Miracle-dz/UniFAR-FTUs huggingface dataset:
One of the Family: Metis Culture in Nineteenth-Century Northwestern Saskatchewan

List of Illustrations Acknowledgments Note on Methodology and Sources Note on Writing Conventions Introduction 1 "They are strongly attached to the country of rivers, lakes, and forests": The Social Landscapes of the Northwest 2 "The bond that connected one human being to another": Social Construction of the Metis Family 3 "To live in the land of my Mother": Residency and Patronymic Connections Across the Northwest 4 "After a man has tasted of the comforts of married life this living alone comes pretty tough": Family, Acculturation, and Roman Catholicism 5 "The only men obtainable who know the country and Indians are all married": Family, Labour, and the HBC 6 "The HalfBreeds of this place always did and always will dance": Competition, Freemen, and Contested Spaces 7 "I Thought it advisable to furnish him": Freemen to Free Traders in the Northwest Fur Trade Conclusion Appendix Glossary Notes Bibliography Index of Names Index of Subjects
Effects of exposure and information structure in native and non-native pronoun resolution in French

Abstract The present study investigated pronoun resolution strategies in French native speakers and in German-speaking learners of French. French and German differ in antecedent preferences in ambiguous constructions such as The postman hit the pirate before he went home: while French shows a N2-preference, German shows a N1-preference. This difference is explained by effects of exposure to an unambiguous alternative construction referring to N1 that exists in French, but not in German (Hemforth et al. 2010, Language specific preferences in anaphor resolution: Exposure or gricean maxims? In Steallan Ohlsson and Richard Catarambone (eds.), 32nd Annual Conference of the Cognitive Science Society, 2218–2223. Austin, TX: Cognitive Science Society). In questionnaires, we tested French active and passive sentences to investigate (a) whether the L2-learners would apply the same strategy as natives in the active condition (i.e. N2-preference), and (b) whether the explicit topicalization of a referent as a consequence of passivization influences interpretation preferences in both groups (resulting in more N1 choices). The results show that German learners prefer the N1 more often than the French natives in the active condition. Crucially, the number of N1 choices increased in both groups in the passive condition. These results suggest that L2-learners might have difficulties acquiring strategies based on the frequency and availability of alternative constructions in the L2, and provide further evidence for the importance of information-structure-based strategies in L1 and L2 pronoun resolution.
Viscoelastic testing: Critical appraisal of new methodologies and current literature

United States Food and Drug Administration (FDA)‐approved viscoelastic testing (VET) methodologies have significantly changed in the last 10 years, with the availability of cartridge‐based VET. Some of these cartridge‐based methodologies use harmonic resonance‐based clot detection. While VET has always allowed for the evaluation of real‐time clot formation, cartridge‐based VET provides increased ease of use as well as greater portability and robustness of results in out‐of‐laboratory environments. Here we review the use of VET in a variety of clinical contexts, including cardiac surgery, trauma, liver transplant, obstetrics, and hypercoagulable states such as COVID‐19. As of now, high quality randomized trial evidence for new generation VET (TEG 6s, HemoSonics Quantra, ROTEM sigma) is limited. Nevertheless, the use of VET‐guided transfusion algorithms appears to result in reduced blood usage without worsening of patient outcomes. Future work comparing the new generation VET instruments and continuing to validate clinically important cut‐offs will help move the field of point‐of‐care coagulation monitoring forward and increase the quality of transfusion management in bleeding patients.
Acute stroke: evaluation and treatment.

Stroke defines an acute vascular event in the brain and is a leading cause of death and disability. Ischemic stroke results from decreased blood flow to a portion of the brain with consequent cell death. Hemorrhagic stroke, on the other hand, is a result of bleeding into the brain. Ischemic stroke is far more common and is potentially treatable with thrombolytic therapy. While effective, the wide application of this therapy has been hampered by restrictive selection criteria based on time since onset of symptoms. Successful treatment requires a system capable of rapidly identifying and evaluating prospective candidates. In this context, use of community education, specific ED protocols and designated treatment centers may demonstrate some advantages. Evidence is emerging that patient selection by time since stroke onset, imaging characteristics, and intra-arterial treatment may increase the probability of recanalisation of occluded vessels. Normalization of serum glucose, acute blood pressure management and surgical extraction of intracerebral clot may be of benefit in some circumstances.
Pilots’ Fatigue Status Recognition Using Deep Contractive Autoencoder Network

