Miracle-dz/UniFAR-FTUs huggingface dataset:
MANTLE STRUCTURE BENEATH EASTERN EURASIA FROM FINITE FREQUENCY SEISMIC TOMOGRAPHY ( FFST )

An accurate, high-resolution 3-D earth model is crucial to the seismic calibration for nuclear monitoring. We use the newly developed Finite Frequency Seismic Tomography (FFST) approach to construct the crustal and mantle structure beneath eastern Eurasia. Traditionally, travel times of seismic waves are calculated based on the ray theory, which is valid strictly for infinite-frequency waves. Observed seismic waves, however, are finite frequency signals. As a result of scattering and diffractive effects, travel times of realistic finite frequency waves are sensitive to 3-D structure around the geometrical rays, and the travel time shifts caused by heterogeneities diminish gradually from the heterogeneities to receivers a phenomena named the wave front healing. The ray theory ignores this ubiquitous effect of the propagation of realistic seismic waves. Tomographic inversions based on it, therefore, tend to underestimate the magnitudes of heterogeneities. The newly developed FFST utilizes the 3-D Born-Fréchet sensitivity kernels of the travel times of finite-frequency seismic waves. The new method accounts for the wave front healing, off-ray scattering and other non-geometrical diffraction phenomena, and significantly improves the resolution of the velocity heterogeneity. In addition to the new methodology, we will use a more comprehensive data set than in previous studies to construct the new earth model beneath eastern Eurasia. We will collect data from the publicly accessible sources e.g., Incorporated Research Institutions for Seismology (IRIS), Global Seismographic Network (GSN), the Program for the Array Seismic Studies of the Continental Lithosphere (PASSCAL), and International Monitoring System (IMS) stations. Efforts will also be made to collect data sets from other networks in the region such as the Japanese Broadband Seismograph Network (F-net), the Japanese International Seismic Network (JISNET in Indonesia), and the Taiwan Broadband Seismic Network. Access to other unique sources including permanent and portable seismic stations throughout the study area further improves the station coverage over eastern Eurasia. This is the beginning of a three-year effort to improve seismic calibration in eastern Eurasia. We will first develop a 3-D FFST velocity model using body waves, along with model uncertainties. Travel time corrections and modeling error surfaces will be derived and applied to relocating ground truth (GT5) events. The final model will be developed using joint body and surface waves. From the 3D velocity models, we will also simulate the full wave propagation in the transition zone. To facilitate data integration, a project database and a web-based tool are being implemented. 479 27th Seismic Research Review: Ground-Based Nuclear Explosion Monitoring Technologies
Transcription Using Virtual Microphone Arrays

We describe a system that generates speaker-annotated transcripts of meetings by using a virtual microphone array, a set of spatially distributed asynchronous recording devices such as laptops and mobile phones. The system is composed of continuous audio stream alignment, blind beamforming, speech recognition, speaker diarization using prior speaker information, and system combination. When utilizing seven input audio streams, our system achieves a word error rate (WER) of 22.3% and comes within 3% of the close-talking microphone WER on the non-overlapping speech segments. The speaker-attributed WER (SAWER) is 26.7%. The relative gains in SAWER over the single-device system are 14.8%, 20.3%, and 22.4% for three, five, and seven microphones, respectively. The presented system achieves a 13.6% diarization error rate when 10% of the speech duration contains more than one speaker. The contribution of each component to the overall performance is also investigated, and we validate the system with experiments on the NIST RT-07 conference meeting test set.
Visualising genre relationships in Icelandic manuscripts

included as episodes (or þ ættir) in longer konungasögur, but since the nineteenth cen-tury have been included in the fornaldarsaga corpus. The XSLT scripts used in the earliest stages of our research confirmed that these texts, among others, were noteworthy for the frequency with which they appear alongside genres such as riddarasögur and konungasögur.
Fast On-Board 3 D Human Torso Pose Recovery and Forecasting *

