nils-herrmann/clean_scirepeval huggingface dataset:
Transcriptional reprogramming in yeast using dCas9 and combinatorial gRNA strategies

Transcriptional reprogramming is a fundamental process of living cells in order to adapt to environmental and endogenous cues. In order to allow flexible and timely control over gene expression without the interference of native gene expression machinery, a large number of studies have focused on developing synthetic biology tools for orthogonal control of transcription. Most recently, the nuclease-deficient Cas9 (dCas9) has emerged as a flexible tool for controlling activation and repression of target genes, by the simple RNA-guided positioning of dCas9 in the vicinity of the target gene transcription start site.



In this study we compared two different systems of dCas9-mediated transcriptional reprogramming, and applied them to genes controlling two biosynthetic pathways for biobased production of isoprenoids and triacylglycerols (TAGs) in baker's yeast Saccharomyces cerevisiae. By testing 101 guide-RNA (gRNA) structures on a total of 14 different yeast promoters, we identified the best-performing combinations based on reporter assays. Though a larger number of gRNA-promoter combinations do not perturb gene expression, some gRNAs support expression perturbations up to ~threefold. The best-performing gRNAs were used for single and multiplex reprogramming strategies for redirecting flux related to isoprenoid production and optimization of TAG profiles. From these studies, we identified both constitutive and inducible multiplex reprogramming strategies enabling significant changes in isoprenoid production and increases in TAG.



Taken together, we show similar performance for a constitutive and an inducible dCas9 approach, and identify multiplex gRNA designs that can significantly perturb isoprenoid production and TAG profiles in yeast without editing the genomic context of the target genes. We also identify a large number of gRNA positions in 14 native yeast target pomoters that do not affect expression, suggesting the need for further optimization of gRNA design tools and dCas9 engineering.
ALAN: adaptive learning for multi-agent navigation

In multi-agent navigation, agents need to move towards their goal locations while avoiding collisions with other agents and obstacles, often without communication. Existingmethods compute motions that are locally optimal but do not account for the aggregated motions of all agents, producing inefficient global behavior especially when agents move in a crowded space. In this work, we develop a method that allows agents to dynamically adapt their behavior to their local conditions. We formulate the multi-agent navigation problem as an action-selection problem and propose an approach, ALAN, that allows agents to compute time-efficient and collision-free motions. ALAN is highly scalable because each agent makes its own decisions on how to move, using a set of velocities optimized for a variety of navigation tasks. Experimental results show that agents using ALAN, in general, reach their destinations faster than using ORCA, a state-of-the-art collision avoidance framework, and two other navigation models.
High Voltage Power Supply Using a T-Type Parallel Resonant DC–DC Converter

In this research, the study and implementation of the t-type parallel resonant dc–dc converter in high-voltage power supplies for travelling wave tube applications is presented. The proposed converter shares the benefits of the traditional full-bridge parallel resonant dc–dc converter, such as zero voltage switching on primary side switches over a wide load range, symmetric three-level voltage across the transformers primary winding, and integration of transformers leakage inductance and winding capacitance during its basic operation. However, only two switches are subjected to the input voltage, while the other two switches are subjected to half of the input voltage. Theoretical analysis, commutation analysis, and design guidelines are presented. The theoretical analysis validation and efficiency investigation are conducted through a t-type parallel resonant converter prototype, which operates with 1.5 kW and 97.12% efficiency. Hence, experimental results of a travelling wave tube power supply prototype operating with 1.5 kW of output power, 400 Vdc input, and four high output voltages, whose measurements results are <inline-formula><tex-math notation="LaTeX">$V_{o1}=1382\,\rm {V}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$V_{o2}=3505\,\rm {V}$</tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$V_{o3}= 1504\,\rm {V}$</tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$ V_{o4}=3338\,\rm {V}$</tex-math></inline-formula>, confirm the feasibility of the proposed implementation.
An Eye Tracking Analysis for Video Advertising: Relationship Between Advertisement Elements and Effectiveness

