Miracle-dz/UniFAR-FTUs huggingface dataset:
Variations to stimulated recall protocols to enhance student reflection: I did, I saw, I remembered

The project, Mental Models and Robotics and Middle Schooling, was an empirical qualitative study centred within information processing theory and linked with the introspection mediating process tracing paradigm. The study involved students and their teacher in a socioeconomically diverse urban primary school and aimed to establish how the identification of participants’ mental models can assist in the authentic assessment of learning through a richer understanding of the cognitive development taking place in a technology based learning experience. The strict protocols of Stimulated Recall methodology were used to externalise participants’ ‘in-action’ mental models, using the opening question ‘What were you thinking? ’ The use of this rigid questioning technique elicited insufficient responses from the students. An additional opening question, ‘What were you doing? ’ was added in the second episode of Stimulated Recall prior to the question, ‘What were you thinking while you were doing that? ’ This change elicited increased quantity and quality of responses because students were able to link their thoughts and feelings with associated actions and reactions. Richer mental models of procedural knowledge but more crucially, conceptual knowledge, were evident in the recall of journaling activities. Social construction mental models were also richer as students more willingly linked thought to action.
Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the “chronnectome”). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a “fingerprint” of the brain. Here, we employed multiband resting‐state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time‐window dynamic network analysis approach to systematically examine individual time‐varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto‐parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.
Word juncture modelling based on the TIMIT database

acoustic signal which simply represents the sequence of the actually pronounced phones. This might in turn reduce recognition accuracy. One solution to this problem is to use "phonological rules" ([2], for both French and English, and [3]) which account for various pronunciation variations in the language. However, it is hard to obtain reliable sets of such rules. Because of the compactness of such rules, many variation phenomena cannot be covered. In this study, we develop data-based word juncture models, which account for the pronunciation variations at word boundaries, as an optional form of phonological rules. We used the American English TIMIT database. Issues in generating the models and using them in a continuous recognition task are discussed. A comparison is given between the coverage of the pronunciation variations by the models and by a set of phonological rules. There is a fairly good agreement between the models and the rules in predicting the pronunciation variations, whereas the models cover a larger set of variation phenomena. Furthermore, use of the models improved recognition performance. In this study, we propose a different approach. We only concentrate on the pronunciation variations at word junctures, although generally within-word deviations may also occur. We directly generate word-juncture models based on the statistics of the pronunciation variations from a training data set. Then we use these models to predict those actual phone sequences deviating from the normative sequences. The actual phone sequences are used in word recognition. The whole process of generating and using the models is illustrated in Figure 1.
Prolonging vase life of carnation flowers using natural essential oils and its impact on microbial profile of vase solutions.

This experiment was conducted during the two summer seasons; 2008 and 2009. Two cultivars of Dianthus caryphyllus L. were used; Farida and Madam Collate. Four essential oil treatments were used versus two controls; Tap water and 8-Hydroxyquinoline (8-HQ). These essential oils were extracted from mandarin, coriander, dill and clove. The maximum vase-life over the two seasons was recorded with dill oil followed by coriander in cv. Farida and with 8-HQ in cv. Madam Collate. The essential oils treatments showed accumulative significant reduction percentages in flower fresh weight, which increased by decreasing the used oil concentrations. The accumulative reduction percentage in flower fresh weight and the average flower dry weight showed negative significant correlation coefficients. The relative increased percentages in net water uptake were 10.93 and 7.95% in 8-HQ for both cultivars. The net water uptake had the greatest values with those flowers kept in solution containing dill followed by clove oils in cv. M adam Collate and by mandarin oil in cv. Farida. The highest positive correlation coefficient was recorded between vase life and net water uptake. The highest pH value was observed in vase containing tap water for both cultivars. In comparison, due to different treatments application, the pH values were significantly changed to a fairly acidic. These treatments were ranked, in this respect, at descending order as follows; dill oil, 8-HQ, mandarin, coriander and clove with both cultivars. The highest mean count of total spore- forming bacteria was recorded in the tap water vase solutions, while the lowest counts were with coriander oil. In vase solution containing dill oil, the log count of molds decreased as the dose concentration increased. The same trend was observed in cv. Madam Collate during the seasons. Counts of cellulose decomposing microorganisms were increased by extending the life of the carnation cut flowers in the vase solutions. All the examined preservatives, in particular 8-HQ, and the oils of dill, clove and coriander greatly suppressed proliferation of cellulose decomposers and resulted in flower densities compared with control solutions. The anatomical study indicated that, cv. Farida flower stalks greatly severe from more exposure to microorganisms attack in vase solution, especially cellulose decomposing microorganisms which penetrate tissues as lethal and blockage parasites. These negative influences on cv. Farida could be an evidence for explaining its rapid senescence and the longevity of the flowers of cv. Madam Collate.
A syllable-based Turkish speech recognition system by using time delay neural networks (TDNNs)