Anticipatory human intent modeling is important for robots operating alongside humans in dynamic or crowded environments. Humans often telegraph intent through posture cues, such as torso or head cues. In this paper, we describe a computationally lightweight approach to human torso pose recovery and forecasting with a view towards limited sensing for easy on-board deployment. Our end-to-end system combines RGB images and point cloud information to recover 3D human pose, bridging the gap between learning-based 2D pose estimation methods and the 3D nature of the environment that robots and autonomous vehicles must reason about, with minimum overhead. In addition to pose recovery, we use a simple filter-and-fit method to forecast torso pose. We focus on rapidly generating short horizon forecasts, which is the most relevant scenario for autonomous agents that iteratively alternate between data gathering and planning steps in highly dynamic environments. While datasets suited to benchmarking multi-person 3D pose prediction in real-world scenarios are scarce, we describe an easily replicable evaluation method for benchmarking in a near real-world setting. We then assess the pose estimation performance using this evaluation procedure. Lastly, we evaluate the forecasting performance quantitatively on the Human3.6M motion capture dataset. Our simple 3D pose recovery method adds minimum overhead to 2D pose estimators, with comparable performance to 3D pose estimation baselines from a computer vision alternative. Furthermore, our uncomplicated forecasting algorithm outperforms complicated recurrent neural network methods while also being faster on the torso pose forecasting task.
2 Functional Constraints : 2 . 2 . 1 Length / Width Ratio Constraint , C

Optimal design of shallow foundations used in supporting various machinery is of vital importance. Badly designed foundations result in serious problems such as soil failure, large soil settlement, low vibration efficiency and resonance of structures near by the foundation. An optimal design approach based on using MATLAB optimization tool box is used to provide the optimal design of a machinery foundation on a sandy soil. To minimize the foundation cost the foundation mass is used as an objective function. To control the high technical quality of the foundation from soil mechanics and vibrations point of view, 10 functional constraints are used. The soil isolation efficiency of the soil is responsible about the vibration transmitted to the surrounding. Therefore, the effect of the minimum isolation efficiency in the range of 70 – 94 % on the foundation optimal design is investigated in details. The performance of the foundation against the minimum isolation efficiency is outlined so that the civil engineer can compromise between cost and performance.
Detection of Scenes in Fiction

Narratology usually defines the event as the smallest unit of a narrative, but computational literary studies has problems to come up with a workable definition and operationalization of event for its purposes (Meister, 2003) . This has proven to be a serious obstacle for research on narrative texts, because similarity of plot is an important aspect for many operations, for example genre classification. Recent operationalizations like syuzhet (Jockers, 2015) capture the emotional arc but not the plot itself. But it is quite obvious even to non-narratologists that a main component especially of conventional fiction consists of parts which contain a lot of direct speech and detailed descriptions of actions. These parts are usually called ‘scenesʼ and in our context they can be understood similarly to plays as “a division [. ] during which the action takes place in a single place without a break in time” (Learner s̓ Dictionary 2018) including the aspect that it is “a part of a play, movie, story, etc., in which a particular action or activity occurs” (Learner s̓ Dictionary 2018).
Diet and niche overlap of the pompano ( Trachinotus carolinus ) and palometa ( Trachinotus goodei ) ( Perciformes , Carangidae ) in a surf zone beach in southeastern Brazil

The diet and niche overlap of the pompanos Trachinotus carolinus and T.goodei in a surf zone were studied based on 352 specimens of T. carolinus and 53 of T. goodei ranging from 10 to 165 mm total length. The study was carried out in a sand beach in southeastern Brazil, where the fishes were captured with a seine net measuring 10 x 2 meters and 4.0 mm mesh. A total of 34 prey items were found for T. carolinus and 26 for T. goodei, represented mainly by Copepoda, Insecta, Decapoda larvae, Polychaeta, Mollusca, Echinodermata, and fishes. The diet showed a remarkable predominance of insects Formicidae and zooplankton represented mainly by copepods, Decapod larva, and Mysida among other prey groups. No differences were observed for day and night periods, stomach fullness, or prey length and predator length relationships. Both species share 72.16% of the prey items. The uncommon high presence of insects in the stomachs may be associated with the sample area near to coastal vegetation, and the presence of human wastes from urban zone.
Real Time Online Visual End Point Detection Using Unidirectional LSTM

