Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating internal magnetic field gradients in aquifer sediments

ABSTRACTInternal magnetic field gradients in porous materials, if sufficiently large, can be a source of error in nuclear magnetic resonance (NMR) measurements of the transverse relaxation time T2 and the diffusion coefficient D. Given that these measurements can provide information about the pore fluid and the pore geometry, it is important to determine the magnitude of internal gradients and assess their potential impact. We estimated the effective internal gradients in aquifer sediment samples using three methods. We used a 2D NMR method to map the distribution of internal gradients versus T2 and found gradients up to 1000 G/cm with peak gradient values in the range of 20−100 G/cm for most of the samples. The average effective gradient values, calculated from the slope of the mean log relaxation rate versus the squared echo time, typically fell above the peak gradient values in the 2D distributions, with a range from 12 to 230 G/cm. The maximum effective gradients, calculated from the magnetic suscep.
Inertia gravity wave generation by the tropospheric midlatitude jet as given by the Fronts and Atlantic Storm‐Track Experiment radio soundings

[1] Generation of inertia gravity waves by the midlatitude tropospheric jet is studied on the basis of the data obtained from the radio soundings over the North Atlantic during the Fronts and Atlantic Storm-Track Experiment campaign. A sample of 224 radio soundings is used to analyze the wave activity as a function of the distance to the jet. It is shown that radio soundings displaying the most intense gravity wave activity, both in the stratosphere and in the troposphere, are the ones closest to the jet axis. Thus the jet region is the dominant source of gravity waves in this region far from orography. Further examination allows for identification of two specific regions of the flow that are associated with intense gravity wave activity: the vicinity of the maximum of the jet velocity and the regions of strong curvature of the jet. The detailed case studies we provide suggest that geostrophic adjustment is the dynamical mechanism responsible for the generation of large-amplitude inertia gravity waves in the regions of the strong curvature of the wind. The generation of waves in the vicinity of the regions where the wind veers, in the deep troughs of the geopotential, appears to be systematic.
An Efficient and Noise-Robust Audiovisual Encoder for Audiovisual Speech Recognition

Boosted by self-supervised learning (SSL) on large amounts of unlabeled data, computationally demanding transformer-based audiovisual ASR (AV-ASR) achieves state-of-the-art performance. In this work, we are the ﬁrst to propose teacher-student model distillation for an efﬁcient and noise-robust AV encoder for AV-ASR. First, we compare two options for the teacher, a non-task-speciﬁc and a task-speciﬁc one. Second, we investigate the design and the components in the student neural network. Third, we explore loss function choices during distillation. By distillation with a simpliﬁed loss function, the ﬁnal efﬁcient conformer-based student has 69% fewer parameters and 23% less computational power than the teacher, but excels the base-line student with a WER of 4.6% (11.4%) in clean condition, and with 20.2% (35.7%) in 0dB babble noise. On average over noise types in 0dB SNR, our proposed student even achieves more than 50% relative WER reduction compared to the baseline student.
Australian cultural built heritage: stakeholders’ perceived conservation barriers and motivations

Abstract Stakeholders are recognised as drivers of effective conservation of cultural built heritage. Yet, as stakeholders have eclectic views in terms of their interest in, knowledge of and perceptions about the management of historic fabric, their practices are often diverse. The objective of this paper is to gain an understanding of the stakeholders’ views drawn from relevant professional field on the issues that act as barriers to conservation and identify the factors that motivate built heritage management in Australia. Using a qualitative research design, two focus groups were conducted in Queensland and New South Wales with purposely selected key informants (N = 14) working in the Australian heritage sector. The study presents stakeholders’ interest in managing built heritage and the perceptions concerning the application of conservation policy and practices in the Australian built heritage sector, as influenced by the interdisciplinary backgrounds of participants. The paper contributes to an in-depth understanding of the conservation barriers and motivators and their implications for policy and practices in the management of Australian built heritage. The study is based on the perceptions of key informants with diverse interests and knowledge about the conservation of cultural built heritage; this makes the research analysis and implications inclusive and influential from both theoretical and practical points of view.
Disturbance and climate change risks to forest carbon sinks and potential management responses

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Habilidades sociales en Engagement y desempeño académico en estudiantes universitarios