In this paper, we present a model for Turkish speech recognition. The model is syllable-based, where the recognition is performed through syllables as speech recognition units. The main goal of the model is to recognize as much as possible of a given continuous speech by identifying only a small set of syllables in the language. For that purpose, only the syllable types with a higher frequency are selected for the recognition. The use of longer recognition units in speech recognition systems increases the success of the recognition since it is easier to detect the endpoints of syllables when compared to phonemes. On the other side, word-based recognition requires a very large dataset that includes all the words and word forms in the language, which is also another challenge. Hereby, we take the advantage of Turkish being an ortographically transparent and syllabified language. Our model employs time delay neural networks (TDNNs) for learning syllables. We achieve an accuracy of %65.6 on our large vocabulary continuous speech corpus. In addition, we define an algorithm for the automatic detection of syllable boundaries which gives an accuracy of %44. The automatic syllable boundary detection module is used for the recognition of isolated syllables rather than a continuous speech.
The role of uncoupling protein 3 in fatty acid metabolism: protection against lipotoxicity?

The physiological function of the mitochondrial uncoupling protein (UCP), UCP3, is still under debate. There is, however, ample evidence to indicate that, in contrast to UCP1, the primary function of UCP3 is not the dissipation of energy. Rather, several lines of evidence suggest that UCP3 is associated with cellular fatty acid metabolism. The highest levels of expression of UCP3 have been found in type 2 glycolytic muscle fibres, and fasting and high-fat diets up regulate UCP3. This up-regulation is most pronounced in muscle with a low fat oxidative capacity. Acute exercise also up regulates UCP3, and this effect has been shown to be a result of the exercise-induced increase in plasma fatty acid levels. In contrast, regular physical activity, which increases fat oxidative capacity, reduces UCP3 content. Based on these data it has been postulated that UCP3 functions to export those fatty acids that cannot be oxidized from the mitochondrial matrix, in order to prevent fatty acid accumulation inside the matrix. Several experiments have been conducted to test this hypothesis. Blocking carnitine palmitoyltransferase 1, thereby reducing fat oxidative capacity, rapidly induces UCP3. High-fat diets, which increase the mitochondrial supply of fatty acids, also up regulate UCP. However, feeding a similar amount of medium-chain fatty acids, which can be oxidized inside the mitochondrial matrix and therefore does not need to be exported from the matrix, does not affect UCP3 protein levels. In addition, UCP3 is increased in patients with defective β-oxidation and is reduced after restoring oxidative capacity. In conclusion, it is suggested that UCP3 has an important physiological function in facilitating outward transport from the mitochondrial matrix of fatty acid anions that cannot be oxidized, thereby protecting against lipid-induced mitochondrial damage.
New Public Governance: A Regime-Centered Perspective