Visual Voice Activity Detection (V-VAD) involves the detection of speech activity of a speaker using visual features. The V-VAD is useful in detecting the end point of an utterance under noisy acoustic conditions or for maintaining speaker privacy. In this paper, we propose a speaker independent, real-time solution for V-VAD. The focus is on real-time aspect and accuracy as such algorithms will play a key role in detecting end point especially while interacting with speech assistants. We propose two novel methods one using CNN and the other using 2D-DCT features. Unidirectional LSTMs are used in both the methods to make it online and learn temporal dependence. The methods are tested on two publicly available datasets. Additionally the methods are also tested on a locally collected dataset which further validates our hypothesis. Additionally it has been observed through experiments that both the approaches generalize to unseen speakers. It has been shown that our best approach gives substantial improvement over earlier methods done on the same dataset.
THE PERCEPTION OF FAMILIAR AND UNFAMILAR ACCENTS BY BILINGUAL AND MONOLINGUAL CHILDREN

In large urban cities, children typically grow-up in a diverse multicultural environment. Depending on their local language environment, children are often exposed to regional as well as foreign-accents. This study investigated whether children’s accent processing is affected by the variability in their ambient language environment. English monolingual and Sylheti-English bilingual children were assessed on their ability to identify sentences in three accent conditions: London-English (familiar to all), Sylheti-accented English (only familiar to the bilinguals), Spanishaccented English (unfamiliar to all). All children were most accurate in the London-English accent condition, however the groups differed with their dominant familiar accent: the monolinguals were more accurate at recalling the London-English sentences than the bilinguals, and the bilinguals were more accurate in the Sylheti-accented condition than the monolinguals. The results suggest that variation in linguistic experience in early life, give rise to differences in the processing of familiar and unfamiliar accented speech.
Improving Code-Switched Language Modeling Performance Using Cognate Features

We have found that cognate words, defined as sets of words used in multiple languages that share a common etymology, can in fact elicit code-switching or language mixing between the languages. This paper focuses on how information about cognate words can improve language modeling performance of codeswitched English-Spanish (EN-ES) language. We have found that the degree of semantic, phonetic or lexical overlap between a pair of cognate words is a useful feature in identifying codeswitching in language. We derive a set of spelling, phonetic and semantic features from a list of of EN-ES cognates and run experiments on a corpus of conversational code-switched EN-ES. First, we show that there exists a strong statistical relationship between these cognate-based features and code-switching in the corpus. Secondly, we demonstrate that language models using these features obtain similar performance improvements as do other manually tagged features including language and part-ofspeech tags. We conclude that cognate features can be a useful set of automatically-derived features that can be easily obtained for any pair of languages.
Understanding and Improving Ontology

Ontologies are the fundamental building blocks of the Semantic Web and Linked Data. Reasoning is critical to ensure the logical consistency of ontologies, and to compute inferred knowledge from an ontology. It has been shown both theoretically and empirically that, despite decades of intensive work on optimising ontology reasoning algorithms, performing core reasoning tasks on large and expressive ontologies is time-consuming and resource-intensive. In this paper, we present the meta-reasoning framework R2O2* to tackle the important problems of understanding the source of TBox reasoning hardness and predicting and optimising TBox reasoning efficiency by exploiting machine learning techniques. R2O2* combines state-of-the-art OWL 2 DL reasoners as well as an efficient OWL 2 EL reasoner as components, and predicts the most efficient one by using an ensemble of robust learning algorithms including XGBoost and Random Forests. A comprehensive evaluation on a large and carefully curated ontology corpus shows that R2O2* outperforms all six component reasoners as well as AutoFolio, a robust and strong algorithm selection system.
Experiment 1 : Do Rituals Enhance Consumption ?

