Miracle-dz/UniFAR-FTUs huggingface dataset:
Trunk Muscle Activation Patterns Differ Between Those With Low and High Back Extensor Strength During a Controlled Dynamic Task

It is proposed that reduced function in one of the spinal systems (active, passive, and neural) outlined by Panjabi could increase the risk of experiencing a low back injury (LBI). Also proposed is that reduced function in any one system can be compensated for by adjusting the time-varying recruitment of trunk muscles. This study addressed whether those with reduced active system function (WEAK), measured as back extensor strength, would have different trunk muscle activation patterns than those with higher function (STRONG), and secondly whether this relationship would be modified following recovery from a LBI. Sixty men participated, 30 recently recovered from LBI (rLBI, 4–12 weeks post injury) and 30 who had not had a LBI in the last year (ASYM). ASYM and rLBI participants were separated into STRONG and WEAK sub-groups if their isometric back extensor strength was above or below their group median, respectively. Trunk electromyograms from 24 muscle sites were recorded during a highly controlled horizontal transfer task. Principal component analysis captured key muscle activation patterns (amplitude and temporal); then analysis of variance models tested for strength or group*strength effects on these patterns consistent with the two main objectives. Significant strength, or group by strength effects were found for 3/4 electromyographic comparisons. In general, the WEAK group required higher activation amplitudes of abdominal and back extensor muscles, and greater temporal responsiveness of back extensor muscles only to the changing external moments than those who were STRONG. Group by strength interactions found that participants in the rLBI group had greater differences between WEAK and STRONG participants for overall muscle activation amplitudes in both abdominal and back extensor muscles. This increase in muscle activation was interpreted as compensation for lower maximum force properties whereas the increased temporal responsiveness captured a greater need to modify the agonist back extensors muscle activation patterns only in response to changes in the dynamic moments. Interactions captured that the recent experience of pain (rLBI) modified the magnitude of adjustment in muscle activation patterns potentially adapting to an increased risk of instability (painful flare) events associated with a deficit (lower strength) of the active system.
Likelihood-based Density Estimation using Deep Architectures

Multivariate density estimation is a central problem in unsupervised machine learning that has been studied immensely in both statistics and machine learning. Several methods have thus been proposed for density estimation including classical techniques like histograms, kernel density estimation methods, mixture models, and more recently neural density estimation that leverages the recent advances in deep learning and neural networks to tractably represent a density function. In today’s age, when large amounts of data are being generated in almost every field, it is of paramount importance to develop density estimation methods that are cheap both computationally and in memory cost. The main contribution of this thesis is in providing a principled study of parametric density estimation methods using mixture models and triangular maps for neural density estimation. The first part of the thesis focuses on the compact representation of mixture models using deep architectures like latent tree models, hidden Markov models, tensorial mixture models, hierarchical tensor formats and sum-product networks. It provides a unifying view of possible representations of mixture models using such deep architectures. The unifying view allows us to prove exponential separation between deep mixture models and mixture models represented using shallow architectures, demonstrating the benefits of depth in their representation. In a surprising result thereafter, we prove that a deep mixture model can be approximated using the conditional gradient algorithm by a shallow architecture of polynomial size w.r. t. the inverse of the approximation accuracy. Next, we address the more practical problem of density estimation of mixture models for streaming data by proposing an online Bayesian Moment Matching algorithm for Gaussian mixture models that can be distributed over several processors for fast computation. Exact Bayesian learning of mixture models is intractable because the number of terms in the posterior grows exponentially w.r. t. to the number of observations. We circumvent this problem by projecting the exact posterior on to a simple family of densities by matching a set of sufficient moments. Subsequently, we extend this algorithm for sequential data modeling using transfer learning by learning a hidden Markov model over the observations with Gaussian mixtures. We apply this algorithm on three diverse applications of activity recognition based on smartphone sensors, sleep stage classification for predicting neurological disorders using electroencephalography data and network size prediction for telecommunication networks. In the second part, we focus on neural density estimation methods where we provide a unified framework for estimating densities using monotone and bijective triangular maps represented using deep neural networks. Using this unified framework we study the limitations and representation power of recent flow based and autoregressive methods. Based
Supporting User Steering In Large-Scale Workflows With Provenance Data. (Support des actions de pilotage dans les workflows à grande échelle avec données de provenance)

