nils-herrmann/clean_scirepeval huggingface dataset:
Unveiling the Self Assembling Behavior of 5 Fluorouracil and its N,N' Dimethyl Derivative: A Spectroscopic and Microscopic Approach.
Venue: Langmuir the ACS journal of surfaces and colloids
Year: 2017

Under physiological conditions, 5 fluorouracil (5 FU) an anticancer drug, self assembles into fibrils by strong hydrogen bonding network, whereas its N,N' dimethyl derivative, 5 fluoro 1,3 dimethyluracil (5 FDMU) does not make fibrils due to lack of strong hydrogen bonding motif. In vitro, 5 FU self assembly is sensitive to physicochemical conditions like the pH and ionic strength of the solution, which tune the strength of the noncovalent driving forces. Here we report a surprising finding that the buffer, which is necessary to control the pH and is typically considered to be inert, also significantly influences 5 FU self assembly, which indicates an important role of counterions in the fibril formation. We have also monitored concentration and time dependent fibrillar growth of 5 FU. Again, fibril growth process is probed under dynamic conditions using microfluidic platform. The self assembly of 5 FU compared with its N,N' dimethyl derivative shows lower cytotoxicity to the cultured human erythroleukemic cells (K562 cells) which plausibly states the reason behind the greater effectiveness of 5 FU derivative drugs than 5 FU itself.
Analisis Konflik Batin pada Tokoh Utama Novel Penggantin Hamas Karya Fanny chrisma w. Tinjauan psikologi sastra
Year: 2020

Generally, literature work is the literature creation which always depended by current development. Recently, the education elements on literature has been faded, it's intended more on teenager and love story. "Pengantin Hamas by Vanny Chrisma W." novel is consist of many religion values and self development norms through the psychological conflicts. Thereby, the writer analyzes psychological conflicts in this novel. Based on this condition, the problems on this research are (1) How does the psychological conflict involve by the main character on Pengantin Hamas by Vanny Chrisma W? (2) What are the factors that causing the psychological conflicts in Pengantin Hamas by Vanny Chrisma W? The method used in this research was qualitative descriptive by using literature psychology approach. The source of this research was Pengantin Hamas by Vanny Chrisma W. published on 2013 and consisting of 240 pages. The analysis was conducted by collecting the data of psychological conflicts on Pengantin Hamas by Vanny Chrisma W, (1) Collecting data, and (2) Analyzing data. This research has found a lot of psychological conflicts that involved the main character. There are the inappropriate decision between his jihad will, but his mother forbids, his doubt to join Asian Muslim Association or stay in Palestine to wait Huriyah's coming, his doubt to decide his love, waiting love respond from Huriyah, hoping bless of Huriyah's father, feeling down because of his love choice where Huriyah has to pass away or 'syahid' however he has hurt her heart. Psychological conflicts that almost happened to the main character are caused by external factors or it comes from the outside of main character that interfere the main character's personality until he got horrible psychological conflicts. Self personality structure of main character Id always able to control. The main character solved the problems by undoing and repression, almost the occurred conflicts that happened in the story were not solved well because he often felt the problems himself. From the novel story, we can conclude that we cannot always felt the problems. The occurred problems must be solved wisely and not to be ignored it, even if we felt the problems in our heart, because that will be something horrible mistake later.
Hybrid extreme learning machine with meta heuristic algorithms for monthly pan evaporation prediction
Venue: Comput. Electron. Agric.
Year: 2020

Accurate estimation of pan evaporation (Ep) is of great significance to the development of agricultural irrigation systems and agricultural water resources management. The purpose of this study was to explore the applicability of coupling extreme learning machine (ELM) model with two new meta heuristic algorithms, i.e. whale optimization algorithm (WOA) and flower pollination algorithm (FPA) for monthly Ep prediction. To achieve this goal, two hybrid models of WOAELM and FPAELM were developed for predicting monthly Ep in the Poyang Lake Basin of Southern China as a case study. Their performances were further compared with the differential evolution algorithm optimized ELM (DEELM) improved M5 model tree (M5P) and artificial neural networks (ANN) models. Monthly climatic parameters, including maximum and minimum temperature (Tmax and Tmin) sunshine duration (n) relative humidity (RH) wind speed (U) and Ep from four weather stations in the basin from 2001 to 2015 were collected, those of which during 2001 2010 were used for model training and those during 2011 2015 for model testing. The obtained results showed that the hybrid FPAELM model exhibited the highest prediction accuracy at all the four stations, followed by the hybrid WOAELM model, both of which were superior to the other traditional models. Heuristic algorithms, especially FPA, are highly recommended for improving performance of standalone machine learning models. Compared with the combination of multi meteorological elements, the combination of Tmax, Tmin and extraterrestrial solar radiation achieved higher but still satisfactory prediction accuracy, with the absolute error less than 0.1 mm d 1 averaged over the four stations. Tmax, Tmin and extraterrestrial solar radiation were thus suggested to be used for monthly Ep estimation in this area considering the convenience of data acquisition.
Soy Infant Formula and Seizures in Children with Autism: A Retrospective Study
Venue: PloS one
Year: 2014

