nils-herrmann/clean_scirepeval huggingface dataset:
Lean healthcare: rhetoric, ritual and resistance.

This paper presents an ethnographic account of the implementation of Lean service redesign methodologies in one UK NHS hospital operating department. It is suggested that this popular management 'technology', with its emphasis on creating value streams and reducing waste, has the potential to transform the social organisation of healthcare work. The paper locates Lean healthcare within wider debates related to the standardisation of clinical practice, the re-configuration of occupational boundaries and the stratification of clinical communities. Drawing on the 'technologies-in-practice' perspective the study is attentive to the interaction of both the intent to transform work and the response of clinicians to this intent as an ongoing and situated social practice. In developing this analysis this article explores three dimensions of social practice to consider the way Lean is interpreted and articulated (rhetoric), enacted in social practice (ritual), and experienced in the context of prevailing lines of power (resistance). Through these interlinked analytical lenses the paper suggests the interaction of Lean and clinical practice remains contingent and open to negotiation. In particular, Lean follows in a line of service improvements that bring to the fore tensions between clinicians and service leaders around the social organisation of healthcare work. The paper concludes that Lean might not be the easy remedy for making both efficiency and effectiveness improvements in healthcare.
Cell Segmentation: 50 Years Down the Road [Life Sciences]

Ever since the establishment of cell theory in the early 19th century, which recognized the cell as the fundamental building unit of life, biologists have sought to explain the underlying principles. Momentous discoveries were made over the course of many decades of research , but the quest to attain full understanding of cellular mechanisms and how to manipulate them to improve health continues to the present day, with bigger budgets, more minds, and more sophisticated tools than ever before. One of the tools to which a great deal of the progress in cell biology can be attributed is light microscopy [2]. The field has come a long way since Antoni van Leeuwenhoeks first steps in the 1670s toward improving and exploiting microscopic imaging for studying life at the cellular level. Not only do biologists today have a plethora of different, complementary microscopic imaging techniques at their disposal that enable them to visualize phenomena even way below the classical diffraction limit of light, advanced microscope systems also allow them to easily acquire very large numbers of images within just a matter of hours. The abundance, heterogeneity, dimensionality, and complexity of the data generated in modern imaging experiments rule out manual image management, processing, and analysis. Consequently, computerized techniques for performing these tasks have become of key importance for further progress in cell biology [3][6]. A central problem in many studies, and often regarded as the cornerstone of image analysis, is image segmentation. Specifically, since cellular morphology is an important phenotypic feature that is indicative of the physiological state of a cell, and since the cell contour is often required for subsequent analysis of intracellular processes (zooming in to nanoscale), or of cell sociology (zooming out to millimeter scale), the problem of cell segmentation has received increasing attention in past years [7]. Here we reflect on how the field has evolved over the years and how past developments can be expected to extrapolate into the future.
Correlation of radiographic parameters and clinical symptoms in adult scoliosis.

This study is a retrospective review of the initial enrollment data from a prospective multicentered study of adult spinal deformity.



The purpose of this study is to correlate radiographic measures of deformity with patient-based outcome measures in adult scoliosis.



Prior studies of adult scoliosis have attempted to correlate radiographic appearance and clinical symptoms, but it has proven difficult to predict health status based on radiographic measures of deformity alone. The ability to correlate radiographic measures of deformity with symptoms would be useful for decision-making and surgical planning.



The study correlates radiographic measures of deformity with scores on the Short Form-12, Scoliosis Research Society-29, and Oswestry profiles. Radiographic evaluation was performed according to an established positioning protocol for anteroposterior and lateral 36-inch standing radiographs. Radiographic parameters studied were curve type, curve location, curve magnitude, coronal balance, sagittal balance, apical rotation, and rotatory subluxation.



The 298 patients studied include 172 with no prior surgery and 126 who had undergone prior spine fusion. Positive sagittal balance was the most reliable predictor of clinical symptoms in both patient groups. Thoracolumbar and lumbar curves generated less favorable scores than thoracic curves in both patient groups. Significant coronal imbalance of greater than 4 cm was associated with deterioration in pain and function scores for unoperated patients but not in patients with previous surgery.