This paper aims to assess which ad element-related eye movement behaviors could predict the traditional advertising effectiveness of high-order for video advertising. The data were obtained from 61 participants, each stimulated by six video ads, via an eye tracking method and questionnaires. A logistic regression was conducted to predict high and low ad effectiveness with regard to element-related eye indicators. Three core constructs of high-order advertising effectiveness commonly used in research address the memory (ad recall), affect (attitude toward ad and attitude toward brand) and desirability (purchase intention) of consumers. Three key advertising elements (product, brand, and endorser) were tracked, presented by three eye movement indicators (transformed fixation time (TFT), transformed fixation number (TFN), and average gaze duration (AGD)). The findings indicated that three items are related to attitude toward ad (product-related AGD, brand-related AGD and endorser-related TFT), attitude toward brand (brand-related TFN and AGD, endorser-related TFT), and purchase intention (product-related AGD, brand-related TFN and endorser-related TFN). However, only two items of them are related to recall (product-related AGD and brand-related TFN). Furthermore, for all ad outcomes, consistently, eye movements on product elements and endorser elements tend to positively related to ad effectiveness, while eye movements on brand elements tent to negatively.
Scholarly use of social media and altmetrics: A review of the literature

Social media has become integrated into the fabric of the scholarly communication system in fundamental ways: principally through scholarly use of social media platforms and the promotion of new indicators on the basis of interactions with these platforms. Research and scholarship in this area has accelerated since the coining and subsequent advocacy for altmetrics—that is, research indicators based on social media activity. This review provides an extensive account of the state-of-the art in both scholarly use of social media and altmetrics. The review consists of two main parts: the first examines the use of social media in academia, examining the various functions these platforms have in the scholarly communication process and the factors that affect this use. The second part reviews empirical studies of altmetrics, discussing the various interpretations of altmetrics, data collection and methodological limitations, and differences according to platform. The review ends with a critical discussion of the implications of this transformation in the scholarly communication system.
A New Approach to Quantify Network Security by Ranking of Security Metrics and Considering Their Relationships

There are several characteristics in computer networks, which play important roles in determining the level of network security. These characteristics known as security metrics can be applied for security quantification in computer networks. Most of the researches on this area has focused on defining the new security metrics to improve the quantification process. In this paper, we present a new approach to analyze and quantify the network security by ranking of security metrics with considering the relationships between them. Our ranking method reveals the importance of each security metric to quantify security in the network under surveillance. The proposed approach helps the network administrators to have a better insight on the level of network security
Clear underwater vision

Underwater imaging is important for scientific research and technology, as well as for popular activities. We present a computer vision approach which easily removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. We show that the main degradation effects can be associated with partial polarization of light. We therefore present an algorithm which inverts the image formation process, to recover a good visibility image of the object. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by product, a distance map of the scene is derived as well. We successfully used our approach when experimenting in the sea using a system we built. We obtained great improvement of scene contrast and color correction, and nearly doubled the underwater visibility range.
Universal coding, information, prediction, and estimation

A connection between universal codes and the problems of prediction and statistical estimation is established. A‘known lower bound for the mean length of universal codes is sharpened and generalized, and optimum universal codes constructed. The bound is defined to give the information in strings relative to the considered class of processes. The earlier derived minimum description length criterion for estimation of parameters, including their number, is given a fundamental information, theoretic justification by showing that its estimators achieve the information in the strings. It is also shown that one cannot do prediction in Manuscript received July 13,1983; revised January 16,1984. This work was presented in part at the IEEE International Symposium on Information Theory, St. Jovite, Canada, September 26-30, 1983. This work was done while the author was Visiting Professor at the Department of System Science, University of California, Los Angeles, while on leave from the IBM Research Laboratory, San Jose, CA 95193. Gaussian autoregressive moving average (ARMA) processes below a bound, which is determined by the information in the data.
Detecting trigger-based behaviors in botnet malware

Malware often hides malicious behaviors which are triggered when constraints are satisfied. The trigger-based behavior makes malware detection harder, and requires manual analysis. The number of daily submitted malware has been increasing, while the scale of the manual analysis of malware still remains not sufficient. We have been studying the system which analyzes trigger-based behavior in various ways. This paper proposes a detection method of trigger-based behavior by satisfying the trigger condition. Symbolic execution is utilized to invoke the behavior, and multiple execution paths are analyzed to detect malicious activities. We have designed BotMelt, a system representing our approach. BotMelt focuses on analyzing the Botnet malware, especially for the C&C worker. We defined network packet data as symbol, and symbolic executes malware samples to invoke trigger-based behavior. We tested 4 in the wild Botnet malware samples: HwDoor, Bisonal, Keyboy and Plez. BotMelt identified most of the trigger-based behaviors, and our analysts successfully detected malicious activities using BotMelt generated traces. We evaluated on the three criteria: the validness of executed codes, the correctness of the malicious activity detection, and the number of detected behaviors compared with all possible behaviors. Although there are some discussions of our proposed method, we think BotMelt could contribute to the trigger-based behavior analysis.
Marketing Theory : Overview of Ontology , Epistemology , and Axiology Aspects

