Miracle-dz/UniFAR-FTUs huggingface dataset:
State Estimation and Control of Active Systems for High Performance Vehicles

In recent days, mechatronic systems are getting integrated in vehicles ever more. While stability and safety systems such as ABS, ESP have pioneered the introduction of such systems in the modern day car, the lowered cost and increased computational power of electronics along with electrification of the various components has fuelled an increase in this trend. The availability of chassis control systems onboard vehicles has been widely studied and exploited for augmenting vehicle stability. At the same time, for the context of high performance and luxury vehicles, chassis control systems offer a vast and untapped potential to improve vehicle handling and the driveability experience. As performance objectives have not been studied very well in the literature, this thesis deals with the problem of control system design for various active chassis control systems with performance as the main objective. A precursor to the control system design is having complete knowledge of the vehicle states, including those such as the vehicle sideslip angle and the vehicle mass, that cannot be measured directly. The first half of the thesis is dedicated to the development of algorithms for the estimation of these variables in a robust manner. While several estimation methods do exist in the literature, there is still some scope of research in terms of the development of estimation algorithms that have been validated on a test track with extensive experimental testing without using research grade sensors. The advantage of the presented algorithms is that they work only with CAN-BUS data coming from the standard vehicle ESP sensor cluster. The algorithms are tested rigorously under all possible conditions to guarantee robustness. The second half of the thesis deals with the design of the control objectives and controllers for the control of an active rear wheel steering system for a high performance supercar and a torque vectoring algorithm for an electric racing vehicle. With the use of an active rear wheel steering, the driver's confidence in the vehicle improves due a reduction in the lag between the lateral acceleration and the yaw rate, which allows drivers to push the vehicle harder on a racetrack without losing confidence in it. The torque vectoring algorithm controls the motor torques to improve the tire utilisation and increases the net lateral force, which allows professional drivers to set faster lap times.
Erosion of cohesive grains by an impinging turbulent jet

The erosion and transport of particles by an impinging turbulent jet in air is observed in various situations, such as the cleaning of a surface or during the landing of a spacecraft. The presence of inter-particle cohesive forces modiﬁes the erosion threshold, beyond which grains are transported. The cohesion also inﬂuences the resulting formation and shape of the crater. In this paper, we characterize the role of the cohesive forces on the erosion of a ﬂat granular bed by an impinging normal turbulent jet in air. We perform experiments using a cohesion-controlled granular material to ﬁnely tune the cohesion between particles while keeping the other properties constant. We investigate the eﬀects of the cohesion on the erosion threshold and show that the results can be rationalized by a cohesive Shields number that accounts for the inter-particles cohesion force. Despite the complex nature of a turbulent jet, we can provide a scaling law to correlate the jet erosion threshold, based on the outlet velocity at the nozzle, to a local cohesive Shields number. The presence of cohesion between the grains also modiﬁes the shape of the resulting crater, the transport of grains, and the local erosion process.
Pessimistic Off-Policy Optimization for Learning to Rank

Off-policy learning is a framework for optimizing policies without deploying them, using data collected by another policy. In recommender systems, this is especially challenging due to the imbalance in logged data: some items are recommended and thus logged more frequently than others. This is further perpetuated when recommending a list of items, as the action space is combinatorial. To address this challenge, we study pessimistic off-policy optimization for learning to rank. The key idea is to compute lower confidence bounds on parameters of click models and then return the list with the highest pessimistic estimate of its value. This approach is computationally efficient and we analyze it. We study its Bayesian and frequentist variants, and overcome the limitation of unknown prior by incorporating empirical Bayes. To show the empirical effectiveness of our approach, we compare it to off-policy optimizers that use inverse propensity scores or neglect uncertainty. Our approach outperforms all baselines, is robust, and is also general.
Finetuning a Kalaallisut-English machine translation system using web-crawled data

West Greenlandic, known by native speakers as Kalaallisut, is an extremely low-resource polysynthetic language spoken by around 56,000 people in Greenland. Here, we attempt to finetune a pretrained Kalaallisut-to-English neural machine translation (NMT) system using web-crawled pseudoparallel sentences from around 30 multilingual websites. We compile a corpus of over 93,000 Kalaallisut sentences and over 140,000 Danish sentences, then use cross-lingual sentence embeddings and approximate nearest-neighbors search in an attempt to mine near-translations from these corpora. Finally, we translate the Danish sentence to English to obtain a synthetic Kalaallisut-English aligned corpus. Although the resulting dataset is too small and noisy to improve the pretrained MT model, we believe that with additional resources, we could construct a better pseudoparallel corpus and achieve more promising results on MT. We also note other possible uses of the monolingual Kalaallisut data and discuss directions for future work. We make the code and data for our experiments publicly available.
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression

This paper presents a language-powered paradigm for ordinal regression. Existing methods usually treat each rank as a category and employ a set of weights to learn these concepts. These methods are easy to overfit and usually attain unsatisfactory performance as the learned concepts are mainly derived from the training set. Recent large pre-trained vision-language models like CLIP have shown impressive performance on various visual tasks. In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space. Specifically, we reformulate this task as an image-language matching problem with a contrastive objective, which regards labels as text and obtains a language prototype from a text encoder for each rank. While prompt engineering for CLIP is extremely time-consuming, we propose OrdinalCLIP, a differentiable prompting method for adapting CLIP for ordinal regression. OrdinalCLIP consists of learnable context tokens and learnable rank embeddings; The learnable rank embeddings are constructed by explicitly modeling numerical continuity, resulting in well-ordered, compact language prototypes in the CLIP space. Once learned, we can only save the language prototypes and discard the huge language model, resulting in zero additional computational overhead compared with the linear head counterpart. Experimental results show that our paradigm achieves competitive performance in general ordinal regression tasks, and gains improvements in few-shot and distribution shift settings for age estimation. The code is available at https://github.com/xk-huang/OrdinalCLIP.
Pre‐ and post‐association barriers to host switching in sympatric mutualists

Coevolution between mutualists can lead to reciprocal specialization, potentially causing barriers to host switching. Here, we conducted assays to identify pre‐ and post‐association barriers to host switching by endosymbiotic bacteria, both within and between two sympatric nematode clades. In nature, Steinernema nematodes and Xenorhabdus bacteria form an obligate mutualism. Free‐living juvenile nematodes carry Xenorhabdus in a specialized intestinal receptacle. When nematodes enter an insect, they release the bacteria into the insect hemocoel. The bacteria aid in killing the insect and facilitate nematode reproduction. Prior to dispersing from the insect, juvenile nematodes must form an association with their symbionts; the bacteria must adhere to the intestinal receptacle. We tested for pre‐association barriers by comparing the effects of bacterial strains on native versus non‐native nematodes via their virulence towards, nutritional support of, and ability to associate with different nematode species. We then assessed post‐association barriers by measuring the relative fitness of nematodes carrying each strain of bacteria. We found evidence for both pre‐ and post‐association barriers between nematode clades. Specifically, some bacteria were highly virulent to non‐native hosts, and some nematode hosts carried fewer cells of non‐native bacteria, creating pre‐association barriers. In addition, reduced infection success and lower nematode reproduction were identified as post‐association barriers. No barriers to symbiont switching were detected between nematode species within the same clade. Overall, our study suggests a framework that could be used to generate predictions for the evolution of barriers to host switching in this and other systems.
Fast Simulated Maximum Likelihood Estimation of the Spatial Probit Model Capable of Handling Large Samples

Abstract We show how to quickly estimate spatial probit models for large data sets using maximum likelihood. Like Beron and Vijverberg (2004), we use the GHK (Geweke-Hajivassiliou-Keane) algorithm to perform maximum simulated likelihood estimation. However, using the GHK for large sample sizes has been viewed as extremely difficult (Wang, Iglesias, & Wooldridge, 2013). Nonetheless, for sparse covariance and precision matrices often encountered in spatial settings, the GHK can be applied to very large sample sizes as its operation counts and memory requirements increase almost linearly with n when using sparse matrix techniques.
Towards an improved understanding of proximity-to-failure in resistance training and its influence on skeletal muscle hypertrophy, neuromuscular fatigue, muscle damage, and perceived discomfort: A scoping review

ABSTRACT While proximity-to-failure is considered an important resistance training (RT) prescription variable, its influence on physiological adaptations and short-term responses to RT is uncertain. Given the ambiguity in the literature, a scoping review was undertaken to summarise evidence for the influence of proximity-to-failure on muscle hypertrophy, neuromuscular fatigue, muscle damage and perceived discomfort. Literature searching was performed according to PRISMA-ScR guidelines and identified three themes of studies comparing either: i) RT performed to momentary muscular failure versus non-failure, ii) RT performed to set failure (defined as anything other than momentary muscular failure) versus non-failure, and iii) RT performed to different velocity loss thresholds. The findings highlight that no consensus definition for “failure” exists in the literature, and the proximity-to-failure achieved in “non-failure” conditions is often ambiguous and variable across studies. This poses challenges when deriving practical recommendations for manipulating proximity-to-failure in RT to achieve desired outcomes. Based on the limited available evidence, RT to set failure is likely not superior to non-failure RT for inducing muscle hypertrophy, but may exacerbate neuromuscular fatigue, muscle damage, and post-set perceived discomfort versus non-failure RT. Together, these factors may impair post-exercise recovery and subsequent performance, and may also negatively influence long-term adherence to RT. KEY POINTS This scoping review identified three broad themes of studies investigating proximity-to-failure in RT, based on the specific definition of set failure used (and therefore the research question being examined), to improve the validity of study comparisons and interpretations. There is no consensus definition for set failure in RT, and the proximity-to-failure achieved during non-failure RT is often unclear and varies both within and between studies, which together poses challenges when interpreting study findings and deriving practical recommendations regarding the influence of RT proximity-to-failure on muscle hypertrophy and other short-term responses. Based on the limited available evidence, performing RT to set failure is likely not superior to non-failure RT to maximise muscle hypertrophy, but the optimal proximity to failure in RT for muscle hypertrophy is unclear and may be moderated by other RT variables (e.g., load, volume-load). Also, RT performed to set failure likely induces greater neuromuscular fatigue, muscle damage, and perceived discomfort than non-failure RT, which may negatively influence RT performance, post-RT recovery, and long-term adherence.
Multiple imputation for cause-specific Cox models: Assessing methods for estimation and prediction

