nils-herrmann/clean_scirepeval huggingface dataset:
Disparity estimation in stereo videos using spatio temporal disparity hyperplane models
Venue: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2017

We propose a method for disparity estimation in stereo video. We address the problems associated with spatially temporally correlated disparity variations (STCDV) STCDV problems are caused by complex motions, e.g. yaw rotation, pan tilt zoom camera movements, etc. The key novelty of this study is to introduce a spatio temporal disparity hyperplane (STDH) model. The proposed STDH model represents a hyperplane defined in four dimensional space spanned by disparity, image plane, and time coordinates. Our STDH model is represented by surface normals varying with the spatially temporally correlated changes in disparity. Thus, our STDH model is effective in estimating disparity in a stereo video including STCDVs. We estimate video disparity by incorporating our STDH model into the PatchMatch brief propagation framework. Our experiments demonstrate that the proposed method outperforms other methods.
Machine learning for cooperative automated driving
Year: 2018

Most of the research in autonomous driving currently involves using the on board sensors on the vehicle to collect the information of the surroundings and using that information for controlling the autonomous vehicle. This research investigates how Machine Learning can be used with cooperative driving for self driving vehicles. Here the self driving vehicles can use the vehicle information of the surrounding vehicles to manoeuvre around them. Therefore, it does not need to rely on just its on board sensors to navigate through the traffic. In this research Reinforcement learning is used for designing a driving algo rithm to control a self driving vehicle in a highway environment which is simulat ed using SUMO (Simulation of Urban Mobility) This research focuses on the method to design the driving algorithm which involves choosing the correct input features and actions for the driving agent and the design of the reward structure. It also explains how the performance of the driving algorithm is effected with change in the reinforcement learning parameters. Except the self driving vehicle, the other vehicles are controlled by SUMO itself. The aim is to check if the self driving vehicle is able to manoeuvre through the traffic in the highway by over taking other vehicles and when needed allowing the faster moving vehicles to overtake it. The driving algorithm is trained and tested in a 2 lane highway environment and a three lane highway environment. It was found that having collision detec tion as just a part of the rewarding policy did not give the desired results, as the collision percentage was found to be in the confidence interval between 20.5% to 28.4% in a 2 lane highway. Hence collision detection and avoidance was done by a separate entity outside the learning algorithm. This helped in reducing the collision percentage to be in the confidence interval of 0.015 and 0.048 for a two lane system and between 0.08 and 0.14 for a 3 lane system. The research also analyses the performance of the driving algorithm in case of packet loss and change in communication range. A method to cope with the packet loss is also discussed in this report. This research can be used as a basis for using cooperative driving for self driving vehicles, but further research needs to be conducted to make the self driving vehicles safer and reliable as the collision rates achieved using this method is still significantly high.
A genome scale CRISPR screen reveals PRMT1 as a critical regulator of androgen receptor signaling in prostate cancer
Year: 2020

Androgen receptor (AR) signaling is the central driver of prostate cancer across disease states. While androgen deprivation therapy (ADT) is effective in the initial treatment of prostate cancer, resistance to ADT or to next generation androgen pathway inhibitors invariably arises, most commonly through re activation of the AR axis. Thus, orthogonal approaches to inhibit AR signaling in advanced prostate cancer are essential. Here, via genome scale CRISPR/Cas9 screening, we identify protein arginine methyltransferase 1 (PRMT1) as a critical mediator of AR expression and signaling. PRMT1 regulates recruitment of AR to genomic target sites and inhibition of PRMT1 impairs AR binding at lineage specific enhancers, leading to decreased expression of key oncogenes, including AR itself. Additionally, AR driven prostate cancer cells are uniquely susceptible to combined AR and PRMT1 inhibition. Our findings implicate PRMT1 as a key regulator of AR output and provide a preclinical framework for co targeting of AR and PRMT1 in advanced prostate cancer.
Multiple insecticide resistance mechanisms in Anopheles gambiae s.l. populations from Cameroon, Central Africa
Venue: Parasites Vectors
Year: 2012

