nils-herrmann/clean_scirepeval huggingface dataset:
A detection algorithm for cherry fruits based on the improved YOLO v4 model
Year: 2021

"Digital" agriculture is rapidly affecting the value of agricultural output. Robotic picking of the ripe agricultural product enables accurate and rapid picking, making agricultural harvesting intelligent. How to increase product output has also become a challenge for digital agriculture. During the cherry growth process, realizing the rapid and accurate detection of cherry fruits is the key to the development of cherry fruits in digital agriculture. Due to the inaccurate detection of cherry fruits, environmental problems such as shading have become the biggest challenge for cherry fruit detection. This paper proposes an improved YOLO V4 deep learning algorithm to detect cherry fruits. This model is suitable for cherry fruits with a small volume. It is proposed to increase the network based on the YOLO V4 backbone network CSPDarknet53 network, combined with DenseNet The density between layers, the a priori box in the YOLO V4 model, is changed to a circular marker box that fits the shape of the cherry fruit. Based on the improved YOLO V4 model, the feature extraction is enhanced, the network structure is deepened, and the detection speed is improved. To verify the effectiveness of this method, different deep learning algorithms of YOLO V3, YOLO V3 dense and YOLO V4 are compared. The results show that the mAP (average accuracy) value obtained by using the improved YOLO V4 model (YOLO V4 dense) network in this paper is 0.15 higher than that of yolov4. In actual orchard applications, cherries with different ripeness of cherries in the same area can be detected, and the fruits with larger ripeness differences can be artificially intervened, and finally, the yield of cherry fruits can be increased.
Hierarchical Linked Views
Venue: Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007)
Year: 2007

Coordinated views have proven critical to the development of effective visualization environments. This results from the fact that a single view or representation of the data cannot show all of the intricacies of a given data set. Additionally, users will often need to correlate more data parameters than can effectively be integrated into a single visual display. Typically, development of multiple linked views results in an adhoc configuration of views and associated interactions. The hierarchical model we are proposing is geared towards more effective organization of such environments and the views they encompass. At the same time, this model can effectively integrate much of the prior work on interactive and visual frameworks. Additionally, we expand the concept of views to incorporate perceptual views. This is related to the fact that visual displays can have information encoded at various levels of focus. Thus, a global view of the display provides overall trends of the data while focusing in on individual elements provides detailed specifics.
Temporal Dynamics of the Driving Factors of Urban Landscape Change of Addis Ababa During the Past Three Decades
Venue: Environmental Management
Year: 2017

Mapping and quantifying urban landscape dynamics and the underlying driving factors are crucial for devising appropriate policies, especially in cities of developing countries where the change is rapid. This study analyzed three decades (1984 2014) of land use land cover change of Addis Ababa using Landsat imagery and examined the underlying factors and their temporal dynamics through expert interview using Analytic Hierarchy Process (AHP) Classification results revealed that urban area increased by 50% while agricultural land and forest decreased by 34 and 16% respectively. The driving factors operated differently during the pre and post 1991 period. The year 1991 was chosen because it marked government change in the country resulting in policy change. Policy had the highest influence during the pre 1991 period. Land use change in this period was associated with the housing sector as policies and institutional setups were permissive to this sector. Population growth and in migration were also important factors. Economic factors played significant role in the post 1991 period. The fact that urban land has a market value, the growth of private investment, and the speculated property market were among the economic factors. Policy reforms since 2003 were also influential to the change. Others such as accessibility, demography, and neighborhood factors were a response to economic factors. All the above mentioned factors had vital role in shaping the urban pattern of the city. These findings can help planners and policymakers to better understand the dynamic relationship of urban land use and the driving factors to better manage the city.
A potential biomarker in sports related concussion: brain functional connectivity alteration of the default mode network measured with longitudinal resting state fMRI over thirty days.
Venue: Journal of neurotrauma
Year: 2015