El estudio del engagement se ha convertido en uno de los temas principales de investigación en contextos académicos. La presente investigación buscó determinar la relación entre engagement y el desempeño académico en estudiantes universitarios. La población-muestra esta conformada por 298 estudiantes de la Universidad Nacional del Centro del Perú; el instrumento utilizado fue la escala UWES sobre engagement académico; para evaluar el desempeño se emplearon los promedios consolidados en tres carreras profesionales. Los resultados indican que no existe correlación significativa entre las variables estudiadas, siendo la correlación significativa en el nivel 0,05 (bilateral), con un p-valor del 0.33. La correlación de Spearman muestra un nivel de relación no significativa, con un coeficiente de 0.123. Los estudiantes se desenvuelven con niveles de motivación interna personal; la principal conclusión es que pese a las condiciones adversas y desmotivación mantienen un desempeño académico equilibrado.
Ground calibrations of the Clouds and the Earth's Radiant Energy System (CERES) tropical rainfall measuring mission spacecraft thermistor bolometers

The clouds and the Earth's Radiant Energy System (CERES) spacecraft scanning thermistor bolometers will measure earth-reflected solar and earth-emitted longwave radiances, at the top-of-the-atmosphere. The measurements are performed in the broadband shortwave and longwave spectral regions as well as in the 8-12 micrometers water vapor window over geographical footprints as small as 10 kilometers at the nadir. The CERES measurements are designed to improve our knowledge of the earth's natural climate processes, in particular those related to clouds, and man's impact upon climate as indicated by atmospheric temperature. November 1997, the first set of CERES bolometers is scheduled for launch on the Tropical Rainfall Measuring Missions (TRMM) Spacecraft. The CERES bolometers were calibrated radiometrically in a vacuum ground facility using absolute reference sources, tied to the International Temperature Scale of 1990. Accurate bolometer calibrations are dependent upon the derivations of the radiances from the spectral properties of both the sources and bolometers. In this paper, the overall calibration approaches are discussed for the longwave and shortwave calibrations. The spectral response for the TRMM bolometer units are presented and applied to the bolometer ground calibrations in order to determine pre-launch calibration gains.
Evaluation of C-band SAR polarimetric parameters for discrimination of first-year sea ice types

In this study, the classification potential of polarimetric parameters derived after Cloude–Pottier decomposition, Touzi decomposition, Freeman–Durden decomposition, normalized radar cross section measurements, phase differences, and statistical synthetic aperture radar correlation measures is evaluated by relating them to three pre-identified sea ice types and wind-roughened open water. A combined approach that constitutes a visual inspection of estimated probability densities of the polarimetric parameters and quantitative analysis using supervised classifications (k means and maximum likelihood) is adopted. Polarimetric parameters are iteratively combined in pairs and triplets to test for their ice type discrimination potential. Sensitivity of polarimetric parameters to radar incidence angle is also examined. Our results demonstrated strong but variable sensitivity of polarimetric parameters to different ice types, which was dependent on radar incidence angle. Results of parameter evaluation demonstrated that no single parameter discriminates significantly (>60%) between all the ice types considered in the study. Combining two low correlated parameters increased the classification accuracy by 10%–22%. Combining the third polarimetric parameter did not necessarily improve the classification results. However, the best classification results were achieved using a combination of three parameters.
Holocene vegetation history and palaeoenvironments at Klaarfontein Springs, Western Cape, South Africa

