nils-herrmann/clean_scirepeval huggingface dataset:
Progressive Latent Models for Self Learning Scene Specific Pedestrian Detectors
Venue: IEEE Transactions on Intelligent Transportation Systems
Year: 2020

The performance of offline learned pedestrian detectors significantly drops when they are applied to video scenes of various camera views, occlusions, and background structures. Learning a detector for each video scene can avoid the performance drop but it requires repetitive human effort on data annotation. In this paper, a self learning approach is proposed, toward specifying a pedestrian detector for each video scene without any human annotation involved. Object locations in video frames are treated as latent variables and a progressive latent model (PLM) is proposed to solve such latent variables. The PLM is deployed as components of object discovery, object enforcement, and label propagation, which are used to learn the object locations in a progressive manner. With the difference of convex (DC) objective functions, PLM is optimized by a concave convex programming algorithm. With specified network branches and loss functions, PLM is integrated with deep feature learning and optimized in an end to end manner. From the perspectives of convex regularization and error rate estimation, detailed optimization analysis and learning stability analysis of the proposed PLM are provided. The extensive experiments demonstrate that even without annotation involved the proposed self learning approach outperforms weakly supervised learning approaches, while achieving comparable performance with transfer learning approaches.
Mapping the Business Systems of 61 Major Economies: A Taxonomy and Implications for Varieties of Capitalism and Business Systems Research
Year: 2018

Efforts to build a universal theory of the world's business systems require empirical grounding in an understanding of the variety that need explaining. To support such theorizing, we analyzed the institutional structures of 61 major economies, accounting for 93.5% of 2013 world GDP at purchasing power parity. We found nine main types of business systems: Highly Coordinated, Coordinated Market, Liberal Market, European Peripheral, Advanced Emerging, Advanced City, Arab Oil Based, Emerging, and Socialist Economies. Our findings illustrate the need to go beyond the Varieties of Capitalism and Business Systems frameworks; provide empirical support for the CME versus LME dichotomy for part of the OECD; identify some of the business systems proposed recently as sub types of larger clusters; indicate that institutional diversity may increase with development level; and cast doubt on the notions of state led and family led capitalism as types of business systems. Our discussion further suggests numerous avenues for theory development and empirical research.
Salicylic acid an emerging growth and flower inducing hormone in marigold (Tagetes sp. L)
Year: 2018

Salicylic acid (SA) is an emerging plant growth regulator that acts as signaling molecule in plants under biotic and abiotic stresses. SA also exerts a stimulatory effect on different physiological processes of plant growth but its association with leaf pigments and flowering is less known. Current experiment was conducted to evaluate the effect of exogenous application of different doses of SA on marigold (Tagetes sp. in greenhouse condition. Marigold (Tagetes sp. L) plants were randomly divided in 4 groups and treated exogenously with four different concentrations of SA (T0: 0 (only water) T1: 40, T2: 80 and T3: 120 mg/L) The solutions were sprayed on aerial parts of plant after 60 days of sowing. Results analysis showed that T3 (120 mg/L SA solution) showed maximum number of leaves plant (30.38) highest plant height (50.63 cm) more number of inflorescence, greater stem diameter (7.84 mm) maximum fresh weight of flowers (11.90 g) and maximum dry weight of flower (1.25 g) Whereas, minimum number of leaves (22.74) lowest plant height (40.8 cm) less number of inflorescence, smaller stem diameter (4.75 mm) minimum fresh flower weight (7.13 g) and minimum dry flower weight (0.7 g) were observed in T0. Furthermore, various leaf pigments were found higher in T3. Present study concluded that T3 treatment of SA improved leaf pigments and morphometric parameters in Marigold. From the aforementioned results, it is suggested that 120 mg/L concentration of SA should be sprayed exogenously before flowering stage, on marigold plants for better growth and flower production.
Are Professional Investment Managers Skilled? Evidence from Syndicated Loan Portfolios
Year: 2015

Theory predicts that individual investor's incentives to uncover new information about asset values are low if asset prices are efficient. This, in turn, implies that heterogeneity in investment manager skill, if present, should be most clearly visible among managers that focus on asset classes with less informationally efficient prices. We investigate this argument using a large sample of syndicated bank loan portfolios managed by collateralized loan obligation (CLO) managers. Using a CLO's equity tranche cash on cash return to measure performance, we find strong persistence that is robust to an extensive set of risk controls. Although investors seem to derive their expectation about management quality from a manager's realized performance and allocate more capital to "skilled" managers, top performers cope to stay ahead of net of fees. This questions the rationality of managers and the efficiency of the syndicated loan market. This paper was accepted by Wei Jiang, finance.
Three dimensional Ultrasound Evaluation of Tongue Posture and Its Impact on Articulation Disorders in Preschool Children with Anterior Open Bite
Venue: Radiology and oncology
Year: 2018

