nils-herrmann/clean_scirepeval huggingface dataset:
Small CRISPR RNAs guide antiviral defense in prokaryotes.

Prokaryotes acquire virus resistance by integrating short fragments of viral nucleic acid into clusters of regularly interspaced short palindromic repeats (CRISPRs). Here we show how virus-derived sequences contained in CRISPRs are used by CRISPR-associated (Cas) proteins from the host to mediate an antiviral response that counteracts infection. After transcription of the CRISPR, a complex of Cas proteins termed Cascade cleaves a CRISPR RNA precursor in each repeat and retains the cleavage products containing the virus-derived sequence. Assisted by the helicase Cas3, these mature CRISPR RNAs then serve as small guide RNAs that enable Cascade to interfere with virus proliferation. Our results demonstrate that the formation of mature guide RNAs by the CRISPR RNA endonuclease subunit of Cascade is a mechanistic requirement for antiviral defense.
Experimental study of OFDM implementation utilizing GNU Radio and USRP - SDR

A Software Defined Radio (SDR) is a radio which can tune to any frequency band, transmit and receive different modulations and different physical parameters across a large frequency spectrum by using a programmable hardware and powerful software. An SDR performs significant amounts of signal processing in a general purpose computer, or a reconfigurable piece of digital electronics or the combination of both. In this paper, we seek to explore the viability of using GNU Radio; an open source SDR implementation and the Universal Software Radio Peripheral (USRP); an SDR hardware platform, to transmit and receive the OFDM radio signal with QPSK and BPSK modulation. Quality of Service (QoS) in terms of Packet Received Ratio (PRR) on the data transmitted will then be investigated and analyzed.
IoTAbench: an Internet of Things Analytics Benchmark

The commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This ``Internet-of-Things" (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer ``Big Data" solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user's specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HP Vertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform ``apples-to-apples" comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster.
Empirical studies of agile software development: A systematic review

Agile software development represents a major departure from traditional, plan-based approaches to software engineering. A systematic review of empirical studies of agile software development up to and including 2005 was conducted. The search strategy identified 1996 studies, of which 36 were identified as empirical studies. The studies were grouped into four themes: introduction and adoption, human and social factors, perceptions on agile methods, and comparative studies. The review investigates what is currently known about the benefits and limitations of, and the strength of evidence for, agile methods. Implications for research and practice are presented. The main implication for research is a need for more and better empirical studies of agile software development within a common research agenda. For the industrial readership, the review provides a map of findings, according to topic, that can be compared for relevance to their own settings and situations.
Evaluation of Awareness and Acceptability of Using e-Government Services in Developing Countries : the Case of Jordan

Similar to other developing countries, Jordan started a national e-Government initiative aiming to streamline government procedures and make information and government services available to business and citizens online. This paper presents the results of a pilot study that aims to assess factors which could influence the awareness and use of e-Government services in Jordan. It investigates issues such as: accessibility of egovernment, citizen's attitude toward various privacy and security, the required services and costs. The data was collected using quantitative and qualitative methods including a survey and interviews with e-Government officials. The results of this preliminary study suggest that awareness of e-government did not reach the required level. These findings are hoped to be useful for researchers, practitioners and policy makers.
Public health implications of meat production and consumption.

The high level of meat and saturated fat consumption in the USA and other high-income countries exceeds nutritional needs and contributes to high rates of chronic diseases such as cardiovascular disease, diabetes mellitus and some cancers. Affluent citizens in middle- and low-income countries are adopting similar high-meat diets and experiencing increased rates of these same chronic diseases. The industrial agricultural system, now the predominant form of agriculture in the USA and increasingly world-wide, has consequences for public health owing to its extensive use of fertilisers and pesticides, unsustainable use of resources and environmental pollution. In industrial animal production there are public health concerns surrounding feed formulations that include animal tissues, arsenic and antibiotics as well as occupational health risks and risks for nearby communities. It is of paramount importance for public health professionals to become aware of and involved in how our food is produced.
Iterative policy learning in end-to-end trainable task-oriented neural dialog models

In this paper, we present a deep reinforcement learning (RL) framework for iterative dialog policy optimization in end-to-end task-oriented dialog systems. Popular approaches in learning dialog policy with RL include letting a dialog agent to learn against a user simulator. Building a reliable user simulator, however, is not trivial, often as difficult as building a good dialog agent. We address this challenge by jointly optimizing the dialog agent and the user simulator with deep RL by simulating dialogs between the two agents. We first bootstrap a basic dialog agent and a basic user simulator by learning directly from dialog corpora with supervised training. We then improve them further by letting the two agents to conduct task-oriented dialogs and iteratively optimizing their policies with deep RL. Both the dialog agent and the user simulator are designed with neural network models that can be trained end-to-end. Our experiment results show that the proposed method leads to promising improvements on task success rate and total task reward comparing to supervised training and single-agent RL training baseline models.
Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft

Electrical power systems play a critical role in spacecraft and aircraft. This paper discusses our development of a diagnostic capability for an electrical power system testbed, ADAPT, using probabilistic techniques. In the context of ADAPT, we present two challenges, regarding modelling and real-time performance, often encountered in real-world diagnostic applications. To meet the modelling challenge, we discuss our novel high-level speci cation language which supports autogeneration of Bayesian networks. To meet the real-time challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuits typically have small footprints and are optimized for the real-time avionics systems found in spacecraft and aircraft. Using our approach, we present how Bayesian networks with over 400 nodes are auto-generated and then compiled into arithmetic circuits. Using real-world data from ADAPT as well as simulated data, we obtain average inference times smaller than one millisecond when computing diagnostic queries using arithmetic circuits that model our realworld electrical power system.
Improving Abstraction in Text Summarization

Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document. However, the level of actual abstraction as measured by novel phrases that do not appear in the source document remains low in existing approaches. We propose two techniques to improve the level of abstraction of generated summaries. First, we decompose the decoder into a contextual network that retrieves relevant parts of the source document, and a pretrained language model that incorporates prior knowledge about language generation. Second, we propose a novelty metric that is optimized directly through policy learning to encourage the generation of novel phrases. Our model achieves results comparable to state-of-the-art models, as determined by ROUGE scores and human evaluations, while achieving a significantly higher level of abstraction as measured by n-gram overlap with the source document.ive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document. However, the level of actual abstraction as measured by novel phrases that do not appear in the source document remains low in existing approaches. We propose two techniques to improve the level of abstraction of generated summaries. First, we decompose the decoder into a contextual network that retrieves relevant parts of the source document, and a pretrained language model that incorporates prior knowledge about language generation. Second, we propose a novelty metric that is optimized directly through policy learning to encourage the generation of novel phrases. Our model achieves results comparable to state-of-the-art models, as determined by ROUGE scores and human evaluations, while achieving a significantly higher level of abstraction as measured by n-gram overlap with the source document.
Density-Based Multiscale Data Condensation

ÐA problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity. Index TermsÐData mining, multiscale condensation, scalability, density estimation, convergence in probability, instance learning.
Grounding language in action.

We report a new phenomenon associated with language comprehension: the action-sentence compatibility effect (ACE). Participants judged whether sentences were sensible by making a response that required moving toward or away from their bodies. When a sentence implied action in one direction (e.g., "Close the drawer" implies action away from the body), the participants had difficulty making a sensibility judgment requiring a response in the opposite direction. The ACE was demonstrated for three sentences types: imperative sentences, sentences describing the transfer of concrete objects, and sentences describing the transfer of abstract entities, such as "Liz told you the story." These dataare inconsistent with theories of language comprehension in which meaning is represented as a set of relations among nodes. Instead, the data support an embodied theory of meaning that relates the meaning of sentences to human action.
Nonlinear System Identification of Soft Robot Dynamics Using Koopman Operator Theory

Soft robots are challenging to model due to their nonlinear behavior. However, their soft bodies make it possible to safely observe their behavior under random control inputs, making them amenable to large-scale data collection and system identification. This paper implements and evaluates a system identification method based on Koopman operator theory. This theory offers a way to represent a nonlinear system as a linear system in the infinite-dimensional space of real-valued functions called observables, enabling models of nonlinear systems to be constructed via linear regression of observed data. The approach does not suffer from some of the shortcomings of other nonlinear system identification methods, which typically require the manual tuning of training parameters and have limited convergence guarantees. A dynamic model of a pneumatic soft robot arm is constructed via this method, and used to predict the behavior of the real system. The total normalized-root-mean-square error (NRMSE) of its predictions over twelve validation trials is lower than that of several other identified models including a neural network, NLARX, nonlinear Hammerstein-Wiener, and linear state space model.
Topic-Specific Image Caption Generation

Recently, image caption which aims to generate a textual description for an image automatically has attracted researchers from various fields. Encouraging performance has been achieved by applying deep neural networks. Most of these works aim at generating a single caption which may be incomprehensive, especially for complex images. This paper proposes a topic-specific multi-caption generator, which infer topics from image first and then generate a variety of topic-specific captions, each of which depicts the image from a particular topic. We perform experiments on flickr8k, flickr30k and MSCOCO. The results show that the proposed model performs better than single-caption generator when generating topic-specific captions. The proposed model effectively generates diversity of captions under reasonable topics and they differ from each other in topic level.
Scheduling Parallel DAG Jobs Online to Minimize Average Flow Time

