nils-herrmann/clean_scirepeval huggingface dataset:
Pooled CRISPR screens with imaging on microRaft arrays reveals stress granule regulatory factors
Venue: Nature Methods
Year: 2020

Genetic screens using pooled CRISPR based approaches are scalable and inexpensive, but restricted to standard readouts, including survival, proliferation and sortable markers. However, many biologically relevant cell states involve cellular and subcellular changes that are only accessible by microscopic visualization, and are currently impossible to screen with pooled methods. Here we combine pooled CRISPR Cas9 screening with microraft array technology and high content imaging to screen image based phenotypes (CRaft ID; CRISPR based microRaft followed by guide RNA identification) By isolating microrafts that contain genetic clones harboring individual guide RNAs (gRNA) we identify RNA binding proteins (RBPs) that influence the formation of stress granules, the punctate protein RNA assemblies that form during stress. To automate hit identification, we developed a machine learning model trained on nuclear morphology to remove unhealthy cells or imaging artifacts. In doing so, we identified and validated previously uncharacterized RBPs that modulate stress granule abundance, highlighting the applicability of our approach to facilitate image based pooled CRISPR screens. CRISPR based microraft followed by guide RNA identification (CRaft ID) combines microraft arrays, microscopy and CRISPR Cas9 technology for high content image based phenotyping. CRaft ID was used to identify proteins involved in stress granule formation.
Defending Against Data Integrity Attacks in Smart Grid: A Deep Reinforcement Learning Based Approach
Venue: IEEE Access
Year: 2019

State estimation plays a critical role in monitoring and managing operation of smart grid. Nonetheless, recent research efforts demonstrate that data integrity attacks are able to bypass the bad data detection mechanism and make the system operator obtain the misleading states of system, leading to massive economic losses. Particularly, data integrity attacks have become critical threats to the power grid. In this paper, we propose a deep Q network detection (DQND) scheme to defend against data integrity attacks in alternating current (AC) power systems. DQND is a deep reinforcement learning scheme, which avoids the problem of curse of dimension that conventional reinforcement learning schemes have. Our strategy in DQND applies a main network and a target network to learn the optimal defending strategy. To improve the learning efficiency, we propose the quantification of observation space and utilize the concept of slide window as well. The experimental evaluation results show that the DQND outperforms the existing deep reinforcement learning based detection scheme in terms of detection accuracy and rapidity in the IEEE 9, 14, and 30 bus systems.
Using social and behavioural science to support COVID 19 pandemic response
Venue: Nature Human Behaviour
Year: 2020

The COVID 19 pandemic represents a massive global health crisis. Because the crisis requires large scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID 19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months. Forty three experts highlight some key insights from the social and behavioural sciences for effective response to the COVID 19 pandemic and point out important gaps researchers should move quickly to fill in the coming weeks and months.
Technology driven developments and policy implications for mathematics education
Year: 2012

The advent of technology has done more than merely increase the range of resources available for mathematics teaching and learning: it represents the emergence of a new culture a virtual culture with new paradigms which differs crucially from preceding cultural forms. In this chapter, the implications of this paradigm shift for policies concerning learning, curriculum design, and teacher education will be discussed. Also, the ubiquitous possibility of emergence of ever new forms of technology brings about both new opportunities for learning and collaborative work (involving students and teachers) as well as potential dangers. Policy measures may give priority to technological access and developments, over the intellectual growth of learners and the professional development of teachers which should be more demanding goals of mathematics education. Such policy issues will be discussed.
Wheelchair Navigation: Automatically Adapting to Evolving Environments
Venue: TAROS
Year: 2019

Power wheelchairs can increase independence by supporting the mobility of their users. However, severe disabilities of users can render controlling the wheelchair difficult, if not impossible, especially over longer periods of time. This paper describes a proposal for research into techniques that would improve the experience and quality of life of wheelchair users by reducing the cognitive burden introduced by repetitive and complicated navigation tasks and manoeuvres. This will be achieved by sharing the control between the user and an autonomous controller. A number of techniques will be used to achieve this aim. Simultaneous Localization and Mapping (SLAM) and topological mapping will be used for navigation between rooms while Computer Vision techniques will allow the (semi) automatic recognition of places in the user's home, based on the detection and categorization of objects. Finally, medium to high level automation will be provided. This includes automatic and transparent assistance with tasks such as navigating through doorways but also autonomous navigation to specific locations using high level constructs "take me to the kitchen table"
Occurrence of Penicillium verrucosum, ochratoxin A, ochratoxin B and citrinin in on farm stored winter wheat from the Canadian Great Lakes Region
Venue: PloS one
Year: 2017