Tongue posture plays an important role in the etiology of anterior open bite (AOB) and articulation disorders, and is crucial for AOB treatment planning and posttreatment stability. Clinical assessment of tongue posture in children is unreliable due to anatomical limitations. The aim of the study was to present functional diagnostics using three dimensional ultrasound (3DUS) assessment of resting tongue posture in comparison to clinical assessment, and the associations between the improper tongue posture, otorhinolaryngological characteristics, and articulation disorders in preschool children with AOB.  A cross sectional study included 446 children, aged 3 7 years, 236 boys and 210 girls, examined by an orthodontist to detect the prevalence of AOB. The AOB was present in 32 children. The control group consisted of 43 children randomly selected from the participants with normocclusion. An orthodontist, an ear, nose and throat (ENT) specialist and a speech therapist assessed orofacial and ENT conditions, oral habits, and articulation disorders in the AOB group and control group. Tongue posture was also assessed by an experienced radiologist, using 3DUS. The 3DUS assessment of tongue posture was compared to the clinical assessment of orthodontist and ENT specialist.  The prevalence of AOB was 7.2% The AOB group and the control group significantly differed regarding improper tongue posture (p 0.001) and articulation disorders (p 0.001) In children without articulation disorders from both groups, the improper tongue posture occured less frequently than in children with articulation disorders (p 0.001) After age adjustment, a statistical regression model showed that the children with the improper tongue posture had higher odds ratios for the presence of AOB (OR 14.63; p 0.001) than the others. When articulation disorders were included in the model, these odds ratios for the AOB became insignificant (p 0.177) There was a strong association between the improper tongue posture and articulation disorders (p 0.002) The 3DUS detected the highest number of children with improper resting tongue posture, though there was no significant difference between the 3DUS and clinical assessments done by orthodontist and ENT specialist.  The 3DUS has proved to be an objective, non invasive, radiation free method for the assessment of tongue posture and could become an important tool in functional diagnostics and early rehabilitation in preschool children with speech irregularities and irregular tongue posture and malocclusion in order to enable optimal conditions for articulation development.
Dynamics modeling and sliding mode control of tractor trailer wheeled mobile robots subject to wheels slip
Venue: Mechanism and Machine Theory
Year: 2019

Wheeled mobile robots have vast applications in various areas of technology, and hence improving their autonomy has been the subject of extensive research. To enhance the payload carrying capacity of single platform wheeled robots, an additional wheeled platform (called trailer) is connected to the active platform (called tractor) and the whole system is called the Tractor Trailer Wheeled Mobile Robot (TTWMR) In the present study, a novel control rule is presented for trajectory tracking control of such robotic systems. To this end, the dynamics model of such nonholonomic systems is for the first time derived such that lateral and longitudinal slip in the wheels are taken into account. To model the interaction between the wheels and the ground, the LuGre friction model has been utilized. Then, the obtained dynamics model has been verified using ADAMS multibody simulation software. In the next step, to track the desired trajectories, a novel controller, based on sliding mode robust approach, is suggested in which slipping is modeled as a set of unknown but finite disturbances in the robot model. Validity and accuracy of the proposed controller have been examined via computer simulations.
Promoting charitable donation campaigns on social media
Year: 2018

It is salient to investigate how to increase persuasiveness of donation campaign messages communicated on social media. The purpose of this paper is to propose that a construal fit between different message frames (loss/gain framing and desirability/feasibility framing) and donation temporal proximity enhances the persuasiveness of charity donation messages. A convenience sample of 120 adults in Izmir voluntarily participated in this study and participants were randomly assigned to one of four experimental conditions. This research shows that gain framed donation promoting messages paired with desirability framed messages are more effective on distant future donation intentions, whereas loss framed messages paired with feasibility framed messages are more effective on near future donation intentions. The findings of this paper are to figure out ways to enable marketers to develop effective Corporate Social Responsibility strategies for telecommunication service companies to encourage consumers' charitable donations in a social media context.
Monetary Policy Shocks and Industrial Sector Performance in South Africa
Year: 2016

