nils-herrmann/clean_scirepeval huggingface dataset:
A CENP-S/X complex assembles at the centromere in S and G2 phases of the human cell cycle

The functional identity of centromeres arises from a set of specific nucleoprotein particle subunits of the centromeric chromatin fibre. These include CENP-A and histone H3 nucleosomes and a novel nucleosome-like complex of CENPs -T, -W, -S and -X. Fluorescence cross-correlation spectroscopy and Förster resonance energy transfer (FRET) revealed that human CENP-S and -X exist principally in complex in soluble form and retain proximity when assembled at centromeres. Conditional labelling experiments show that they both assemble de novo during S phase and G2, increasing approximately three- to fourfold in abundance at centromeres. Fluorescence recovery after photobleaching (FRAP) measurements documented steady-state exchange between soluble and assembled pools, with CENP-X exchanging approximately 10 times faster than CENP-S (t1/2 ∼ 10 min versus 120 min). CENP-S binding to sites of DNA damage was quite distinct, with a FRAP half-time of approximately 160 s. Fluorescent two-hybrid analysis identified CENP-T as a uniquely strong CENP-S binding protein and this association was confirmed by FRET, revealing a centromere-bound complex containing CENP-S, CENP-X and CENP-T in proximity to histone H3 but not CENP-A. We propose that deposition of the CENP-T/W/S/X particle reveals a kinetochore-specific chromatin assembly pathway that functions to switch centromeric chromatin to a mitosis-competent state after DNA replication. Centromeres shuttle between CENP-A-rich, replication-competent and H3-CENP-T/W/S/X-rich mitosis-competent compositions in the cell cycle.
DLVM: A MODERN COMPILER INFRASTRUCTURE FOR DEEP LEARNING SYSTEMS

Deep learning software demands reliability and performance. However, many of the existing deep learning frameworks are software libraries that act as an unsafe DSL in Python and a computation graph interpreter. We present DLVM, a design and implementation of a compiler infrastructure with a linear algebra intermediate representation, algorithmic differentiation by adjoint code generation, domainspecific optimizations and a code generator targeting GPU via LLVM. Designed as a modern compiler infrastructure inspired by LLVM, DLVM is more modular and more generic than existing deep learning compiler frameworks, and supports tensor DSLs with high expressivity. With our prototypical staged DSL embedded in Swift, we argue that the DLVM system enables a form of modular, safe and performant frameworks for deep learning.
Parallel Simulations of Manufacturing Processing using Simpy, a Python-Based Discrete Event Simulation Tool

SimPy is a Python-based, interpreted simulation tool that offers the power and convenience of Python. It is able to launch processes and sub-processes using generators, which act autonomously and may interact using interrupts. SimPy offers other advantages over competing commercial codes in that it allows for modular development, use of a version control system such as CVS, can be made self-documenting with PyDoc, and is completely extensible. The convenience of an interpreted language, however, is offset for large models by slower than desired run times. This disadvantage can be compensated for by parallelizing the system using PyMPI, from the Lawrence Livermore National Laboratory. This work was performed under the auspices of the U.S. Department of Energy by University of California, Lawrence Livermore National Laboratory under Contract W-7405-Eng-48.
A holistic model of user experience for living lab experiential design

This paper introduces a holistic approach for describing a User eXperience (UX) model that is composed of different experience types, concepts and elements. Looking at the current UX body of knowledge, several theoretical and experimental models already exist and address various aspects of the resulting user experience when interacting with a product, such as perceived usefulness, ease of use, hedonic quality and visual attractiveness to cite just of few (Forlizzi & Ford, 2000; Morville, 2004; Mahlke, 2002; Hassenzahl et al., 2000). The resulting holistic model of UX is currently undergoing empirical experiments ly in three different use case domains appertaining to the Internet of Things (IoT) based services namely, wellbeing, environment and logistics. Previous results suggest that different aspects of experience are mostly independently perceived by the users while all significantly contribute to the intention to use a product/service. The iterative nature of the Living Lab experiential design and relevance of existing elements of UX as well as value co-creation are discussed. Finally, a view of the resulting holistic model is presented with a first set of preliminary conclusions.
A Comparative Analysis of MRI Brain Tumor Segmentation Technique