Current diagnosis and monitoring of sports related concussion rely on clinical signs and symptoms, and balance, vestibular, and neuropsychological examinations. Conventional brain imaging often does not reveal abnormalities. We sought to assess if the longitudinal change of functional and structural connectivity of the default mode network (DMN) can serve as a potential biomarker. Eight concussed Division I collegiate football student athletes in season (one participated twice) and 11 control subjects participated in this study. ImPACT (Immediate Post Concussion Assessment and Cognitive Testing) was administered over the course of recovery. High resolution three dimensional T1 weighted, T2* weighted diffusion tensor images and resting state functional magnetic resonance imaging (rs fMRI) scans were collected from each subject within 24 h, 7+ 1 d and 30+ 1 d after concussion. Both network based and whole brain based functional correlation analyses on DMN were performed. ImPACT findings demonstrated significant cognitive impairment across multiple categories and a significant increase of symptom severity on Day 1 following a concussion but full recovery by 6.0+ 2.4 d. While the structural connectivity within DMN and gross anatomy appeared unchanged, a significantly reduced functional connectivity within DMN from Day 1 to Day 7 was found in the concussed group in this small pilot study. This reduction was seen in eight of our nine concussion cases. Compared with the control group, there appears a general trend of increased DMN functional connectivity on Day 1, a significant drop on Day 7, and partial recovery on Day 30. The results of this pilot study suggest that the functional connectivity of DMN measured with longitudinal rs fMRI can serve as a potential biomarker to monitor the dynamically changing brain function after sports related concussion, even in patients who have shown clinical improvement.
Level of Thyroid Stimulating Hormone Concentration in A sample of Hyperactive Versus Pro social school children in Ir aqi City
Year: 2014

Attention deficit hyperactivity disorder (ADHD) is a common recognized psychiatric disorder of Childhood and its cause is still unknow n.during the last years number of studies have appearedon the subject of relationships between thy roid hormones and psychiatric disease.  To assess the association between thyroid stimulati ng hormone (TSH) concentrations, for the Purpose of comparing the hormone levels among schoo l children with ADHD, and those healthy school Children with pro social traits.  A cross sectional study was conducted for the peri od from the first of June 2013 to the 31 st March 2014. The study sample was (100) school chi ldren diagnosedwith behavioral disorders including ADHD with or without conduct or aggressiv e disorder. In comparison with (100)school children characterized by pro social traits as a co ntrol group. Assessment of behavioural disorders depended on Rutter behavioralQuestionnaire (RBQ) a s a behavioral scale used by teachers for assessment of school children behavior. Measurement of serum TSH concentration level was done to school children with ADHD, those with conduct disor der, and to the controlsgroup.  Out of 100 school children who are diagnosed as hav ing ADHD, 65% were males and 35% were females. School children who were found to have con duct disorder comorbid with ADHD constituted 49% of them 32% were males and 17% were females. S chool children diagnosed to have ADHD, without
Live stranded fin whale Balaenoptera physalus in Libyan waters reported via social media platform
Year: 2020

A live movie was streamed by a recreational fisherman on social media on 2 August 2020 featuring a fin whale Balaenoptera physalus stranded alive off the coast of Bomba Bay, 70 km east of Derna, Libya. This short note highlights the importance of social media for recording and monitoring marine megafauna in remote and scarcely surveyed regions.
Proportional Volume Sampling and Approximation Algorithms for A Optimal Design
Venue: SODA
Year: 2019