Preface Part I. Developing a Theory of Governance for NPG: What Should Be at Its Center? 1. The Foundations of New Public Governance, Douglas F. Morgan and Craig W. Shinn 2. Why a Constitutional Approach Matters for Advancing New Public Governance, Stephanie P. Newbold 3. Institutional History and New Public Governance, Richard T. Green 4. A Value-Based Global Framework for New Public Governance, Douglas F. Morgan, Gary L. Larsen, Guoxian Bao, and Xuejun Wang 5. History as a Source of Values for New Public Governance, Donald P. Moynihan 6. Two Kinds of Rationality and Their Implications for New Public Governance, Michael W. Spicer Part II. NPG and Local Governing Institutions: Transforming Deliberative Democracy into Action 7. The American Constitutional Legacy and the Deliberative Democracy Environment of New Public Governance, Vera Vogelsang-Coombs 8. Civic Capacity Assessment Framework, Margaret Banyan 9. Building Capacity in Culturally Diverse Communities Through Community Engagement in Hard Times, Claudia Maria Vargas 10. Forging Vertical and Horizontal Integration in Public Administration Leadership and Management, Gary L. Larsen 11. Civic Infrastructure and Capacity Building: Lessons from the Field, Don Bohn 12. Local Government as Polity Leadership: Implications for New Public Governance, Kent S. Robinson and Douglas F. Morgan 13. Interest-Based Deliberative Democracy in Natural Resource Management, Debra Whitall, Craig Thomas, Steve Brink, and Gina Bartlett 14. Multilevel Environmental Governance of Conservation Programs, Shpresa Halimi and Craig W. Shinn Part III. Public Service Education and Leadership in an Age of NPG 15. Regime Leadership for Public Servants, Brian J. Cook 16. Competing Needs, Expectations, and Realities in Local Government Education, Phillip J. Cooper 17. Agency in Networks: Implications for Theory and Practice in the New Public Governance, Eric T. (Rick) Mogren 18. EMERGE: Public Leadership for Sustainable Development, Kristen Magis, Marcus Ingle, and Ngo Huy Duc 19. Preparing the Next Generation for Local Government Leadership, Scott Lazenby 20. Educating Leaders for New Public Governance: Public Administration as a Liberal Art, Douglas F. Morgan 21. Rethinking U.S. Public Administration Doctoral Education in the Age of New Public Governance, Stephanie P. Newbold and David H. Rosenbloom 22. Educating for New Public Governance: Civic Engagement and the Liberal Arts, Kevin Kecskes, Masami Nishishiba, and Douglas F. Morgan 23. Epilogue: Research Questions to Improve the Theory and Practice of NPG, Douglas F. Morgan, Craig W. Shinn, and Brian J. Cook References About the Authors Index
Finding Fair and Efficient Allocations

We study the problem of allocating a set of indivisible goods among a set of agents in a fair and efficient manner. An allocation is said to be fair if it is envy-free up to one good (EF1), which means that each agent prefers its own bundle over the bundle of any other agent up to the removal of one good. In addition, an allocation is deemed efficient if it satisfies Pareto efficiency. While each of these well-studied properties is easy to achieve separately, achieving them together is far from obvious. Recently, Caragiannis et al. (2016) established the surprising result that when agents have additive valuations for the goods, there always exists an allocation that simultaneously satisfies these two seemingly incompatible properties. Specifically, they showed that an allocation that maximizes the Nash social welfare objective is both EF1 and Pareto efficient. However, the problem of maximizing Nash social welfare is NP-hard. As a result, this approach does not provide an efficient algorithm for finding a fair and efficient allocation. In this paper, we bypass this barrier, and develop a pseudopolynomial time algorithm for finding allocations that are EF1 and Pareto efficient; in particular, when the valuations are bounded, our algorithm finds such an allocation in polynomial time. Furthermore, we establish a stronger existence result compared to Caragiannis et al. (2016): For additive valuations, there always exists an allocation that is EF1 and fractionally Pareto efficient. Another key contribution of our work is to show that our algorithm provides a polynomial-time 1.45-approximation to the Nash social welfare objective. This improves upon the best known approximation ratio for this problem (namely, the 2-approximation algorithm of Cole et al., 2017), and also matches the lower bound on the integrality gap of the convex program of Cole et al. (2017). Unlike many of the existing approaches, our algorithm is completely combinatorial, and relies on constructing integral Fisher markets wherein specific equilibria are not only efficient, but also fair.
ASSESSMENT OF THE RADIAL ARTERY IN CORONARY BYPASS SURGERY