Four experiments tested the novel hypothesis that ritualistic behavior potentiates and enhances ensuing consumption— an effect found for chocolates, lemonade, and even carrots. Experiment 1 showed that participants who engaged in ritualized behavior, compared with those who did not, evaluated chocolate as more flavorful, valuable, and deserving of behavioral savoring. Experiment 2 demonstrated that random gestures do not boost consumption as much as ritualistic gestures do. It further showed that a delay between a ritual and the opportunity to consume heightens enjoyment, which attests to the idea that ritual behavior stimulates goal-directed action (to consume). Experiment 3 found that performing a ritual oneself enhances consumption more than watching someone else perform the same ritual, suggesting that personal involvement is crucial for the benefits of rituals to emerge. Finally, Experiment 4 provided direct evidence of the underlying process: Rituals enhance the enjoyment of consumption because of the greater involvement in the experience that they prompt.
UvA-DARE ( Digital Academic Repository ) Quantitatively evaluating interventions in the influenza A ( H 1 N 1 ) epidemic on China campus grounded on individual-based simulations

The novel Influenza A (H1N1) virus is attacking the world in 2009. Among others, campuses in China, particularly most university/college campuses for bachelor students, are at-risk areas where many susceptible youngsters live. They most likely interact with each other quite often in dormitories, classrooms and refectories. We model the pandemic influenza A (H1N1) transmission through campus contacts and then forecast the effectiveness of interventions, based on a previously presented Complex Agent Network model for simulating infectious diseases [1]. Our results suggest that pandemic influenza A (H1N1) on campus will die out even with no intervention taken; the most effective intervention is still quarantining confirmed cases as early as possible and, in addition, vaccinating susceptible people can further decrease the maximum daily number of the infected. This study can support quantitative experimentation and prediction of infectious diseases within predefined areas, and assessment of intervention strategies.
Corpus Building for Low Resource Languages in the DARPA LORELEI Program

We describe corpora for the LORELEI (Low Resource Languages for Emergent Incidents) Program, whose goal is to build human language technologies to provide situational awareness during emergent incidents, with a particular focus on low resource languages. Incident Language packs are used for system development and testing in machine translation, entity disambiguation and linking, and the “situation frame” task, which requires aggregation of information about the emergent incident. Incident languages, as well as the incidents themselves, remain unknown until the evaluation begins, and no labeled training data is provided; systems developers must rapidly adapt technology for the incident language and return initial results within 24 hours. Given this surprise language evaluation scenario, Representative Language packs are designed to support research into cross-language projection and language universals rather than to provide training data. They contain large volumes of monolingual and parallel text, basic annotations, lexical resources and simple NLP tools for 23 languages selected for typological diversity and coverage. We discuss the creation of the LORELEI language packs with a special focus on resources for machine translation, as well as techniques for maintaining consistency across the language packs.
Development of the Bonding with Dog Checklist (BoDC)

The growing interest in the mechanisms through which human-animal interaction (HAI) benefits humans suggests that new measures are needed to assess human bonding, especially to non-owned animals (i.e., animals that are not the human’s pet). The current study addressed this need by developing and testing a new measure to assess human bonding behavior with non-owned animals, during an animal-assisted intervention that incorporated shelter dogs. An observational measure, a 12-item Bonding with Dog Checklist (BoDC), was created based on prior work on dog bonding and attachment. Participants in the study were incarcerated adolescents residing in two juvenile detention centers (N = 131). An Exploratory Factor Analysis resulted in a final 7-item measure with a single factor structure and good inter-item reliability ( alpha = .864). Raters used the 7-item BoDC to assess bonding of youth with dogs as they trained or walked shelter dogs during a 10-week intervention. Seventeen raters were trained in use of the observational measure and attended weekly supervision meetings. BoDC scores increased over the sessions and the BoDC was moderately correlated with a measure of dog attachment in the first half of the intervention, providing some evidence for construct validity. These results provide preliminary evidence for the BoDC as a reliable and valid measure to assess human bonding to animals that are not their personal companions.
Automated Diagnosis of Alzheimer ’ s Disease using PET Images : A study of alternative procedures for feature extraction and selection

