Miracle-dz/UniFAR-FTUs huggingface dataset:
Driver behavior recognition using recurrent neural network in multiple depth cameras environment

To improve the driving safety triggered by driver’s behavior recognition in an in-car environment, we propose to use depth cameras mounted in a car to generate behavior models generated by a deep learning algorithm for a driver’s behavior classification. The contribution of this paper is trifold: 1) The proposed multi-view driver behavior recognition system can handle the occlusion problem happened in one of the cameras; 2) Using the recurrent neural network can effectively recognize the continuous time behavior; 3) the average recognition accuracy of proposed systems can achieve 83% and 88%, respectively. Introduction A driver’s behavior plays an important role to affect the traffic safety. For example, answering a phone, watching a video, or chatting with the people with a head turning behavior often lead the following car accidents. To increase a driving safety, a driver’s behavior is analyzed, understood, and recognized [1, 2] to assist a driver to behave in a proper manner in a car. For example, Jain et al. [2] proposed to utilize cameras to understand a driver’s behavior in an in-vehicle environment. However, it is challenging to use an in-car camera for behavior recognition due to the light changing, occlusion, and the clutter issues. Furthermore, in a limited in-car space, as shown in Fig. 1 (a), mounting positions of a camera to capture a driver’s behavior is also very limited. Based on a limited mounting position, the captured content of a frame leads severe self-occlusion issue, as shown in Fig. 1 (b). Figure 1. In-vehicle environment: (a) In-vehicle environment is a narrow space, (b) Driver in a sitting position and whole body was occluded by other
Robust predictive control for anti-rolling path following of underactuated surface vessels using adaptive Kalman filter

With the strong time-varying disturbances in wave fields, the large sway motion of underactuated surface vessels can be caused leading to the control performance reduction and large range of roll angle, such that a novel path following control with roll constraints is proposed. Firstly, the affine switching system model is constructed with coupled surge, sway, roll, and yaw. Then, with augmented state adaptive Kalman filter, the estimations of system state and disturbance can be obtained for the feedforward compensation. Considering the existing estimation error between real and estimated states, the state estimation has been adopted directly in the controller design for the performance improvements. With the convex optimization of linear matrix inequalities, the invariant ellipsoid constraint ensures the close-loop system uniformly bounded and stable. Lastly, the simulation results have verified the effectiveness of the designed control approach.
Toward Collision-Free and Efficient Coordination for Automated Vehicles at Unsignalized Intersection

With the significant advance of vehicle-to-everything (V2X) techniques, unsignalized intersection coordination has been widely recognized to facilitate the development of automated vehicles (AVs) for the intelligent transportation system. However, how to guarantee driving safety while improving the unsignalized intersection management efficiency is a challenging issue. In this article, we investigate the collision-free and efficient V2X-enabled AV scheduling problem at unsignalized intersections. First, by dividing the intersection zone into different collision sections (CSs), we formulate the intersection collision-free model into an absolute value programming (AVP) problem, which is proved to be NP-hard. We consider both nonplatoon and platoon traffic scenarios, and unlike previous algorithms, which require to control all the AVs at each scheduling step with computational intractability, our scheduling algorithm can assign a feasible time for each arriving AV with low complexity. Further, we propose an alternately iterative descent method (AIDM) to solve the AVP problem by assigning the optimal entering time for each arriving AV. Through extensive simulations with various traffic data generated by SUMO, we demonstrate that our proposed AIDM algorithm can significantly enhance the scheduling performance in terms of passing delay and scheduling throughput. Even though the AIDM algorithm achieves the same level of transportation performances with the state-of-the-art algorithm, it advances dramatically in computational complexity and communication overhead, which is easier to be implemented in practice.
Frequent acquisition of multiple strains of methicillin-resistant Staphylococcus aureus by healthcare workers in an endemic hospital environment.

