nils-herrmann/clean_scirepeval huggingface dataset:
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells. We evaluate DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols. DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability.
Engineering the CA(2+)-activated photoprotein aequorin with reduced affinity for calcium.

Two stage PCR has been used to introduce single amino acid substitutions into the EF hand structures of the Ca(2+)-activated photoprotein aequorin. Transcription of PCR products, followed by cell free translation of the mRNA, allowed characterisation of recombinant proteins in vitro. Substitution of D to A at position 119 produced an active photoprotein with a Ca2+ affinity reduced by a factor of 20 compared to the wild type recombinant aequorin. This recombinant protein will be suitable for measuring Ca2+ inside the endoplasmic reticulum, the mitochondria, endosomes and the outside of live cells.
EvolView, an online tool for visualizing, annotating and managing phylogenetic trees

EvolView is a web application for visualizing, annotating and managing phylogenetic trees. First, EvolView is a phylogenetic tree viewer and customization tool; it visualizes trees in various formats, customizes them through built-in functions that can link information from external datasets, and exports the customized results to publication-ready figures. Second, EvolView is a tree and dataset management tool: users can easily organize related trees into distinct projects, add new datasets to trees and edit and manage existing trees and datasets. To make EvolView easy to use, it is equipped with an intuitive user interface. With a free account, users can save data and manipulations on the EvolView server. EvolView is freely available at: http://www.evolgenius.info/evolview.html.
Mid-second Trimester Measurement of Nasal Bone Length in the Indian Population.

The purpose of this study was to establish an Indian reference for normal fetal nasal bone length at 16-26 weeks of gestation.



The fetal nasal bone was measured by ultrasound in 2,962 pregnant women at 16-26 weeks of gestation from 2004 to 2009 by a single operator, who performed three measurements for each woman when the fetus was in the midsagittal plane and the nasal bone was between a 45 and 135° angle to the ultrasound beam. All neonates were examined after delivery to confirm the absence of congenital abnormalities.



The median nasal bone length increased with gestational age from 3.3 mm at 16 weeks to 6.65 mm at 26 weeks in a linear relationship. The fifth percentile nasal bone lengths were 2.37, 2.4, 2.8, 3.5, 3.6, 3.9, 4.3, 4.6, 4.68, 4.54, and 4.91 mm at 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, and 26 weeks, respectively.



We have established the nasal bone length in South Indian fetuses at 16-26 weeks of gestation and there is progressive increase in the fifth percentile of nasal bone length with advancing gestational age. Hence, gestational age should be considered while defining hypoplasia of the nasal bone.
Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition

DeepFashion is a widely used clothing dataset with 50 categories and more than overall 200k images where each image is annotated with fine-grained attributes. This dataset is often used for clothes recognition and although it provides comprehensive annotations, the attributes distribution is unbalanced and repetitive specially for training fine-grained attribute recognition models. In this work, we tailored DeepFashion for fine-grained attribute recognition task by focusing on each category separately. After selecting categories with sufficient number of images for training, we remove very scarce attributes and merge the duplicate ones in each category, then we clean the dataset based on the new list of attributes. We use a bilinear convolutional neural network with pairwise ranking loss function for multi-label fine-grained attribute recognition and show that the new annotations improve the results for such a task. The detailed annotations for each of the selected categories are provided for public use.
DECOM: DOA estimation with combined MUSIC for coprime array

In this paper, we propose a direction-of-arrival (DOA) estimation method by combining multiple signal classification (MUSIC) of two decomposed linear arrays for the corresponding coprime array signal processing. The title “DECOM” means that, first, the nonlinear coprime array needs to be DECOMposed into two linear arrays, and second, Doa Estimation is obtained by COmbining the MUSIC results of the linear arrays, where the existence and uniqueness of the solution are proved. To reduce the computational complexity of DECOM, we design a two-phase adaptive spectrum search scheme, which includes a coarse spectrum search phase and then a fine spectrum search phase. Extensive simulations have been conducted and the results show that the DECOM can achieve accurate DOA estimation under different SNR conditions.
Comparison of the rheological properties of viscosity and elasticity in two categories of soft tissue fillers: calcium hydroxylapatite and hyaluronic acid.

Two types of soft tissue filler that are in common use are those formulated primarily with calcium hydroxylapatite (CaHA) and those with cross-linked hyaluronic acid (cross-linked HA).