The occurrence of P. verrucosum and ochratoxin A (OTA) were surveyed for 3 and 4 years, respectively. A total of 250 samples was collected from an average of 30 farms during the 2011, 2012, 2013 and 2014 winter seasons. Most storage bins surveyed were typically 11 m high round bins made of corrugated, galvanized steel, with flat bottoms and conical roofs. Samples of clumped grain contained the most P. verrucosum (p<0.05, n 10) followed by samples taken from the first load (n 24, mean 147+ 87 CFU/g) and last load (n 17, mean 101+ 77 CFU/g) Five grain samples (2.2% tested positive for OTA, citrinin and OTB at concentrations of 14.7+ 7.9, 4.9+ 1.9 and 1.2+ 0.7 ng/g, with only three samples exceeding 5 ng/g. Grain samples positive for OTA were related to moisture resulting from either condensation or migrating moist warm air in the bin or areas where precipitation including snow entered the bin. Bins containing grain and clumps contaminated with OTA were studied in detail. A number of statistically significant risk factors for OTA contamination were identified. These included 1) grain clumps accumulated around or directly under manhole openings, 2) debris and residue of old grain or grain clumps collected from the bin walls or left on storage floor and augers and 3) grain clumps accumulated around side doors. Even when grain enters storage below the 14.5% threshold of moisture, condensation and moisture migration occurs in hotspots in modern corrugated steel storage bins. Hot spots of OTA contamination were most often in areas affected by moisture migration due to inadequate aeration and exposure to moisture from precipitation or condensation. Further, we found that the nature of the condensation affects the nature and distribution of small and isolated areas with high incidence of toxin contamination and/or P. verrucosum prevalence in the grain bins examined.
Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms
Venue: ArXiv
Year: 2019

Pre season prediction of crop production outcomes such as grain yields and N losses can provide insights to stakeholders when making decisions. Simulation models can assist in scenario planning, but their use is limited because of data requirements and long run times. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of five machine learning (ML) algorithms as meta models for a cropping systems simulator (APSIM) to inform future decision support tool development. We asked: 1) How well do ML meta models predict maize yield and N losses using pre season information? 2) How many data are needed to train ML algorithms to achieve acceptable predictions? 3) Which input data variables are most important for accurate prediction? and 4) Do ensembles of ML meta models improve prediction? The simulated dataset included more than 3 million genotype, environment and management scenarios. Random forests most accurately predicted maize yield and N loss at planting time, with a RRMSE of 14% and 55% respectively. ML meta models reasonably reproduced simulated maize yields but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10 40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables. Averaged across all ML models, weather conditions, soil properties, management information and initial conditions were roughly equally important when predicting yields. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre season management.
Multi level image software assembly technology based on OpenStack and Ceph
Venue: 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference
Year: 2016

With the development of virtualization technology, it is a prevailing trend that users deploy highly reliable, scalable, high efficiency application services via virtual machines. OpenStack, the most fiery open source virtual machines management platform, focuses on IaaS(Infrastructure as a Service) and now expands to PaaS(Platform as a Service) and SaaS(Software as a Service) As a Mature IaaS cloud computing product, it's no doubt that it should take data replication and storage mechanism into consideration as a precondition, in order to ensure the integrity and continuity of service provided for users. At the mention of storage, Ceph, a unified, high reliability, high performance and scalable distributed file system, comes to OpenStack's mind, providing block storage, object storage and file storage. To solve the problem that OpenStack only supported Single level image management which wastes a fairly large number of storage space, we have studied Ceph storage cluster with OpenStack integration architecture and achieved the multi level image software assembly technology. From the aspect of storage space, the technology eliminate duplication of shared operating system image data and shared application software image data. From the aspect of time, users don't have to install the required application softwares manually, providing the time saving as well. Once you launched your virtual machine instances, the required softwares have been installed already.
Generic Social Processes: Reimagining a Conceptual Schema for Grounded Theory in the Contemporary Era
Year: 2019

