Miracle-dz/UniFAR-FTUs huggingface dataset:
Space disaggregation in models of route and mode choice : method and application to the Paris area

La repr? sentation spatiale de l? aire de mod? lisation dans les mod? les de la demande de transports a peu chang? au cours des derni? res d? cennies. A cet ? gard, l? tat-de l? art repose encore largement sur le syst? me de centro? de-connecteur qui est utilis? e dans les mod? les classiques. Elle est une approche agr? g? e qui ignore la variabilit? physique li? e ? la dispersion des lieux d? sagr? g? s de r? sidence et d? activit? dans l? espace local. En cons? quence, le pouvoir explicatif des mod? les quant aux comportements de choix d? itin? raire et de mode demeure limit? l? chelle locale : Par exemple, la localisation d? sagr? g? e influence sur le choix entre une autoroute dont l? changeur est ? loign? , et un autre itin? raire non-autoroutier. Egalement, le rabattement terminal influence sur le partage modal auto vs. transports en commun. Nous pr? sentons une approche d? sagr? g? e pour la repr? sentation spatiale. Dans un d? coupage zonal, l? espace ? l? int? rieur d? une zone est repr? sent? de mani? re d? sagr? g? e stochastique. Pour chaque zone, les points d? ancrage sont d? finis relative aux n? uds du r? seau qui peuvent ? tre utilis? s pour acc? der au r? seau. Un itin? raire entre une paire de zones est ensuite consid? r? comme une chaine, compos? e de deux trajets terminaux, correspondants aux sections intrazonales de l? itin? raire, et d? un trajet principal correspondant ? la section entre deux points d? ancrage. En cons? quence, le mod? le de choix d? itin? raire est transform? un mod? le de choix conjoint d? une paire de point d? ancrage. Le vecteur des temps al? atoires terminaux est Normal Multidimensionnel donnant lieu ? un mod? le Probit de choix conjoint de points d? ancrage.Pour ? tendre au cadre multimodal, un mode collectif composite est d? fini comme une chaine compos? e des trois trajets modaux d? acc? s, principal, et de sortie, et les stations sont consid? r? es comme les points d? ancrage, connectant les trajets de rabattement au trajet principal. Un mod? le Logit Multinomial de choix de mode est estim? partir de l? Enqu? te Globale de Transport de 2001 pour le mode auto et le faisceau des modes collectifs composites, et est combin? avec les deux mod? les Probit correspondants au choix des stations
Data-Enabled Distribution Grid Management

In 2020, U.S. electric utilities installed more than 94 million advanced meters, which brought the percentage of residential customers equipped with smart meters to 75%. This significant investment allows collecting extensive customer data at the distribution level, however, the data are not currently leveraged effectively to help with system operations. This dissertation aims to use the smart meters’ data to improve the grid’s reliability, stability, and controllability by solving two of the most challenging problems at the distribution level, namely distribution network phase identification and outage identification. Distribution networks have typically been the least observable and most dynamic and locally controlled elements in the power grid. Complete information about the network topology is continuously changing and is not always readily available when needed. Lack of phase connectivity information is a challenge, especially when rebalancing the grid and also in the aftermath of outages caused by extreme events. Traditionally, phase identification is executed manually. In this dissertation, a machine learning-based data mining method for accurate and efficient phase identification of residential customers is proposed by leveraging power consumption data collected through smart meters. The proposed method uses a high-pass filter to remove the redundant and irrelevant segments
Toward generalized Milankovitch theory (GMT)

Abstract. In recent decades, numerous paleoclimate records and results of model simulations have provided strong support for the astronomical theory of Quaternary glacial cycles formulated in its modern form by Milutin Milankovitch. At the same time, new findings have revealed that the classical Milankovitch theory is unable to explain a number of important facts, such as the change in the dominant periodicity of glacial cycles from 41 to 100 kyr about 1 million years ago. This transition was also accompanied by an increase in the amplitude and asymmetry of the glacial cycles. Here, based on the results of a hierarchy of models and data analysis, a framework of the extended (generalized) version of the Milankovitch theory is presented. To illustrate the main elements of this theory, a simple conceptual model of glacial cycles was developed using the results of an Earth system model, CLIMBER-2. This conceptual model explicitly assumes the multistability of the climate–cryosphere system and the instability of the “supercritical” ice sheets. Using this model, it is shown that Quaternary glacial cycles can be successfully reproduced as the strongly nonlinear response of the Earth system to the orbital forcing, where 100 kyr cyclicity originates from the phase locking of the precession and obliquity-forced glacial cycles to the corresponding eccentricity cycle. The eccentricity influences glacial cycles solely through its amplitude modulation of the precession component of orbital forcing, while the long timescale of the late Quaternary glacial cycles is determined by the time required for ice sheets to reach their critical size. The postulates used to construct this conceptual model were justified using analysis of relevant physical and biogeochemical processes and feedbacks. In particular, the role of climate–ice sheet–carbon cycle feedback in shaping and globalization of glacial cycles is discussed. The reasons for the instability of the large northern ice sheets and the mechanisms of the Earth system escape from the “glacial trap” via a set of strongly nonlinear processes are presented. It is also shown that the transition from the 41 to the 100 kyr world about 1 million years ago can be explained by a gradual increase in the critical size of ice sheets, which in turn is related to the gradual removal of terrestrial sediments from the northern continents. The implications of this nonlinear paradigm for understanding Quaternary climate dynamics and the remaining knowledge gaps are finally discussed.
Effects of Rumination and Co-Rumination on the Relationship Between Self-Efficacy and Depressive Symptoms