Magnetic Resonance Imaging (MRI) is a powerful visualization tool that permits to acquire images of internal anatomy of human body in a secure and non-invasive manner. The important task in the diagnosis of brain tumor is to determine the exact location, orientation and area of the abnormal tissues. This paper presents a performance analysis of image segmentation techniques, viz., Genetic algorithm, K-Means Clustering and Fuzzy C-Means clustering for detection of brain tumor from brain MRI images. The performance evaluation of these techniques is carried out on the real time database on the basis of error percentage compared to ground truth.
Calibrating the end-Permian mass extinction.

The end-Permian mass extinction was the most severe biodiversity crisis in Earth history. To better constrain the timing, and ultimately the causes of this event, we collected a suite of geochronologic, isotopic, and biostratigraphic data on several well-preserved sedimentary sections in South China. High-precision U-Pb dating reveals that the extinction peak occurred just before 252.28 ± 0.08 million years ago, after a decline of 2 per mil (‰) in δ(13)C over 90,000 years, and coincided with a δ(13)C excursion of -5‰ that is estimated to have lasted ≤20,000 years. The extinction interval was less than 200,000 years and synchronous in marine and terrestrial realms; associated charcoal-rich and soot-bearing layers indicate widespread wildfires on land. A massive release of thermogenic carbon dioxide and/or methane may have caused the catastrophic extinction.
Correlation of MRI findings with clinical findings of trochanteric pain syndrome

Greater trochanter pain syndrome due to tendinopathy or bursitis is a common cause of hip pain. The previously reported magnetic resonance (MR) findings of trochanteric tendinopathy and bursitis are peritrochanteric fluid and abductor tendon abnormality. We have often noted peritrochanteric high T2 signal in patients without trochanteric symptoms. The purpose of this study was to determine whether the MR findings of peritrochanteric fluid or hip abductor tendon pathology correlate with trochanteric pain. We retrospectively reviewed 131 consecutive MR examinations of the pelvis (256 hips) for T2 peritrochanteric signal and abductor tendon abnormalities without knowledge of the clinical symptoms. Any T2 peritrochanteric abnormality was characterized by size as tiny, small, medium, or large; by morphology as feathery, crescentic, or round; and by location as bursal or intratendinous. The clinical symptoms of hip pain and trochanteric pain were compared to the MR findings on coronal, sagittal, and axial T2 sequences using chi-square or Fisher’s exact test with significance assigned as p < 0.05. Clinical symptoms of trochanteric pain syndrome were present in only 16 of the 256 hips. All 16 hips with trochanteric pain and 212 (88%) of 240 without trochanteric pain had peritrochanteric abnormalities (p = 0.15). Eighty-eight percent of hips with trochanteric symptoms had gluteus tendinopathy while 50% of those without symptoms had such findings (p = 0.004). Other than tendinopathy, there was no statistically significant difference between hips with or without trochanteric symptoms and the presence of peritrochanteric T2 abnormality, its size or shape, and the presence of gluteus medius or minimus partial thickness tears. Patients with trochanteric pain syndrome always have peritrochanteric T2 abnormalities and are significantly more likely to have abductor tendinopathy on magnetic resonance imaging (MRI). However, although the absence of peritrochanteric T2 MR abnormalities makes trochanteric pain syndrome unlikely, detection of these abnormalities on MRI is a poor predictor of trochanteric pain syndrome as these findings are present in a high percentage of patients without trochanteric pain.
High-performance lithium battery anodes using silicon nanowires.

