Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining Pathways from Innovation Orientation to Patient Satisfaction: A Relational View of Healthcare Delivery

The federal government and industry leaders view innovation as a potentially fruitful way to improve hospital performance, specifically patient satisfaction. However, translating a hospital's innovation orientation into improved performance is challenging given that important network participants—namely physicians—may possess different aims. Grounded in Relational RBV, this study tests a model linking innovation orientation to patient satisfaction through a pathway of knowledge-sharing routines (physician partnering and customer relationship management) and complementary capabilities (hospital responsiveness). Further, this study investigates the moderating role of physician employment (a form of governance) by examining hospitals with high and low levels of employed physicians. Structural Equation Modeling results from a paired sample of primary survey and secondary data from 173 acute care hospitals in the USA reveal the following. Hospitals with high levels of employed physicians translate innovation orientation into patient satisfaction by using customer relationship management (CRM) programs to influence hospital responsiveness directly, ultimately leading to patient satisfaction. Hospitals with low levels of physician employment use CRM programs in a fully mediated fashion to inform physician partnering activities, which influence hospital responsiveness, driving patient satisfaction
Optimal control allocation of quadrotor UAVs subject to actuator constraints

In this paper the control allocation problem is revisited in the context of quadrotor unmanned aerial vehicles (UAVs). Two control allocation strategies are presented, both aiming to avoid output saturation. The first strategy is based on the optimization of a weighted least squares (WLS) criterion subject to boundary constraints. The second, called direct control allocation (DCA), aims to maintain constant the direction of the desired virtual efforts vector. As a modification of this strategy, we introduce the partial control allocation (PCA) scheme. In the latter, only the direction of a fraction of the desired virtual efforts vector is kept constant. It is also presented a discussion on the performance of the DCA and PCA schemes.
Harmonic approximation of velocity oscillations in electrical drives

The accuracy of an electrical drive moving at constant velocity significantly depends on the performance of the applied velocity estimation algorithm. In this paper, this dependence is investigated for estimation algorithms, which use digital filters and quantized encoder measurements. Assuming that the velocity controller, the estimation algorithm and the input-output behavior of the drive from desired torque to velocity are linear, the velocity controlled electrical drive is shown to have almost always no steady-state. Moreover, the velocity is found to be oscillating with a bounded oscillation amplitude. As the main result, a formula is derived which yields an approximation of the velocity oscillation in terms of Fourier series. The formula reveals the influence the encoder resolution and other electrical drive parameters have on the characteristics of the velocity oscillation. To verify the relations predicted by the formula, simulations of an electrical drive are performed.
Why aren't SE techniques being used by KBS systems developers already?

Software engineering methods can be viewed as five layers which constrain simple hacking. The layers have been individually developed over the last twenty years, and the early layers are more developed than the later ones. The first layer of software engineering technology is the use of life cycle models to structure software development projects. These allow managerial decisions to be made, and emphasise the consequences for later stages of early design decisions; the extreme case being the cost in maintenance of failures to develop systems to meet the real requirements of users. The second layer focusses on the design and implementation core of the life cycle and states the documentation and control procedures for version control, quality control, easier maintenance and project management. The third layer uses data from previous development projects as the basis for predictions of the time to develop systems, the cost and the resources required. In order to make such decisions in any engineering discipline it is necessary to collect data from a large number of commercial projects as has been done for software metrics such as the COCOMO model (Boehm, 1981). The fourth layer stipulates that the representations developed at each stage of the life cycle must be subject to processes of verification against previous representations and validation against the user or client’s need for a solution to their problem. With this stage comes the introduction of notations for the representations and guidelines on the assessments which should be made. Many sets of guidelines and notations are available in structured development methods from Yourdon, Jackson, Mascot, SSADM and others. The fifth layer requires that these guidelines for verification become formalised, firstly so that the verification can be proven to be correct, and secondly so that the development stage itself is replaced by a transformation process which operates on one representation to produce the second.
HOXC8 promotes breast tumorigenesis by transcriptionally facilitating cadherin-11 expression