We study the optimal design problems where the goal is to choose a set of linear measurements to obtain the most accurate estimate of an unknown vector in $d$ dimensions. We study the $A$ optimal design variant where the objective is to minimize the average variance of the error in the maximum likelihood estimate of the vector being measured. The problem also finds applications in sensor placement in wireless networks, sparse least squares regression, feature selection for $k$ means clustering, and matrix approximation. In this paper, we introduce proportional volume sampling to obtain improved approximation algorithms for $A$ optimal design. Our main result is to obtain improved approximation algorithms for the $A$ optimal design problem by introducing the proportional volume sampling algorithm. Our results nearly optimal bounds in the asymptotic regime when the number of measurements done, $k$ is significantly more than the dimension $d$ We also give first approximation algorithms when $k$ is small including when $k=d$ The proportional volume sampling algorithm also gives approximation algorithms for other optimal design objectives such as $D$ optimal design and generalized ratio objective matching or improving previous best known results. Interestingly, we show that a similar guarantee cannot be obtained for the $E$ optimal design problem. We also show that the $A$ optimal design problem is NP hard to approximate within a fixed constant when $k=d$
Prevalence and psychological stress in recurrent aphthous stomatitis among female dental students in Saudi Arabia
Venue: Clujul medical
Year: 2018

Recurrent Aphthous Stomatitis (RAS) is a common oral mucosal disorder which is characterized by recurrent ulcerations mainly confined to non keratinized oral mucosa. Though the exact etiology is imprecise, stress and anxiety were found to be linked with the onset of RAS. The present study was directed to know the prevalence of RAS among female dental students in King Khalid University, to find out the association between RAS and psychological stress and the effectiveness of Hospital Anxiety and Depression scale (HADS) in finding out the psychological profile of RAS patients. Method 122 female dental students of age group 17 to 25 years were selected for the study. Subjects with relevant medical problems and deleterious habits were excluded. A questionnaire comprising general stress related questions and HAD scale were used to assess stress. Those who were giving history of ulcer were diagnosed as RAS based on the clinical features. The questionnaire results were used for statistical analysis and processed. Result 14% of the total students were having RAS. 70% could correlate the onset of ulcer with certain specific factors; stress being the major causative agent (91% In HAD scale, 88% of students were having anxiety and 65% had depression; all patients with depression had anxiety.  The prevalence of RAS in female dental students in KKU was around 14% There is a strong relationship between psychological stress and RAS, as in most of the other studies. HAD scale alone can be used for detecting the psychological factor in RAS.
Predicting missing values: a comparative study on non parametric approaches for imputation
Venue: Comput. Stat.
Year: 2019

Missing data is an expected issue when large amounts of data is collected, and several imputation techniques have been proposed to tackle this problem. Beneath classical approaches such as MICE, the application of Machine Learning techniques is tempting. Here, the recently proposed missForest imputation method has shown high imputation accuracy under the Missing (Completely) at Random scheme with various missing rates. In its core, it is based on a random forest for classification and regression, respectively. In this paper we study whether this approach can even be enhanced by other methods such as the stochastic gradient tree boosting method, the C5.0 algorithm, BART or modified random forest procedures. In particular, other resampling strategies within the random forest protocol are suggested. In an extensive simulation study, we analyze their performances for continuous, categorical as well as mixed type data. An empirical analysis focusing on credit information and Facebook data complements our investigations.
Frequency Noise and Phase Locking of a Quantum Cascade Laser Pumped, 1.073 Terahertz Molecular Laser using a 1560nm Frequency Comb
Venue: 2019 Conference on Lasers and Electro Optics Europe European Quantum Electronics Conference (CLEO/Europe EQEC)
Year: 2019