There is great interest in developing rechargeable lithium batteries with higher energy capacity and longer cycle life for applications in portable electronic devices, electric vehicles and implantable medical devices. Silicon is an attractive anode material for lithium batteries because it has a low discharge potential and the highest known theoretical charge capacity (4,200 mAh g(-1); ref. 2). Although this is more than ten times higher than existing graphite anodes and much larger than various nitride and oxide materials, silicon anodes have limited applications because silicon's volume changes by 400% upon insertion and extraction of lithium which results in pulverization and capacity fading. Here, we show that silicon nanowire battery electrodes circumvent these issues as they can accommodate large strain without pulverization, provide good electronic contact and conduction, and display short lithium insertion distances. We achieved the theoretical charge capacity for silicon anodes and maintained a discharge capacity close to 75% of this maximum, with little fading during cycling.
Convolutional Random Walk Networks for Semantic Image Segmentation

Most current semantic segmentation methods rely on fully convolutional networks (FCNs). However, their use of large receptive fields and many pooling layers cause low spatial resolution inside the deep layers. This leads to predictions with poor localization around the boundaries. Prior work has attempted to address this issue by post-processing predictions with CRFs or MRFs. But such models often fail to capture semantic relationships between objects, which causes spatially disjoint predictions. To overcome these problems, recent methods integrated CRFs or MRFs into an FCN framework. The downside of these new models is that they have much higher complexity than traditional FCNs, which renders training and testing more challenging. In this work we introduce a simple, yet effective Convolutional Random Walk Network (RWN) that addresses the issues of poor boundary localization and spatially fragmented predictions with very little increase in model complexity. Our proposed RWN jointly optimizes the objectives of pixelwise affinity and semantic segmentation. It combines these two objectives via a novel random walk layer that enforces consistent spatial grouping in the deep layers of the network. Our RWN is implemented using standard convolution and matrix multiplication. This allows an easy integration into existing FCN frameworks and it enables end-to-end training of the whole network via standard back-propagation. Our implementation of RWN requires just 131 additional parameters compared to the traditional FCNs, and yet it consistently produces an improvement over the FCNs on semantic segmentation and scene labeling.
Producing gelatin nanoparticles as delivery system for bovine serum albumin.

Progress in the field of biology and biochemistry has led to the discovery of numerous bioactive peptides and proteins in the last few decades. Delivery of therapeutic proteins/peptides has received a considerable amount of attention in recent years.



In this study, a two-step desolvation method was used to produce biodegradable hydrophilic gelatin nanoparticles (GNP) as a delivery system of protein model (BSA). The size and shape of the nanoparticles were examined by dynamic light scattering and scanning electron microscopy.



Particles with a mean diameter of 200-300 nm were produced and the percentage of entrapment efficiency was found to be 87.4. The optimum amount of theoretical BSA loading was obtained, the release of BSA was monitored in vitro, and the mechanism of release was studied. The BSA release profile showed a biphasic modulation characterized by an initial, relatively rapid release period, followed by a slower release phase.



Results show that the two-step desolvation is an appropriate method for preparing GNP as a delivery vehicle for BSA.
Image super-resolution via deterministic-stochastic synthesis and local statistical rectification

Single image superresolution has been a popular research topic in the last two decades and has recently received a new wave of interest due to deep neural networks. In this paper, we approach this problem from a different perspective. With respect to a downsampled low resolution image, we model a high resolution image as a combination of two components, a deterministic component and a stochastic component. The deterministic component can be recovered from the low-frequency signals in the downsampled image. The stochastic component, on the other hand, contains the signals that have little correlation with the low resolution image. We adopt two complementary methods for generating these two components. While generative adversarial networks are used for the stochastic component, deterministic component reconstruction is formulated as a regression problem solved using deep neural networks. Since the deterministic component exhibits clearer local orientations, we design novel loss functions tailored for such properties for training the deep regression network. These two methods are first applied to the entire input image to produce two distinct high-resolution images. Afterwards, these two images are fused together using another deep neural network that also performs local statistical rectification, which tries to make the local statistics of the fused image match the same local statistics of the groundtruth image. Quantitative results and a user study indicate that the proposed method outperforms existing state-of-the-art algorithms with a clear margin.
StereoTrust: a group based personalized trust model

