Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Conventions

Abstract It is shown that player mobility has important consequences for the long-run equilibrium distribution in dynamic evolutionary models of strategy adjustment, when updating is prone to small probability perturbations, i.e. “mistakes" or “mutations. " Ellison (1993) concluded that the effect on the matching process of localized “neighborhoods" was to strengthen the stability of risk-dominant outcomes, originally demonstrated by Kandori, Mailath, and Rob (1993) (KMR) and Young (1993). I consider a model in which players can choose the neighborhoods to which they belong. When strategies and locations are updated simultaneously, only efficient strategies survive. The robustness of this conclusion is emphasized in a general locational model in which strategy revision opportunities are allowed to arrive at a faster rate than opportunities to change locations. The efficient strategy persists in all cases in which the locational structure is non-trivial. Moreover, even as the relative frequency of player mobility approaches zero, the efficient strategy occurs with boundedly positive relative frequency. This result is in stark contrast to the conclusions of the previous models.
The Best of Both Worlds: A Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks

To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: (A) train standard SR networks on synthetic low-resolution–high-resolution (LR–HR) pairs or (B) predict the degradations of an LR image and then use these to inform a customised SR network. Despite significant progress, subscribers to the former miss out on useful degradation information and followers of the latter rely on weaker SR networks, which are significantly outperformed by the latest architectural advancements. In this work, we present a framework for combining any blind SR prediction mechanism with any deep SR network. We show that a single lightweight metadata insertion block together with a degradation prediction mechanism can allow non-blind SR architectures to rival or outperform state-of-the-art dedicated blind SR networks. We implement various contrastive and iterative degradation prediction schemes and show they are readily compatible with high-performance SR networks such as RCAN and HAN within our framework. Furthermore, we demonstrate our framework’s robustness by successfully performing blind SR on images degraded with blurring, noise and compression. This represents the first explicit combined blind prediction and SR of images degraded with such a complex pipeline, acting as a baseline for further advancements.
Scalable sparse machine learning methods for big data

Sparse machine learning models have become increasingly popular in analyzing high-dimensional data. With the evolving era of Big Data, ultrahigh-dimensional, large-scale data sets are constantly collected in many areas such as genetics, genomics, biomedical imaging, social media analysis, and high-frequency finance. Mining valuable information efficiently from these massive data sets requires not only novel statistical models but also advanced computational techniques. This thesis focuses on the development of scalable sparse machine learning methods to facilitate Big Data analytics. Built upon the feature screening technique, the first part of this thesis proposes a family of hybrid safe-strong rules (HSSR) that incorporate safe screening rules into the sequential strong rule to remove unnecessary computational burden for solving the lasso-type models. We present two instances of HSSR, namely SSR-Dome and SSR-BEDPP, for the standard lasso problem. We further extend SSR-BEDPP to the elastic net and group lasso problems to demonstrate the generalizability of the hybrid screening idea. In the second part, we design and implement an R package called biglasso to extend the lasso model fitting to Big Data in R. Our package biglasso utilizes memory-mapped files to store the massive data on the disk, only reading data into memory when necessary during model fitting, and is thus able to handle data-larger-than-RAM cases seamlessly. Moreover, it’s built upon our redesigned algorithm incorporated with the proposed HSSR screening, making it much more memoryand computation-efficient than existing R packages. Extensive numerical experiments with synthetic and real data sets are conducted in both parts to show the effectiveness of the proposed methods. In the third part, we consider a novel statistical model, namely the overlapping group logistic regression model, that allows for selecting important groups of features
Synchronization of an inertial measurement unit and standalone camera system based on attitude and optical flow data

The fusion of camera and inertial data is well established and useful for a wide range of tasks. With advancements in camera technology and renewed interest in computer vision, standalone cameras have emerged as a possible research tool. These cameras offer high quality video recording, in a small, lightweight form factor, but are limited by connectivity, thus limiting their usefulness. This thesis presents a software correlation method which correlates inertial data and camera data based on attitude estimates, eliminating the need for additional hardware or connections. This thesis presents a brief background and description of the correlation process, along with a review of similar work. The underlying methods for feature detection, optical flow, and signal comparison are explained in detail in the context of the software correlation method. The software correlation method is then demonstrated in an experiment that looks at a single axis of rotation. Finally, the error in the software correlation process is quantified and compared to that in an uncorrelated system. Software correlation proves to be a viable option for the correlation of inertial measurement units and standalone cameras.
Innovations in integrating machine learning and agent-based modeling of biomedical systems