This study suggests that restoration of a more normal sagittal balance is the critical goal for any reconstructive spine surgery. The study suggests that magnitude of coronal deformity and extent of coronal correction are less critical parameters.
Gender differences in seasonal affective disorder (SAD)

146 women and 44 men (out- and inpatients; treatment sample) with Seasonal Affective Disorder (SAD; winter type) were tested for gender differences in demographic, clinical and seasonal characteristics. Sex ratio in prevalence was (women : men) 3.6 : 1 in unipolar depressives and 2.4 : 1 in bipolars (I and II). Sex ratios varied also between different birth cohorts and men seemed to underreport symptoms. There was no significant difference in symptom-profiles in both genders, however a preponderance of increased eating and different food selection on a trend level occured in women. The female group suffered significantly more often from thyroid disorders and from greater mood variations because of dark and cloudy weather. Women referred themselves to our clinic significantly more frequently as compared to men. In summary gender differences in SAD were similar to those of non-seasonal depression: the extent of gender differences in the prevalence of affective disorders appears to depend on case criteria such as diagnosis (unipolar vs. bipolar), birth cohort and number of symptoms as minimum threshold for diagnosis. We support the idea of applying sex-specific diagnostic criteria for diagnosing depression on the basis of our data and of the literature.
Exposure to hate speech increases prejudice through desensitization.

In three studies (two representative nationwide surveys, N = 1,007, N = 682; and one experimental, N = 76) we explored the effects of exposure to hate speech on outgroup prejudice. Following the General Aggression Model, we suggest that frequent and repetitive exposure to hate speech leads to desensitization to this form of verbal violence and subsequently to lower evaluations of the victims and greater distancing, thus increasing outgroup prejudice. In the first survey study, we found that lower sensitivity to hate speech was a positive mediator of the relationship between frequent exposure to hate speech and outgroup prejudice. In the second study, we obtained a crucial confirmation of these effects. After desensitization training individuals were less sensitive to hate speech and more prejudiced toward hate speech victims than their counterparts in the control condition. In the final study, we replicated several previous effects and additionally found that the effects of exposure to hate speech on prejudice were mediated by a lower sensitivity to hate speech, and not by lower sensitivity to social norms. Altogether, our studies are the first to elucidate the effects of exposure to hate speech on outgroup prejudice.
Interactive Visualization Tools for Exploring the Semantic Graph of Large Knowledge Spaces

While the amount of available information on the Web is increasing rapidly, the problem of managing it becomes more difficult. We present two applications, Thinkbase and Thinkpedia, which aim to make Web content more accessible and usable by utilizing visualizations of the semantic graph as a means to navigate and explore large knowledge repositories. Both of our applications implement a similar concept: They extract semantically enriched contents from a large knowledge spaces (Freebase and Wikipedia respectively), create an interactive graph-based representation out of it, and combine them into one interface together with the original text based content. We describe the design and implementation of our applications, and provide a discussion based on an informal evaluation.
High performance ultra-low-precision convolutions on mobile devices

Many applications of mobile deep learning, especially real-time computer vision workloads, are constrained by computation power. This is particularly true for workloads running on older consumer phones, where a typical device might be powered by a singleor dual-core ARMv7 CPU. We provide an open-source implementation and a comprehensive analysis of (to our knowledge) the state of the art ultra-low-precision (<4 bit precision) implementation of the core primitives required for modern deep learning workloads on ARMv7 devices, and demonstrate speedups of 4x-20x over our additional state-of-the-art float32 and int8 baselines.
Hand gesture to speech conversion using Matlab

Hand gesture is one of the typical methods used in sign language for non-verbal communication. Sign gestures are a non-verbal visual language, different from the spoken language, but serving the same function. It is often very difficult for the hearing impaired community to communicate their ideas and creativity to the normal humans. This paper presents a system that will not only automatically recognize the hand gestures but also convert it into corresponding speech output so that speaking impaired person can easily communicate with normal people. The gesture to speech system, G2S, has been developed using the skin colour segmentation. The system consists of camera attached to computer that will take images of hand gestures. Image segmentation & feature extraction algorithm is used to recognize the hand gestures of the signer. According to recognized hand gestures, corresponding pre-recorded sound track will be played.
Degrees of Freedom of Interference Networks with Transmitter-Side Caches