The evaluation of pilots’ fatigue status is of substantial significance in aviation safety, which faces two major issues. They are how to get the fatigue status feature representation and how to identify the fatigue behavior status of pilots via electroencephalogram (EEG) signals. To solve the first issue, we propose a novel fatigue evaluation index via different window functions to compute the power spectrum of relative rhythms from EEG signals. Wavelet packet transform is used to decompose EEG signals from pilots to form four major rhythms, i.e., <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\delta }$ </tex-math></inline-formula> wave, <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\theta }$ </tex-math></inline-formula> wave, <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\alpha }$ </tex-math></inline-formula> wave, and <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\beta }$ </tex-math></inline-formula> wave, and the combined representation of their power spectrum curve area is the features of pilots’ mental status. To solve the second issue, we propose a new deep contractive autoencoder (AE) network with a softmax (SM) classifier to detect the multistatuses of mental fatigue workload. The recognition results of our model are also compared with that of other models such as the deep AE network with a SM classifier model. The experimental results show that our deep learning model has superior classification performance, and the recognition accuracy of fatigue mental status is up to 91.67%, which shows that the proposed method performs excellently compared with the state-of-the-art methods.
Deep generative models for unsupervised delamination detection using guided waves

With the rising demands for robust structural health monitoring procedures for aerospace structures, the scope of intelligent algorithms and learning techniques is expanding. Supervised algorithms have shown promising results in the field provided a large, balanced, and labeled amount of data for training. For some applications like aerospace, the data collection process is cumbersome, time-taking, and costly. Also, generating possible damage scenarios in a laboratory setup is challenging because of the complexity of the damage initiation and failure mechanism. Besides this, the uncertainties of the real-time operation restrict the online prediction accuracy with supervised learning. In this paper, deep generative models are proposed for unsupervised delamination prediction as an anomaly detection problem. In this one-class-based model, the deep learning network is trained to learn the distribution of baseline signals. In the testing phase, damage signals and unseen baseline signals are fed into the trained network to predict the state of the structure, i.e., healthy or unhealthy (delamination). It is seen that the proposed method can successfully predict the delamination with high accuracy.
Active Darcy's Law

While bacterial swarms can exhibit active turbulence in vacant spaces, they naturally inhabit crowded environments. We numerically show that driving disorderly active fluids through porous media enhances Darcy's law. While purely active flows average to zero flux, hybrid active/driven flows display greater drift than pure-driven fluids. This enhancement is non-monotonic with activity, leading to an optimal activity to maximize flow rate. We incorporate the active contribution into an active Darcy's law, which may serve to help understand anomalous transport of swarming in porous media.
Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving

Environmental perception is a key element of autonomous driving because the information received from the perception module influences core driving decisions. An outstanding challenge in real-time perception for autonomous driving lies in finding the best trade-off between detection quality and latency. Major constraints on both computation and power have to be taken into account for real-time perception in autonomous vehicles. Larger object detection models tend to produce the best results, but are also slower at runtime. Since the most accurate detectors cannot run in real-time locally, we investigate the possibility of offloading computation to edge and cloud platforms, which are less resource-constrained. We create a synthetic dataset to train object detection models and evaluate different offloading strategies. Using real hardware and network simulations, we compare different trade-offs between prediction quality and end-to-end delay. Since sending raw frames over the network implies additional transmission delays, we also explore the use of JPEG and H.265 compression at varying qualities and measure their impact on prediction metrics. We show that models with adequate compression can be run in real-time on the cloud while outperforming local detection performance.
Revisiting Planetary Systems in Okayama Planet Search Program: A new long-period planet, RV astrometry joint analysis, and multiplicity-metallicity trend around evolved stars

In this study, we revisit 32 planetary systems around evolved stars observed within the framework of the Okayama Planet Search Program and its collaborative framework of the EAPS-Net to search for additional companions and investigate the properties of stars and giant planets in multiple-planet systems. With our latest radial velocities obtained from Okayama Astrophysical Observatory (OAO), we confirm an additional giant planet in the wide orbit of 75 Cet system ($P_{\rm{c}} = 2051.62_{-40.47}^{+45.98}\ \rm{d}$, $M_{\rm{c}}\sin i=0.912_{-0.090}^{+0.088}\ M_{\rm{J}}$, and $a_{\rm{c}}=3.929_{-0.058}^{+0.052}\ \rm{au}$), along with five stars exhibiting long-term radial velocity accelerations, which indicates massive companions in the wide orbits. We have also found that the radial velocity variations of several planet-harboring stars may indicate additional planet candidates, stellar activities, or other understudied sources. These stars include $\epsilon$ Tau, 11 Com, 24 Boo, 41 Lyn, 14 And, HD 32518, and $\omega$ Ser. We further constrain the orbital configuration of the HD 5608, HD 14067, HD 120084, and HD 175679 systems by combining radial velocities with astrometry, as their host central stars exhibit significant astrometric accelerations. For other systems, we simply refine their orbital parameters. Moreover, our study indicates that the OPSP planet-harboring stars are more metal-poor compared to the currently known planet-harboring stars, and this is likely due to the $B-V$ color upper limit at 1.0 for star selection in the beginning of the survey. Finally, by investigating the less-massive giant planets ($<5 M_{\rm{J}}$) around currently known planet-harboring evolved stars, we have found that metallicity positively correlates with the multiplicity and total planet mass of the system, which can be evidence for the core-accretion planet formation model.
Data-Driven Intelligence can Revolutionize Today's Cybersecurity World: A Position Paper