Currently, there is no cure for Alzheimer’s disease (AD), but its early detection is essential to an effective treatment, slowing down the progression of symptoms. Consequently, the development of automatic diagnostic tools, which use as principal source of information three-dimensional images of the brain, has attracted great interest in recent years. This work focused on PET images and studied alternatives to two of the main building blocks of a computerized diagnostic system: the extraction and selection of features. Regarding the common approach based on Voxel Intensities (VI), the FDG-PET image was studied for different scales and resolutions. In addition, the use of a measure of local contrast was also tested, as well as the widely known texture descriptor, Local Binary Patterns (LBP), to which a novel extension to three-dimensional data was proposed. As regards selection, a new method based on data acquired by the Eye Track technology during the inspection of PET images by an expert physician was proposed. The aim of this method is to model the behavior of the gaze over time, and use the model to select the features that the expert found most interesting. Moreover, other more conventional methods based on correlation measures and mutual information were also studied. The Support Vector Machine (SVM) classifier was used to perform binary classifications among AD patients, patients with Mild Cognitive Impairment (MCI) and a control group (in a dichotomous fashion), obtaining comparable or superior performances to those achieved by most systems found in the literature.
ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES STUDIES ON METAL COMPLEXES DERIVED FROM PHYSIOLOGICALLY ACTIVE QUINOXALINE DERIVATIVES GRADUATE PROJECT CHEM. 774 BY GOJJE GAMO

Coordination compounds, with bonds between a central metal atom and surrounding ligands, play critical roles in biology, biochemistry and medicine, controlling the structure and function of many enzymes and their metabolism. They play similarly vital roles in many industrial processes and in the development of new materials with specifically designed properties. Thus synthesis and study of such complexes is very important. In this lab work, equimolar quantities of o-phenyldiamine and oxalic acid dihydrate were condensed in 5N HCl under reflux to 2,3-quinoxalinedione. Refluxing of 2,3-quinoxalinedione with phosphorus (V) chloride as well as with thionylchloride gave 2,3 dichloroquinoxaline which was then treated first with hydroxylamine hydrochloride to afford a new ligand 2,3quinoxalinedioxime (QXDO) and secondly with N-(aminoformimidoyl)phenol to give quinoxaline-2,3-bis-[N-(aminoformimidoyl)phenol] (QXAP) both of which contain nitrogen and oxygen donor atoms. However, the formation of QXAP was unsuccessful as it was confirmed by H NMR. Thus QXDO was further complexed with divalent metal salts to give different complexes. New metal complexes of nickel and copper with quinoxalinedioxime (QXDO) were prepared. The complexes were characterized by elemental analysis, conductance measurements, atomic absorption, IR, NMR and UV spectroscopy and magnetic moments. QXDO behaves as a bidetate ligand. Key terms: Quinoxaline, o-phenyldiamine, salicylaldehyde, hydrazine, 2,3-quinoxalinedione (QXD), 2,3-dichloroquinoxaline (DCQX), 2,3-quinoxalinedioxime (QXDO).
A Study of a Cross-Language Perception Based on Cortical Analysis Using Biomimetic STRFs

For those in the early stage of learning a foreign language, they commonly experience difficulties in understanding spoken words in the second language, while they have no problem in recognizing words spoken in their mother tongue. This paper examines this phenomenon using biomimetic receptive fields that can be interpreted as a transfer function between acoustic stimulus and cortical responses in the brain. While receptive fields of individual subjects are often optimized to recognize unique phonemes in their mother language, it is unclear whether challenges associated with acquiring a new language (especially in adulthood) is due to a mismatch between phonemic characteristics in the new language and optimized processing in the system. We explore this question by contrasting biomimetic systems optimized for four different languages with sufficiently different characteristics. We perform English phoneme classification with these languageoptimized systems. We observed distinctive characteristics in receptive fields emerging from each language, and the differences of English phoneme recognition performance accordingly.