This article discusses about the marketing theory from the perspective of philosophy of science. The discussion focused on the aspects of the ontology, epistemology, and axiology. From the aspect of the ontology, it seems that the essence of marketing is a useful science and mutually beneficial for both the marketer and the stakeholders, or in other words that marketing knowledge is useful knowledge for the benefit of humankind that can be realized through the exchange process. Side of the ontology covers what the substance of marketing knowledge, the substance of truth and reality that is inherent with marketing. Meanwhile, aspects of epistemology cover a variety of approaches, methods, sources, structure and validation or marketing truth. Finally, axiology fields are related to ethics in marketing and marketing research. Marketing ethics and ethics in marketing research is a crucial matter, including in this case is trust.
Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control

MOBAs represent a huge segment of online gaming and are growing as both an eSport and a casual genre. The natural starting point for AI researchers interested in MOBAs is to develop an AI to play the game better than a human but MOBAs have many more challenges besides adversarial AI. In this paper we introduce the reader to the wider context of MOBA culture, propose a range of challenges faced by the community today, and posit concrete AI projects that can be undertaken to begin solving them.
A 98.55% Efficiency Switched-Tank Converter for Data Center Application

Achieving high efficiency and high power density features of intermediate bus converter (IBC) in data center application is very critical. This paper proposes a nonisolated switched-tank dc–dc converter (STC) to meet the high requirement of IBC. The proposed STC has fixed voltage conversion ratio. Theoretical analysis provides the guideline for designing resonant tank and nonresonant tank as well as selecting proper deadtime for the proposed circuit. With resonant operation feature of the proposed circuit, zero current switching is realized on all switching devices to help the high-efficiency operation of the proposed converter. Through the proposed design methodology in this paper, power loss on switching devices and planar inductors can be minimized. In addition, detailed power loss analysis and breakdown provide hints for further efficiency improvement. Simulation has been performed to validate the operating principle of the proposed STC. A designed 6x STC (450 W nominal/600 W maximum) based on eGaNFETs is demonstrated. With 54 V typical input voltage, the output of the 6x STC is 9 V. The GaN-based prototype achieves a peak efficiency of 98.55%. Also, it achieves 97.18% efficiency and 750 W/in3 power density at 450 W.
Robust camera calibration of soccer video using genetic algorithm

This paper proposes an exact and robust genetic algorithm-based method for the calibration of soccer camera. According to the FIFA official soccer field layout we defined a field model for the soccer court. Camera calibration is done through finding the homography transform between the field model and the input frame which is followed with DLT decomposition. The intersection of lines in the field model and input frame form feature points and by means of a genetic algorithm, we found the correspondence between those points. Our algorithm was applied to a couple of soccer video frames and the achieved results demonstrate its robustness, accuracy and high performance.
Generating Single Subject Activity Videos as a Sequence of Actions Using 3D Convolutional Generative Adversarial Networks

Humans have the remarkable ability of imagination, where within the human mind virtual simulations are done of scenarios whether visual, auditory or any other senses. These imaginations are based on the experiences during interaction with the real world, where human senses help the mind understand their surroundings. Such level of imagination has not yet been achieved using current algorithms, but a current trend in deep learning architectures known as Generative Adversarial Networks (GANs) have proven capable of generating new and interesting images or videos based on the training data. In that way, GANs can be used to mimic human imagination, where the resulting generated visuals of GANs are based on the data used during training. In this paper, we use a combination of Long Short-Term Memory (LSTM) Networks and 3D GANs to generate videos. We use a 3D Convolutional GAN to generate new human action videos based on trained data. The generated human action videos are used to generate longer videos consisting of a sequence of short actions combined creating longer and more complex activities. To generate the sequence of actions needed we use an LSTM network to translate a simple input description text into the required sequence of actions. The generated chunks are then concatenated using a motion interpolation scheme to form a single video consisting of many generated actions. Hence a visualization of the input text description is generated as a video of a subject performing the activity described.
Deriving optimal weights in deep neural networks