BackgroundIncreasing incidence of DDT and pyrethroid resistance in Anopheles mosquitoes is seen as a limiting factor for malaria vector control. The current study aimed at an in depth characterization of An. gambiae s.l. resistance to insecticides in Cameroon, in order to guide malaria vector control interventions.MethodsAnopheles gambiae s.l. mosquitoes were collected as larvae and pupae from six localities spread throughout the four main biogeographical domains of Cameroon and reared to adults in insectaries. Standard WHO insecticide susceptibility tests were carried out with 4% DDT, 0.75% permethrin and 0.05% deltamethrin. Mortality rates and knockdown times (kdt50 and kdt95) were determined and the effect of pre exposure to the synergists DEF, DEM and PBO was assessed. Tested mosquitoes were identified to species and molecular forms (M or S) using PCR RFLP. The hot ligation method was used to depict kdr mutations and biochemical assays were conducted to assess detoxifying enzyme activities.ResultsThe An. arabiensis population from Pitoa was fully susceptible to DDT and permethrin (mortality rates 98% and showed reduced susceptibility to deltamethrin. Resistance to DDT was widespread in An. gambiae s.s. populations and heterogeneous levels of susceptibility to permethrin and deltamethrin were observed. In many cases, prior exposure to synergists partially restored insecticide knockdown effect and increased mortality rates, suggesting a role of detoxifying enzymes in increasing mosquito survival upon challenge by pyrethroids and, to a lower extent DDT. The distribution of kdr alleles suggested a major role of kdr based resistance in the S form of An. gambiae. In biochemical tests, all but one mosquito population overexpressed P450 activity, whereas baseline GST activity was low and similar in all field mosquito populations and in the control.ConclusionIn Cameroon, multiple resistance mechanisms segregate in the S form of An. gambiae resulting in heterogeneous resistance profiles, whereas in the M form and An. arabiensis insecticide tolerance seems to be essentially mediated by enzyme based detoxification. Synergists partially restored susceptibility to pyrethroid insecticides, and might help mitigate the impact of vector resistance in the field. However, additional vector control tools are needed to further impact on malaria transmission in such settings.
Unusual defect physics in CH3NH3PbI3 perovskite solar cell absorber
Year: 2014

Thin film solar cells based on Methylammonium triiodideplumbate (CH3NH3PbI3) halide perovskites have recently shown remarkable performance. First principle calculations show that CH3NH3PbI3 has unusual defect physics: (i) Different from common p type thin film solar cell absorbers, it exhibits flexible conductivity from good p type, intrinsic to good n type depending on the growth conditions; (ii) Dominant intrinsic defects create only shallow levels, which partially explain the long electron hole diffusion length and high open circuit voltage in solar cell. The unusual defect properties can be attributed to the strong Pb lone pair s orbital and I p orbital antibonding coupling and the high ionicity of CH3NH3PbI3.
Unsupervised Question Answering for Fact Checking
Venue: EMNLP
Year: 2019

Recent Deep Learning (DL) models have succeeded in achieving human level accuracy on various natural language tasks such as question answering, natural language inference (NLI) and textual entailment. These tasks not only require the contextual knowledge but also the reasoning abilities to be solved efficiently. In this paper, we propose an unsupervised question answering based approach for a similar task, fact checking. We transform the FEVER dataset into a Cloze task by masking named entities provided in the claims. To predict the answer token, we utilize pre trained Bidirectional Encoder Representations from Transformers (BERT) The classifier computes label based on the correctly answered questions and a threshold. Currently, the classifier is able to classify the claims as "SUPPORTS" and "MANUAL_REVIEW" This approach achieves a label accuracy of 80.2% on the development set and 80.25% on the test set of the transformed dataset.
Deep learning based model for detecting 2019 novel coronavirus pneumonia on high resolution computed tomography
Venue: Scientific reports
Year: 2020

Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID 19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID 19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019 CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per patient accuracy of 95.24% and a per image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96% With the assistance of the model, the reading time of radiologists was greatly decreased by 65% The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.
Logistic Regression Machine Learning Algorithm On Mri Brain Image For Fast And Accurate Diagnosis
Year: 2020

In medical field fast and accurate diagnosis has required for treatment, but present technologies do not have such facility. So need to implement an efficient diagnosis application for effective treatment. In this work LR (Logistic Regression) based Machine Learning (ML) method for classification and global threshold segmentation for pre processing. At 1st stage image acquisition and de noising has been performed, 2nd stage classification and regression has attained with ML techniques. In this MRI brain Image computer aided automatic detection system for brain disorders and tumors has been proposed. Computational methods of this work are threshold segmentation followed by LR ML classifier. This experiment consists of 120 brain images of 15 normal and 105abnormal from real time MRI brain database. The performance metrics like accuracy on training and testing images is 99.46% has been obtain. This technique is compared to recently published techniques conclude that LR ML with Thsegmentation has Fastreal and accurate brain diagnosis system
Hybrid Semi Dense 3D Semantic Topological Mapping From Stereo Visual Inertial Odometry SLAM With Loop Closure Detection
Venue: IEEE Transactions on Vehicular Technology
Year: 2020