Grounded theory (GT) has gone through a number of changes since Barney Glaser and Anselm Strauss's seminal introduction to the tradition. Newer constructivist versions of GT allow for pre formed theoretical and conceptual strategies to guide data collection, for the idea that knowledge should emerge only inductively from the field is no longer seen as tenable. Within this context, we present Robert Prus's "generic social processes" framework as a set of conceptual tools especially useful in guiding constructivist GT. Yet this framework has been challenged in light of contemporary debates on the epistemological status of generalized sociological claims, the ontological nature of the social world, and the importance of explanatory knowledge. These debates lead us to consider new adaptions to this now classic schema of generic social processes with an eye to the future of GT and a more conceptually rich ethnography.
Method Optimization Study on Isolation of Volatile Compounds by Headspace Solid Phase Microextraction (HS SPME) from Custard Apple (Annona squamosa L. Pulp.
Year: 2020

Aroma is an important quality parameter, which can help in delaying degradative processes and hence understanding the volatile compounds that are part of an aroma is extremely important. Thus, the aim of this study was to isolate the volatile compounds present in custard apple (Annona squamosa L. pulp by solid phase microextraction (SPME) technique using 3 different fibers (DVB/CAR/PDMS, PDMS and PDMS/DVB) An experimental design was set up to evaluate the best extraction conditions wherein the variables were extraction temperature (minimum 28degC, maximum 62degC) ionic strength (minimum 0, maximum 30.0% and sample concentration (min 1, max 99.0% The separation of volatiles was achieved in chromatographic columns of different polarity (polar and non polar) while identification of volatile compounds was performed by high resolution gas chromatography system coupled with mass spectrometry. The results obtained using 3 different fibers revealed the isolation of 71 compounds with fiber DVB/CAR/PDMS, while only 30 and 21 compounds were isolated using fibers PDMS/DVB and PDMS, respectively. The major classes of compounds identified were terpenes, esters, alcohols and aldehydes. Optimum conditions for obtaining higher number of volatiles for non polar column were 45&degC for extraction temperature, 15% of sodium chloride and 50% of pulp amount which resulted in isolation of a maximum of 78 peaks.
Multiparticulates Drug Delivery Systems: A Review
Year: 2013

Pharmaceutical research and development are increasingly focusing on delivery systems which enhance desirable therapeutic objectives while minimising side effects. Recent trends indicate that multiparticulate drug delivery systems are especially suitable for achieving controlled and delayed release oral formulations with low risk of dose dumping. These oral multiparticulate drug delivery systems offer biopharmaceutical advantages with respect to predictable and even distribution and transportation in the gastro intestinal tract. Pelletization is novel drug delivery system that converts fine powder particles into pellets and it is usefull in order to develop a site specific drug delivery system. There are different techniques in the preparation of pellets. Consequently, multiparticulate drug delivery systems provide tremendous opportunities for designing new controlled and delayed release oral formulations, thus extending the frontier of future pharmaceutical development.
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
Venue: IEEE Transactions on Image Processing
Year: 2019

Facial expression recognition in the wild is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect on analyzing constrained frontal faces, they fail to perform well on partially occluded faces that are common in the wild. In this paper, we propose a convolution neutral network (CNN) with attention mechanism (ACNN) that can perceive the occlusion regions of the face and focus on the most discriminative un occluded regions. ACNN is an end to end learning framework. It combines the multiple representations from facial regions of interest (ROIs) Each representation is weighed via a proposed gate unit that computes an adaptive weight from the region itself according to the unobstructedness and importance. Considering different RoIs, we introduce two versions of ACNN: patch based ACNN (pACNN) and global local based ACNN (gACNN) pACNN only pays attention to local facial patches. gACNN integrates local representations at patch level with global representation at image level. The proposed ACNNs are evaluated on both real and synthetic occlusions, including a self collected facial expression dataset with real world occlusions, the two largest in the wild facial expression datasets (RAF DB and AffectNet) and their modifications with synthesized facial occlusions. Experimental results show that ACNNs improve the recognition accuracy on both the non occluded faces and occluded faces. Visualization results demonstrate that, compared with the CNN without Gate Unit, ACNNs are capable of shifting the attention from the occluded patches to other related but unobstructed ones. ACNNs also outperform other state of the art methods on several widely used in the lab facial expression datasets under the cross dataset evaluation protocol.
Quantum Representation of Indexed Images and its Applications
Year: 2019