Seizures are a common phenotype in many neurodevelopmental disorders including fragile X syndrome, Down syndrome and autism. We hypothesized that phytoestrogens in soy based infant formula were contributing to lower seizure threshold in these disorders. Herein, we evaluated the dependence of seizure incidence on infant formula in a population of autistic children. Medical record data were obtained on 1,949 autistic children from the SFARI Simplex Collection. An autism diagnosis was determined by scores on the ADI R and ADOS exams. The database included data on infant formula use, seizure incidence, the specific type of seizure exhibited and IQ. Soy based formula was utilized in 17.5% of the study population. Females comprised 13.4% of the subjects. There was a 2.6 fold higher rate of febrile seizures [4.2% versus 1.6% OR 2.6, 95% CI 1.3 5.3] a 2.1 fold higher rate of epilepsy comorbidity [3.6% versus 1.7% OR 2.2, 95% CI 1.1 4.7] and a 4 fold higher rate of simple partial seizures [1.2% versus 0.3% OR 4.8, 95% CI 1.0 23] in the autistic children fed soy based formula. No statistically significant associations were found with other outcomes including: IQ, age of seizure onset, infantile spasms and atonic, generalized tonic clonic, absence and complex partial seizures. Limitations of the study included: infant formula and seizure data were based on parental recall, there were significantly less female subjects, and there was lack of data regarding critical confounders such as the reasons the subjects used soy formula, age at which soy formula was initiated and the length of time on soy formula. Despite these limitations, our results suggest that the use of soy based infant formula may be associated with febrile seizures in both genders and with a diagnosis of epilepsy in males in autistic children. Given the lack of data on critical confounders and the retrospective nature of the study, a prospective study is required to confirm the association.
Malware Detection Method Focusing on Anti debugging Functions
Venue: 2014 Second International Symposium on Computing and Networking
Year: 2014

Malware has received much attention in recent years. Antivirus software is widely used as a countermeasure against malware. However, some kinds of malware can evade detection by antivirus software, hence, a new detection method is required. In this paper, we propose a malware detection method that focuses on Anti Debugging functions. An Anti Debugging function is a method that prevents malware analysts from analyzing an application program (AP) The function can form part of benign as well as malicious APs. Our method focuses on a behavioral difference between benign and malicious APs and detects malware by comparing the two behavioral patterns. Evaluation results with malware confirmed our method to be capable of successfully detecting malware.
Simulating the council specific impact of anti malaria interventions: A tool to support malaria strategic planning in Tanzania
Venue: PloS one
Year: 2020

The decision making process for malaria control and elimination strategies has become more challenging. Interventions need to be targeted at council level to allow for changing malaria epidemiology and an increase in the number of possible interventions. Models of malaria dynamics can support this process by simulating potential impacts of multiple interventions in different settings and determining appropriate packages of interventions for meeting specific expected targets.  The OpenMalaria model of malaria dynamics was calibrated for all 184 councils in mainland Tanzania using data from malaria indicator surveys, school parasitaemia surveys, entomological surveillance, and vector control deployment data. The simulations were run for different transmission intensities per region and five interventions, currently or potentially included in the National Malaria Strategic Plan, individually and in combination. The simulated prevalences were fitted to council specific prevalences derived from geostatistical models to obtain council specific predictions of the prevalence and number of cases between 2017 and 2020. The predictions were used to evaluate in silico the feasibility of the national target of reaching a prevalence of below 1% by 2020, and to suggest alternative intervention stratifications for the country.  The historical prevalence trend was fitted for each council with an agreement of 87% in 2016 (95%CI: 0.84 0.90) and an agreement of 90% for the historical trend (2003 2016) (95%CI: 0.87 0.93) The current national malaria strategy was expected to reduce the malaria prevalence between 2016 and 2020 on average by 23.8% (95% CI: 19.7% 27.9% if current case management levels were maintained, and by 52.1% (95% CI: 48.8% 55.3% if the case management were improved. Insecticide treated nets and case management were the most cost effective interventions, expected to reduce the prevalence by 25.0% (95% CI: 19.7% 30.2) and to avert 37 million cases between 2017 and 2020. Mass drug administration was included in most councils in the stratification selected for meeting the national target at minimal costs, expected to reduce the prevalence by 77.5% (95%CI: 70.5% 84.5% and to avert 102 million cases, with almost twice higher costs than those of the current national strategy. In summary, the model suggested that current interventions are not sufficient to reach the national aim of a prevalence of less than 1% by 2020 and a revised strategic plan needs to consider additional, more effective interventions, especially in high transmission areas and that the targets need to be revisited.  The methodology reported here is based on intensive interactions with the NMCP and provides a helpful tool for assessing the feasibility of country specific targets and for determining which intervention stratifications at sub national level will have most impact. This country led application could support strategic planning of malaria control in many other malaria endemic countries.
Machine Learning Techniques to Understand Partial and Implied Data Values for Conversion of Natural Language to SQL Queries on HPCC Systems
Venue: 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)
Year: 2019