Several sedimenit cores ai-ounid the coastal lake of Verlorenvlei, Westem Cape, have thus far revealed important details of the development of this semi-arid winter-rainfall environment during the late Quatemary. Complex interplay between climate change, sea-level fluctuation and human activities have characterized the Verlorenvlei record and suggests that these environments have been subject to high degrees of disturbance over time. However, due to the discontiniuous nature of the sedimentary record, the Holocene record has remained fragmentary. Investigation of a 6 in vibracore sequence at Klaarfontein (32δ25′26″S; 18δ29′40″E). an artesian spring site some 18 km inland of Eland's Bay. facilitates a more complete Holocene palaeoenivironmental reconstruction. Pollen concentrations in 37 spectra distributed through the 6 m core vary with organic content, but at all depths are stufficient to facilitate interpretation of vegetation history and reveal a high degree of dynamism in the local and regional environments. The lower sediments are characterized by elements strongly indicative of more xeric conditions and the catchment appears to have been dominated by drought-resisting woody shrubs. Local vegetation in the mid-Holocene is consistent with the occurrence of two sealevel transgressions resulting in each case in the development of a salt marsh at the site. The uppermost pollen spectra are all relatively modern (post 200 BP). Grass pollen declines from 1900 BP, possibly as a result of the occupation of the area by pastoralists, although climate change cannot be ruled out. A decline in pollen diversity towards the surface is associated with a further reduction in grass pollen and ani increase in the proportion of succulents, hence offerinig support for the hypothesis of significant colonial and post-colonial disturbance of the vegetation. The evaluation of the pollen sequence at Klaarfontein supports previous palaeoenvironmental reconstructions and at once reveals new insights regarding the complex Holocene limnology, hydrology and geomorphology of the Verlorenvlei system.
Design and Implementation of a Software Platform Based on Artificial Intelligence for Product Recommendation

 Abstract — Nowadays, artificial intelligence is used successfully in the field of e-commerce for its ability to learn from a large amount of data. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them the most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Also, Long Short-Term Memory algorithms have been implemented and trained on historical data in order to predict customer scores of the different items. Items with the highest scores are recommended to customers.
Combining Metrics and Graphs for Object Oriented Reverse Engineering

A software system may become very large during its evolution, getting less maintainable while its complexity rises. Since replacing the system with a new one is often out of question because of economic considerations, reengineering techniques are being developed to change the system into a form which makes it easier to maintain and to further develop. However, before a system can be reengineered, it has to be reverse engineered in order to understand its nature and inner logic. This work deals with a lightweight approach to software reverse engineering combining simple graphs with simple object oriented metrics. Our goal is to obtain a simple and scalable graphical display of a system and its parts through which we succeed to visually extract information, which is useful to the understanding of the system and the detection of its design problems. The primary goal of this work is to put up a repository of combinations of graphs and metrics which are useful to reverse engineer an object oriented system. To validate our approach we implemented a tool called CodeCrawler, which can graphically display source code while providing a layer of interactivity to the user: we use the term navigating the code. We ran CodeCrawler on two Smalltalk case studies and one large industrial case study written in C++. The positive experiences and reactions which we obtained are a proof of the usefulness of our idea.
Cross-grained Contrastive Representation for Unsupervised Lesion Segmentation in Medical Images

Automatic segmentation of lesions in medical images plays a crucial role in the quantitative assessment of disease progression. While supervised deep learning-based methods have been successful in numerous segmentation tasks, they rely on a large number of labeled images for training, which can be expensive and time-consuming to acquire. Although unsupervised learning shows potential in addressing this challenge, the performance of current unsupervised algorithms is mostly unsatisfactory. To overcome this issue, we propose a new unsupervised framework for medical lesion segmentation using a novel cross-granularity contrastive (CGC) module. Our module contains coarse-grained and fine-grained discrimination paths that enable the network to capture the distinctions between lesions and normal tissues at different levels of context. We evaluate our method on two large public datasets of CT/MRI scans and demonstrate that our approach improves a Gaussian mixture model-based segmentation by up to 9%, which surpasses all other unsupervised segmentation methods by a large margin. Additionally, our module can also be integrated with other existing unsupervised segmentation methods to further enhance their performance. Therefore, our framework shows great potential for use in medical image applications with limited labeled data availability. The code of this work will be released via https://github.com/yu02019.
New Bounds on Quantum Sample Complexity of Measurement Classes

—This paper studies the sample complexity of learning in a quantum environment under a quantum counterpart of the well-known probably approximately correct (PAC) model. In this model, available are n randomly generated quantum states with classical labels for the training. The objective is to find a quantum measurement that with high probability predicts the labels of unseen samples correctly. The model is agnostic as the labeling law and the quantum states are unknown to the learner. It subsumes well-studied problems such as state discrimination, quantum property testing, and even classical PAC. Moreover, it abides by quantum mechanical laws such as no-cloning, state collapse, and measurement incompatibility. Such properties prohibit sample reuse and, thus, raise unique challenges for learning in quantum settings. We propose a new learning algorithm called shadow quantum risk minimizer and derive upper bounds on the sample complexity of several quantum concept classes. Particularly, we prove that the sample complexity of any quantum concept class C grows with O (cid:16) 1 ϵ 2 log |C ∗ | δ (cid:17) , where C ∗ is the set of extreme points of the convex closure of C . Hence, in the worst case, the bound grows with the logarithm of the size of the concept class. This is a significant improvements compared to prior works.
Mapping small elevation changes over large areas: Differential radar interferometry