Methicillin-resistant Staphylococcus aureus (MRSA) has been an endemic nosocomial pathogen at the VA medical center (VAMC) in Providence, Rhode Island since 1981. From 1985 to 1987, more than 30% of all unique S aureus isolates were methicillin resistant. To evaluate the frequency of acquisition of MRSA isolates by healthcare workers, we compared the antimicrobial susceptibility patterns, multilocus enzyme genotypes and plasmid profiles of isolates recovered from nasal and hand cultures from VAMC nurses and house staff on rotation at the VAMC with those of clinical isolates from patients at the VAMC and four other affiliated hospitals. Fifty-six percent of ward nurses cultured (n = 112) were colonized with S aureus, of which 65% was methicillin resistant. Six isolates of MRSA were identified on the initial culturing of house staff (n = 65); 16 MRSA isolates were recovered at the end of a four-week rotation (p less than .02). Phenotypic and genotypic analyses demonstrated that numerous distinct MRSA strains were recovered in the study period. The incidence of MRSA among clinical isolates at the VAMC and affiliated institutions was remarkably constant throughout the three-year study period. Moreover, despite regularly sharing resident physicians, interns and medical students, MRSA isolates were commonly recovered at the other university-affiliated hospitals. Our study failed to reveal evidence of significant interhospital transmission of MRSA isolates by healthcare workers. While healthcare workers may contribute to the dissemination of MRSA within institutions, they appear to be less important in spreading MRSA between institutions.
Generalised networks for protein interaction analysis

Protein interaction networks (PINs) are mathematical representations of interactions between proteins within organisms. Studying their properties can give insights into biological functions and the importance of proteins, and it can therefore aid in drug- discovery. Graphs are the most common mathematical object used to represent PINs. In this thesis, we investigate generalised mathematical representations of PINs. In particular, we examine multilayer networks (MLNs) and node-weighted networks. These mathematical objects allow the construction of temporal PINs and tissue-specific PINs by combining gene-expression data with PINs. We introduce promiscuity as an information-theoretical measure of a node’s distribution of neighbours across different layers in MLNs. We examine promiscuity in synthetic networks and tissue-specific PINs and find that the vast majority of proteins are not cell-type specific. Integrating temporal gene-expression data with PINs allows us to create temporal PINs in the form of MLNs. We investigate an eigenvector-based temporal centrality in a temporal PIN of yeast during the cell cycle. We thereby examine the change of proteins’ importance over time, which reflects their activity during the cell cycle. We then discuss the detection of community structure in node-weighted networks. For synthetic networks, we show that considering node weights can alter detected community structure. We combine a human PIN with gene-expression data to construct tissue-specific PINs and investigate their community structure. Comparing the detected communities with gene-ontology information, we find some tissue-specific functions of these PINs. Overall, the case studies in this thesis suggest that MLN and node-weighted networks are suitable for the integration of protein-interaction data with other biological data sets.
A Flexible Collision Avoidance Strategy for the Formation of Multiple Unmanned Aerial Vehicles

This paper develops a flexible collision avoidance strategy for the formation of multiple unmanned aerial vehicles (multi-UAV). Firstly, To make full use of the information flow from the communication of the formation, an improved artificial potential field (IAPF) function is constructed by adding the communication topology and communication weights. Based on the IAPF functions, both collision avoidance within the formation and obstacle avoidance outside the formation are well organized to ensure flight safety. Secondly, to fuse collision avoidance and formation keeping, we design the null space behavioral (NSB) approach to combine them into a single motion command, where the formation keeping algorithm is designed as a second-order consensus algorithm. In this way, the flexible collision avoidance strategy is proposed based on IAPF and consensus, handling the relationship between collision avoidance and formation keeping flexibly. Finally, three-dimensional multi-UAV flight simulation validates the effectiveness of the proposed strategy that the formation can both implement collision avoidance quickly and keep a good geometric formation configuration simultaneously.
On the Assumptions That We Make About the World Around Us: A Conceptual Framework for Feature Transformation Effects

Various phenomena such as halo effects, spontaneous trait inferences, and evaluative conditioning have in common that assumptions about object features (e.g., whether a person is intelligent or likeable) are influenced by other object features (e.g., whether that person is attractive or co-occurs with other liked persons). Surprisingly, these phenomena have rarely been related to each other, most likely because different phenomena are described using different terms. To overcome this barrier, we put forward a conceptual framework that can be used to describe a wide range of these phenomena. After introducing the four core concepts of the framework, we illustrate how it can be applied to various phenomena. Doing so helps to reveal similarities and differences between those phenomena, thus improving communication and promoting interactions between different areas of research. Finally, we illustrate the generative power of the framework by discussing some of the new research questions that it highlights.
Optimal Learning Theory and Approximate Optimal Learning Algorithms