To provide physicians with a scientific rationale for determining which soft tissue fillers are most appropriate for volume replacement.



Six cross-linked HA soft tissue fillers (Restylane and Perlane from Medicis, Scottsdale, AZ; Restylane SubQ from Q-Med, Uppsala, Sweden; and Juvéderm Ultra, Juvéderm Ultra Plus, and Juvéderm Voluma from Allergan, Pringy, France) and a soft tissue filler consisting of CaHA microspheres in a carrier gel containing carboxymethyl cellulose (Radiesse, BioForm Medical, Inc., San Mateo, CA).  The viscosity and elasticity of each filler gel were quantified according to deformation oscillation measurements conducted using a Thermo Haake RS600 Rheometer (Newington, NH) using a plate and plate geometry with a 1.2-mm gap. All measurements were performed using a 35-mm titanium sensor at 30°C. Oscillation measurements were taken at 5 pascal tau (τ) over a frequency range of 0.1 to 10 Hz (interpolated at 0.7 Hz). Researchers chose the 0.7-Hz frequency because it elicited the most reproducible results and was considered physiologically relevant for stresses that are common to the skin.  The rheological measurements in this study support the concept that soft tissue fillers that are currently used can be divided into three groups.  Rheological evaluation enables the clinician to objectively classify soft tissue fillers, to select specific filler products based on scientific principles, and to reliably predict how these products will perform--lifting, supporting, and sculpting--after they are appropriately injected.
Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support Vector Machine

Fault detection in photovoltaic (PV) arrays becomes difficult as the number of PV panels increases. Particularly, under low irradiance conditions with an active maximum power point tracking algorithm, line-to-line (L-L) faults may remain undetected because of low fault currents, resulting in loss of energy and potential fire hazards. This paper proposes a fault detection algorithm based on multiresolution signal decomposition for feature extraction, and two-stage support vector machine (SVM) classifiers for decision making. This detection method only requires data of the total voltage and current from a PV array and a limited amount of labeled data for training the SVM. Both simulation and experimental case studies verify the accuracy of the proposed method.
Agency informing techniques: communicating player agency in interactive narratives

Within interactive narrative research, agency is largely considered in terms of a player's autonomy in a game, defined as theoretical agency. Rather than in terms of whether or not the player feels they have agency, their perceived agency. An effective interactive narrative needs to provide a player a level of agency that satisfies their desires and must do that without compromising its own structure. Researchers frequently turn to techniques for increasing theoretical agency to accomplish this. This paper proposes an approach to categorize and explore techniques in which a player's level of perceived agency is affected without requiring more or less theoretical agency.
LTE PHY layer vulnerability analysis and testing using open-source SDR tools

This paper provides a methodology to study the PHY layer vulnerability of wireless protocols in hostile radio environments. Our approach is based on testing the vulnerabilities of a system by analyzing the individual subsystems. By targeting an individual subsystem or a combination of subsystems at a time, we can infer the weakest part and revise it to improve the overall system performance. We apply our methodology to 4G LTE downlink by considering each control channel as a subsystem. We also develop open-source software enabling research and education using software-defined radios. We present experimental results with open-source LTE systems and shows how the different subsystems behave under targeted interference. The analysis for the LTE downlink shows that the synchronization signals (PSS/SSS) are very resilient to interference, whereas the downlink pilots or Cell-Specific Reference signals (CRS) are the most susceptible to a synchronized protocol-aware interferer. We also analyze the severity of control channel attacks for different LTE configurations. Our methodology and tools allow rapid evaluation of the PHY layer reliability in harsh signaling environments, which is an asset to improve current standards and develop new and robust wireless protocols.
Decomposition of Organometal Halide Perovskite Films on Zinc Oxide Nanoparticles.