A technique that uses synthetic aperture radar (SAR) images to measure very small (1 cm or less) surface motions with good resolution (10 m) over large swaths (50 km) is presented along with experimental results. The method could be used for accurate measurements of many geophysical phenomena, including swelling and buckling in fault zones, residual displacements from seismic events, and prevolcanic swelling. The method is based on SAR interferometry, where two images are made of a scene by simultaneously flying two physically separated antennas. Then the phases of corresponding pixels are differenced, and altitude formation is deduced from some simple computation and image rectification. It is also possible to use one antenna flown twice over the same scene; then, if the second flight exactly duplicates the track of the first, an interesting possibility occurs. There would be no phase changes between the images at all unless there was a physical change in the scene, such as ground swelling, that would alter the distance from some resolution element to the antenna. Since the phase changes all occur at the short carrier wavelength, the basic limitation on sensitivity is only the phase noise in the system. When the two imaging passes are made from flight tracks that are separated (which is the case with the Seasat images used here), it is no longer possible to distinguish surface changes from the parallax caused by topography. However, with some additional computation, a third image made at some other baseline may be used to remove the topography and leave only the surface changes. This method was applied using Seasat data to an imaging site in Imperial Valley, California, where motion effects were observed that were ascribed to the expansion of water-absorbing clays. Phase change images of this area are shown, along with associated ground truth about the presence of water. Problems with the technique are explored, along with a discussion of future experimental possibilities on upcoming SAR missions like Earth Observing System (EOS), Earth Resources Satellite (ERS 1), SIR-C, and the Venus imaging radar, Magellan.
Computers to help with conversations : affective framework to enhance human nonverbal skills

Nonverbal behavior plays an integral part in a majority of social interaction scenarios. Being able to adjust nonverbal behavior and influence other’s responses are considered valuable social skills. A deficiency in nonverbal behavior can have detrimental consequences in personal as well as in professional life. Many people desire help, but due to limited resources, logistics, and social stigma, they are unable to get the training that they require. Therefore, there is a need for developing automated interventions to enhance human nonverbal behaviors that are standardized, objective, repeatable, low-cost, and can be deployed outside of the clinic. In this thesis, I design and validate a computational framework designed to enhance human nonverbal behavior. As part of the framework, I developed My Automated Conversation coacH (MACH)—a novel system that provides ubiquitous access to social skills training. The system includes a virtual agent that reads facial expressions, speech, and prosody, and responds with verbal and nonverbal behaviors in real-time. As part of explorations on nonverbal behavior sensing, I present results on understanding the underlying meaning behind smiles elicited under frustration, delight or politeness. I demonstrate that it is useful to model the dynamic properties of smiles that evolve through time and that while a smile may occur in positive and in negative situations, its underlying temporal structures may help to disambiguate the underlying state, in some cases, better than humans. I demonstrate how the new insights and developed technology from this thesis became part of a real-time system that is able to provide visual feedback to the participants on their nonverbal behavior. In particular, the system is able to provide summary feedback on smile tracks, pauses, speaking rate, fillers and intonation. It is also able to provide focused feedback on volume modulation and enunciation, head gestures, and smiles for the entire interaction. Users are able to practice as many times as they wish and compare their data across sessions. I validate the MACH framework in the context of job interviews with 90 MIT undergraduate students. The findings indicate that MIT students using MACH are perceived as stronger candidates compared to the students in the control group. The results were reported based on the judgments of the independent MIT career counselors and Mechanical Turkers’, who did not participate in the study, and were blind to the study conditions. Findings from this thesis could motivate further interaction possibilities of helping people with public speaking, social-communicative difficulties, language learning, dating and more. Thesis Supervisor: Rosalind W. Picard Title: Professor of Media Arts and Sciences, Program in Media Arts and Sciences, MIT