in Finnish Värttinävaltimoa käytetään yleisesti ohitussiirteenä sepelvaltimo-ohituskirurgiassa. Se on helppo preparoida siirteeksi ja sen aukipysyvyys siirteenä on erinomainen. Värttinävaltimon preparointiin ja käyttöön liittyy kuitenkin tiettyjä haasteita. Luonnollisessa asemassaan värttinävaltimo huolehtii käden verenkierrosta yhdessä kyynärvaltimon kanssa. Ennen värttinävaltimon preparointia on tärkeää varmistua ettei sen poisto aiheuta verenkierron puutetta kädessä. Toiseksi, värttinävaltimo on yksi lihasvaltimoista, jotka ovat alttiita valttimokovettumataudille, josta kaikki sepelvaltimopotilaat kärsivät. Valtimokovettumatauti voi estää värttinävaltimon käytön sepelvaltimosiirteenä. Perinteisesti käden verenkiertoa on arvioitu ennen leikkausta Allenin testillä. Allenin testi on yksinkertainen kliininen koe, joka on helppo suorittaa missä tahansa eikä se vaadi laitteita tai muita ylimääräisiä panostuksia. Allenin testiä on arvosteltu siitä, että se on subjektiivinen eikä tarpeeksi luotettava. Väitöskirjatyö pyrkii selvittämään, voidaanko Allenin testiä käyttää arvioimaan käden verenkiertoa luotettavasti. Lisäksi pyritään selvittämään, onko värttinävaltimon käytön sepelvaltimosiirteenä estävät tekijät toispuoleisia, vai ovatko yleensä molemmat värttinävaltimot käyttökelvottomia, ja onko kyynärvarren valtimoiden ja kaulavaltimoiden valtimokovettumataudilla yhteyttä. Lopuksi pyritään vielä selvittämään leikkauksen aikaisen värttinävaltimon paineenmittauksen käyttökelpoisuutta värttinävaltimon siirrekelpoisuuden arvioinnissa. Tutkimukseen osallistui kaksi potilasryhmää. Molempien ryhmien potilaat olivat tulossa sepelvaltimo-ohitusleikkaukseen. Ensimmäiseen ryhmään kuului 145 potilasta, joille tehtiin Allenin testi, kyynärvarren valtimoiden ultraäänitutkimus sekä sormien verenpainemittaus. Yli 60-vuotiaita ja päivystyspotilaita
Contiguitas: The Pursuit of Physical Memory Contiguity in Datacenters