Solution processed zinc oxide (ZnO) nanoparticles (NPs) with excellent electron transport properties and a low-temperature process is a viable candidate to replace titanium dioxide (TiO2) as electron transport layer to develop high-efficiency perovskite solar cells on flexible substrates. However, the number of reported high-performance perovskite solar cells using ZnO-NPs is still limited. Here we report a detailed investigation on the chemistry and crystal growth of CH3NH3PbI3 perovskite on ZnO-NP thin films. We find that the perovskite films would severely decompose into PbI2 upon thermal annealing on the bare ZnO-NP surface. X-ray photoelectron spectroscopy (XPS) results show that the hydroxide groups on the ZnO-NP surface accelerate the decomposition of the perovskite films. To reduce the decomposition, we introduce a buffer layer in between the ZnO-NPs and perovskite layers. We find that a commonly used buffer layer with small molecule [6,6]-phenyl-C61-butyric acid methyl ester (PC61BM) can slow down but cannot completely avoid the decomposition. On the other hand, a polymeric buffer layer using poly(ethylenimine) (PEI) can effectively separate the ZnO-NPs and perovskite, which allows larger crystal formation with thermal annealing. The power conversion efficiencies of perovskite photovoltaic cells are significantly increased from 6.4% to 10.2% by replacing PC61BM with PEI as the buffer layer.
RF Sensor Networks for Device-Free Localization: Measurements, Models, and Algorithms

In this paper, we discuss the emerging application of device-free localization (DFL) using wireless sensor networks, which find people and objects in the environment in which the network is deployed, even in buildings and through walls. These networks are termed “RF sensor networks” because the wireless network itself is the sensor, using radio-frequency (RF) signals to probe the deployment area. DFL in cluttered multipath environments has been shown to be feasible, and in fact benefits from rich multipath channels. We describe modalities of measurements made by RF sensors, the statistical models which relate a person's position to channel measurements, and describe research progress in this area.
Cognitive Modeling, Symbolic

(1995). Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Distributed memory and the representation of general and specific information. On the interaction of selective attention and lexical knowledge: a connectionist account of neglect dyslexia. Implicit and explicit memory in amnesia: some explanations and predictions of the tracelink model. On language and connectionism: analysis of a parallel distributed processing model of language acquisition. From rote learning to system building: acquiring verb morphology in children and connectionist nets. From simple associations to systematic reasoning: a connectionist representation of rules, variables and dynamic bindings using temporal synchrony. Constituent attachment and thematic role assignment in sentence processing: influences of content-based expectations. Symbolic cognitive models are theories of human cognition that take the form of working computer programs. A cogni-tive model is intended to be an explanation of how some aspect of cognition is accomplished by a set of primitive computational processes. A model performs a specific cog-nitive task or class of tasks and produces behavior that constitutes a set of predictions that can be compared to data from human performance. Task domains that have received considerable attention include problem solving, language comprehension, memory tasks, and human-device interaction. The scientific questions cognitive modeling seeks to answer belong to cognitive psychology, and the computational techniques are often drawn from artificial intelligence. Cognitive modeling differs from other forms of
Linear Array CCD Image Sub-pixel Edge Detection Based on Wavelet Transform

In order to increase accuracy of the linear array CCD edge detection system, a wavelet-based sub-pixel edge detection method is proposed, the basic process is like this: firstly, according to the step gradient features, automatically calculate the pixel-level border of the CCD image. Then use the wavelet transform algorithm to devide the image’s edge location in sub-pixel level, thus detecting the sub-pixel edge. In this way we prove that the method has no principle error and at the same time possesses a good anti-noise performance. Experiments show that under the circumstance of no special requirements, the accuracy of the method is greater than 0.02 pixel, thus verifying the correctness of the theory.
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting. The improved performance of these losses over alternatives, along with further tricks and refinements described in this work, allow for an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly. We further demonstrate the performance gain of the RNN over baselines in an online A/B test.
Annotating tense, mood and voice for English, French and German

We present the first open-source tool for annotating morphosyntactic tense, mood and voice for English, French and German verbal complexes. The annotation is based on a set of language-specific rules, which are applied on dependency trees and leverage information about lemmas, morphological properties and POS-tags of the verbs. Our tool has an average accuracy of about 76%. The tense, mood and voice features are useful both as features in computational modeling and for corpuslinguistic research.
A Data Mining Framework for Prevention and Detection of Financial Statement Fraud

Financial statement fraud has reached the epidemic proportion globally. Recently, financial statement fraud has dominated the corporate news causing debacle at number of companies worldwide. In the wake of failure of many organisations, there is a dire need of prevention and detection of financial statement fraud. Prevention of financial statement fraud is a measure to stop its occurrence initially whereas detection means the identification of such fraud as soon as possible. Fraud detection is required only if prevention has failed. Therefore, a continuous fraud detection mechanism should be in place because management may be unaware about the failure of prevention mechanism. In this paper we propose a data mining framework for prevention and detection of financial statement fraud.
Recommending the World's Knowledge: Application of Recommender Systems at Quora