Computational Science and Engineering (CSE) workflows are large-scale, require High Performance Computing (HPC) execution, and have the exploratory nature of science. During the long run, which often lasts for hours or days, users need to steer the workflow by dynamically analyzing it and adapting it to improve the quality of results or to reduce the execution time. However, to steer the workflow, users typically perform several interactions (called user steering actions), which need to be tracked. Otherwise, users find it harder to understand how and what needs to be steered, they can steer in a misleading way, it can be difficult to explain the results that were consequences of their actions, and it can be impossible to reproduce the results. This thesis addresses this problem by proposing an approach that defines the fundamental concepts for user steering action; introduces the notion of provenance of steering actions; and contemplates a W3C PROV-compliant data diagram to model steering action data with provenance. Also, the approach presents system design principles to enable the management of steering action data by capturing, explicitly relating the actions to the rest of the workflow data, and storing these data efficiently. Two instances of this approach were designed and built: one is a lightweight tool to be plugged into parallel scripts and the other is to be used within a Parallel Workflow Management System, which are the two typical ways to conduct CSE experiments in HPC. Using real use cases in the Oil and Gas industry, the experiments show that the proposed approach enables users to understand how their actions directly affect the workflow results at runtime and that the system design principles were essential to add negligible overhead to the HPC workflows.
Invited Paper: Subsumption of Information Systems Education towards a Discipline of Design

Disruptive innovations continue to reshape channels of learning. The Information Systems discipline may be among the least immune to these disruptions. As such, students have greater access to the acquisition of the computing skills and knowledge that are commonly presumed to suffice entry-level employment positions sought after by graduates of Information Systems programs. Further, these same technologies disrupting education are shaping the organizational and business environment such that it is fair to reflect on the disposition and complexion of the discipline as a whole and surmise whether this past will predict the future. Moreover, businesses and organizations are finding that the supply chain of workers needed to harness these disruptive technologies flows neither exclusively, nor even optimally, through academia. Upon reflection of this disruptive circumstance of skills and knowledge development, we consider subsuming the IS discipline into the broader auspice of design buttressed equally by emphases on technical excellence, business acumen, and leadership. We explore principles for a design-focused philosophy for Information Systems education that assumes that while higher education programs may have lost the lead in technology skills development focused on entry-level employment, we may reassert our role in computing education through the embrace of design at the philosophical, epistemological, and pedagogical levels.
Handling the speed-accuracy trade-off in deep-learning based pedestrian detection systems. (Gestion du compromis vitesse- précision dans les systèmes de détection de piétons basés sur apprentissage profond)

The main objective of this thesis is to improve the detection performance of deep learning based pedestrian detection systems without sacrificing detection speed. Detection speed and accuracy are traditionally known to be at trade-off with one another. Thus, this thesis aims to handle this trade-off in a way that amounts to faster and better pedestrian detection. To achieve this, we first conduct a systematic quantitative analysis of various deep learning techniques with respect to pedestrian detection. This analysis allows us to identify the optimal configuration of various deep learning components of a pedestrian detection pipeline. We then consider the important question of convolutional layer selection for pedestrian detection and propose a pedestrian detection system called Multiple-RPN, which utilizes multiple convolutional layers simultaneously. We propose Multiple-RPN in two configurations -- early-fused and late-fused; and go on to demonstrate that early fusion is a better approach than late fusion for detection across scales and occlusion levels of pedestrians. This work furthermore, provides a quantitative demonstration of the selectivity of various convolutional layers to pedestrian scale and occlusion levels. We next, integrate the early fusion approach with that of pseudo-semantic segmentation to reduce the number of processing operations. In this approach, pseudo-semantic segmentation is shown to reduce false positives and false negatives. This coupled with reduced number of processing operations results in improved detection performance and speed (~20 fps) simultaneously; performing at state-of-art level on caltechreasonable (3.79% miss-rate) and citypersons (7.19% miss-rate) datasets. The final contribution in this thesis is that of an anchor classification layer, which further reduces the number of processing operations for detection. The result is doubling of detection speed (~40 fps) with a minimal loss in detection performance (3.99% and 8.12% miss-rate in caltech-reasonable and citypersons datasets respectively) which is still at the state-of-art standard.
Finite-Time Synchronization of Uncertain Complex Dynamic Networks with Nonlinear Coupling

The finite-time synchronization control is studied in this paper for a class of nonlinear uncertain complex dynamic networks. The uncertainties in the network are unknown but bounded and satisfy some matching conditions. The coupling relationship between network nodes is described by a nonlinear function satisfying the Lipchitz condition. By introducing a simple Lyapunov function, two main results regarding finite-time synchronization of a class of complex dynamic networks with parameter uncertainties are derived. By employing some analysis techniques like matrix inequalities, suitable controllers can be designed based on the obtained synchronization criteria. Moreover, with the obtained control input, the time instant required for the system to achieve finite-time synchronization can be estimated if a set of LMIs are feasible or an assumption on the eigenvalues of some matrices can be satisfied. Finally, the effectiveness of the proposed results is verified by numerical simulation.
Phonology