In this work, we study the problem of scheduling parallelizable jobs online with an objective of minimizing average flow time. Each parallel job is modeled as a DAG where each node is a sequential task and each edge represents dependence between tasks. Previous work has focused on a model of parallelizability known as the arbitrary speed-up curves setting where a scalable algorithm is known. However, the DAG model is more widely used by practitioners, since many jobs generated from parallel programming languages and libraries can be represented in this model. However, little is known for this model in the online setting with multiple jobs. The DAG model and the speed-up curve models are incomparable and algorithmic results from one do not immediately imply results for the other. Previous work has left open the question of whether an online algorithm can be O(1)-competitive with O(1)-speed for average flow time in the DAG setting. In this work, we answer this question positively by giving a scalable algorithm which is (1 + ǫ)-speed O( 1 ǫ )-competitive for any ǫ > 0. We further introduce the first greedy algorithm for scheduling parallelizable jobs — our algorithm is a generalization of the shortest jobs first algorithm. Greedy algorithms are among the most useful in practice due to their simplicity. We show that this algorithm is (2 + ǫ)-speed O( 1 ǫ )competitive for any ǫ > 0. ∗Department of Computer Science and Engineering, Washington University in St. Louis, 1 Brookings Drive, St. Louis, MO 63130. {kunal, li.jing, kefulu, bmoseley}@wustl.edu. B. Moseley and K. Lu work was supported in part by a Google Research Award and a Yahoo Research Award. K. Agrawal and J. Li were supported in part by NSF grants CCF-1150036 and CCF-1340571.
Autopilots for Small Unmanned Aerial Vehicles : A Survey

This paper presents a survey of the autopilot systems for small or micro unmanned aerial vehicles (UAVs). The objective is to provide a summary of the current commercial, open source and research autopilot systems for convenience of potential small UAV users. The UAV flight control basics are introduced first. The radio control system and autopilot control system are then explained from both the hardware and software viewpoints. Several typical off-the-shelf autopilot packages are compared in terms of sensor packages, observation approaches and controller strengths.  Conclusion is made with a summary of the current autopilot market and a remark on the future development.
Discourse semantics and ideology

This article presents fragments of a new, multidisciplinary theory of ideology and its relations with discourse, formulated in the broader framework of a critical discourse analysis. Ideologies are defined as basic systems of fundamental social cognitions and organizing the attitudes and other social representations shared by members of groups. They thus indirectly control the mental representations (models) that form the interpretation basis and contextual embeddedness of discourse and its structures. In this framework, it is examined how semantic structures of discourse (such as topic, focus, propositional structure, local coherence, level of description, implications and macrostructures) are monitored by underlying ideologies, as expressed in opinion articles in the New York Times and the Washington Post.
Euler angles based loss function for camera relocalization with Deep learning

-Deep learning has been applied to camera relocalization, in particular, PoseNet and its extended work are the convolutional neural networks which regress the camera pose from a single image. However there are many problems, one of them is expensive parameter selection. In this paper, we directly explore the three Euler angles as the orientation representation in the camera pose regressor. There is no need to select the parameter, which is not tolerant in the previous works. Experimental results on the 7 Scenes datasets and the King’s College dataset demonstrate that it has competitive performances.
Deep learning-based electroencephalography analysis: a systematic review

Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.  In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.  Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends.  Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About 40% of the studies used convolutional neural networks (CNNs), while 14% used recurrent neural networks (RNNs), most often with a total of 3 to 10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was 5.4% across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. ∗The first two authors contributed equally to this work.  To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.
Working memory.

The term working memory refers to a brain system that provides temporary storage and manipulation of the information necessary for such complex cognitive tasks as language comprehension, learning, and reasoning. This definition has evolved from the concept of a unitary short-term memory system. Working memory has been found to require the simultaneous storage and processing of information. It can be divided into the following three subcomponents: (i) the central executive, which is assumed to be an attentional-controlling system, is important in skills such as chess playing and is particularly susceptible to the effects of Alzheimer's disease; and two slave systems, namely (ii) the visuospatial sketch pad, which manipulates visual images and (iii) the phonological loop, which stores and rehearses speech-based information and is necessary for the acquisition of both native and second-language vocabulary.
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play

We describe a simple scheme that allows an agent to explore its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another. Alice proposes a task for Bob to complete; and then Bob attempts to complete the task. In this work we will focus on (nearly) reversible environments, or environments that can be reset, and Alice will “propose” the task by running a set of actions and then Bob must partially undo, or repeat them, respectively. Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent. When deployed on an RL task within the environment, this unsupervised training reduces the number of episodes needed to learn.