This paper employs an eight variable Structural Vector Auto regression (SVAR) model to examine how monetary policy shocks affect industrial sector performance in South Africa using monthly data from 1994:1 to 2012:12.The study finds no direct link between exchange rate and interest rate shocks and industrial output growth. A money supply shock, however, is observed to exert a significant positive impact on industrial output growth from about the eighth month. The study also reveals that the interest rate response to an unanticipated increase in the rate of inflation is insignificant, reflecting the infrequent changes of the repo rate in the country. We also find evidence of a symbiotic relationship between industrial output growth and other sectors of the economy that form components of aggregate output. The study further demonstrates that monetary authorities have very limited control over industrial output growth using instruments of monetary policy. In addition, it is found that relatively large proportions of the variations in the rate of inflation are explained by changes in money supply, exchange rates and industrial output. We also observe that variations in exchange rates are largely explained by unexpected changes in the exchange rates themselves, which supports the Martingale Hypothesis of exchange rates.
Effects of climate warming on photosynthesis in boreal tree species depend on soil moisture
Venue: Nature
Year: 2018

Climate warming will influence photosynthesis via thermal effects and by altering soil moisture1 11. Both effects may be important for the vast areas of global forests that fluctuate between periods when cool temperatures limit photosynthesis and periods when soil moisture may be limiting to carbon gain4 6,9 11. Here we show that the effects of climate warming flip from positive to negative as southern boreal forests transition from rainy to modestly dry periods during the growing season. In a three year open air warming experiment with juveniles of 11 temperate and boreal tree species, an increase of 3.4 degC in temperature increased light saturated net photosynthesis and leaf diffusive conductance on average on the one third of days with the wettest soils. In all 11 species, leaf diffusive conductance and, as a result, light saturated net photosynthesis decreased during dry spells, and did so more sharply in warmed plants than in plants at ambient temperatures. Consequently, across the 11 species, warming reduced light saturated net photosynthesis on the two thirds of days with driest soils. Thus, low soil moisture may reduce, or even reverse, the potential benefits of climate warming on photosynthesis in mesic, seasonally cold environments, both during drought and in regularly occurring, modestly dry periods during the growing season.Low soil moisture may reduce, or even reverse, the potential benefits of climate warming on photosynthesis in mesic, seasonally cold environments, both during drought and in regularly occurring, modestly dry periods during the growing season.
Local Path Planning Using Artificial Potential Field for Waypoint Tracking with Collision Avoidance
Venue: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Year: 2020

In this paper, we propose a waypoint tracking scheme with a collision avoidance system using the artificial potential field (APF) with speed planning. It is shown that there is little literature about the combination work of waypoint tracking and APF based local path planner. We present a new path planning method to smoothly join the local path predicted by APF to avoid obstacles and the path given by irregular waypoints to eliminate severe yawing of the heading angle of the vehicle. The proposed speed planning combined with APF effectively prevents possible excessive tire slip angle of the vehicle driving at high speed. The simulation results show that the proposed path planning algorithm with speed planning is effective in collision avoidance with static and/or dynamic obstacles for the waypoint tracking scenario. This proposed method is validated with the vehicle dynamic lateral motion model in MATLAB/Simulink and CarSim.
CrowdQ: Crowdsourced Query Understanding
Venue: CIDR
Year: 2013

Work in hybrid human machine query processing has thus far focused on the data: gathering, cleaning, and sorting. In this paper, we address a missed opportunity to use crowdsourcing to understand the query itself. We propose a novel hybrid human machine approach that leverages the crowd to gain knowledge of query structure and entity relationships. The proposed system exploits a combination of query log mining, natural language processing (NLP) and crowdsourcing to generate query templates that can be used to answer whole classes of different questions rather than focusing on just a specific question and answer.
An open labeled study to evaluate safety and efficacy of Unicough syrup in children with acute bronchitis.
Year: 2012