In studies analyzing competing time-to-event outcomes, interest often lies in both estimating the effects of baseline covariates on the cause-specific hazards and predicting cumulative incidence functions. When missing values occur in these baseline covariates, they may be discarded as part of a complete-case analysis or multiply imputed. In the latter case, the imputations may be performed either compatibly with a substantive model pre-specified as a cause-specific Cox model [substantive model compatible fully conditional specification (SMC-FCS)], or approximately so [multivariate imputation by chained equations (MICE)]. In a large simulation study, we assessed the performance of these three different methods in terms of estimating cause-specific regression coefficients and predicting cumulative incidence functions. Concerning regression coefficients, results provide further support for use of SMC-FCS over MICE, particularly when covariate effects are large and the baseline hazards of the competing events are substantially different. Complete-case analysis also shows adequate performance in settings where missingness is not outcome dependent. With regard to cumulative incidence prediction, SMC-FCS and MICE are performed more similarly, as also evidenced in the illustrative analysis of competing outcomes following a hematopoietic stem cell transplantation. The findings are discussed alongside recommendations for practising statisticians.
On Computing Prestige in a Network with Negative Relations

AbstractThe computation of network prestige is an important issue in studyingnetworks such as the Internet and social networks. A number of iterativemethods have been proposed for the measurement of prestige of symmetricor asymmetric network. The PageRank algorithm has been successfullyapplied in the computation of ranking of webpages and data mining in theInternet. In this paper, we propose a revised PageRank algorithm for thecomputation of prestige of a general networks and extend it to handle thecase of negative relations.Key Words. Markov chain, Networks, Prestige, Negative Relations.Subject Classiﬂcations. 65C20, 65F10. 1. Introduction The measurement of prestige in a network is an important issue [4, 15] and ithas many applications such as the study of ranking of webpages [11, 12], socialnetworks [4, 15] and disease transmission [3]. A number of methods based on thecomputation of eigenvectors have been proposed in the literatures [4, 15]. In anetwork, being chosen or nominated by a popular or powerful person would addone’s popularity [4]. In the following, we give an example of a social network ofﬂve persons [4]. In the network, 3 is chosen (or supported) by 1 and 2, 4 is chosen by 3 and5 is chosen by 4. The relations can be represented by using an 5
Stress-Induced Mutagenesis, Gambler Cells, and Stealth Targeting Antibiotic-Induced Evolution

Mechanisms of evolution and evolution of antibiotic resistance are both fundamental and world health problems. Stress-induced mutagenesis defines mechanisms of mutagenesis upregulated by stress responses, which drive adaptation when cells are maladapted to their environments—when stressed. ABSTRACT Mechanisms of evolution and evolution of antibiotic resistance are both fundamental and world health problems. Stress-induced mutagenesis defines mechanisms of mutagenesis upregulated by stress responses, which drive adaptation when cells are maladapted to their environments—when stressed. Work in mutagenesis induced by antibiotics had produced tantalizing clues but not coherent mechanisms. We review recent advances in antibiotic-induced mutagenesis that integrate how reactive oxygen species (ROS), the SOS and general stress responses, and multichromosome cells orchestrate a stress response-induced switch from high-fidelity to mutagenic repair of DNA breaks. Moreover, while sibling cells stay stable, a mutable “gambler” cell subpopulation is induced by differentially generated ROS, which signal the general stress response. We discuss other evolvable subpopulations and consider diverse evolution-promoting molecules as potential targets for drugs to slow evolution of antibiotic resistance, cross-resistance, and immune evasion. An FDA-approved drug exemplifies “stealth” evolution-slowing drugs that avoid selecting resistance to themselves or antibiotics.
Fluorescence exclusion: a rapid, accurate and powerful method for measuring yeast cell volume

