Miracle-dz/UniFAR-FTUs huggingface dataset:
Au th or M an us cr ipt Detection of Very Early Dental Caries in Independent Living Older Adults

BACKGROUND AND OBJECTIVES: Dental caries is an important healthcare challenge in adults over 65 years of age. Integration of oral health screening into non-dental primary care practice may improve access to preventive dental care for vulnerable populations such as the elderly. Such integration would require easy, fast, and accurate early caries detection tools. Primary goal of this study was to evaluate the diagnostic performance of Optical Coherence Tomography (OCT) imaging for detecting very early caries in the elderly living in communitybased settings. The International Caries Detection and Assessment System (ICDAS) served as Au th or M an us cr ipt gold standard. Secondary goal of this study was to provide baseline prevalence data of very early caries lesions in independent living adults aged 65+ years. STUDY DESIGN: MATERIALS AND METHODS: Seventy-two subjects were recruited from three sites in Southern California: a retirement community, a senior health fair, and a convalescent hospital. Clinical examination was performed using the ICDAS visual criteria and this was followed by OCT imaging. The 2-dimensional OCT images (B-scan) were analyzed with simple software. Locations with a log of back-scattered light intensity (BSLI) below 2.9 were scored as sound, and areas equaling or exceeding 2.9 BSLI were considered carious. Diagnostic performance of OCT imaging was compared with ICDAS score. RESULTS: OCT–based diagnosis demonstrated very good sensitivity (95.1%) and good specificity (85.8%). 54.7% of dentate subjects had at least one tooth with very early coronal caries. CONCLUSIONS: Early coronal decay is prevalent in the unrestored pits and fissures of coronal surfaces of teeth in independent living adults aged 65+ years. Though OCT imaging coupled with a simple diagnostic algorithm can accurately detect areas of very early caries in community-based settings, existing devices are expensive and not well-suited for use by nondental health care providers. Simple, inexpensive, fast and accurate tools for early caries detection by field health care providers working in non-traditional settings are urgently needed to support inter-professional dental health management.
DigiVoice: Voice Biomarker Featurization and Analysis Pipeline

In recent years, data-driven models have enabled significant advances in medicine. Simultaneously, voice has shown potential for analysis in precision medicine as a biomarker for screening illnesses. There has been a growing trend to pursue voice data to understand neuropsychiatric diseases. In this paper, we present DigiVoice, a comprehensive feature extraction and analysis pipeline for voice data. DigiVoice supports raw . WAV files and text transcriptions in order to analyze the entire content of voice. DigiVoice supports feature extraction including acoustic, natural language, linguistic complexity, and semantic coherence features. DigiVoice also supports machine learning capabilities including data visualization, feature selection, feature transformation, and modeling. To our knowledge, DigiVoice provides the most comprehensive voice feature set for data analysis to date. With DigiVoice, we plan to accelerate research to correlate voice biomarkers with illness to enable data-driven treatment. We have worked closely with our industry partner, NeuroLex Laboratories, to make voice computing open source and accessible. DigiVoice enables researchers to leverage our technology across the domains of voice computing and precision medicine without domain-specific expertise. Our work allows any researchers to use voice as a biomarker in their past, current, or future studies.
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)

Distributed learning across coalitions is becoming popular for multi-centric implementation of deep learning models. However, the level of trust between the members of a coalition can vary and requires different security architectures. Privacy of the training data has been largely described in distributed learning. In this paper, we present a scalable security architecture providing additional features such as validation on the sources quality, confidentiality on the model within a trusted coalition or confidentiality among untrusted partners inside the coalition. More specifically, we propose solutions that guarantee preservation not only of data privacy but also of data quality, enforce a trustworthy sequence of iterative learning, and that lead to equitable sharing of the learned model among the coalition’s members. We give an example of its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.
Non-uniform phase in superconducting wires with spin-orbit coupling

The interplay of superconductivity, Zeeman coupling and Rashba spin-orbit effect is studied theoretically for a one-dimensional system, within the framework of Green's functions formalism. Using the self-consistency equation, we show that the combination of both spin-orbit and Zeeman interactions results in the emergence of a modulated phase at low exchange field and any temperature below the critical one $T_{c0}$. We point out that at least two components of the Zeeman field (one longitudinal and one transverse to the spin-orbit field) are needed to generate this 'high-temperature' inhomogeneous phase, and provide its wavevector for both the weak and strong spin-orbit coupling regimes. Finally, we compute numerically the field-temperature phase diagram of the system.
Learning to Discover, Ground and Use Words with Segmental Neural Language Models