What is Phonology? Phonology can be described as an aspect of language that deals with rules for the structure and sequencing of speech sounds. Every language has a wide variety of speech sounds (phonemes). For example in English, the ng sound, as in ring, will never appear at the beginning of a word. Phonology rules also determine which sounds may be combined. For example, the combination of dn will not appear in sequence in the same syllable.
Robustly Complete Temporal Logic Control Synthesis for Nonlinear Systems

Modern systems such as spacecrafts and autonomous vehicles are complex yet safety-critical, and therefore the control methods that can deal with di erent dynamics and constraints while being provably correct are sought after. Formal methods are rigorous techniques originally used for developing and verifying nite-state systems with respect to speci cations in formal languages. This thesis is concerned with using formal methods in control synthesis for nonlinear systems, which can guarantee the correctness of the resulting control strategies. For nonlinear continuous-state dynamical systems, formal control synthesis relies on nite abstractions of the original system by discretizing the system state space and over approximating system transitions. Without further assumptions, control synthesis is usually not complete in the way that no control strategies can be found even if there exists one. To deal with this problem, this thesis proposes a formal control synthesis approach that is sound and robustly complete in the sense that correct control strategies can be found whenever the speci cations can be realized for the system with additional disturbance. Fundamental to the soundness and robust completeness is a xed-point characterization of the winning set of the system with respect to a given speci cation, which is the set of initial conditions that can be controlled to satisfy the speci cation. Regarding discrete-time systems, such characterizations are rst presented by using iterative computation of predecessors for basic linear temporal logic (LTL) speci cations, including invariance, reachability and reachand-stay. A more general class of LTL formulas, which can be translated into deterministic Büchi automata (DBA), is also considered, and an algorithm guided by the graph structure of the LTL-equivalent DBA is proposed for characterizing the winning set in this situation. It is then shown that the computational complexity of the algorithm can be reduced by using a pre-processing procedure to the graphs of the DBA. Because of the general nonlinearity, exact computation of winning sets is currently almost impossible. In this work, the conditions for set approximations are derived so that control synthesis is robustly complete. To meet such conditions, the proposed approach adopts interval arithmetic and a subdivision scheme in the approximation of predecessors. Under such a scheme, the system state space is adaptively partitioned with respect to both the given dynamics and speci cation and set approximation can be made arbitrarily precise to satisfy the robust completeness conditions. The proposed method is also shown applicable to sampled-data systems by computing validated solutions over one sampling period based on high-order Taylor expansion. Applications such as converter voltage regulation, parallel parking, and reactive locomotion planning problems are studied to show the e ectiveness and e ciency of the proposed approach.
A Bayesian-based framework for making inspection and maintenance decisions from data and expert knowledge

It is estimated that more than one-third of current infrastructure maintenance expenditure is wasted through poor decision-making. To make better decisions about maintenance, there is a need to provide better predictions of asset deterioration, and further, to use this information to plan inspections and appropriate repair actions. A number of statistical modelling techniques have been proposed to predict deterioration. However, these approaches can be difficult to apply in practice, for example when the time of deterioration is only known approximately from periodic inspections. Also, these approaches lack an easy way to incorporate knowledge about the deterioration process that can readily be considered when judgements are made by experienced maintainers. Moreover, in practice, the size of available datasets on deterioration is often limited; hence there is a need to blend data with knowledge. This thesis presents a framework for predicting deterioration and reasoning about the effects of repair using both the available data and expert knowledge that can support inspection and maintenance-related decisions. The framework uses Bayesian modelling, combining two types of Bayesian approaches: Bayesian statistical models and Bayesian Networks (BNs). Bayesian statistical models are used to estimate the parameter of statistical distributions, modelled as continuous variables. On the other hand, BNs model causal or influential relationships between (primarily) discrete variables to make predictions and can be based on elicited knowledge. This thesis builds on earlier work that combines these two forms of model, with both the continuous variables from Bayesian statistical models and the discrete variables of BNs. We refer this type of model to as a hybrid BN. The use of hybrid BNs is possible using an already existing algorithm that dynamically discretises continuous variables in a BN. BNs within the framework can be combined to model the different aspects of deterioration needed in different circumstances. The rate of deterioration can be learnt from censored deterioration data inferred from inspection records and knowledge elicited from engineers. Asset sharing similar characteristics can be grouped, and when a group contains only a few instances in the available data, data from related groups can be used to constrain the parameter learning. Deterioration through multiple condition states can be modelled. The deterioration of different components of complex structures can be combined. Finally, we model the effect of repair actions and show how to plan maintenance.
An Efficient, Lung-Targeted, Drug-Delivery System To Treat Asthma Via Microparticles