Cells exist in an astonishing range of volumes across and within species. However, our understanding of cell size control remains limited, due in large part to the challenges associated with accurate determination of cell volume. Much of our comprehension of size regulation derives from models such as budding and fission yeast, but even for these morphologically stereotypical cells, assessment of cell volume has relied on proxies and extrapolations from two-dimensional measurements. Recently, the fluorescence exclusion method (FXm) was developed to evaluate the size of mammalian cells, but whether it could be applied to smaller cells remained unknown. Using specifically designed microfluidic chips and an improved data analysis pipeline, we show here that FXm reliably detects subtle difference in the volume of fission yeast cells, even for those with altered shapes. Moreover, it allows for the monitoring of dynamic volume changes at the single-cell level with high time resolution. Collectively, our work reveals how coupling FXm with yeast genetics will bring new insights into the complex biology of cell growth. SUMMARY STATEMENT Fluorescence exclusion provides a unique method to accurately measure the volume of yeast cells at both the population and single-cell levels.
Coping Style, Insomnia, and Psychological Distress Among Persons With Gastrointestinal Cancer

Background Although insomnia has been shown to play an important role in psychological distress among gastrointestinal cancer patients, little is known about the internal mechanisms underlying this relation. Coping styles have been shown to moderate the effect of sleep on emotion, and this moderating effect may be age-dependent. Objective The aim of this study was to test how age and coping styles moderate the role of insomnia on psychological distress among gastrointestinal cancer patients. Methods This was a descriptive cross-sectional study with a convenience sample. A total of 352 patients with gastrointestinal cancer from two tertiary hospitals were recruited to participate in this study. The Kessler Psychological Distress Scale, the Insomnia Severity Index, and the Simplified Coping Style Questionnaire were used to assess patients’ psychological distress, insomnia, and coping styles, respectively. We performed hierarchical multiple regression and Johnson–Neyman statistical analysis to probe the moderating effect of coping styles and age. Results The mean age of the participants was 58.32 years. Nearly half of the participants reported psychological distress, and many reported insomnia. Insomnia was significantly positively associated with psychological distress. Coping styles and age moderated the relationship between insomnia and distress. Specifically, positive coping buffered the effect of insomnia on psychological distress only for subjects aged 34 years and younger; negative coping exacerbated the above relationship in patients aged 68 years and older while weakening the relationship in participants aged 51 years and younger. Discussion There are age differences in the moderating role of coping styles on the relationship between insomnia and distress among gastrointestinal cancer patients. This study provides preliminary evidence to inform tailored guidance on coping styles by age groups to attenuate the risk for psychological distress related to insomnia in the cancer population.
Significance of the Ontological/Utilitarian Distinction Among Measures of Individualism and Collectivism

A meta-analysis reported in this article compared the effects of ontological and utilitarian measures of individualism and collectivism on the findings of published research. Ontological differences in individualism and collectivism concern the degree to which either individuals or collectivities are interpreted as the primary entities comprising social reality. Reflecting these differences are measures that include subjects such as the degree of permanence and sense of collective obligation associated with interpersonal, group, or community relationships. Utilitarian distinctions in individualism and collectivism involve beliefs that either individuals or collectivities are instrumental in the pursuit of valued outcomes. Related measures include items concerning the attractiveness or consequences of working alone versus working in a group. Results of the meta-analysis indicated that ontological and utilitarian measures have produced differing findings in several notable instances, with utilitarian measures producing larger effects.
Embedded implementation of active damping in hydraulic valves

The objective of this thesis is to implement an embedded solution of active damping in hydraulic valves, which aims at reducing vibrations in mobile machines with on-board electronics. Oscillations in mobile machines are usually problematic and have been for a long time. They often occur due to the fast movements with requirements of heavy loads. The solutions are often in form of limiting the operator’s speed of using built-in damping in the valve. However, these solutions have often drawbacks in form of decreased efficiency and moderate damping effect. With the developments in electrification in hydraulics it is now more convenient to use control strategies to reduce oscillations and vibrations with software. Active damping has previously been done but experiments with embedded implementations needs to be investigated further. This thesis derives an adaptive control solution which is based on different papers and previous master’s thesis projects, and implements it on a mobile machine, a forwarder. The main conclusion that can be drawn from this thesis is that the embedded processor, with fixed-point arithmetic, is not sufficient for the derived algorithms to be fully adaptive to system changes. Instead a simpler method has been used for the experiments in laboratory environment and on the forwarder. The experiments indicate that the simpler method for the active damping solution will still dampen the system drastically, and approximately reduce pressure oscillations by 30-50%.