Agent-based modeling (ABM) is a well-established computational paradigm for simulating complex systems in terms of the interactions between individual entities that comprise the system’s population. Machine learning (ML) refers to computational approaches whereby algorithms use statistical methods to “learn” from data on their own, i.e., without imposing any a priori model/theory onto a system or its behavior. Biological systems—ranging from molecules, to cells, to entire organisms, to whole populations and even ecosystems—consist of vast numbers of discrete entities, governed by complex webs of interactions that span various spatiotemporal scales and exhibit nonlinearity, stochasticity, and variable degrees of coupling between entities. For these reasons, the macroscopic properties and collective dynamics of biological systems are generally difficult to accurately model or predict via continuum modeling techniques and mean-field formalisms. ABM takes a “bottom-up” approach that obviates common difficulties of other modeling approaches by enabling one to relatively easily create (or at least propose, for testing) a set of well-defined “rules” to be applied to the individual entities (agents) in a system. Quantitatively evaluating a system and propagating its state over a series of discrete time-steps effectively simulates the system, allowing various observables to be computed and the system’s properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, at least in an unbiased way, there is a uniquely synergistic opportunity to employ ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, running ABM calculations can generate a wealth of data, and ML can be applied in that context too—for example, to generate statistical measures that accurately and meaningfully describe the stochastic outputs of a system and its properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate plausible (realistic) datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision a variety of synergistic ABM⇄ML loops. After introducing some basic ideas about ABMs and ML, and their limitations, this Review describes examples of how ABM and ML have been integrated in diverse contexts, spanning spatial scales that include multicellular and tissue-scale biology to human population-level epidemiology. In so doing, we have used published studies as a guide to identify ML approaches that are well-suited to particular types of ABM applications, based on the scale of the biological system and the properties of the available data.
Validating continuous gravitational-wave candidates from a semicoherent search using Doppler modulation and an effective point spread function

Following up large numbers of candidates in continuous gravitational wave searches presents a challenge, particularly in regard to computational power and the time required to manually scrutinize each of the candidates. It is important to design and test good follow-up procedures that are safe (i.e., minimize false dismissals) and computationally efficient across many search configurations. We investigate two follow-up procedures, or"vetoes,"both of which exploit the Doppler modulation predicted in astrophysical signals. In particular, we introduce the concept of using an effective point spread function as part of our veto criteria. We take advantage of a well-established semicoherent search algorithm based on a hidden Markov model to study various search configurations and to generalize the veto criteria by considering the overall veto performance in terms of efficiency and safety. The results can serve as a guideline for follow-up studies in future continuous gravitational wave searches using a hidden Markov model algorithm. The results also apply qualitatively to other semicoherent search algorithms.
Introducing Intermediate Domains for Effective Self-Training during Test-Time

Experiencing domain shifts during test-time is nearly inevitable in practice and likely results in a severe performance degradation. To overcome this issue, test-time adaptation continues to update the initial source model during deployment. A promising direction are methods based on self-training which have been shown to be well suited for gradual domain adaptation, since reliable pseudo-labels can be provided. In this work, we address two problems that exist when applying self-training in the setting of test-time adaptation. First, adapting a model to long test sequences that contain multiple domains can lead to error accumulation. Second, naturally, not all shifts are gradual in practice. To tackle these challenges, we introduce GTTA. By creating artificial intermediate domains that divide the current domain shift into a more gradual one, effective self-training through high quality pseudo-labels can be performed. To create the intermediate domains, we propose two independent variations: mixup and light-weight style transfer. We demonstrate the effectiveness of our approach on the continual and gradual corruption benchmarks, as well as ImageNet-R. To further investigate gradual shifts in the context of urban scene segmentation, we publish a new benchmark: CarlaTTA. It enables the exploration of several non-stationary domain shifts.
Factors affecting target perceptibility in object substitution masking