We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented character sequences. In contrast to previous segmentation models that treat word segmentation as an isolated task, our model unifies word discovery, learning how words fit together to form sentences, and, by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. Experiments show that the unconditional model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and that modeling language conditional on visual context improves performance on both.
AGN-driven quenching of satellite galaxies

We explore the effect of active galactic nucleus (AGN) feedback from central galaxies on their satellites by comparing two sets of cosmological zoom-in runs of 27 haloes with masses ranging from 1012 to 1013.4$\rm {M}_{\odot}$ at z = 0, with (wAGN) and without (noAGN) AGN feedback. Both simulations include stellar feedback from multiple processes, including powerful winds from supernovae, stellar winds from young massive stars, AGB stars, radiative heating within Strömgren spheres and photoelectric heating. Our wAGN model is identical to the noAGN model except that it also includes a model for black hole seeding and accretion, as well as AGN feedback via high-velocity broad absorption line winds and Compton/photoionization heating. We show that the inclusion of AGN feedback from the central galaxy significantly affects the star formation history and the gas content of the satellite galaxies. AGN feedback starts to affect the gas content and the star formation of the satellites as early as z = 2. The mean gas-rich fraction of satellites at z = 0 decreases from 15 per cent in the noAGN simulation to 5 per cent in the wAGN simulation. The difference between the two sets extends as far out as five times the virial radius of the central galaxy at z = 1. We investigate the quenching mechanism by studying the physical conditions in the surroundings of pairs of satellites matched across the wAGN and noAGN simulations and find an increase in the temperature and relative velocity of the intergalactic gas.
Distilling Translations with Visual Awareness

Previous work on multimodal machine translation has shown that visual information is only needed in very specific cases, for example in the presence of ambiguous words where the textual context is not sufficient. As a consequence, models tend to learn to ignore this information. We propose a translate-and-refine approach to this problem where images are only used by a second stage decoder. This approach is trained jointly to generate a good first draft translation and to improve over this draft by (i) making better use of the target language textual context (both left and right-side contexts) and (ii) making use of visual context. This approach leads to the state of the art results. Additionally, we show that it has the ability to recover from erroneous or missing words in the source language.
Improving the performance of solar flare prediction using active longitudes information

Context. Solar flare prediction models normally depend on properties of active regions, such as sunspot area, McIntosh classifications, Mount Wilson classifications, and various measures of the magnetic field. Nevertheless, the positional information of active regions has not been used. Aims. We define a metric, DARAL (distance between active regions and predicted active longitudes), to depict the positional relationship between active regions and predicted active longitudes and add DARAL to our solar flare prediction model to improve its performance. Methods. Combining DARAL with other solar magnetic field parameters, we build a solar flare prediction model with the instancebased learning method, which is a simple and effective algorithm in machine learning. We extracted 70 078 active region instances from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) magnetograms containing 1055 National Oceanic and Atmospheric Administration (NOAA) active regions within 30◦ of the solar disk center from 1996 to 2007 and used them to train and test the solar flare prediction model. Results. Using four performance measures (true positive rate, true negative rate, true skill statistic, and Heidke skill score), we compare performances of the solar flare prediction model with and without DARAL. True positive rate, true negative rate, true skill statistic, and Heidke skill score increase by 6.7% ± 1.3%, 4.2% ± 0.5%, 10.8% ± 1.4% and 8.7% ± 1.0%, respectively. Conclusions. The comparison indicates that the metric DARAL is beneficial to performances of the solar flare prediction model.
Multiwavelength Raman spectroscopy of ultranarrow nanoribbons made by solution-mediated bottom-up approach

Here we present a combined experimental and theoretical study of graphene nanoribbons (GNRs), where detailed multi-wavelength Raman measurements are integrated by accurate ab initio simulations. Our study covers several ultra-narrow GNRs, obtained by means of solution-based bottom-up synthetic approach, allowing to rationalize the effect of edge morphology, position and type of functional groups as well as the length on the GNR Raman spectrum. We show that the low-energy region, especially in presence of bulky functional groups is populated by several modes, and a single radial breathing-like mode cannot be identified. In the Raman optical region, we find that, except for the fully-brominated case, all GNRs functionalized at the edges with different side groups show a characteristic dispersion of the D peak (8-22 cm-1/eV). This has been attributed to the internal degrees of freedom of these functional groups, which act as dispersion-activating defects. The G peak shows small to negligible dispersion in most of the cases, with larger values only in presence of poor control of the edges functionalization, exceeding the values reported for highly defected graphene. In conclusion, we have shown that the characteristic dispersion of the G and D peaks offer further insight on the GNR structure and functionalization, by making Raman spectroscopy an important tool for the characterization of GNRs.
Marketing to the Poor: An Integrative Justice Model for Engaging Impoverished Market Segments