Trust plays important roles in diverse decentralized environments, including our society at large. Computational trust models help to, for instance, guide users' judgements in online auction sites about other users; or determine quality of contributions in web 2.0 sites. Most of the existing trust models, however, require historical information about past behavior of a specific agent being evaluated - information that is not always available. In contrast, in real life interactions among users, in order to make the first guess about the trustworthiness of a stranger, we commonly use our "instinct" - essentially stereotypes developed from our past interactions with "similar" people. We propose StereoTrust, a computational trust model inspired by real life stereotypes. A user forms stereotypes using her previous transactions with other agents. A stereotype contains certain features of agents and an expected outcome of the transaction. These features can be taken from agents' profile information, or agents' observed behavior in the system. When facing a stranger, the stereotypes matching stranger's profile are aggregated to derive his expected trust. Additionally, when some information about stranger's previous transactions is available, StereoTrust uses it to refine the stereotype matching. According to our experiments, StereoTrust compares favorably with existing trust models that use different kind of information and more complete historical information. Moreover, because evaluation is done according to user's personal stereotypes, the system is completely distributed and the result obtained is personalized. StereoTrust can be used as a complimentary mechanism to provide the initial trust value for a stranger, especially when there is no trusted, common third parties.
Comparative Studies of Passive Imaging in Terahertz and Mid-Wavelength Infrared Ranges for Object Detection

We compared the possibility of detecting hidden objects covered with various types of clothing by using passive imagers operating in a terahertz (THz) range at 1.2 mm (250 GHz) and a mid-wavelength infrared at 3-6 μm (50-100 THz). We investigated theoretical limitations, performance of imagers, and physical properties of fabrics in both the regions. In order to investigate the time stability of detection, we performed measurements in sessions each lasting 30 min. We present a theoretical comparison of two spectra, as well as the results of experiments. In order to compare the capabilities of passive imaging of hidden objects, we combined the properties of textiles, performance of imagers, and properties of radiation in both spectral ranges. The paper presents the comparison of the original results of measurement sessions for the two spectrums with analysis.
Developing an extended technology acceptance model: Doctor's acceptance of electronic medical records in Jordan

The purpose of this study is to construct doctors’ acceptance model of Electronic Medical Records (EMR) in private hospitals. The model extends the Technology Acceptance Model (TAM) with two factors of Individual Capabilities; Self-Efficacy (SE) and Perceived Behavioral Control (PBC). The initial findings proposes additional factors over the original factors in TAM making Perceived Usefulness (PU), Perceived Ease Of Use (PEOU), Behavioral Intention to use (BI), SE, and PBC working in incorporation. A cross-sectional survey was used in which data were gathered by a personal administered questionnaire as the instrument for data collection. Doctors of public hospitals were involved in this study which proves that all factors are reliable.
DESIGNING OF INSULIN PUMP

58 Abstract— Bio-Medical Instrumentation is an expanding and demanding area in which many fields are available to explore, among which diabetes is one of them. Glucose and Insulin are both interrelated terms. Glucose is a sugar which provides energy to the cells. Insulin is a hormone which supports the absorption of glucose. This paper deals with designing of insulin pump, which is used to regulate the blood glucose level in the body according to the requirement. Insulin delivery system is a low cost, portable, automated pump which is used by diabetic patients to administer insulin as and when they require it at regular intervals. The purpose of designing an insulin pump is to deliver precise and accurate amount of insulin to the patient. Insulin pump is used to deliver extremely small volume of insulin in predefined time duration, at a constant flow rate which allows the patient to modify the amount of insulin using keypad at any time of the day. A processing module controls the position of the piston which is instructed to move in clockwise and anticlockwise direction and is attached to the syringe pump. This paper introduces the use of low power embedded system with the help of microcontroller which gives the idea about use of embedded system in portable insulin syringe to deliver predefined dosage of insulin with the help of servo motor.
Robot-learning - Three case studies in robotics and machine learning