Object substitution masking (OSM) refers to the observation that reporting of a briefly presented target item is considerably reduced when a mask remains visible after the target offset. The size of OSM has been said to be critically dependent on the speed with which attention is deployed towards the target. One line of evidence in favour of this view is that when set size increases so does OSM. In addition, when the target location is known in advance because of a pre-cue OSM is reduced or eliminated. However, all the studies that reported an interaction between set size and OSM or between pre-cue and OSM performance was at ceiling/floor or there was no control condition. This might have led to an over-interpretation of the statistical interactions which were evidently a consequence of ceiling and floor effects in performance, or in other cases the failure to correct for clear response biases. In Chapters 2&3 the purported role of attention in OSM is investigated by manipulating orthogonally set size and mask duration. The principal finding is that, when performance is not influenced by ceiling/floor effects and the scores are corrected for response bias set size and mask duration do not interact although their individual effects are highly significant. Chapter 4 shows that the set size effect in OSM is not due to crowding; the two factors affect OSM independently. In Chapter 5 attention is manipulated in a direct manner by employing a spatial pre-cue and manipulating the cue-target onset asynchrony. Although pre-cueing the target improves performance it does not affect OSM. Finally, in Chapter 6 the participants phenomenal experience in OSM is investigated. The results show that at a relatively large number of masking trials OSM is “complete”; participants report seeing a blank space at the target location although the target is present. Collectively, the results of the present thesis show that attention does not influence OSM when it is controlled either indirectly (i.e. set size, crowding) or directly (pre-cue). What these findings show is that Di Lollo et al.'s specific implementation of the general model of the re-entrant account of awareness is invalid. The findings are also discussed in relation to other accounts of OSM , Moore and Llleras's (2003) object-updating account and Poder's (2012) attentional gating model (Chapter 7).
Results FeHV-1 Pneumonia Results From the Extension of Upper Respiratory Tract Lesions Along the Airways

Feline herpesvirus type 1 (FeHV-1) is one of the etiological agents of feline respiratory disease. FeHV-1 is an epitheliotropic and cytopathic virus that mainly causes rhinitis and conjunctivitis, although pneumonia is also occasionally seen. In this study, the authors investigated the pathogenesis of FeHV-1-associated pneumonia, comparing natural cases with viral infection of tracheal ring and cell cultures in vitro, using histology, immunohistology, double immunofluorescence, and transmission electron microscopy as investigative tools. The results confirm that FeHV-1 targets both respiratory epithelial cells and pneumocytes and indicate that FeHV-1 pneumonia is the consequence of continuous cell-to-cell viral spread from the upper airways via the trachea into the lungs. They provide strong evidence that FeHV-1–infected cells die primarily via apoptosis, following loss of cell-to-cell contact, rounding, and detachment. However, virus-induced lesions in vivo are dominated by marked neutrophil infiltration and extensive necrosis with less prominent apoptosis; in the airways, the tissue necrosis can extend into the submucosa. The necrosis appears to result from virus-induced neutrophil influx and release of proteolytic enzymes, such as matrix metalloproteinase-9, from the neutrophils.
Use of a Smartphone for Self-Management of Pulmonary Diseases

Chronic obstructive pulmonary disease is a serious health problem which places a significant economic burden on health systems worldwide, due mostly to a significant number of acute exacerbation events. The prevention of these events is a common goal of COPD treatments, being imperative that patient education and self-management programs are developed in order to arouse patient’s awareness of their activity behavior and health condition. Home telemonitoring has gained interest as a potential solution to the global challenge of providing home care and encouraging patients to become more involved in the management of their own health condition, and also as a way to bridge the gap between professional care and patient self-management. Making use of smartphone technology, the COPDHelper was developed, an application aimed to improve the patient’s self-management of the pulmonary disease, assisting and guiding in the diverse fields of pulmonary rehabilitation and treatment. Developed only for Android OS, in order to be integrated in the Fraunhofer’s SmartCompanion, this telemonitoring system is composed by two major modules: (1) Monitoring and (2) Counseling and Education. However, and since self-awareness of the actual state of the disease is as much important as monitoring of the COPD symptoms and patient’s vital signs, the focus of COPDHelper rests more precisely in detecting and solving the small mistakes on the part of the patient when managing this pulmonary disease, marking its main difference when compared with available methods. By successfully adopting already implemented features of the SmartCompanion and making use of external biomedical sensors, COPDHelper became an in-depth application, capable of covering the most relevant aspects of pulmonary disease self-management. The evaluation of the developed system was performed via usability tests, to assess its efficiency and coherence, and also user satisfaction when interacting with the system. The results indicated that COPDHelper would be a successfully management tool for COPD patients, but with some reservations regarding its use by senior patients. As commonly encountered in other systems in its early stages of development, some COPDHelper features and components need further improvements before this application is fit for routine use.
A boutique personal librarian program for transfer students