This paper studies cache-aided interference networks with arbitrary number of transmitters and receivers, whereby each transmitter has a cache memory of finite size. Each transmitter fills its cache memory from a content library of files in the placement phase. In the subsequent delivery phase, each receiver requests one of the library files, and the transmitters are responsible for delivering the requested files from their caches to the receivers. The objective is to design schemes for the placement and delivery phases to maximize the sum degrees of freedom (sum-DoF) which expresses the capacity of the interference network at the high signal-to-noise ratio regime. Our work mainly focuses on a commonly used uncoded placement strategy. We provide an information-theoretic bound on the sumDoF for this placement strategy. We demonstrate by an example that the derived bound is tighter than the bounds existing in the literature for small cache sizes. We propose a novel delivery scheme with a higher achievable sum-DoF than those previously given in the literature. The results reveal that the reciprocal of sum-DoF decreases linearly as the transmitter cache size increases. Therefore, increasing cache sizes at transmitters translates to increasing the sum-DoF and, hence, the capacity of the interference networks. Index Terms Coded caching, Interference networks, Degrees of freedom, Interference management.
Citizen-centric evaluation framework for e-government systems in Kenya. The case of public service commission of Kenya Online Recruitment & Selection system

E-government services is growing at a considerable pace, especially in developing countries as government seeks to make use of ICT to serve its citizens efficiently and effectively. E-government projects cost are enormous and therefore it becomes imperative for governments to continuously evaluate these projects with a view of identifying the benefits, justifying investment made and improving the quality of services they offer to the citizens among other reasons through constant evaluation to gauge the success of these projects. Like the evaluation of all other information systems initiatives, the evaluation of e-governments in both theory and practice has proved to be important but complex. The complexity of evaluation is mostly due to the multiple perspectives involved, the difficulties of quantifying benefits, and the social and technical context of use. Existing frameworks for e-government software projects evaluation were analyzed with the aim of developing a post-implementation evaluation framework for e-government systems with the citizens/clients as the central focus. The main aim of this study was to investigate the citizen's perspective in evaluating e-government systems and to propose a set of evaluating factors that can be used for evaluation of e-government systems. A survey of users of Kenya Public Service Commission of Kenya (PSCK) Online Recruitment and Selection Database System was conducted. The study used four constructs for evaluation of e-government system namely; financial, social, technical and delivery platform constructs. Furthermore, specific factors to measure these four constructs with a consideration of the level of e-government in Kenya were identified. The study found that about 74% of the respondents were satisfied with the online job application system of Public Service Commission of Kenya.
Frequential and color analysis for hair mask segmentation

In this paper, we present an automatic method for hair segmentation. Our algorithm is divided into two steps. Firstly, we take information from frequential and color analysis in order to create binary masks as descriptor of the hair location. Secondly, we perform a 'matting treatment' which is a process to extract foreground object from an image. This approach is based on markers which positions are initialized from the fusion of frequential and color masks. At the end the matting treatment result is use to segment the hair. Results are evaluated using semi- manual segmentation references.
Synthesis of full-adder circuit using reversible logic

A reversible gate has the equal number of inputs and outputs and one-to-one mappings between input vectors and output vectors; so that, the input vector states can be always uniquely reconstructed from the output vector states. This correspondence introduces a reversible full-adder circuit that requires only three reversible gates and produces least number of "garbage outputs ", that is two. After that, a theorem has been proposed that proves the optimality of the propounded circuit in terms of number of garbage outputs. An efficient algorithm is also introduced in this paper that leads to construct a reversible circuit.
Improving Person Re-identification via Pose-Aware Multi-shot Matching

Person re-identification is the problem of recognizing people across images or videos from non-overlapping views. Although there has been much progress in person re-identification for the last decade, it still remains a challenging task because of severe appearance changes of a person due to diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person reidentification by analyzing camera viewpoints and person poses, so-called Pose-aware Multi-shot Matching (PaMM), which robustly estimates target poses and efficiently conducts multi-shot matching based on the target pose information. Experimental results using public person reidentification datasets show that the proposed methods are promising for person re-identification under diverse viewpoints and pose variances.
Morphologic changes in the retina after selective retina therapy