Intelligent mobile vehicles asks for the ability to learn to comprehend immediate surroundings similar to human cognition and autonomously navigate in an unknown scene. Although simultaneous localization and mapping (SLAM) can construct a geometrically surrounding map, the map contains little semantic information. This paper presents a novel stereo visual inertial system with loop closure detection based on a semantic topological map framework. In our system, a hybrid 3D point cloud semantic topological mapping framework is used to realize autonomous navigation simply by providing a map for path planning and meanwhile for storing semantic information under a dynamic environment. A stereo visual inertial system can obtain more accurate visual inertial odometry than a monocular system, and a visual inertial system is capable of real time map correction when the localization module detects a loop closure. This paper adopts state of the art Mask R CNN components to obtain 2D semantic information and project it to a semi dense 3D semantic topological map built by stereo visual inertial SLAM. We perform experimental evaluation of our proposed system on the EuRoC MAV datasets and we also compare it with other competitive methods. Our results demonstrate that the proposed method is more effective than the other competitive methods.
Performance Comparison of Mutual Automotive Radar Interference Mitigation Algorithms
Venue: 2019 IEEE Radar Conference (RadarConf)
Year: 2019

The number of cars equipped with radar sensors is steadily increasing. Hence, mutual sensor interference will inevitably occur frequently. Interference results in decreased sensing performance, potentially leading to dangerous traffic situations. For the chirp sequence (CS) radars prevalent in automotive applications, different processing algorithms for interference mitigation have been proposed. However, the performances of these algorithms have not typically been evaluated and compared in a systematic way. In this paper we describe a framework based on the statistical evaluation of performance measures, suitable for a general comparison of mutual radar interference mitigation algorithms. The proposed methodology is then applied to analyze selected algorithms based on radar simulations.
Total Factor Energy Efficiency in BRI Countries: An Estimation Based on Three Stage DEA Model
Year: 2018

The Belt and Road Initiative (BRI) is showing its great influence and leadership on the international energy cooperation. Based on the three stage DEA model, total factor energy efficiency (TFEE) in 35 BRI countries in 2015 was measured in this article. It shows that the three stage DEA model could eliminate errors of environment variable and random, which made the result better than traditional DEA model. When environment variable errors and random errors were eliminated, the mean value of TFEE was declined. It demonstrated that TFEE of the whole sample group was overestimated because of external environment impacts and random errors. The TFEE indicators of high income countries like South Korea, Singapore, Israel and Turkey are 1, which is in the efficiency frontier. The TFEE indicators of Russia, Saudi Arabia, Poland and China are over 0.8. And the indicators of Uzbekistan, Ukraine, South Africa and Bulgaria are in a low level. The potential of energy saving and emissions reduction is great in countries with low TFEE indicators. Because of the gap in energy efficiency, it is necessary to distinguish different countries in the energy technology options, development planning and regulation in BRI countries.
Op Ed The Ivory Tower vs. the Dark Side: A Rebuttal to "Joining the Dark Side"
Year: 2016

The April, 2012, issue of Against the Grain included an article by Laura Harris, "Joining the Dark Side,"1 which I read with interest.Harris described her transition from working in an academic library to working for Spring share, the developer and vendor for the wonderful LibGuides. The article held interest for me personally, since I experienced a similar change earlier in my career.I had spent seven years in academic libraries when I received an unexpected and unsolicited job offer from EBSCO Subscription Services (later EBSCO Information Services) Like Laura,I had not considered working for a vendor. Well, that's not quite true. I had actually talked with a couple of folks at EBSCO before, but only for a possible position at their headquarters in Birmingham, AL. I thought I was completely uninterested in any sort of customer facing position. And yet,that is what I ended up doing (in three different positions) for fifteen years!
Reliability and Validity of the Revised Individualized Structured Mastery Tasks in Children with Developmental Delay
Year: 2016

Pei Jung Wang1, George A Morgan2, Li Chiou Chen3, Ai Wen Hwang4, Lu Lu5 and Hua Fang Liao3* 1Department of Medicine, School and Graduate Institute of Physical Therapy, National Taiwan University, Taipei, Taiwan 2Department of Education and Human Development, Colorado State University, Fort Collins, Colorado, USA 3Department of Medicine, School and Institute of Physical Therapy, National Taiwan University, Taipei, Taiwan 4Department of Medicine, Graduate Institute of Early Intervention, Chang Gung University, Tao Yuan, Taiwan 5Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan
Vocal motor and auditory connectivity of the midbrain periaqueductal gray in a teleost fish
Venue: The Journal of comparative neurology
Year: 2013