At Quora, our mission is to share and grow the world's knowledge. Recommender systems are at the core of this mission: we need to recommend the most important questions to people most likely to write great answers, and recommend the best answers to people interested in reading them. Driven by the above mission statement, we have a variety of interesting and challenging recommendation problems and a large, rich data set that we can work with to build novel solutions for them. In this talk, we will describe several of these recommendation problems and present our approaches solving them.
Measuring social media activity of scientific literature: an exhaustive comparison of scopus and novel altmetrics big data

This paper measures social media activities of 15 broad scientific disciplines indexed in Scopus database using Altmetric.com data. First, the presence of Altmetric.com data in Scopus database is investigated, overall and across disciplines. Second, a zero-truncated negative binomial model is used to determine the association of various factors with increasing or decreasing citations. Lastly, the effectiveness of altmetric indices to identify publications with high citation impact is comprehensively evaluated by deploying area under the curve (AUC)—an application of receiver operating characteristic. Results indicate a rapid increase in the presence of Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. It was found that Blog count was the most important factor in the field of Health Professions and Nursing as it increased the number of citations by 38.6%, followed by Twitter count increasing the number of citations by 8% in the field of Physics and Astronomy. The results of receiver operating characteristic show that altmetric indices can be a good indicator to discriminate highly cited publications, with an encouragingly AUC = 0.725 between highly cited publications and total altmetric count. Overall, findings suggest that altmetrics can be used to distinguish highly cited publications. The implications of this research are significant in many different directions. Firstly, they set the basis for a further investigation of altmetrics efficiency to predict publications impact and most significantly promote new insights for the measurement of research outcome dissemination over social media.
The Misuse of IEEE Device 52- Medium Voltage Contactors and Breakers Are Not the Same!

Many standards and specifications, related to medium voltage (MV) motor and feeder circuits, continue to center around the generic use of the IEEE Device 52 as a circuit identifier for the switching device used within those power systems.  Device 52 is defined as a device that is used to close and interrupt an ac power circuit under normal conditions or to interrupt this circuit under fault or emergency conditions. Many of the current standards include detailed protection and coordination recommendations that are centered on circuit breaker protective settings. Some of these recommended protective settings can be detrimental when MV vacuum contactors are used instead of ac circuit breakers to interrupt the circuit under fault conditions such as in motor and feeder applications. This paper will outline MV contactor and breaker rating differences, compare the appropriate protection variations and requirements between the two, and highlight the dangers to personnel and equipment when assuming that a MV vacuum contactor can provide the same performance and require the same protective settings as a MV circuit breaker.
A Motion Recognition Method Using Foot Pressure Sensors

This paper proposes a method for recognizing postures and gestures using foot pressure sensors, and we investigate optimal positions for pressure sensors on soles are the best for motion recognition. In experiments, the recognition accuracies of 22 kinds of daily postures and gestures were evaluated from foot-pressure sensor values. Furthermore, the optimum measurement points for high recognition accuracy were examined by evaluating combinations of two foot pressure measurement areas on a round-robin basis. As a result, when selecting the optimum two points for a user, the recognition accuracy was about 93.6% on average. Although individual differences were seen, the best combinations of areas for each subject were largely divided into two major patterns. When two points were chosen, combinations of the near thenar, which is located near the thumb ball, and near the heel or point of the outside of the middle of the foot were highly recognized. Of the best two points, one was commonly the near thenar for subjects. By taking three points of data and covering these two combinations, it will be possible to cope with individual differences. The recognition accuracy of the averaged combinations of the best two combinations for all subjects was classified with an accuracy of about 91.0% on average. On the basis of these results, two types of pressure sensing shoes were developed.
Evaluations of Crossover and Mutation Probability of Genetic Algorithm in an Optimal Facility Layout Problem

Facility Layout Problem (FLP) is logic based combinatorial optimization problem. It is a meta-heuristic solution approach that gained significant attention to obtained optimal facility layout. This paper examines the convergence analysis by changing the crossover and mutation probability in an optimal facility layout. This algorithm is based on appropriate techniques that include multipoint swapped crossover and swap mutation operators. Two test cases were used for the implementations of the said technique and evaluate the robustness of the proposed method compared to other approaches in the literature. facility layout problem, genetic algorithm, material handling cost, meta-heuristics