Depressive symptoms have far-reaching and negative implications on both an individual and societal level, with college students generally considered to be a particularly vulnerable population in terms of risk for depressive symptoms. Two internal cognitive processes, self-efficacy, and rumination, as well as the interpersonal form of rumination, co-rumination, have all been uniquely linked to depressive symptoms. The literature linking these four constructs is not nearly as extensive as it is with any of the constructs uniquely relating to depressive symptoms. Rumination is related to lower levels of self-efficacy, but the interaction of self-efficacy and co-rumination as well as the effects of rumination and co-rumination on the relationship between self-efficacy and depressive symptoms have yet to be explored to the same extent. The current study assessed students enrolled in undergraduate psychology classes at a medium-sized public university in New England (N=222) involving five self-report survey assessments. Results indicated that rumination predicted self-efficacy over and above co-rumination. Interestingly, co-rumination but not rumination moderated the association between selfefficacy and depressive symptoms. Clinical and educational implications of these findings and directions for future research are discussed.
Experimental and Numerical Analysis of Fracture in 41Cr4 Steel – Issues of the Stationary Cracks

Abstract This paper analyzes the process of fracture in 41Cr4 steel on the basis of experimental and numerical data obtained for non-propagating cracks. The author’s previous and latest experimental results were used to determine the apparent crack initiation moment and fracture toughness for the material under plane strain conditions. Numerical simulations were carried out to assess changes in the J-integral, the crack tip opening displacement, the size of the plastic region and the distribution of stresses around the crack tip. A complex numerical analysis based on the true stress-strain curve was performed to determine the behavior of 41Cr4 steel under increasing external loads.
Exploring key-stakeholder perceptions on non-communicable disease care during the COVID-19 pandemic in Kenya

Introduction over one third of total Disability-Adjusted-Life-Years lost in Kenya are due to non-communicable diseases (NCD). In response, the Government declared significant commitment towards improving NCD care. The COVID-19 pandemic increased the burden on the already overstretched health systems in Kenya. The aims of this study are to assess whether health care providers perceived NCD care to be optimal during the pandemic and explore how to improve responses to future emergencies. Methods this cross-sectional online survey included healthcare personnel with non-clinical roles (public health workers and policy-makers) and those delivering health care (doctors and nurses). Respondents were recruited between May and September 2021 by random sampling, completed by snowball sampling. Results among 236 participants (42% in clinical, 58% in non-clinical roles) there was an overall consensus between respondents on NCD care being disrupted and compromised during the pandemic in Kenya. Detracted supplies, funding, and technical resources affected the continuity of NCDs’ response, despite government efforts. Respondents agreed that the enhanced personnel capacity and competencies to manage COVID-19 patients were positive, but noted a lack of guidance for redirecting care for chronic diseases, and advocated for digital innovation as a solution. Conclusion this paper explores the perceptions of key stakeholders involved in the management of NCDs in Kenya to improve planning for future emergency responses. Gaps were identified in health system response and preparedness capacity during the pandemic including the perceived need to strengthen NCD services, with solutions offered to guide resilience efforts to protect the health system from disruption.
The Meanings of Physics Equations and Physics Education

Physics equation represents a relationship among symbols by modelling conceptual knowledge of physics mathematically. Teaching and learning the rich meanings of physics equations can enhance learners’ understanding of physics concepts. From this expectation, we introduced various discussions which categorised the meaning of the physics equation according to the semantic relationship between the related concepts and the function and status of the physics equation. The physics equations are classified into interaction relationship, state relationship, and causal relationship according to the semantic relationship among the concepts included in the equation. This classification is based on the distinction of several ontological categories. Physics equations are also divided into empirical laws, definitions and principles according to their function and conceptual status. This classification of equations can provide a basis for teaching and learning physics in-depth.
Inhibition of RNA degradation integrates the metabolic signals induced by osmotic stress into the Arabidopsis circadian system