Indexed images have been widely used in classical digital image processing, however there have been no reports on the representation and applications of indexed images in quantum image processing so far. To solve the representation problem of indexed images on a quantum computer, a quantum indexed image representation (QIIR) method is proposed in the paper. A quantum indexed image consists of a quantum data matrix and a quantum palette matrix. Each data structure is based on the basic states of qubit sequence to represent information, including pixel positions and pixel values in the data matrix, and indexes and color values in the palette matrix. Several simple geometric and color transformations of quantum indexed images are presented later, including orthogonal rotation, cyclic shift, color inversion, color replacement and color look up. Finally, a quantum indexed image steganography based on EzStego is proposed. In this scheme, the distance between two arbitrary color values in the quantum palette is first calculated, and then several effective color pairs are obtained. At last, according to embedded message bits, pixel values in the data matrix are updated in light of effective color pairs, and a new quantum data matrix with embedded message is obtained. The proposed scheme can be executed on a future quantum computer. The feasibility and validness of this scheme are verified on a classical computer from four aspects: visual quality, embedding capacity, robustness and security.
A Novel Adaptive Controller for Robot Manipulators Based on Active Inference
Venue: IEEE Robotics and Automation Letters
Year: 2020

More adaptive controllers for robot manipulators are needed, which can deal with large model uncertainties. This letter presents a novel active inference controller (AIC) as an adaptive control scheme for industrial robots. This scheme is easily scalable to high degrees of freedom, and it maintains high performance even in the presence of large unmodeled dynamics. The proposed method is based on active inference, a promising neuroscientific theory of the brain, which describes a biologically plausible algorithm for perception and action. In this work, we formulate active inference from a control perspective, deriving a model free control law which is less sensitive to unmodeled dynamics. The performance and the adaptive properties of the algorithm are compared to a state of the art model reference adaptive controller (MRAC) in an experimental setup with a real 7 DOF robot arm. The results showed that the AIC outperformed the MRAC in terms of adaptability, providing a more general control law. This confirmed the relevance of active inference for robot control.
Diagnosis and management of psoriasis.
Venue: Canadian family physician Medecin de famille canadien
Year: 2017

To provide primary care clinicians with an up to date and practical overview of the diagnosis and management of psoriasis. QUALITY OF EVIDENCE PubMed, MEDLINE, EMBASE, and Cochrane databases were searched for relevant meta analyses, randomized controlled trials, systematic reviews, and observational studies about the diagnosis and management of psoriasis. MAIN MESSAGE Psoriasis is a chronic, multisystem inflammatory disease with predominantly skin and joint involvement. Beyond the physical dimensions of disease, psoriasis has an extensive emotional and psychosocial effect on patients, affecting social functioning and interpersonal relationships. As a disease of systemic inflammation, psoriasis is associated with multiple comorbidities, including cardiovascular disease and malignancy. The diagnosis is primarily clinical and a skin biopsy is seldom required. Depending on the severity of disease, appropriate treatment can be initiated. For mild to moderate disease, first line treatment involves topical therapies including corticosteroids, vitamin D3 analogues, and combination products. These topical treatments are efficacious and can be safely initiated and prescribed by primary care physicians. Patients with more severe and refractory symptoms might require further evaluation by a dermatologist for systemic therapy.  Many patients with psoriasis seek initial evaluation and treatment from their primary care providers. Recognition of psoriasis, as well as its associated medical and psychiatric comorbidities, would facilitate timely diagnosis and appropriate management with effective and safe topical therapies and other medical and psychological interventions, as needed. More severe and refractory cases might warrant referral to a dermatologist for further evaluation and possible systemic therapy.
El Hajj Malik El Shabazz: the Continuity and Legacy of a Critical Africana Human Rights Consciousness
Year: 2020

This article places El Hajj Malik El Shabazz's praxis, as well as the Africana [Black] movement for freedom, into a human rights discourse. Exploring his evolution into an internationalist activist theoretician in five rhythmic movements, if you will, the opening the transitioning the solidification the influencing and the continuities it is the central premise of this article that in order to truly grasp the breadth and depth of the Africana [Black] struggle for freedom we must explore those involved and their contributions as a product of a critical human rights consciousness. Accordingly, El Hajj Malik El Shabazz becomes an archetype in this regard. This idea rests upon the assertion that his praxis, albeit incomplete, due to his untimely death, represents an important nexus in the Africana struggle for freedom, merging various manifestations of radical praxis into a clearer articulation of the Black radical tradition.