To investigate structural changes in the retina by histologic evaluation and in vivo spectral domain optical coherence tomography (SD-OCT) following selective retina therapy (SRT) controlled by optical feedback techniques (OFT). SRT was applied to 12 eyes of Dutch Belted rabbits. Retinal changes were assessed based on fundus photography, fluorescein angiography (FAG), SD-OCT, light microscopy, transmission electron microscopy (TEM), and scanning electron microscopy (SEM) at each of the following time points: 1 h, and 1, 3, 7, 14 and 28 days after SRT. BrdU (5’-bromo-2’-deoxy-uridine) incorporation assay was also conducted to evaluate potential proliferation of RPE cells. SRT lesions at1 h after SRT were ophthalmoscopically invisible. FAG showed leakage in areas corresponding to SRT lesions, and hyperfluorescence disappeared after 7 days. SD-OCT showed that decreased reflectivity corresponding to RPE damage was restored to normal over time in SRT lesions. Histologic analysis revealed that the damage in SRT lesions was primarily limited to the retinal pigment epithelium (RPE) and the outer segments of the photoreceptors. SEM and TEM showed RPE cell migration by day 3 after SRT, and restoration of the RPE monolayer with microvilli by 1 week after SRT. At 14 and 28 days, ultrastructures of the RPE, including the microvilli and tight junctions, were completely restored. The outer segments of the photoreceptors also recovered without sequelae. Interdigitation between the RPE and photoreceptors was observed. BrdU incorporation assay revealed proliferation of RPE on day 3 after SRT, and peak proliferation was observed on day 7 after SRT. Based on multimodal imaging and histologic assessment, our findings demonstrate that SRT with OFT could selectively target the RPE without damaging the neurosensory retina. Therefore, the use of SRT with OFT opens the door to the possibility of clinical trials of well-defined invisible and nondestructive retina therapy, especially for macular disease.
Small Moving Vehicle Detection in a Satellite Video of an Urban Area

Vehicle surveillance of a wide area allows us to learn much about the daily activities and traffic information. With the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately-sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information. In this paper, we look into the problem of moving vehicle detection in satellite imagery. To the best of our knowledge, it is the first time to deal with moving vehicle detection from satellite videos. Our approach consists of two stages: first, through foreground motion segmentation and trajectory accumulation, the scene motion heat map is dynamically built. Following this, a novel saliency based background model which intensifies moving objects is presented to segment the vehicles in the hot regions. Qualitative and quantitative experiments on sequence from a recent Skybox satellite video dataset demonstrates that our approach achieves a high detection rate and low false alarm simultaneously.
Virtual Head-up Displays for Augmented Reality in Cars: a User Testing to Validate the Congruence

Head-up displays (HUD) permit augmented reality (AR) information in cars. Simulation is a convenient way to design and evaluate the benefit of such innovation for the driver. For this purpose, we have developed a virtual HUD that we compare to real AR HUDs from depth perception features. User testing was conducted with 24 participants in a stereoscopic driving simulator. It showed the ability of the virtual HUD to reproduce the perception of the distance between real objects and their augmentation. Three AR overlay designs to highlight the car ahead were compared: the trapezoid shape was perceived as more congruent that the U shape overlay.
Cellulases and beyond: the first 70 years of the enzyme producer Trichoderma reesei

More than 70 years ago, the filamentous ascomycete Trichoderma reesei was isolated on the Solomon Islands due to its ability to degrade and thrive on cellulose containing fabrics. This trait that relies on its secreted cellulases is nowadays exploited by several industries. Most prominently in biorefineries which use T. reesei enzymes to saccharify lignocellulose from renewable plant biomass in order to produce biobased fuels and chemicals. In this review we summarize important milestones of the development of T. reesei as the leading production host for biorefinery enzymes, and discuss emerging trends in strain engineering. Trichoderma reesei has very recently also been proposed as a consolidated bioprocessing organism capable of direct conversion of biopolymeric substrates to desired products. We therefore cover this topic by reviewing novel approaches in metabolic engineering of T. reesei.
Multi-modal human action recognition using deep neural networks fusing image and inertial sensor data

Human action recognition has been studied in many fields including computer vision and sensor networks using inertial sensors. However, there are limitations such as spatial constraints, occlusions in images, sensor unreliability, and the inconvenience of users. In order to solve these problems we suggest a sensor fusion method for human action recognition exploiting RGB images from a single fixed camera and a single wrist mounted inertial sensor. These two different domain information can complement each other to fill the deficiencies that exist in both image based and inertial sensor based human action recognition methods. We propose two convolutional neural network (CNN) based feature extraction networks for image and inertial sensor data and a recurrent neural network (RNN) based classification network with long short term memory (LSTM) units. Training of deep neural networks and testing are done with synchronized images and sensor data collected from five individuals. The proposed method results in better performance compared to single sensor-based methods with an accuracy of 86.9% in cross-validation. We also verify that the proposed algorithm robustly classifies the target action when there are failures in detecting body joints from images.