Abstract The circadian clock system acts as an endogenous timing reference that coordinates many metabolic and physiological processes in plants. Previous studies have shown that the application of osmotic stress delays circadian rhythms via 3ʹ-phospho-adenosine 5ʹ-phosphate (PAP), a retrograde signalling metabolite that is produced in response to redox stress within organelles. PAP accumulation leads to the inhibition of exoribonucleases (XRNs), which are responsible for RNA degradation. Interestingly, we are now able to demonstrate that post-transcriptional processing is crucial for the circadian response to osmotic stress. Our data show that osmotic stress increases the stability of specific circadian RNAs, suggesting that RNA metabolism plays a vital role in circadian clock coordination during drought. Inactivation of XRN4 is sufficient to extend circadian rhythms as part of this response, with PRR7 and LWD1 identified as transcripts that are post-transcriptionally regulated to delay circadian progression.
A bivariate geometric distribution with positive or negative correlation

In many fields, there are practical situations that require the construction of bivariate or multivariate distributions. In this paper, we propose a new bivariate geometric model, derived by linking two univariate geometric distributions through a specific copula function, allowing for positive and negative correlations. Some properties of this joint distribution are presented and discussed, with particular reference to reliability concepts and parameter estimation. Finally an application to real data is presented.
On the nonlinear development of disturbances in boundary layers assumed as parallel

The initial boundary value problem describing the nonlinear development of disturbances in a locally parallel, two or three-dimensional boundary layer is investigated. An asymptotic analysis of spatially periodic solutions with respect to weak temporal stability or instability is performed. The result is a Landau equation, which is coupled with an inhomogeneous heat equation, for determining the amplitude of the disturbance and the mean deformation of the boundary-layer profile. The limit of solutions for long times is calculated under the assumption that such a limit exists. It can be interpreted as a temporally and spatially periodic bifurcating solution of the nonlinear disturbance differential equation in the case of weak temporal instability. The three-dimensional flow over an infinite yawed wedge and Blasius flow over a flat plate are considered as numerical examples. The analysis also shows that the assumption of a locally parallel boundary layer has an important mathematical consequence. While the time-dependent mean-flow distortion decays far from the wall, its limit as time goes to infinity defines a constant vector at the outer edge of the boundary layer, which in general is not equal to zero.
Empowering human anatomy education through gamification and artificial intelligence: An innovative approach to knowledge appropriation

Gamification has appeared as an alternative educational methodology to traditional tools. Specifically, in anatomy teaching, multiple technological applications have emerged in response to the difficulties of accessing cadaveric material; however, there is insufficient information about the effects of these applications on the performance achieved by students, or about to the best way to adapt learning to meet their educational needs. In this study, we investigated how teaching human anatomy through a mobile gamified technological tool containing recommendation systems can be combined with a virtual assistant to improve the learning and academic performance of medical students in the Anatomy Department at the Universidad de La Frontera in Temuco, Chile and the Anatomy Department at the Pontificia Universidad Católica de Chile. In total, 131 students participated in the experiment, which was divided into two case studies. The main findings led to the conclusion that gamified components support students in learning anatomy. In addition, the predictions and recommendations provided by the virtual assistant enabled the academic aspects that the students needed to improve to be extracted adequately. Future work is expected to support adaptive learning by incorporating new artificial intelligence in education elements that can generate personalized scenarios for studying anatomy based on the application.
Evolution in the frontline treatment of patients with chronic lymphocytic leukemia: experience from one European center

Abstract Treatment of chronic lymphocytic leukemia (CLL) has dramatically evolved over the last decades thanks to the introduction of targeted therapies. We aimed to describe retrospectively the evolution in the frontline prescription in the CLL patients from our institution. As a secondary objective, the impact of frontline therapy on the time-to-next-treatment (TTNT) and overall survival (OS). After a median of 6.4 years (0.1–36.4) of follow-up from diagnosis, 323 of 780 CLL patients (41.4%) required therapy. Alkylating agents in monotherapy (chlorambucil) were the most used until 2012, and from then, chemoimmunotherapy. Since 2018, targeted therapies were the most common therapeutic strategy (74.1%). Patients who received targeted therapies had significantly longer TTNT compared to other regimens. In the multivariable analyses, mutated IGHV genes targeted therapies and chemoimmunotherapy regimens were related to longer TTNT, and sex female, age younger than 65, and mutated IGHV genes were associated with better OS.
Comparison of retinal regions-of-interest imaged by OCT for the classification of intermediate AMD