The exploration/exploitation dilemma is a fundamental but often computationally intractable problem in reinforcement learning. The dilemma also impacts data efficiency which can be pivotal when the interactions between the agent and the environment are constrained. Traditional optimal control theory has some notion of objective criterion, such as regret, maximizing which results in optimal exploration and exploitation. This approach has been successful in multi-armed bandit problem but becomes impractical and mostly intractable to compute for multi-state problems. For complex problems with large state space when function approximation is applied, exploration/exploitation during each interaction is in practice generally decided in an ad hoc approach with heavy parameter tuning, such as -greedy. Inspired by different research communities, optimal learning strives to find the optimal balance between exploration and exploitation by applying principles from optimal control theory. The contribution of this thesis consists of two parts: 1. to establish a theoretical framework of optimal learning based on reinforcement learning in a stochastic (non-Markovian) decision process and through the lens of optimal learning unify the Bayesian (model-based) reinforcement learning and the partially observable reinforcement learning. 2. to improve existing reinforcement learning algorithms in the optimal learning view and the improved algorithms will be referred to as approximate optimal learning algorithms. Three classes of approximate optimal learning algorithms are proposed drawing from the following principles respectively: (1) Approximate Bayesian inference explicitly by training a recurrent neural network entangled with a feed forward neural network; (2) Approximate Bayesian inference implicitly by training and sampling from a pool of prediction neural networks as dynamics models; (3) Use memory based recurrent neural network to extract features from observations. Empirical evidence is provided to show the improvement of the proposed algorithms.
An Overview of Natural Language Processing

: Natural Language Processing is branch of machine learning that deals with text and speech. Natural Language Processing is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing Natural Language Processing, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis
Topical Review: Cortical Malformation and Pediatric Epilepsy: A Molecular Genetic Approach

Genetic malformations of the cerebral cortex are important causes of neurologic morbidity in children because they are often associated with developmental delay, motor disturbances (cerebral palsy), and epilepsy. Primary autosomal recessive microcephaly is a cortical malformation with a low incidence of epilepsy. One of its causative genes, ASPM, might play an important role in regulating proliferation of neuronal progenitor cells. Mutations in ASPM do not seem to affect later stages of cortical development, such as neuronal migration, and this might be responsible for the low epileptogenicity of this malformation. ASPM might also have played an important role in the evolutionary expansion of the human brain. Bilateral frontoparietal polymicrogyria, on the other hand, is a highly epileptogenic malformation. Its causative gene, GPR56 , is also expressed in the neurogenic regions of the cortex, but its primary function might be in the determination of cell fate and/or cortical patterning. Further studies of these genes will likely lead to a better understanding of human brain development and epilepsy. (J Child Neurol 2005;20:300—303).
Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics

Increasingly learning analytics (LA) has begun utilising staff- and student-facing dashboards capturing visualisations to present data to support student success and improve learning and teaching. The use of LA is complex, multifaceted and raises many issues for consideration, including ethical and legal challenges, competing stakeholder views and implementation decisions. It is widely acknowledged that LA development requires input from various stakeholders. This conceptual article explores the LA literature to determine how student perspectives are positioned as dashboards and visualisations are developed. While the sector acknowledges the central role of students, as demonstrated here, much of the literature reflects an academic, teacher-centric or institutional view. This view reflects some of the key ethical concerns related to informed consent and the role of power translating to a somewhat paternalistic approach to students. We suggest that as students are the primary stakeholders – they should be consulted in the development and application of LA. An ethical approach to LA requires that we engage with our students in their learning and the systems and information that support that process rather than assuming we know we know what students want, what their concerns are or how they would like data presented.
From Concept Models to Conceptual Data Models

In order to develop a harmonised and efficient IT system, such as a database, it is important to be familiar with the underlying concept model (concept systems) for the relevant domain which the IT system should be designed to accommodate, as this forms the necessary firm foundation for designing the conceptual data model. Although there is no one-to-one correlation between concept and characteristic features in the concept model and classes and attributes in the conceptual data model, there are many similarities between concept modelling and conceptual data modelling, and by closely examining the relationship between the two models, we have strived to construct an algorithm for creating conceptual data models in Unified Modelling Language (UML) on the basis of concept models that adhere to the traditional principles and methods of terminology work.
Slow‐stream rehabilitation: is it effective?