Purpose The purpose of this paper is to discuss the planning, implementation and assessment of a personal librarian (PL) program for transfer students at a four-year private university. It highlights best practices to ensure the success of such a program, emphasizes the importance of collaboration with other campus units and explores the possible applications for other underserved student populations. Design/methodology/approach This paper explains why the PL model is particularly appropriate to serve the needs of transfer students. It clearly describes the assessment methods to evaluate such a program and proposes best practices to ensure success and sustainability. Findings Transfer students respond very positively to a PL program as shown by the number of interactions they have with their PLs and their responses to a survey. Librarians also appreciate the chance to develop meaningful relationships with students despite the addition to their workload. Practical implications Institutions looking at ways to reach out to transfer students or other underserved populations can easily adapt the PL program described here. Originality/value The PL concept is not new but, to the best of the authors’ knowledge, it is the first time it has been applied to transfer students. The program described here is also unique because of its “boutique” approach, which emphasizes customization and personalization.
Acoustic analysis of rock cutting process for impregnated diamond drilling

xiii Statement of Originality . xv . xvii . xix Introduction . 1 1.1 Background . 1 1.2 Literature review . 2 1.2.1 Acoustic emission . 2 1.2.2 Impregnated diamond drilling . 12 1.2.3 Pattern recognition . 21 1.2. 4 Rock drilling studies and AE . 25 1.3 Shortcoming of previous contributions . 27 1.4 Objectives and thesis overview . 28 1.5 References . 30 Experimental Methodology . 37 2.1 Experimental setups . 37 2.1.1 Echidna drilling rig . 37 2.1.2 Cerchar apparatus . 38 2.1.3 AE system . 38 2.2 Experimental procedure . 39 2.2.1 Drilling tests . 39 2.2.2 Abrasivity tests . 41 2.3 References . 41 Acoustic emission analysis for rock–bit interactions in impregnated diamond drilling . 45
Synthesis of Amorphous Hydrogenated Boron Carbide from Orthocarborane Using Argon Bombardment: A Reaxff Molecular Dynamics Study

In this study, the synthesis process of a-BxC:Hy using argon bombardment from the orthocarborane precursor was modeled by using reactive molecular dynamics (MD). Utilizing the MD simulations, the formation of free radicals as a result of ion bombardment was identified and quantified. Then, the densification process that is aided by the mixture of free radicals and orthocarborane was analyzed. The densification process by creating the initial structure composed of free radicals and orthocarborane with active sites created by partially removing some of the hydrogen atoms from the icosahedral cage was also modelled. Overall, a better understanding of the mechanism of the densification of hydrogenated boron carbide and the roles of Ar bombardment and radical species toward the deposition process were obtained.
Moisture susceptibility of hydrated lime modified mastics using adhesion test methods and surface free energy techniques

ABSTRACT Moisture damage is one of the major factors that has been reported to affect the performance of asphalt pavements which depends mainly on the bond strength between bitumen, aggregates and mineral filler. In the presence of moisture, this bond weakens and results in premature pavement failure. One of the many ways of mitigating moisture damage effects is the use of anti-stripping agents. Hydrated lime has been found to be one of the most efficient anti-stripping agents among the common additives used with asphalt mixtures. This study attempts to quantify the bond strength of hydrated lime modified mastics with different aggregates (granite and limestone) under moisture conditions using a combination of surface free energy techniques and conventional adhesion test methods. From the surface free energy technique, the moisture susceptibility was studied in the form of dry work of adhesion, work of debonding and various energy parameters. Two conventional techniques, the PATTI test and the rolling bottle test were used to assess bond strength under wet conditions. The results of all three techniques lead to the same conclusion that hydrated lime is an active filler that improves moisture damage resistance compared to the other natural fillers but that the improvement is highly dependent on aggregate mineralogy.
Quality Indexes Design for Online Monitoring Polymer Injection Molding

Quality control is a crucial issue in the injection molding process with target of obtaining a high yield rate and reducing production cost. Consequently, effective methods for monitoring the injection conditions (e.g., pressure, velocity, and temperature) in real-time and adjusting these conditions adaptively as required to ensure a consistent part quality are essential. This study proposes a quality index based on the clamping force increment during the injection cycle, as determined by four strain gauges attached to the tie bars of the injection molding machine. Also, various quality indexes for online quality monitoring and prediction purposes based on the pressure, viscosity, and energy features extracted from the pressure profiles obtained at the load cell, nozzle, and molding cavity, respectively, are compared. The feasibility of the proposed quality indexes is investigated experimentally for various settings of the barrel temperature, back pressure, and rotational speed of the plasticizing screw. It is shown that all of the quality indexes are correlated with the injection-molded quality and hence provide a feasible basis for the realization of an on-line quality monitoring and control system. Particularly, the tie-bar elongation quality index requires no modification or invasion of the injection molding system or cavity and hence provides a particularly attractive solution for monitoring and controlling the part quality.