Recently, a novel type of molecular THz laser with ~mW level output power was demonstrated  This laser is optically pumped by a mid IR Quantum Cascade Laser (QCL) therefore resulting in a much more compact, frequency agile and deployable source compared to standard, CO2 laser pumped, far infrared gas lasers. This achievement has revived the interest for molecular THz lasers, in particular for their exploitation as low phase noise THz local oscillators (LOs) operating at room temperature. Compared to CO2 laser pumping, a QCL pump offers an unrivalled frequency agility that allows accessing an unprecedented number of molecular transitions. On the other hand, its lower frequency stability can potentially impact the frequency noise of the THz laser source  Here we report the first measurement of the frequency noise power spectral density (PSD) of a 1.073THz ammonia laser pumped by a 10.3mm QCL. This is achieved by using a commercial harmonic mixer to generate a beating between the ammonia laser frequency and the 1080th harmonic of the 1GHz repetition rate of a custom made, 1560nm frequency comb (FC) We then exploit a voltage controlled oscillator to track the beat note signal and realize the frequency to amplitude conversion  The measured frequency noise PSD is shown in Fig.1. In the range ~30 100kHz we find a frequency noise 10Hz2/Hz 95dBc) limited by the noise of the QCL current driver. We will show that the value of the frequency noise PSD depends on the relative position of the pump frequency with respect to center of the Doppler broadened pump transition line. To further elucidate the effect of the stability of the pump laser we also measured the frequency noise PSD of a CO2 pumped, 2.5THz methanol laser, reaching 0.1Hz2/Hz 110dBc) at 40kHz from the carrier, limited by the frequency noise of the FC harmonic. Finally, we will show that it is possible to actively phase lock the QCL pumped ammonia laser to the FC repetition rate harmonic by controlling the QCL drive current, demonstrating a sub Hz linewidth. These results pave the way to the development of QCL pumped molecular lasers as compact, low phase noise THz sources operating a room temperature.
Genetics of restless legs syndrome: An update.
Venue: Sleep medicine reviews
Year: 2018

A major role of genetic factors in the risk of developing restless legs syndrome (RLS) is supported by the high frequency of positive family history of RLS in patients affected with this disease, and the higher concordance rates in monozygotic twins compared with dizygotic ones in twin studies. In this review we have focused on those reports describing inheritance patterns of RLS, genetic anticipation, the results of studies performed on positivity of family history of RLS, twin studies, linkage studies in familial RLS, genome wide association studies (GWAS) exome sequencing studies, and case control association studies on candidate genes in RLS. Although to date the causative gene(s) has(ve) not been definitively identified, a number of variants of several genes, most of them through GWAS, have been associated with RLS risk, the strongest candidates being variants of PTPRD, BTBD9, and MEIS1 genes. Despite results of several recent case control association studies which have suggested a possible contribution of heme oxygenase 1 (HMOX1) rs2071746 and vitamin D3 receptor (VDR) rs731236 variants, or the presence of allele 2 of the complex microsatellite repeat Rep1 within the alpha synuclein (SNCA) gene promoter in modifying the risk for RLS, these studies need to be replicated in further studies involving different populations.
Does organizational reputation matter in Pakistan's higher education institutions? The mediating role of person organization fit and person vocation fit between organizational reputation and turnover intention
Year: 2020

Drawing on social identity theory (SIT) signaling theory, and attraction selection attrition (ASA) model, we proposed and developed a model linking organizational reputation to turnover intention, both directly and indirectly through person vocation fit (P V fit) and person organization fit (P O fit) The model was tested using partial least squares structural equation modeling (PLS SEM) on a sample of 250 full time faculty members from Pakistan's higher educational institutions. The findings supported the model and found that organization reputation had a negative and direct influence on turnover intention and positive impact on P V fit and P O fit. P O fit and P V fit, in turn, had negative relationships with faculty turnover intention. Moreover, the study revealed that P O fit and P V fit partially mediated the negative relationship between organizational reputation and faculty turnover intention. The findings of the study has important practical implications for the adoption of marketing concepts in non profit seeking organizations, especially in higher educational institutions. The practical and theoretical implications and some interesting avenues for future research are discussed at the end of the study.
Pentad and triangular structures behind the Racah matrices
Year: 2020