Fifty‐two patients, who were not considered to be suitable for traditional therapy, were evaluated on their admission (53 admissions) to the Slow Stream Rehabilitation Unit at Greenwich Hospital, and on and after their discharge from hospital, with a minimum follow‐up period after discharge from hospital of 13 months. Mobility and the capacity to perform basic self‐care activities were assessed by means of a set protocoi while traditional rehabilitation therapies were employed. Of the 53 admissions to hospital, there were eight inpatient deaths and 45 discharges from the Unit: 19 discharges to home and 26 discharges to institutional care. This represents a considerable improvement over the initial assessment that all patients would need nursing home accommodation or prolonged hospitalization. At the second follow‐up examination, seven of the 19 patients who were discharged home originally, still resided at home. There were 15 patients in nursing care while 23 patients had died. Most patients had maintained their discharge levels of mobility and self‐care, unless a further disability had supervened. The majority of carers and patients expressed gratitude for the opportunity that the patients had had to undergo therapy, with the possibility of an eventual return to their own homes, rather than to have proceeded directly to a nursing home.
FREQUENCY OF VANCOMYCIN RESISTANT STAPHYLOCOCCUS AUREUS AMONG CLINICAL ISOLATES OF MRSA COLLECTED FROM TERTIARY CARE HOSPITALS OF LAHORE, PAKISTAN

Objective: To determine the frequency of vancomycin resistant Staphylococcus aureus (VRSA) among clinical isolates of methicillin resistant Staphylococcus aureus (MRSA). Study Design: Descriptive study.Place and Duration of Study: Department of Microbiology, University of Health Sciences, Lahore from Jul 2014 to Dec 2014.Material and Methods: A total of 240 (n=240) clinical isolates of MRSA were collected by consecutive sampling from different tertiary care hospitals of Lahore. Re-confirmation of MRSA was done by the standard microbiological methods using disc diffusion technique according to Clinical Laboratory Standards Institute (CLSI) guidelines 2014. Minimum inhibitory concentration (MIC) of the vancomycin was done by agar dilution method.Results: It was found that vancomycin inhibited MRSA strains in the range of 1.0–2.0 µg/ml. Ninety percent (90%) of the strains inhibited at 1 µg/ml while 25 (10.41%) strains showed growth at 1 µg/ml which indicates that their MIC was 2 µg/ml. No vancomycin resistant (VRSA) or intermediate strains (VISA) of MRSA were found during the study but there were significant numbers of isolates having ≥1 µg/ml MIC of vancomycin.Conclusion: Vancomycin has until now excellent activity against clinical isolates of Methicillin-resistant Staphylococcus aureus.
Cooperative Information Systems: A Manifesto *

Information systems technology, computer-supported cooperative work practice, and organizational modeling and planning theories have ev olved with on ly accidental contact t o each o ther. Cooperative information systems is a relatively y oung research a rea which tries to systematically investigate the synergies between these research fields, driven by the observation that change management is the central issue facing all three areas today and that all three fields have indeed developed rather similar strategies to cope with change. In this paper, we therefore propose a framework which views cooperative information systems as composed from three interrelated facets, viz. the system facet, the group collaboration facet, and the organizational facet. We present an overview of these facets, emphasizing strategies they have developed over the past f ew years to accommodate change. We also d iscuss the propagation o f change across the facets, and sketch a basic software architecture intended to support the rapid construction and evolution of cooperative information systems on top of existing organizational and technical legacy.
Breathing pattern in chronic quadriplegia

The resting breathing pattern in 14 chronic C6 and C7 traumatic quadriplegics was compared with six age-matched healthy controls. All quadriplegics had complete motor loss below the lesion level and were at least two years postinjury. Tests were performed with subjects seated. Forced vital capacity (FVC), forced expiratory volume in one second (FEV1.0), inspiratory capacity (IC), and maximum inspiratory mouth pressure (Pimax) were measured. Resting breathing pattern was assessed for 20 minutes using mercury in rubber strain gauges and a computer-assisted data acquisition and analysis program. Inspiratory time (Ti), expiratory time (Te), and tidal volume (Vt) were measured, and the remaining timing components were calculated from these values. The variability of breathing was assessed by comparing the coefficients of variation of each variable. The FVC, IC, and Pimax were significantly reduced; Vt was significantly lower (p less than 0.01) and frequency significantly elevated (p less than 0.05) in quadriplegics. The decreased Vt in quadriplegics was due entirely to a significantly decreased mean inspiratory flow (p less than 0.01); Ti was the same in quadriplegics as in controls. The ratio of mean Ti to total cycle time (Ti/Ttot) was significantly longer in quadriplegics (p less than 0.005). There was no difference in variability of breathing between the two groups for any timing component of ventilation. There was no significant difference in sighing frequency between groups for either breaths greater than 2x mean Vt or breaths greater than 3x mean Vt. Chronic quadriplegics demonstrated a rapid, shallow breathing pattern, probably due to the mechanical restrictions resulting from paralysis of the thorax musculature. They retained the ability to sigh, suggesting that chest wall afferents may not be required in this process.