As cyber threats evolve and grow progressively more sophisticated, cyber security is becoming a more significant concern in today's digital era. Traditional security measures tend to be insufficient to defend against these persistent and dynamic threats because they are mainly intuitional. One of the most promising ways to handle this ongoing problem is utilizing the potential of data-driven intelligence, by leveraging AI and machine learning techniques. It can improve operational efficiency and saves response times by automating repetitive operations, enabling real-time threat detection, and facilitating incident response. In addition, it augments human expertise with insightful information, predictive analytics, and enhanced decision-making, enabling them to better understand and address evolving problems. Thus, data-driven intelligence could significantly improve real-world cybersecurity solutions in a wide range of application areas like critical infrastructure, smart cities, digital twin, industrial control systems and so on. In this position paper, we argue that data-driven intelligence can revolutionize the realm of cybersecurity, offering not only large-scale task automation but also assist human experts for better situation awareness and decision-making in real-world scenarios.
Thermodynamic Linear Algebra

Linear algebraic primitives are at the core of many modern algorithms in engineering, science, and machine learning. Hence, accelerating these primitives with novel computing hardware would have tremendous economic impact. Quantum computing has been proposed for this purpose, although the resource requirements are far beyond current technological capabilities, so this approach remains long-term in timescale. Here we consider an alternative physics-based computing paradigm based on classical thermodynamics, to provide a near-term approach to accelerating linear algebra. At first sight, thermodynamics and linear algebra seem to be unrelated fields. In this work, we connect solving linear algebra problems to sampling from the thermodynamic equilibrium distribution of a system of coupled harmonic oscillators. We present simple thermodynamic algorithms for (1) solving linear systems of equations, (2) computing matrix inverses, (3) computing matrix determinants, and (4) solving Lyapunov equations. Under reasonable assumptions, we rigorously establish asymptotic speedups for our algorithms, relative to digital methods, that scale linearly in matrix dimension. Our algorithms exploit thermodynamic principles like ergodicity, entropy, and equilibration, highlighting the deep connection between these two seemingly distinct fields, and opening up algebraic applications for thermodynamic computing hardware.
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control

Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing cost efficiency. We benchmark two popular classical and deep RL methods (Q-Learning and Deep-Q-Networks) across multiple HVAC environments and explore the practical consideration of model hyper-parameter selection and reward tuning. The findings provide insight for configuring RL agents in HVAC systems, promoting energy-efficient and cost-effective operation.
Bayesian record linkage with variables in one file

In many healthcare and social science applications, information about units is dispersed across multiple data files. Linking records across files is necessary to estimate the associations of interest. Common record linkage algorithms only rely on similarities between linking variables that appear in all the files. Moreover, analysis of linked files often ignores errors that may arise from incorrect or missed links. Bayesian record linking methods allow for natural propagation of linkage error, by jointly sampling the linkage structure and the model parameters. We extend an existing Bayesian record linkage method to integrate associations between variables exclusive to each file being linked. We show analytically, and using simulations, that the proposed method can improve the linking process, and can result in accurate inferences. We apply the method to link Meals on Wheels recipients to Medicare enrollment records.
Refining the IceCube detector geometry using muon and LED calibration data

The IceCube Neutrino Observatory deployed 5160 digital optical modules (DOMs) on 86 cables, called strings, in a cubic kilometer of deep glacial ice below the geographic South Pole. These record the Cherenkov light of passing charged particles. Knowledge of the DOM positions is vital for event reconstruction. While vertical positions have been calibrated, previous in-situ geometry calibration methods have been unable to measure horizontal deviations from the surface positions, largely due to degeneracies with ice model uncertainties. Thus the lateral position of the surface position of each hole is to date in almost all cases used as the lateral position of all DOMs on a given string. With the recent advances in ice modeling, two new in-situ measurements have now been undertaken. Using a large sample of muon tracks, the individual positions of all DOMs on a small number of strings around the center of the detector have been fitted. Verifying the results against LED calibration data shows that the string-average corrections improve detector modeling. Directly fitting string-average geometry corrections for the full array using LED data agrees with the average corrections as derived from muons where available. Analyses are now ongoing to obtain per-DOM positions using both methods and in addition, methods are being developed to correct the recorded arrival times for the expected scattering delay, allowing for multilateration of the positions using nanosecond-precision propagation delays.
Vector quantization loss analysis in VQGANs: a single-GPU ablation study for image-to-image synthesis