Cell-cell adhesion molecule cadherin-11(CDH11) is preferentially expressed in basal-like breast cancer cells and facilitates breast cancer cell migration by promoting small GTPase Rac activity. However, how the expression of CDH11 is regulated in breast cancer cells is not understood. Here, we show that CDH11 is transcriptionally controlled by homeobox C8 (HOXC8) in human breast cancer cells. HOXC8 serves as a CDH11-specific transcription factor and binds to the site of nucleotides −196 to −191 in the CDH11 promoter. Depletion of HOXC8 leads to the decrease in anchorage-independent cell growth, cell migration/invasion and spontaneous metastasis of breast cancer cells; however, suppressed tumorigenic events were fully rescued by ectopic CDH11 expression in HOXC8-knockdown cells. These results indicate that HOXC8 impacts breast tumorigenesis through CDH11. The analysis of publically available human breast tumor microarray gene expression database demonstrates a strong positive linear association between HOXC8 and CDH11 expression (ρ = 0.801, p < 0.001). Survival analysis (Kaplan-Meier method, log-rank test) shows that both high HOXC8 and CDH11 expression correlate with poor recurrence-free survival rate of patients. Together, our study suggests that HOXC8 promotes breast tumorigenesis by maintaining high level of CDH11 expression in breast cancer cells.
LEARNING FOR FINE-GRAINED IMAGE CLASSIFICATION

Fine-grained image classification is quite challenging due to high inter-class similarity and large intra-class variations. Another issue is the small amount of training images with a large number of classes to be identified. To address the challenges, we propose a model for fine-grained image classification with its application to bird species recognition. Based on the features extracted by bilinear convolutional neural network (BCNN), we propose an on-line dictionary learning algorithm where the principle of sparsity is integrated into classification. The features extracted by BCNN encode pairwise neuron interaction in a translation-invariant manner. This property is valuable to fine-grained classification. The proposed algorithm for dictionary learning further carries out sparsity based classification, where training data can be represented with a less number of dictionary atoms. It alleviates the problems caused by insufficient training data, and makes classification much more efficient. Our approach is evaluated and compared with the state-of-the-art approaches on the CUB-200-2011 dataset. The promising experimental results demonstrate its efficacy and superiority.
Numerical Study on Onset Condition of Scour Below Offshore Pipeline Under Reversing Tidal Flow

It is important to study the onset condition of scour below offshore pipelines for the protection of pipeline. In this paper a new and small scale numerical reversing tidal flow flume is established using FLOW-3D Software. This numerical flume can simulate the time process of tidal level as well as the flow velocity and shows good reliability. Using this numerical flume, the onset condition of scour below offshore pipelines under reversing tidal flow is discussed. The flow fields at different times in a period are analyzed and the influences of middle tidal level, flow velocity and buried depth on pressure difference are studied systematically. The onset condition of scour below offshore pipeline for reversing tidal flow is determined, which supplies the technical support for practical engineering.
Distributed Bargaining Mechanisms for MIMO Dynamic Spectrum Access Systems

Dynamic spectrum access (DSA) and MIMO communications are among the most promising solutions to address the ever increasing wireless traffic demand. An integration that successfully embraces the two is far from trivial due to the dynamics of spectrum opportunities as well as the requirement to jointly optimize both spectrum allocation and spatial/antenna pattern in a distributed fashion. Regardless of spectrum dynamics and heterogeneity, existing literature on channel/power allocation in MIMO DSA systems is only applicable to centralized cases. Our objective here is to design distributed algorithms that jointly allocate opportunistic channels to various links and to simultaneously optimize the MIMO precoding matrices so as to achieve fairness or maximize network throughput. For self-interested DSA links, our distributed algorithm allows links to negotiate channel allocation based on Nash bargaining (NB) and configure the precoding matrices so that links' rate demands are guaranteed while the surplus resources (after meeting minimum rate demands) are fairly allocated. Next, we consider a network throughput maximization formulation (NET-MAX). Both the NB-based and NET-MAX problems are combinatorial with mixed variables. To tackle them, we first transform the original problems by incorporating the concept of timesharing. Using dual decomposition, we develop optimal distributed algorithms for timesharing case, which shed light on how to derive a distributed algorithm for the original problems. Our work fills a gap in the literature of channel allocation where a central controller is not available.
Fudan at MediaEval 2013: Violent Scenes Detection Using Motion Features and Part-Level Attributes

The Violent Scenes Detection Task of MediaEval provides a valuable platform for algorithm evaluation and performance comparison. This is a very challenging task as there exist many forms of violent scenes, which vary significantly in their visual and auditory clues. In this notebook paper, we describe our system used in MediaEval 2013, which focuses on the use of motion-based features and part-level semantic attributes. One of the key components of the system is a set of trajectory-based motion features that have been observed effective in last year’s evaluation. We also adopt a newly developed part-level attribute feature, which consists of detection scores of object and scene parts. Our results indicate that the trajectory-based motion features can still offer very competitive performance, and the attribute feature is also helpful under several situations. In addition, temporally smoothing detection scores can lead to a significant performance gain. We conclude that a successful violent scenes detection system should use truly multimodal features, ranging from motion-based to static visual descriptors, as well as audio and attribute features.
Emission Factors Derived from 13 Euro 6b Light-Duty Vehicles Based on Laboratory and On-Road Measurements