The relatively recent entry of multinational corporations (MNCs) into low-income markets, particularly in developing countries, affords the opportunity for the more inclusive capitalism envisioned by globalists. Alternatively, an expansion of MNC marketing in less developed economies might foreshadow the greater exploitation of disadvantaged consumers predicted by many critics of expanded free trade. To diffuse the charge of “exploitative” marketing, it is imperative that corporate marketing efforts seeking to engage impoverished segments be grounded in a strong ethical framework. This article unveils one such framework—the “integrative justice model” (IJM). The IJM is an aspirational model that outlines how to market ethically to disadvantaged consumers in both developed and developing countries. The authors derive the elements of this model from frameworks of moral philosophy and management theory. Although the IJM is normative in nature, the authors connect it to real-world examples, which provides MNCs that market to the poor practical benchmarks for conducting their business operations with fairness and equity. The article concludes with a discussion of the implications of the IJM for public policy.
Broadening the Definition of College and Career Readiness: A Holistic Approach

, but the United States will produce far too few college graduates to fill these openings. Even if the United States did produce a sufficient number of college graduates, other research strongly suggests that many would be unprepared for the world of work. Large international studies (e. g. , OECD, 2013b) and survey research by Casner-Lotto and Barrington (2006) involving US employers demonstrates that a substantial percentage of US college graduates are not prepared to adequately perform in entry-level positions. Importantly, this general lack of readiness is evident even before individuals enter the workforce. The high remediation rates and relatively low graduation rates for college students suggest that many students do not graduate from high school ready for college (US Department of Education, 2013a, 2013b). Compounding these problems is the fact that about 20% of US students fail to even earn a high school diploma (Education Week, 2013). In summary, far too many US students lack college and career readiness (CCR). Typically, discussion and standards surrounding CCR center on a student's standing on core academic skills such as mathematics and English language arts (ELA). The thesis of this report is that the readiness issue can be remedied in part by broadening this discussion in two ways. First, discussion around CCR can be broadened to include students' standing on cross-cutting capabilities and noncognitive skills, as evidence suggests that these skills are also crucially important for education and work success (see Section 3 of this report). Second, discussions of students' CCR should happen at much younger ages. Although a student's CCR level typically is not assessed until late in high school, research shows that college and career success can be predicted, and thus Traditional indicators of core academic skills, such as high school grade point average (HSGPA), class rank, scores on the ACT college readiness assessment and the SAT, and the rigor of coursework, have typically been used to define CCR, even though the various definitions of CCR employ unique sets of indicators Differences among the various definitions of CCR often lead to different conclusions about whether a student is ready for college and a career as well as vastly different implications about the readiness of US 4 ACT Research Report Broadening the Definition of College and Career Readiness: A Holistic Approach students more generally. These definitions vary on many factors, such as the indicators included, the operational definition of college success …
Une évaluation de l ’ utilisation du RSAIS par le personnel infirmier en soins à domicile

Résultats dans le domaine de la santé pour l’amélioration de l’information et des soins (RSAIS) [Health Outcomes for Better Information and Care (HOBIC)], un programme financé par le ministère de la Santé et des Soins de longue durée de l’Ontario, introduit un ensemble de mesures de résultats cliniques fondés sur des données probantes et reflétant les réalités vécues dans le domaine des soins infirmiers. Les auteures font état d’une évaluation des expériences vécues par le personnel infirmier ayant participé au programme RSAIS dès ses débuts, en contexte de soins à domicile. Les résultats ont révélé la présence de nuances et de défis associés à l’introduction du RSAIS et l’utilisation de technologies d’appui dans le cadre de la prestation de soins infirmiers à domicile. Les futurs efforts de mise en œuvre doivent chercher à optimiser la convivialité de la technologie et l’utilité du RSAIS dans la pratique infirmière. D’autres efforts doivent être déployés pour soutenir la pleine intégration et l’utilisation maximale des données de résultats par le personnel infirmier et les gestionnaires en vue d’étayer les orientations en matière de pratique.
Oxygen mass transfer for an immobilised biofilm of Phanerochaete chrysosporium in a membrane gradostat reactor