The midbrain periaqueductal gray (PAG) plays a central role in the descending control of vocalization across vertebrates. The PAG has also been implicated in auditory vocal integration, although its precise role in such integration remains largely unexplored. Courtship and territorial interactions in plainfin midshipman fish depend on vocal communication, and the PAG is a central component of the midshipman vocal motor system. We made focal neurobiotin injections into the midshipman PAG to both map its auditory vocal circuitry and allow evolutionary comparisons with tetrapod vertebrates. These injections revealed an extensive bidirectional pattern of connectivity between the PAG and known sites in both the descending vocal motor and the ascending auditory systems, including portions of the telencephalon, dorsal thalamus, hypothalamus, posterior tuberculum, midbrain, and hindbrain. Injections in the medial PAG produced dense label within hindbrain auditory nuclei, whereas those confined to the lateral PAG preferentially labeled hypothalamic and midbrain auditory areas. Thus, the teleost PAG may have functional subdivisions playing different roles in vocal auditory integration. Together the results confirm several pathways previously identified by injections into known auditory or vocal areas and provide strong support for the hypothesis that the teleost PAG is centrally involved in auditory vocal integration.  2013.
Propagation of elastic wave in nanoporous material with distributed cylindrical nanoholes
Year: 2013

The effective propagation constants of plane longitudinal and shear waves in nanoporous material with random distributed parallel cylindrical nanoholes are studied. The surface elastic theory is used to consider the surface stress effects and to derive the nontraditional boundary condition on the surface of nanoholes. The plane wave expansion method is used to obtain the scattering waves from the single nanohole. The multiple scattering effects are taken into consideration by summing the scattered waves from all scatterers and performing the configuration averaging of random distributed scatterers. The effective propagation constants of coherent waves along with the associated dynamic effective elastic modulus are numerically evaluated. The influences of surface stress are discussed based on the numerical results.
A Systematic Review on Mobile Learning in Higher Education: The African Perspective.
Year: 2017

The spread and popularity of mobile devices has led to their increased application in higher education. While studies have reviewed mobile learning initiatives in different contexts, none has explored this subject in Africa. This systematic review collates and compares studies published between 2010 and 2016 on mobile learning in higher education in the African context to explore the application, impact and challenges of mobile technology supported learning. Findings show that mobile learning within higher education institutions in Africa increased student and lecturer collaboration and, provide dinstant communication, increased student participation and engagement, facilitating authentic learning and reflective practice, as well as fostering learning communities. A change in the lecturers' approaches to teaching also occurred. The findings also indicate significant challenges in integrating mobile learning in higher education institutions within Africa: poor technological infrastructure, lack of access to modern mobile devices, lack of mobile learning pedagogical skills among lecturers, poor attitudes among students and lecturers, and incompatibility of mobile devices with the university online management systems. Policies to guide the implementation of mobile learning were also lacking. Large scale studies assessing the effectiveness of mobile learning within African higher education institutions are lacking and existing studies lacked a theoretical framework. The review highlights enabling conditions for successful integration of mobile learning in African institutions addressing access, training, curriculum design, support and technical requirements. The absence of studies reporting on existing mobile learning projects reflects the limited penetration of this technology and associated pedagogies and a need to strengthen research in this emerging field.
Gut Ischemia Reperfusion Injury Induces Lung Inflammation via Mesenteric Lymph Mediated Neutrophil Activation
Venue: Frontiers in Immunology
Year: 2020

Gut ischemia/reperfusion (I/R) injury is a common clinical problem associated with significant mortality and morbidities that result from systemic inflammation and remote organ dysfunction, typically acute lung injury. The mechanisms underlying the dissemination of gut derived harmful mediators into the circulation are poorly understood. The objective of our study was to determine the role of mesenteric lymphatic circulation in the systemic and pulmonary inflammatory response to gut I/R. Using a murine intestinal I/R model, we evaluated whether and how blocking mesenteric lymph flow affects the inflammatory response in local tissues (gut) and remote organs (lungs) We further explored the mechanisms of post I/R lymph induced systemic inflammation by examining neutrophil activity and interaction with endothelial cells in vitro. Mice subjected to intestinal I/R displayed a significant inflammatory response in local tissues, evidenced by neutrophil infiltration into mucosal areas, as well as lung inflammation, evidenced by increased myeloperoxidase levels, neutrophil infiltration, and elevated microvascular permeability in the lungs. Mesenteric lymph duct ligation (MLDL) had no effect on gut injury per se, but effectively attenuated lung injury following gut I/R. Cell experiments showed that lymph fluid from post I/R animals, but not pre I/R, increased neutrophil surface CD11b expression and their ability to migrate across vascular endothelial monolayers. Moreover, post I/R lymph upregulated neutrophil expression of pro inflammatory cytokines and chemokines, which was mediated by a mechanism involving nuclear factor (NF) kB signaling. Consistently, gut I/R activated NF kB in lung neutrophils, which was alleviated by MLDL. In conclusion, all these data indicate that mesenteric lymph circulation contributes to neutrophil activation and lung inflammation following gut I/R injury partly through activating NF kB.