explicitly shown.
Low-Rank Sparse Representation with Pre-Learned Dictionaries and Side Information for Singing Voice Separation

At present, although the human speech separation has achieved fruitful results, it is not ideal for the separation of singing and accompaniment. Based on low-rank and sparse optimization theory, in this paper, we propose a new singing voice separation algorithm called Low-rank, Sparse Representation with pre-learned dictionaries and side Information (LSRi). The algorithm incorporates both the vocal and instrumental spectrograms as sparse matrix and low-rank matrix, meanwhile combines pre-learning dictionary and the reconstructed voice spectrogram form the annotation. Evaluations on the iKala dataset show that the proposed methods are effective and efficient for singing voice separation.
Progressive Monte Carlo rendering of atmospheric flow features across scales

We demonstrate a technique to visualize multiple scales of atmospheric flow fields and the Lagrangian patterns therein, simulated by state-of-the-art simulation models for each scale. They provide insight into the structural differences and patterns that occur on each scale and highlight the complexity of flow phenomena in our atmosphere. For visualization, we employ unbiased and consistent Monte Carlo rendering to avoid discretization of finite-time Lyapunov exponent fields. High-resolution climate data can improve existing weather prediction and reanalysis capabilities. High-resolution numerical simulations of atmospheric flows help scientists to understand aerodynamic processes and to visualize the structural flow patterns across scales.
Strategic and Nonstrategic Explanations for Attentional Inertia

Previous research and theory have focused on nonstrategic explanations for attentional inertia phenomena in television viewing (that looks become more stable and reflect greater cognitive engagement as they get longer). However, varying relatedness of episodes, for which strategic inertial processes should vary in strength, produces a corresponding difference in inertia of looks crossing those boundaries. Although an additive model would suggest that all these within-narrative boundaries should show stronger relationships than at unrelated-content boundaries, this was not the case. It may thus be that results previously interpreted as reflecting nonstrategic processes instead result from strategic processes not before considered.
Is the use of Christoffel connection in gravity theories conceptually correct?

Christoffel connection did not enter gravity as an axiom of minimal length for the free fall of particles (where anyway length action is not defined for massless particles), nor out of economy, but from the weak equivalence principle (gravitational force is equivalent to acceleration according to Einstein) together with the identification of the local inertial frame with the local Lorentz one. This identification implies that the orbits of all particles are given by the geodesics of the Christoffel connection. Here, we show that in the presence of only massless particles (absence of massive particles) the above identification is inconsistent and does not lead to any connection. The proof is based on the existence of projectively equivalent connections and the absence of proper time for null particles. If a connection derived by some kinematical principles for the particles is to be applied in the world, it is better these principles to be valid in all relevant spacetime rather than different principles to give different connections in different spacetime regions. Therefore, our result stated above may imply a conceptual insufficiency of the use of the Christoffel connection in the early universe where only massless particles are expected to be present (whenever at least some notions, like orbits, are meaningful), and thus of the total use of this connection. If in the early universe the notion of a massive particle, which appears latter in time, cannot be used, in an analogous way in a causally disconnected high-energy region (maybe deep interior of astrophysical objects or black holes) the same conclusions could be extracted if only massless particles are present.
Transcriptome Analysis in Chickpea (Cicer arietinum L.): Applications in Study of Gene Expression, Non-Coding RNA Prediction, and Molecular Marker Development

Extensive analyses of transcriptome have been carried out in chickpea, which is the third most important legume valued as a source of dietary protein and micronutrients. Over the last two decades, several laboratories have used a wide range of techniques encompassing expressed sequence tag (EST) analysis, serial analysis of gene expression (SAGE), microarray and next-generation sequencing (NGS) technologies for analysing the chickpea transcriptomes. However, chickpea transcriptome analysis witnessed significant progress with the advent of the NGS platforms. Gene expression analyses using NGS platforms were carried out in the vegetative and reproductive tissues such as shoot, root, mature leaf, flower bud, young pod, seed and nodule by various groups which resulted in identification of several tissue-specific transcripts. Some laboratories have utilized transcriptomics to explore the response of chickpea to abiotic and biotic stresses such as drought, salinity, heat, cold, Fusarium oxysporum and Ascochyta rabiei differentially expressed genes and also established crosstalk between biotic and abiotic stress responses. Transcriptome analysis has been utilized extensively to identify non-coding RNAs such as miRNAs and long intergenic non-coding (LINC) RNAs. Transcriptome analysis has facilitated the development of molecular markers such as simple sequence repeats (SSRs), single-nucleotide polymorphisms (SNPs) and potential intron polymorphisms (PIPs) that are being used to expedite the chickpea breeding programmes. The available chickpea transcriptomes will continue to serve as the foundation for devising strategies for chickpea improvement.
Efficient Currency Determination Algorithms for Dynamic Data

Data quality is an important aspect in data application and management, and currency is one of the major dimensions influencing its quality. In real applications, datasets timestamps are often incomplete and unavailable, or even absent. With the increasing requirements to update real-time data, existing methods can fail to adequately determine the currency of entities. In consideration of the velocity of big data, we propose a series of efficient algorithms for determining the currency of dynamic datasets, which we divide into two steps. In the preprocessing step, to better determine data currency and accelerate dataset updating, we propose the use of a topological graph of the processing order of the entity attributes. Then, we construct an Entity Query B-Tree (EQB-Tree) structure and an Entity Storage Dynamic Linked List (ES-DLL) to improve the querying and updating processes of both the data currency graph and currency scores. In the currency determination step, we propose definitions of the currency score and currency information for tuples referring to the same entity and use examples to discuss methods and algorithms for their computation. Based on our experimental results with both real and synthetic data, we verify that our methods can efficiently update data in the correct order of currency.