Abstract - A novel system, the membrane gradostat reactor (MGR), designed for the continuous production of secondary metabolites, has been shown to have higher production per reactor volume than batch culture systems. The MGR system mimics the natural environment in which wild occurring microorganism biofilms flourish. The biofilms are immobilised on the external surface of an ultrafiltration membrane where substrate distribution gradients are established across the biofilm. The hypothesis that, dissolved oxygen (DO) mass transfer parameters obtained in submerged pellets can be used to describe and model DO mass transfer parameters in the MGR, was refuted. Phanerochaete chrysosporium biofilms, immobilised on ultrafiltration capillary membranes in the MGR systems were used to quantify DO distribution using a Clark-type microsensor. The DO penetration depth decreased with increasing biofilm thickness, which resulted in the formation of anaerobic zones in the biofilms. Oxygen flux values of 0.27 to 0.7 g/(m
Efficient storage of versioned matrices

Versioned-matrix storage is increasingly important in scientific applications. Various computer-based scientific research, from astronomy observations to weather predictions to mechanical finite-element analyses, results in the generation of large matrices that must be stored and retrieved. Such matrices are often versioned; an initial matrix is stored, then a subsequent matrix based on the first is produced, then another subsequent matrix after that. For large databases of matrices, available disk storage can be a substantial constraint. I propose a framework and programming interface for storing such versioned matrices, and consider a variety of intra-matrix and inter-matrix approaches to data storage and compression, taking into account disk-space usage, performance for inserting data, and performance for retrieving data from the database. For inter-matrix “delta” compression, I explore and compare several differencing algorithms, and several means of selecting which arrays are differenced against each other, with the aim of optimizing both disk-space usage and insert and retrieve performance. This work shows that substantial disk-space savings and performance improvements can be achieved by judicious use of these techniques. In particular, a combination of Lempel-Ziv compression and a proposed form of delta compression, it is possible to both decrease disk usage by a factor of 10 and increase query performance for a factor of two or more, for particular data sets and query workloads. Various other strategies can dramatically improve query performance in particular edge cases; for example, a technique called “chunking”, where a matrix is broken up and saved as several files on disk, can cause query runtime to be approximately linear in the amount of data requested rather than the size of the raw matrix on disk. Thesis Supervisor: Samuel Madden Title: Associate Professor
Millimeter Wave Channel Recommendation System Aided by Tensor Completion

Accurate and fast beam-alignment is important to cope with the fast-varying environment in millimeter-wave communications. A data-driven approach is a promising solution to reduce the training overhead by leveraging side information and on the field measurements. In this work, a two-stage tensor completion algorithm is proposed to predict the received power on a set of possible users' positions, given received power measurements on a small subset of positions; based on these predictions, a small subset of beams is recommended to reduce the training overhead of beam-alignment, based on positional side information. The proposed method is evaluated with the DeepMIMO dataset, generated from Wireless Insite, which provides parameterized channels for the experiment. The numerical results demonstrate that, with high probability, the proposed algorithm recommends a small set of beams which contain the best beam, thus achieving correct alignment with small training overhead. Given power measurements on only 8 random positions out of 25, our algorithm attains a probability of incorrect alignment of only 2%, with only 2.7% of trained beams. To the best of our knowledge, this is the first work to consider the beam recommendation problem with only the knowledge at neighboring positions but none at the user's position.
Gas accretion damped by dust back-reaction at the snow line

Context. The water snow line divides dry and icy solid material in protoplanetary disks. It has been thought to significantly affect planet formation at all stages. If dry particles break up more easily than icy ones, then the snow line causes a traffic jam because small grains drift inward at lower speeds than larger pebbles. Aims. We aim to evaluate the effect of high dust concentrations around the snow line onto the gas dynamics. Methods. Using numerical simulations, we modeled the global radial evolution of an axisymmetric protoplanetary disk. Our model includes particle growth, the evaporation and recondensation of water, and the back-reaction of dust onto the gas. The model takes into account the vertical distribution of dust particles. Results. We find that the dust back-reaction can stop and even reverse the net flux of gas outside the snow line, decreasing the gas accretion rate onto the star to under 50% of its initial value. At the same time, the dust accumulates at the snow line, reaching dust-to-gas ratios of ɛ ≳ 0.8, and it delivers large amounts of water vapor towards the inner disk as the icy particles cross the snowline. However, the accumulation of dust at the snow line and the decrease in the gas accretion rate only take place if the global dust-to-gas ratio is high (ε0 ≳ 0.03), the viscous turbulence is low (αν ≲ 10−3), the disk is large enough (rc ≳ 100 au), and only during the early phases of the disk evolution (t ≲ 1 Myr). Otherwise the dust back-reaction fails to perturb the gas motion.