This paper describes methodologies applied and results achieved in the framework of the ESPRIT Basic Research Action B-Learn II (project no. 7274). B-Learn II is one of the rst projects working towards an application of Machine Learning techniques in elds of industrial relevance, which are much more complex than the domains usually treated in ML research. In particular, B-Learn II aims at easing the programming of robots and enhancing their ability to cooperate with humans. The paper gives a short introduction to learning in robotics and to the three applications under consideration in B-Learn II. Afterwards, learning methodologies used in each of the applications, the experimental setups, and the results obtained are described. In general, it can be found that providing good examples and a good interface between the learning and the performance components is crucial for success, so the extension of the "Programming by Demonstration" paradigm to robotics has become one of the key aspects of B-Learn II.
ELECTROSTATIC SEPARATION AND RECOVERY OF MIXED PLASTICS

A triboelectric separator manufactured by PlasSep, Ltd., Canada was evaluated at MBA Polymers, Inc. as part of a project sponsored by the American Plastics Council (APC) to explore the potential of triboelectric methods for separating commingled plastics from end-oflife durables. The separator works on a very simple principle: that dissimilar materials will transfer electrical charge to one another when rubbed together, the resulting surface charge differences can then be used to separate these dissimilar materials from one another in an electric field. Various commingled plastics were tested under controlled operating conditions. The feed materials tested include commingled plastics derived from electronic shredder residue (ESR), automobile shredder residue (ASR), refrigerator liners, and water bottle plastics. The separation of ESR ABS and HIPS, and water bottle PC and PVC were very promising. However, this device did not efficiently separate many plastic mixtures, such as rubber and plastics; nylon and acetal; and PE and PP from ASR. All tests were carried out based on the standard operating conditions determined for ESR ABS and HIPS. There is the potential to improve the separation performance for many of the feed materials by individually optimizing their operating conditions. Cursory economics shows that the operation cost is very dependent upon assumed throughput, separation efficiency and requisite purity. Unit operation cost could range from $0.03/lb. to $0.05/lb. at capacities of 2000 lb./hr. and 1000 lb./hr.
LOOKING FOR ATTACHMENT SOLUTIONS IN ALL THE WRONG PLACES : OUT OF CONTROL SEXUAL BEHAVIOR AS A SYMPTOM OF INSECURE ATTACHMENT IN MEN

Hypersexual behavior is often misunderstood and minimized, and we continue to lack an understanding of what underlies this behavior. Without an understanding of the function of hypersexual behavior, we cannot ascertain the most effective treatment. This study was designed to examine the underlying function of such behavior by exploring whether insecure attachment in men relates to the development of hypersexual behavior. A total of 45 men who were assessed as having Out-of-Control Sexual Behavior (OCSB), utilizing the recently proposed Hypersexual Disorder (HD) diagnosis were compared to 32 men who did not present with OCSB. Participants were directed to an online survey where they completed assessments for hypersexual behavior (The HBI) and attachment style (The ECR-S). Multivariate analysis indicated that high ECR-S scores predicted high HBI scores, high HBI scores tended to show high levels of attachment avoidant behavior and that high ECR-S scores were predictive of the clinical determination of HD. High scores on attachment avoidance, rather than attachment anxiety, were most predictive of the clinical determination of HD. Overall, the avoidant behavior score was a better predictor of OCSB than were attachment anxiety scores. Hypersexual behavior may be a particular manifestation of avoidant attachment and it is this underlying issue that must be addressed to effectively treat HD. Degree Type Dissertation Degree Name Doctor of Social Work (DSW) First Advisor Phyllis Solomon Second Advisor Andrea Doyle Third Advisor John Giugliano