The unabating growth of the memory needs of emerging datacenter applications has exacerbated the scalability bottleneck of virtual memory. However, reducing the excessive overhead of address translation will remain onerous until the physical memory contiguity predicament gets resolved. To address this problem, this paper presents Contiguitas, a novel redesign of memory management in the operating system and hardware that provides ample physical memory contiguity. We identify that the primary cause of memory fragmentation in Meta's datacenters is unmovable allocations scattered across the address space that impede large contiguity from being formed. To provide ample physical memory contiguity by design, Contiguitas first separates regular movable allocations from unmovable ones by placing them into two different continuous regions in physical memory and dynamically adjusts the boundary of the two regions based on memory demand. Drastically reducing unmovable allocations is challenging because the majority of unmovable pages cannot be moved with software alone given that access to the page cannot be blocked for a migration to take place. Furthermore, page migration is expensive as it requires a long downtime to (a) perform TLB shootdowns that scale poorly with the number of victim TLBs, and (b) copy the page. To this end, Contiguitas eliminates the primary source of unmovable allocations by introducing hardware extensions in the last-level cache to enable the transparent and efficient migration of unmovable pages even while the pages remain in use. We build the operating system component of Contiguitas into the Linux kernel and run our experiments in a production environment at Meta's datacenters. Our results show that Contiguitas's OS component successfully confines unmovable allocations, drastically reducing unmovable 2MB blocks from an average of 31% scattered across the address space down to 7% confined in the unmovable region, leading to significant performance gains. Specifically, we show that for three major production services, Contiguitas achieves end-to-end performance improvements of 2--9% for partially fragmented servers, and 7--18% for highly fragmented servers, which account for nearly a quarter of Meta's fleet. We further use full-system simulations to demonstrate the effectiveness of the hardware extensions of Contiguitas. Our evaluation shows that Contiguitas-HW enables the efficient migration of unmovable allocations, scales well with the number of victim TLBs, and does not affect application performance. We are currently in the process of upstreaming Contiguitas into Linux.
A Novel Peptide Reagent for Investigating Disulfide-Coupled Folding Intermediates of Mid-Size Proteins

Investigations of protein folding have largely involved the use of disulfide-containing proteins, since the disulfide-coupled folding of proteins allows folding intermediates to be trapped and their conformations determined. However, studies of the folding mechanisms of mid-size proteins face several problems, one of which is that detecting folding intermediates is difficult. Therefore, to solve this issue, a novel peptide reagent, maleimidohexanoyl-Arg5-Tyr-NH2, was designed and applied to the detection of folding intermediates of model proteins. BPTI was chosen as a model small protein to estimate the ability of the novel reagent to detect folding intermediates. In addition, a precursor protein (prococoonase) of Bombyx mori cocoonase was used as a model mid-size protein. Cocoonase is classified as a serine protease and has a high homology with trypsin. We recently found that the propeptide sequence of prococoonase (proCCN) is important for the folding of cocoonase. However, it was difficult to study the folding pathway of proCCN since the folding intermediates could not be separated on a reversed-phase HPLC (RP-HPLC). Therefore, to separate the folding intermediates by RP-HPLC, the novel labeling reagent was used to accomplish this for proCCN. The results indicated that the peptide reagent allowed the intermediates to be captured, separated on SDS-PAGE, and analyzed by RP-HPLC without the occurrence of undesirable disulfide-exchange reactions during the labeling reactions. The peptide reagent reported herein is a practical tool for investigating the mechanisms of disulfide-coupled folding of mid-size proteins.
DOMINO: Domain-Invariant Hyperdimensional Classification for Multi-Sensor Time Series Data

With the rapid evolution of the Internet of Things, many real-world applications utilize heterogeneously connected sensors to capture time-series information. Edge-based machine learning (ML) methodologies are often employed to analyze locally collected data. However, a fundamental issue across data-driven ML approaches is distribution shift. It occurs when a model is deployed on a data distribution different from what it was trained on, and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) have been proposed to capture spatial and temporal dependencies in multi-sensor time series data, requiring intensive computational resources beyond the capacity of today's edge devices. While brain-inspired hyperdimensional computing (HDC) has been introduced as a lightweight solution for edge-based learning, existing HDCs are also vulnerable to the distribution shift challenge. In this paper, we propose DOMINO, a novel HDC learning framework addressing the distribution shift problem in noisy multi-sensor time-series data. DOMINO leverages efficient and parallel matrix operations on high-dimensional space to dynamically identify and filter out domain-variant dimensions. Our evaluation on a wide range of multi-sensor time series classification tasks shows that DOMINO achieves on average 2.04% higher accuracy than state-of-the-art (SOTA) DNN-based domain generalization techniques, and delivers $16.34\times$ faster training and $2.89\times$ faster inference. More importantly, DOMINO exhibits notably better performance when learning from partially labeled data and highly imbalanced data, and provides $10.93\times$ higher robustness against hardware noises than SOTA DNNs.