This study performs an ablation analysis of Vector Quantized Generative Adversarial Networks (VQGANs), concentrating on image-to-image synthesis utilizing a single NVIDIA A100 GPU. The current work explores the nuanced effects of varying critical parameters including the number of epochs, image count, and attributes of codebook vectors and latent dimensions, specifically within the constraint of limited resources. Notably, our focus is pinpointed on the vector quantization loss, keeping other hyperparameters and loss components (GAN loss) fixed. This was done to delve into a deeper understanding of the discrete latent space, and to explore how varying its size affects the reconstruction. Though, our results do not surpass the existing benchmarks, however, our findings shed significant light on VQGAN's behaviour for a smaller dataset, particularly concerning artifacts, codebook size optimization, and comparative analysis with Principal Component Analysis (PCA). The study also uncovers the promising direction by introducing 2D positional encodings, revealing a marked reduction in artifacts and insights into balancing clarity and overfitting.
Pulse Sequences to Observe NMR Coupled Relaxation in AX$_n$ Spin Systems

NMR pulse sequences that are modifications of the HSQC experiment are proposed to observe ${}^{13}\textrm{C}$-coupled relaxation in AX, AX$_2$, and AX$_3$ spin systems. ${}^{13}\textrm{CH}$ and ${}^{13}{\textrm{CH}}_2$ moieties are discussed as exemplary AX and AX$_2$ spin systems. The pulse sequences may be used to produce 1D or 2D proton NMR spectra.
Ergodicity and central limit theorems for stochastic wave equations in high dimensions

We consider stochastic wave equations in spatial dimensions $d \geq 4$. We assume that the driving noise is given by a Gaussian noise that is white in time and has some spatial correlation. We show that the solution process is spatially ergodic under the spatial shift using the tools of Malliavin calculus. When the spatial correlation is given by the Riesz kernel, we also establish that the spatial integral of the solution with proper normalization converges to the standard normal distribution under the Wasserstein distance. The convergence is obtained by first constructing the approximation sequence to the solution and then applying Malliavin-Stein's method to the normalized spatial integral of the sequence. The corresponding functional central limit theorem is presented as well.
Exploring governance in Galapagos Marine Reserve

The image of Galapagos has been communicated by the conservation rhetoric as the paradisiacal and pristine destination for tourism and science in the last decades. This discourse has served to motivate, convince, and persuade audiences about why and how Galapagos Marine Reserve (GMR) has been a positive outcome in marine conservation. However, the role of humans in the GMR agency has intentionally been left unnoticed, disregarding its influence in the GMR governability. In recent years, the visibility of the human element in the GMR management has been raised by developing more social-science-based research, mainly linked to economic assessments. Although these initiatives have brought positive outcomes for local interest groups, in many cases, this research-transition has proven to be insufficient to address (and solve) the challenges in GMR governance. Consequently, the ruling bodies' and interest groups' frustration, and the natural environment's degradation have deteriorated the mutual interactions, compromising the MPA long-term viability. Generally, the success or failure of the Marine Protected Areas (MPAs) is attributed to the governance model placed to govern it. In GMR, the challenges to its governance have remained unsolved along the years as issues linked to current events, when in reality, GMR success or failure was incubated even before the reserve was created. In fact, its current condition has been endorsed to the co-governance mode, to the availability of funds, to the fulfillment of regulations, and to the enforcement of law. This idea has disregarded the relevance of the institutional structure, the interactions between interest groups, the meta-governance elements (e.g., images of users), and their attitudes toward the area governance. This dissertation recognizes that alternative perspectives and instruments to look at this MPA agency are needed and argues that a shift from the managerial framework towards a governance paradigm to rule the GMR is urged in order to addresses high complex, diverse, and dynamic governance issues occurring at multiple scales. This thesis is inspired by the interactive governance theory and the governability notion, both of which highlight the importance of the three governance dimensions (i.e., first, second-, and third-order governance) and their mutual linkages, in addressing conflicts and suggesting alternatives. Here it is argued that the horizontal model of governance (or co-management) used in GMR has extensively been promoted as the solution for problems with marine resources, MPAs, and fisheries. However in reality, it is shown that it has been far from being