Acute bronchitis is inflammation of the main air passage to the lungs generally follows a viral respiratory infection. Children generally suffer from acute bronchitis at any stage. Fumes, dusts, decreased immunity, indigestion, viral infections are some causes due to which air passage get inflamed and irritated. Acute bronchitis is a clinical syndrome produced by inflammation of the trachea, bronchi, and bronchioles. In children, acute bronchitis usually occurs in association with viral respiratory tract infection. Acute bronchitis is rarely a primary bacterial infection in otherwise healthy children. In modern therapies using excessive expectorant, antihistaminics, and cough suppressants causes certain side effects and has no roll in rejuvenating the respiratory system. Unicough is a herbal remedy with the herbs which acts as a mucolytic and mild expectorant by its smoothening effect which helps in easy evacuation of cough and rejuvenate the respiratory passages. Current open labeled study on forty four children of acute bronchitis showed significant improvement after first and second week of the treatment in all associated symptoms which proves that Unicough syrup is a safe and effective remedy for acute bronchitis. 3 Antibiotics are commonly prescribed, this type of therapy remains controversial, particularly since only half of all exacerbations are bacterial in origin, the rest being viral or chemical. Thus some believe that antibiotic therapy of acute exacerbation of bronchitis is an abuse of antibiotics, and can contribute to antimicrobial resistance 4 The natural approach to treating bronchitis avoids creating new disease problems. It involves two goals: stimulating the normal processes that expel mucus, and stimulating the immune system to deal with viral infection. Expectorant herbs increase the fluidity and volume of phlegm, and stimulate cough. They are not cough suppressants, since cough suppression traps phlegm in the bronchi and lungs 5 Unicough is a herbal remedy with Khaira Chhal (Acacia catechu),Vasakapatra (Adhatoda vasika) Vach (Acorus calamus) Elaichi (Elettaria cardamomum) Nagkeshar (Mesua ferrea) Talispatra (Abies webbina) Tulsi (Ocimum scantum) Dalchini (Cinnamomum zeylanicum) Sounth (Zingiber officinale) Pippali (Piper longum) Kababchini (Piper cubeba) Mulethi (Glycyrrhiza glabra) Somlata (Ephedra gerardiana) Dhatura panchang (Datura metel) Ajowan sat (Thymol) Pudina sat (Menthol) All these ingredients act as a smoothening, expectorant, bronchodilator and also improve the metabolism. This was an open labeled, non comparative study in the patients of acute bronchitis for the period of 15 days.
Commentary on "Motor system dysfunction in the schizophrenia diathesis: Neural systems to neurotransmitters"
Venue: European Psychiatry
Year: 2018

We were very pleased to read the excellent article by Abboud et al.  who provided a thorough synthesis of the bio behavioral relevance of motor dysfunction in schizophrenia by presenting basic movement physiology, schizophrenia related brain mechanisms, and putative molecular pathways, particularly in the context of dopamine hypothesis. We fully agree with the authors that multistage approach on this particular research topic is important and their work will supplement calls arguing for renewed interest in this rapidly developing research domain  In this commentary we want to add some comments to the research framework outlined by Abboud et al. since there are a few key milestones that need to be reached in order to demonstrate the great value of the motor domain for both neuroscientific research and clinical practice
Eye Contact Detection Algorithms Using Deep Learning and Generative Adversarial Networks
Venue: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2018

Eye contact (mutual gaze) is a foundation of human communication and social interactions; therefore, it is studied in many fields such as psychology, social science, and medicine. Our group have been studied wearable vision based eye contact detection techniques using a first person camera for the purpose of evaluating the gaze skills in the tender dementia care. In this work, we search for deep learning based eye contact detection techniques from small number of labeled images. We implemented and tested two eye contact detection algorithms: naive deep learning based algorithm and generative adversarial networks (GAN) based semi supervised learning (SSL) algorithm. These methods are learned and verified by using Columbia Gaze Dataset, Facescrub and our original datasets. The results show the effectiveness and limitations of the deep learning based and GAN based approaches. Interestingly, we found the bilateral difference of the accuracy of eye contact detection with respect to the facial pose with respect to the camera, which is expected to be caused by the learning datasets.
Utilizing graph neural networks to improving dialogue based relation extraction
Venue: Neurocomputing
Year: 2021

Relation extraction has been an active research interest in the field of Natural Language Processing (NLP) The past works primarily focused on a corpus of formal text which is inherently non dialogic. Recently, the dialogue based relation extraction task, which detects relations among speaker aware entities scattering in dialogues, has been gradually arousing people's attention. Some sequence based neural methods have been carried out to obtain the relevant information. However, identifying cross sentence relations remains unsolved, especially in the context of a specific domain dialogue system. In this paper, we propose a Relational Attention Enhanced Graph Convolutional Network (RAEGCN) which constructs the whole dialogue as a semantic interactive graph by emphasizing the speaker related information and leveraging various inter sentence dependencies. A dense connectivity mechanism is also introduced to empower the multi hop relational reasoning across sentences, which can capture both local and non local features simultaneously. Experiments show the significant superiority and robustness of our model on a real world dataset DialogRE, as compared with previous approaches.
Image Embedding of PMU Data for Deep Learning Towards Transient Disturbance Classification
Venue: 2018 IEEE International Conference on Energy Internet (ICEI)
Year: 2018

This paper presents a study on power grid disturbance classification by Deep Learning (DL) A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.