Tailpipe emissions of a pool of 13 Euro 6b light-duty vehicles (eight diesel and five gasoline-powered) were measured over an extensive experimental campaign that included laboratory (chassis dynamometer), and on-road tests (using a portable emissions measurement system). The New European Driving Cycle (NEDC) and the Worldwide harmonised Light-duty vehicles Test Cycle (WLTC) were driven in the laboratory following standard and extended testing procedures (such as low temperatures, use of auxiliaries, modified speed trace). On-road tests were conducted in real traffic conditions, within and outside the boundary conditions of the regulated European Real-Driving Emissions (RDE) test. Nitrogen oxides (NOX), particle number (PN), carbon monoxide (CO), total hydrocarbons (HC), and carbon dioxide (CO2) emission factors were developed considering the whole cycles, their sub-cycles, and the first 300 s of each test to assess the cold start effect. Despite complying with the NEDC type approval NOX limit, diesel vehicles emitted, on average, over the WLTC and the RDE 2.1 and 6.7 times more than the standard limit, respectively. Diesel vehicles equipped with only a Lean NOX trap (LNT) averaged six and two times more emissions over the WLTC and the RDE, respectively, than diesel vehicles equipped with a selective catalytic reduction (SCR) catalyst. Gasoline vehicles with direct injection (GDI) emitted eight times more NOX than those with port fuel injection (PFI) on RDE tests. Large NOX emissions on the urban section were also recorded for GDIs (122 mg/km). Diesel particle filters were mounted on all diesel vehicles, resulting in low particle number emission (~1010 #/km) over all testing conditions including low temperature and high dynamicity. GDIs (~1012 #/km) and PFIs (~1011 #/km) had PN emissions that were, on average, two and one order of magnitude higher than for diesel vehicles, respectively, with significant contribution from the cold start. PFIs yielded high CO emission factors under high load operation reaching on average 2.2 g/km and 3.8 g/km on WLTC extra-high and RDE motorway, respectively. The average on-road CO2 emissions were ~33% and 41% higher than the declared CO2 emissions at type-approval for diesel and gasoline vehicles, respectively. The use of auxiliaries (AC and lights on) over the NEDC led to an increase of ~20% of CO2 emissions for both diesel and gasoline vehicles. Results for NOX, CO and CO2 were used to derive average on-road emission factors that are in good agreement with the emission factors proposed by the EMEP/EEA guidebook.
FBK: Cross-Lingual Textual Entailment Without Translation

This paper overviews FBK's participation in the Cross-Lingual Textual Entailment for Content Synchronization task organized within SemEval-2012. Our participation is characterized by using cross-lingual matching features extracted from lexical and semantic phrase tables and dependency relations. The features are used for multi-class and binary classification using SVMs. Using a combination of lexical, syntactic, and semantic features to create a cross-lingual textual entailment system, we report on experiments over the provided dataset. Our best run achieved an accuracy of 50.4% on the Spanish-English dataset (with the average score and the median system respectively achieving 40.7% and 34.6%), demonstrating the effectiveness of a "pure" cross-lingual approach that avoids intermediate translations.
Overwintering of Encarsia tricolor on the cabbage whitefly

Encarsia tricolor is the dominant parasitoid species of the cabbage whitefly, Aleyrodes proletella. The latter finds sufficient overwintering habitats to appear in masses on cabbage crops during cultivation periods, whereas habitats with suitable overwintering hosts for E. tricolor are hardly available. Therefore, specific management strategies are needed to facilitate parasitoid overwintering. As a first step, this study aimed to provide general knowledge about the overwintering stages, the overwintering period and the overwintering success of E. tricolor on its primary host A. proletella. Results show that Encarsia tricolor successfully survived winter as immature stages, but no adults were found during late winter months. Visual observations revealed that at least 2.4% of A. proletella nymphs actually enclosed vital parasitoid eggs/ larvae during winter (n = 1,603), because they started to turn dark (parasitoid pupation) between 13-20 April. The proportion of adult emergence from these subsequently developed parasitoid pupae was 41%. In contrast, only 1.1% of parasitoid pupae collected in January overwintered successfully (n = 356). First adult E. tricolor were found on yellow sticky traps in the field between 4-11 May. The gained insights on the overwintering of E. tricolor are compared with the population dynamics of A. proletella on cabbage crops and conclusions for additional management strategies are discussed.
Evolutionary Energy Performance Feedback for Design ( EEPFD ) Interaction and automation for a design exploration process framework