Somewhat unexpectedly, the study of the family of twisted knots revealed a hidden structure behind exclusive Racah matrices S, which control non associativity of the representation product in a peculiar channel R RR R. These S are simultaneously symmetric and orthogonal, and therefore admit two decompositions: as quadratic forms, S EtrE, and as operators: T ST ST 1S 1. Here T and T consist of the eigenvalues of the quantum R matrices in channels R R and R R respectively, S is the second exclusive Racah matrix for RRR R (still orthogonal, but no longer symmetric) and E is a triangular matrix. It can be further used to construct the KNTZ evolution matrix B ET 2E 1, which is also triangular and explicitly expressible through the skew Schur and Macdonald functions what makes Racah matrices calculable. Moreover, B is somewhat similar to Ruijsenaars Hamiltonian, which is used to define Macdonald functions, and gets triangular in the Schur basis. Discovery of this pentad structure (T S, S, E,B) associated with the universal R matrix, can lead to further insights about representation theory, knot invariants and Macdonald Kerov functions. Triangular structures are long believed to be intimately related to integrability. For example, triangular are the pseudo differential "dressing operators" in the description of KdV/KP hierarchies  In a complementary development, triangularity in the form of Gauss decomposition proved to be crucial for the free field description of generic conformal theories [2] Since then triangular transforms appeared in many places, in particular, in the description of Macdonald Kerov functions, of their "generalized" multi time deformations and, somewhat surprisingly, in the study of "evolution" along the family of twist knots which had direct implication to the theory of Racah matrices (6j symbols)  and allowed to explicitly calculate some of them, what remained an unsolvable problem for quite a long period of time. A purpose of this short note is a brief review of this newly emerging and promisingly diverse field. 1. Another example of the fundamental importance is description of Macdonald [4] and, more generally, Kerov functions [5,6] as triangular transformation of Schur functions, see [7] for a recent reminder. In this case one just applies a triangular orthogonalization procedure for the scalar product p|p (g) z d, l i=1 gdi (1) on the space of time variables pk. Here p is the monomial basis, p l i=1 pdi labeled by Young diagrams [d1 d2 dl 0] 2 m2 11 with z k k mk *mk! It is well known, that Schur functions khR{p} form another orthonormal basis when all gi 1, khR|khR' (I) dR,R' (2) and Kerov functions are defined as their triangular transformation, which diagonalizes the product for arbitrary {g} Ker (g) R {p} khR{p} R' 0] R R if r1 r 1 or if r1 r 1, but r2 r 2, or if r1 r 1 and r2 r 2, but r3 r 3, and so on (4)
Using machine learning tools for forecasting natural gas consumption in the province of Istanbul
Venue: Energy Economics
Year: 2019

Commensurate with unprecedented increases in energy demand, a well constructed forecasting model is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the dynamic structure of the country. In this study, we employ three alternative popular machine learning tools for rigorous projection of natural gas consumption in the province of Istanbul, Turkey's largest natural gas consuming mega city. These tools include multiple linear regression (MLR) an artificial neural network approach (ANN) and support vector regression (SVR) The results indicate that the SVR is much superior to ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time series forecasting of natural gas consumption. This study could well serve a useful benchmarking study for many emerging countries due to the data structure, consumption frequency, and consumption behavior of consumers in various time periods.
A new image denoising framework using bilateral filtering based non subsampled shearlet transform
Year: 2020

In this paper, we propose an advanced framework for image denoising using bilateral filtering based non subsampled shearlet transform (NSST) Initially, we apply the NSST to decompose noisy input image into high and low frequency coefficients. The weighted bilateral filter (WBF) is then used to remove the noise from the low frequency coefficients while; thresholding is used to remove noise from the high frequency coefficients. The outputs of both the process are combined to form the resultant image. Finally, the inverse NSST is applied on the resultant output to estimate the final denoised image. To ensure validity of the proposed model, we conduct several experiments by considering different grayscale images with various noise variances. The qualitative and quantitative comparison is illustrated and it shows the improved performance of the proposed method as compared to the other conventional image denoising methods. Mathematical and simulation results are presented to show the validity of our work.