There has been an exponential growth in the amount of data produced daily in recent years, owing to the widespread use of technology. Ease of access to this data is of utmost importance in this day and age. Although in the past, use of structured query languages to query the data stored in the hard drive was satisfactory, use of natural language to access the data is more desired. This paper talks about mapping partial data values to its corresponding data values and attributes in the schema to enrich the natural language query. Machine learning algorithm, Long Short Term Memory, preceded by an Embedding layer has been used on the HPCC Systems platform. The resulting model gives an accuracy of 99.6% while its implementation with the experimental setup gives an accuracy of 92%
Optimal controller design for control affine stochastic systems using neural networks and path integrals
Venue: 2016 American Control Conference (ACC)
Year: 2016

For deterministic nonlinear dynamical systems, approximate dynamic programming based on Pontryagin's maximum principle provides a systematic way to solve optimal control problems. However, in the presence of noise, this approach becomes cumbersome. Hence, in current optimal control solution methodologies noise effect is typically ignored in the adjoint equations. Alternatively, in the Hamilton Jacobi Bellman (HJB) framework, presence of noise results in the second order stochastic HJB equation. Furthermore, through a unique exponential transformation, the stochastic HJB equation of control affine nonlinear stochastic systems with quadratic control cost function can be transformed into a path integral. In this paper, an offline approximate dynamic programming approach using neural networks and path integrals is proposed for solving the above class of finite horizon stochastic optimal control problems. Simulation results using Vanderpol oscillator model are presented to demonstrate the potential of the proposed approach.
On Love Poetry and It's Cultural Background in the Pre Song Dynasty
Year: 2009

In "The Book of Songs" it first depicts our ancestors,s natural and simple love screen.Its contents are rich,and its feelings and scenarios are blended.Qu Yuan depicts the love between people and God which is beyond the fantasy reality against the backdrop of nature.The feelings are sincere and the styles are clear cut in the folk songs of the Wei and Jin Dynasties;the love poems focus on women's physical appearance and clothing description,pay much attention to rhetoric and sound patterns.Love Poems in the Tang Dynasty absorbs nutrition from folk love songs,becoming lively,secular,classical and overshadowed by the rich sentimental color.Love Poetry in the Song Dynasty is replaced by new poems and it declines gradually.In the early Song Dynasty it lays emphasis on the choice of words and form;after the Northern Song Dynasty it becomes more elegant impacted by Ci.
THE CMA EVOLUTION STRATEGY BASED SIZE OPTIMIZATION OF TRUSS STRUCTURES
Year: 2011

Evolution Strategies (ES) are a class of Evolutionary Algorithms based on Gaussian mutation and deterministic selection. Gaussian mutation captures pair wise dependencies between the variables through a covariance matrix. Covariance Matrix Adaptation (CMA) is a method to update this covariance matrix. In this paper, the CMA ES, which has found many applications in solving continuous optimization problems, is employed for size optimization of steel space trusses. Design examples reveal competitive performance of the algorithm compared to the other advanced metaheuristics.
Compression of Deep Learning Models for Text: A Survey
Venue: ArXiv
Year: 2020

In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs) Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTMs) networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) But these models are humongous in size. On the other hand, real world applications demand small model size, low response times and low computational power wattage. In this survey, we discuss six different types of methods (Pruning, Quantization, Knowledge Distillation, Parameter Sharing, Tensor Decomposition, and Linear Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects. Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this survey organizes the plethora of work done by the 'deep learning for NLP' community in the past few years and presents it as a coherent story.
Advanced packaging methods for high power LED modules
Venue: Photonics West Optoelectronic Materials and Devices
Year: 2014

LED luminaires are already beyond retrofit systems, which are limited in heat dissipation due to the old fitting standards. Actual LED luminaries are based on new LED packages and modules. Heat dissipation through the first and second level interconnect is a key issue for a successful LED package. Therefore the impact of known bonding technologies as gluing and soldering and new technologies like sintering and transient liquid phase soldering were analyzed and compared. A realized hermetic high power LED package will be shown as example. The used new techniques result in a module extremely stable against further assembly processes and harsh operating conditions.
A Spatial Temporal Attention Based Method and a New Dataset for Remote Sensing Image Change Detection
Venue: Remote. Sens.
Year: 2020

Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images. Given two co registered images taken at different times, the illumination variations and misregistration errors overwhelm the real object changes. Exploring the relationships among different spatial temporal pixels may improve the performances of CD methods. In our work, we propose a novel Siamese based spatial temporal attention neural network. In contrast to previous methods that separately encode the bitemporal images without referring to any useful spatial temporal dependency, we design a CD self attention mechanism to model the spatial temporal relationships. We integrate a new CD self attention module in the procedure of feature extraction. Our self attention module calculates the attention weights between any two pixels at different times and positions and uses them to generate more discriminative features. Considering that the object may have different scales, we partition the image into multi scale subregions and introduce the self attention in each subregion. In this way, we could capture spatial temporal dependencies at various scales, thereby generating better representations to accommodate objects of various sizes. We also introduce a CD dataset LEVIR CD, which is two orders of magnitude larger than other public datasets of this field. LEVIR CD consists of a large set of bitemporal Google Earth images, with 637 image pairs (1024 x 1024) and over 31 k independently labeled change instances. Our proposed attention module improves the F1 score of our baseline model from 83.9 to 87.3 with acceptable computational overhead. Experimental results on a public remote sensing image CD dataset show our method outperforms several other state of the art methods.
Direct Time Domain TAN Model of 3D Multilayer Hybrid PCB: Experimental Validation
Venue: IEEE Access
Year: 2018

The multilayer technology constitutes the ultimate solution for the design of high density printed circuit board (PCB) Challenging modeling method is required to predict the signal integrity (SI) of the multilayer PCB. This paper addresses an unfamiliar direct time domain model of a 3 D multilayer hybrid PCB. The subnetwork primitive elements of the equivalent graph are constituted by lumped components, interconnect lines, vias, pads, and anti pads. The tensorial analysis of networks (TANs) is used to solve the problem related to the graph topology in the function of the PCB design parameters. The TAN concept is based on the interaction between the primitive elements. The mesh currents constitute the proposed computational unknowns. The unfamiliar model is validated with a three port network prototype constituted by the six layer PCB, including passive SMD components. In the frequency domain, S parameter validation from 100 kHz to 5 GHz is presented. By using 80 Mb/s and 0.5 Gb/s rate data patterns, the proposed TAN model is validated by both simulations and measurements in the time domain. The transient results present vector magnitude relative error accuracy lower than 15% Thanks to the computation speed and adaptability to multilayer hybrid structures, the TAN model is a prominent approach for the SI and power integrity analyses of 3 D multilayer structures.
TEACHING DIGITAL SIGNAL PROCESSING USING SKITS A CASE STUDY
Year: 2018

Digital Signal Processing (DSP) involves acquiring real time signals, digitizing them and mathematically manipulate to extract useful information. Traditionally DSP has been taught in engineering colleges using simulation software to develop interest among students. Connections between abstract signal processing theory and real world signal processing system design are difficult to illustrate in a packed undergraduate curriculum. We have proposed usage of skits for explaining concepts and employing students to act and interactive audiovisual aids as tools for learning DSP. Based on the pretest and posttest scores, we found that group using Skits and audiovisual tools got better scores than group using only audiovisual tools in their ability to understand concepts by applying to simple real world examples. Hence the usage of these interactive teaching aids helps in increase of student's interest to learn, visualize and understand key DSP concepts.
Vaccine optimization for COVID 19: Who to vaccinate first?
Venue: Science Advances
Year: 2020

The optimal use of COVID 19 vaccine depends mainly on how efficacious the vaccine is and how many doses are available. Vaccines, when available, will likely become our best tool to control the COVID 19 pandemic. Even in the most optimistic scenarios, vaccine shortages will likely occur. Using an age stratified mathematical model paired with optimization algorithms, we determined optimal vaccine allocation for four different metrics (deaths, symptomatic infections, and maximum non ICU and ICU hospitalizations) under many scenarios. We find that a vaccine with effectiveness =50% would be enough to substantially mitigate the ongoing pandemic, provided that a high percentage of the population is optimally vaccinated. When minimizing deaths, we find that for low vaccine effectiveness, irrespective of vaccination coverage, it is optimal to allocate vaccine to high risk (older) age groups first. In contrast, for higher vaccine effectiveness, there is a switch to allocate vaccine to high transmission (younger) age groups first for high vaccination coverage. While there are other societal and ethical considerations, this work can provide an evidence based rationale for vaccine prioritization.