Training deep neural networks generally requires massive amounts of data and is very computation intensive. We show here that it may be possible to circumvent the expensive gradient descent procedure and derive the parameters of a neural network directly from properties of the training data. We show that, near convergence, the gradient descent equations for layers close to the input can be linearized and become stochastic equations with noise related to the covariance of data for each class. We derive the distribution of solutions to these equations and discover that it is related to a “supervised principal component analysis.” We implement these results on image datasets MNIST, CIFAR10 and CIFAR100 and find that, indeed, pretrained layers using our findings performs comparable or superior to neural networks of the same size and architecture trained with gradient descent. Moreover, our pretrained layers can often be calculated using a fraction of the training data, owing to the quick convergence of the covariance matrix. Thus, our findings indicate that we can cut the training time both by requiring only a fraction of the data used for gradient descent, and by eliminating layers in the costly backpropagation step of the training. Additionally, these findings partially elucidate the inner workings of deep neural networks and allow us to mathematically calculate optimal solutions for some stages of classification problems, thus significantly boosting our ability to solve such problems efficiently.
Role of Emotional Intelligence for Academic Achievement for Students

In the current competitive environment where students are expected to perform multi roles with efficiency and effectiveness, it is highly needed to develop their right attitude and emotional intelligence towards the unseen complexities of life and quality education. As emotional intelligence is a subset of social intelligence with the ability to understand and monitor one’s own feelings and others too which allows a student to mine the required data for his academic achievement which is an outcome of education and the extent at which the educational goal has been achieved. The emphasis of this paper was to determine the factors which are affecting the development of emotional intelligence and its role in academic achievement for students. In this research secondary data has been collected out of which we find out the correlation between emotional intelligence and academic achievement and teaching emotional and social skills at school not only positively influence academic achievement during the year when these were taught but also leaves the impact in long term achievement. Findings of this paper present that academic achievement without emotional intelligence does not indicate future success and absence of emotional intelligence also indicate the week personality and ability to build relations at working place as well in schools and it is highly important for quality education.
General Video Game AI: Competition, Challenges and Opportunities

The General Video Game AI framework and competition pose the problem of creating artificial intelligence that can play a wide, and in principle unlimited, range of games. Concretely, it tackles the problem of devising an algorithm that is able to play any game it is given, even if the game is not known a priori. This area of study can be seen as an approximation of General Artificial Intelligence, with very little room for game-dependent heuristics. This talk summarizes the motivation, infrastructure, results and future plans of General Video Game AI, stressing the findings and first conclusions drawn after two editions of our competition, presenting the tracks that will be held in 2016 and outlining our future plans.
Business Intelligence and Business Process Management in Banking Operations

Success of banking operations is strongly correlated with the quality of customer relations and efficacy of banks processes. Banks seek means for efficient analysis of vast amount of gathered data from their IT systems. They are exploiting business intelligence (BI) technology to analyze every aspect of their data to understand behavior of their clients, striving to satisfy client's needs, in an endless race for a competitive advantage in the market. The intention of this paper is to review and discuss the most significant applications of business intelligence in banking as well as point to new technology trends that will affect bank's development.
Joint segmentation of multivariate time series with hidden process regression for human activity recognition

The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the variability of the human activities over time (dynamic models) rather than static ones which can have some limitations in such a dynamic context. In this paper, the problem of activity recognition is analyzed through the segmentation of the multidimensional time series of the acceleration data measured in the 3-d space using body-worn accelerometers. The proposed model for automatic temporal segmentation is a specific statistical latent process model which assumes that the observed acceleration sequence is governed by sequence of hidden (unobserved) activities. More specifically, the proposed approach is based on a specific multiple regression model incorporating a hidden discrete logistic process which governs the switching from one activity to another over time. The model is learned ∗Corresponding author: Faicel Chamroukhi Université de Toulon, LSIS, UMR CNRS 7296 Bâtiment R, BP 20132 83957 La Garde Cedex, France Tel: +33(0) 4 94 14 20 06 Fax: +33(0) 4 94 14 28 97 Email addresses: faicel.chamroukhi@univ-tln.fr (F. Chamroukhi ), samer.mohammed@u-pec.fr (S. Mohammed), dorra.trabelsi@u-pec.fr (D. Trabelsi), latifa.oukhellou@ifsttar.fr (L. Oukhellou), amirat@u-pec.fr (Y. Amirat)  2013 in an unsupervised context by maximizing the observed-data log-likelihood via a dedicated expectation-maximization (EM) algorithm. We applied it on a real-world automatic human activity recognition problem and its performance was assessed by performing comparisons with alternative approaches, including well-known supervised static classifiers and the standard hidden Markov model (HMM). The obtained results are very encouraging and show that the proposed approach is quite competitive even it works in an entirely unsupervised way and does not requires a feature extraction preprocessing step.