To study whether it is possible to differentiate intermediate age-related macular degeneration (AMD) from healthy controls using partial optical coherence tomography (OCT) data, that is, restricting the input B-scans to certain pre-defined regions of interest (ROIs). A total of 15744 B-scans from 269 intermediate AMD patients and 115 normal subjects were used in this study (split on subject level in 80% train, 10% validation and 10% test). From each OCT B-scan, three ROIs were extracted: retina, complex between retinal pigment epithelium (RPE) and Bruch membrane (BM), and choroid (CHO). These ROIs were obtained using two different methods: masking and cropping. In addition to the six ROIs, the whole OCT B-scan and the binary mask corresponding to the segmentation of the RPE-BM complex were used. For each subset, a convolutional neural network (based on VGG16 architecture and pre-trained on ImageNet) was trained and tested. The performance of the models was evaluated using the area under the receiver operating characteristic (AUROC), accuracy, sensitivity, and specificity. All trained models presented an AUROC, accuracy, sensitivity, and specificity equal to or higher than 0.884, 0.816, 0.685, and 0.644, respectively. The model trained on the whole OCT B-scan presented the best performance (AUROC = 0.983, accuracy = 0.927, sensitivity = 0.862, specificity = 0.913). The models trained on the ROIs obtained with the cropping method led to significantly higher outcomes than those obtained with masking, with the exception of the retinal tissue, where no statistically significant difference was observed between cropping and masking (p = 0.47). This study demonstrated that while using the complete OCT B-scan provided the highest accuracy in classifying intermediate AMD, models trained on specific ROIs such as the RPE-BM complex or the choroid can still achieve high performance.
Thermoelastic properties of bridgmanite using deep-potential molecular dynamics

MgSiO_3-perovskite (MgPv) plays a crucial role in the Earth's lower mantle. This study combines deep-learning potential (DP) with density functional theory (DFT) to investigate the structural and elastic properties of MgPv under lower mantle conditions. To simulate complex systems, we developed a series of potentials capable of faithfully reproducing DFT calculations using different functionals, such as LDA, PBE, PBEsol, and SCAN meta-GGA functionals. The obtained predictions exhibit remarkable reliability and consistency, closely resembling experimental measurements. Our results highlight the superior performance of the DP-SCAN and DP-LDA in accurately predicting high-temperature equations of states and elastic properties. This hybrid computational approach offers a solution to the accuracy-efficiency dilemma in obtaining precise elastic properties at high pressure and temperature conditions for minerals like MgPv, which opens a new way to study the Earth's interior state and related processes.
Critical charge and spin instabilities in superconducting La$_3$Ni$_2$O$_7$

Motivated by the recent discovery of superconductivity in La$_3$Ni$_2$O$_7$ under high pressure, we explore its potential charge and spin instabilities through combined model analysis and first-principles calculations. Taking into account the negative charge-transfer nature of high valence nickel, a fully correlated two-cluster model identifies a lattice-coupled rocksalt-type charge instability characterized by substantial fluctuations of oxygen holes. This instability is corroborated by density-functional-theory plus $U$ calculations that also reveal a strong tendency towards concurrent antiferromagnetic ordering. The charge, spin, and associated lattice instabilities are significantly suppressed with increasing external pressure, contributing to the emergence of superconductivity in pressurized La$_3$Ni$_2$O$_7$. Carrier doping is found to effectively suppress these instabilities, suggesting a viable strategy to stabilize a superconducting phase under ambient pressure.
Representation Learning With Hidden Unit Clustering for Low Resource Speech Applications

In this paper, we describe an approach to self-supervised representation learning from raw audio using a hidden unit clustering (HUC) framework. The input to the model consists of audio samples that are windowed and processed with 1-D convolutional layers. The learned “time-frequency” representations from the convolutional neural network (CNN) module are further processed with long short term memory (LSTM) layers which generate a contextual vector representation for every windowed segment. The HUC framework, allowing the categorization of the representations into a small number of phoneme-like units, is used to train the model for learning semantically rich speech representations. The targets consist of phoneme-like pseudo labels for each audio segment and these are generated with an iterative $k$-means algorithm. We explore techniques that improve the speaker invariance of the learned representations and illustrate the effectiveness of the proposed approach on two settings, i) completely unsupervised speech applications on the sub-tasks described as part of the ZeroSpeech 2021 challenge and ii) semi-supervised automatic speech recognition (ASR) applications on the TIMIT dataset and on the GramVaani challenge Hindi dataset. In these experiments, we achieve state-of-art results for various Zerospeech tasks. Further, on the ASR experiments, the HUC representations are shown to improve over other established benchmarks based on wav2vec, HuBERT and Best-RQ.