Background Chronic diseases such as diabetes, asthma, and heart disease are the leading causes of death in developing countries. Public health plays an important role in preventing such diseases to improve individuals’ quality of life. Conventional dosage schemes used in public health to cure various diseases generally lead to undesirable side effects and renders the overall treatment ineffective. For example, a required concentration of drug cannot reach the lungs using conventional methods to cure asthma. Microspheres have emerged as a confirmed drug-delivery system to cure asthma. Method In this paper, a salbutamol-loaded poly lactic acid-co-glycolic acid-polyethylene glycol (PLGA-PEG) microsphere (SPP)-based formulation was prepared using a Buchi B-90 nanospray drier. Face-centered central composite design (CCD) was applied to optimize the spray-drying process. Results The drug content and product yield were found to be 72%±0.8% and 86%±0.4%, respectively; drug release (91.1%) peaked for up to 12 hrs in vitro. Microspheres obtained from the spray dryer were found to be shriveled. The experiments were carried out and verified using various groups of rabbits. In our study, the particle size (8.24 µm) was observed to be an essential parameter for drug delivery. The in vivo results indicated that the targeting efficacy and drug concentration in the lung was higher with the salbutamol-loaded PLGA-PEG SPP formulation (1,410.1±10.11 µg/g, 15 mins), as compared to the conventional formulation (92±0.56 µg/g, 10 min). The final product was stable under 5°C±2°C, 25°C±2°C, and 40°C±2°C/75%±5% relative humidity. In addition, these co-polymers have a good safety profile, as determined by testing on human alveolar basal epithelium A549 cell lines. Conclusion Our results prove that microspheres are an alternative drug-delivery system for lung-targeted asthma treatments used in public health.
Nostoc sphaeroids Kütz powder ameliorates diet-induced hyperlipidemia in C57BL/6j mice

Background Hypercholesterolemia is a disease associated with numerous health problems. Growing evidence indicates that hypercholesterolemia, hyperlipidemia is closely linked to chronic inflammation, which can lead to cardiovascular disease, fatty liver disease, and type 2 diabetes. Objective The purpose of this study was to investigate the protective effect of Nostoc sphaeroids Kütz (NO) on diet-induced hyperlipidemia in mice. Design At first, experimental animals received a high-fat diet (HFD) for 4 weeks, and then received a HFD supplemented with 2.5% or 7.5% NO for 6 weeks. In the current study, results show that treatment with NO decreases weight gain and liver index induced by HFD. In addition, the serum levels of TC, TG and LDL are significantly decreased in NO treatment groups. Results From the results of Oil Red staining and Hematoxylin and eosin staining (HE), treatment with NO significantly reduces liver lipid accumulation and protect liver structure. Further analysis reveals that NO has positive effects on liver lipid metabolism and inflammation, as showed by the lower protein expressions of FAS and SREBP-1, the lower concentrations of TNF-α, IL-1β, IL-6, and the lower gene expressions of TNF-α, IL-1β, IL-6 and NF-kB. Conclusions Our results indicate that NO may significantly ameliorate diet-induced hyperlipidemia, which is possibly associated with improving liver lipid metabolism and reducing chronic inflammation.
Urinary Microbiome Evaluation in Patients Presenting with Hematuria with a Focus on Exposure to Tobacco Smoke

Purpose To better characterize the urinary microbiome in males and contribute to overall understanding of the urinary microbiota specifically in patients undergoing evaluation for possible bladder cancer, stratified by risk exposure to smoking. Patients and Methods Recruitment of 43 male patients in a sequential manner presenting for hematuria evaluation to a single institution was undertaken. Mid-stream urine specimen pellets were processed through a DNA isolation protocol before undergoing PCR amplification, purification, and 16S rRNA gene sequencing. Gene sequences were clustered into operational taxonomic units and statistical analysis was performed to determine specimen diversity and phylogenetic trends. Results No significant difference in microbial diversity was found between the specimens. On subgroup analysis, no significant difference was observed when stratified by either tobacco smoking history or by newly diagnosed urothelial bladder cancer. Variation in microbial diversity was seen amongst all analyzed specimens. Conclusion The results of our analysis of carefully selected subjects help to better characterize the urinary microbiome in males and supplements the limited available information on the interrelationship between the urinary microbiome and development of genitourinary malignancy. No significant difference was observed in our small sample size when stratified by tobacco exposure or newly diagnosed bladder cancer.