In order to understand the applicability of multidisciplinary design optimization (MDO) to the building design process, a MDO framework, titled Evolutionary Energy Performance Feedback for Design (EEPFD), along with the prototype tool, H.D. S. Beagle, were developed to support designers with the incorporation of partially automated performance feedback during the early stages of design. This paper presents 2 experimental case studies, one from the design profession and the other from a design studio, that evaluate the applicability and impact of EEPFD on the early stage design process. Through these two case studies two different interaction and automation approaches for applying EEPFD are explored as part of the framework validation. Observed benefits, challenges and suggestions of EEPFD’s implementation are then presented and discussed.
Generalization Training in Special Education Teacher Preparation: Does It Exist?

Enormous resources are committed by universities to prepare special educators to impact student outcomes of our most vulnerable and neediest learners. Generalizing teaching skills from university to K-12 classrooms must be a component of teacher preparation curriculum; otherwise, we continue to merely train our teacher candidates and hope they sustain and generalize their newly acquired skills. Through self-reported surveys and extant data analysis, we identify the extent to which our sample of special education teacher preparation programs are teaching their teacher candidates to generalize newly acquired teaching skills to in-service settings in K-12 classrooms. Results indicate ambiguity with familiarity of generalization techniques and fidelity of implementation. Programming for generalization is absent in reviewed course syllabi, and student teaching supervisors report a disconnect between university and classroom realities. We recommend a systematic approach to programming for generalization by increasing awareness of generalization techniques through professional development, including accountability measures in course syllabi, focusing on high-leverage practices to create more cohesive preparation programs, and improving communication between instructors and student teaching supervisors. Limitations and future research recommendations are discussed.
A Brain Controlled Robotic Gait Trainer for Neurorehabilitation

This paper discusses a brain controlled robotic gait trainer for neurorehabilitation of Spinal Cord Injury (SCI) patients. Patients suffering from Spinal Cord Injuries (SCI) become unable to execute motion control of their lower proximities due to degeneration of spinal cord neurons. The presented approach can help SCI patients in neuro-rehabilitation training by directly translating patient motor imagery into walkers motion commands and thus bypassing spinal cord neurons completely. A non-invasive EEG based brain-computer interface is used for capturing patient neural activity. For signal processing and classification, an open source software (OpenVibe) is used. Classifiers categorize the patient motor imagery (MI) into a specific set of commands that are further translated into walker motion commands. The robotic walker also employs fall detection for ensuring safety of patient during gait training and can act as a support for SCI patients. The gait trainer is tested with subjects, and satisfactory results were achieved. Keywords—Brain Computer Interface (BCI), gait trainer, Spinal Cord Injury (SCI), neurorehabilitation.
Bayesian uncertainty quanti cation in linear models for di usion MRI

Di usion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By tting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coe cients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are a ne functions of the coe cients, the posterior distribution can be expressed in closed-form. We simulated measurements from singleand double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several di erent models: Di usion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quanti cation.
RedLeaf: Towards An Operating System for Safe and Verified Firmware

RedLeaf is a new operating system being developed from scratch to utilize formal verification for implementing provably secure firmware. RedLeaf is developed in a safe language, Rust, and relies on automated reasoning using satisfiability modulo theories (SMT) solvers for formal verification. RedLeaf builds on two premises: (1) Rust's linear type system enables practical language safety even for systems with tightest performance and resource budgets (e.g., firmware), and (2) a combination of SMT-based reasoning and pointer discipline enforced by linear types provides a unique way to automate and simplify verification effort scaling it to the size of a small OS kernel.
Fuzzy clustering using local and global region information for cell image segmentation

In high-throughput applications, accurate segmentation of biomedical images can be considered as an important step for recognizing cells that have the phenotype of interest. In this paper, while conventional fuzzy clustering is not able to implement the local and global spatial information, a novel spatial fuzzy clustering cell image segmentation algorithm is proposed. The segmentation procedure is divided into two stages: the first stage involves processing the local and global spatial information of the given cell image and a final segmentation stage which is based on the idea of conventional fuzzy clustering. Our idea can be considered as a sequential integration of region based methods and fuzzy clustering for cell image segmentation. Experimental results show that the proposed model yields significantly better performance in comparison with several existing methods.