nils-herrmann/clean_scirepeval huggingface dataset:
Understanding Probabilistic Sparse Gaussian Process Approximations

Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have surprisingly different theoretical properties and behave differently in practice. We thoroughly investigate the two methods for regression both analytically and through illustrative examples, and draw conclusions to guide practical application.
Combining Design and Performance in a Data Visualization Management System

Database Research Group, Columbia University Research Assistant, Advised by Prof. Eugene Wu Sep 2015 – Dec 2016 ■ Data Visualization Management System ● Built a predictive model for user mouse interations on web-browsers and implemented a JavaScript library for the model ● Developed experiments using a Chrome extension to collect user interaction data ● Proposed a data streaming framework to improve the response time of client-server-based data visualizations, which supports prediction for user query intent and progressive data transmission
CMOS design near the limit of scaling

Beginning with a brief review of CMOS scaling trends from 1 m to 0.1 m, this paper examines the fundamental factors that will ultimately limit CMOS scaling and considers the design issues near the limit of scaling. The fundamental limiting factors are electron thermal energy, tunneling leakage through gate oxide, and 2D electrostatic scale length. Both the standby power and the active power of a processor chip will increase precipitously below the 0.1m or 100-nm technology generation. To extend CMOS scaling to the shortest channel length possible while still gaining significant performance benefit, an optimized, vertically and laterally nonuniform doping design (superhalo) is presented. It is projected that room-temperature CMOS will be scaled to 20-nm channel length with the superhalo profile. Low-temperature CMOS allows additional design space to further extend CMOS scaling to near 10 nm.
How effective is the Grey Wolf optimizer in training multi-layer perceptrons

This paper employs the recently proposed Grey Wolf Optimizer (GWO) for training Multi-Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification and three function-approximation datasets are utilized to benchmark the performance of the proposed method. For verification, the results are compared with some of the most well-known evolutionary trainers: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolution Strategy (ES), and Population-based Incremental Learning (PBIL). The statistical results prove the GWO algorithm is able to provide very competitive results in terms of improved local optima avoidance. The results also demonstrate a high level of accuracy in classification and approximation of the proposed trainer.
The COG database: an updated version includes eukaryotes

The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. We describe here a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after euk aryotic o rthologous g roups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The euk aryotic o rthologous g roups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or ~54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of ~20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (~1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes. The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies.
Measuring cognitive and psychological engagement : Validation of the Student Engagement Instrument

A review of relevant literatures led to the construction of a self-report instrument designed to measure two subtypes of student engagement with school: cognitive and psychological engagement. The psychometric properties of this measure, the Student Engagement Instrument (SEI), were assessed based on responses of an ethnically and economically diverse urban sample of 1931 ninth grade students. Factor structures were obtained using exploratory factor analyses (EFAs) on half of the dataset, with model fit examined using confirmatory factor analyses (CFAs) on the other half of the dataset. The model displaying the best empirical fit consisted of six factors, and these factors correlated with expected educational outcomes. Further research is suggested in the iterative process of developing the SEI, and the implications of these findings are discussed. D 2006 Society for the Study of School Psychology.
Why do i keep interrupting myself?: environment, habit and self-interruption

Self-interruptions account for a significant portion of task switching in information-centric work contexts. However, most of the research to date has focused on understanding, analyzing and designing for external interruptions. The causes of self-interruptions are not well understood. In this paper we present an analysis of 889 hours of observed task switching behavior from 36 individuals across three high-technology information work organizations. Our analysis suggests that self-interruption is a function of organizational environment and individual differences, but also external interruptions experienced. We find that people in open office environments interrupt themselves at a higher rate. We also find that people are significantly more likely to interrupt themselves to return to solitary work associated with central working spheres, suggesting that self-interruption occurs largely as a function of prospective memory events. The research presented contributes substantially to our understanding of attention and multitasking in context.
English-to-Sanskrit Statistical Machine Translation with Ubiquitous Application

In this paper by utilizing the capabilities of modern ubiquitous operating systems we introduce a comprehensive framework for a ubiquitous translation and language learning environment for English to Sanskrit Machine Translation. We present an application for learning Sanskrit characters, sentences and English Sanskrit translation. For the implementation, we have used the open-source Android platform on the Samsung Mini2440, a state-of-the-art development board. We present our current state of implementation, the architecture of our framework,and the findings we have gathered so far. In addition to this, here we describes the Phrase-Based Statistical Machine Translation Decoder for English to Sanskrit translation in ubiquitous environment. Our goal is to improve the translation quality by enhancing the translation table and by preprocessing the Sanskrit language text .
Fast accurate fish detection and recognition of underwater images with Fast R-CNN

This paper aims at detecting and recognizing fish species from underwater images by means of Fast R-CNN (Regions with Convolutional Neural and Networks) features. Encouraged by powerful recognition results achieved by Convolutional Neural Networks (CNNs) on generic VOC and ImageNet dataset, we apply this popular deep ConvNets to domain-specific underwater environment which is more complicated than overland situation, using a new dataset of 24277 ImageCLEF fish images belonging to 12 classes. The experimental results demonstrate the promising performance of our networks. Fast R-CNN improves mean average precision (mAP) by 11.2% relative to Deformable Parts Model (DPM) baseline-achieving a mAP of 81.4%, and detects 80× faster than previous R-CNN on a single fish image.
Dante vision: In-air and touch gesture sensing for natural surface interaction with combined depth and thermal cameras

Researchers have paid considerable attention to natural user interfaces, especially sensing gestures and touches upon an un-instrumented surface from an overhead camera. We present a system that combines depth sensing from a Microsoft Kinect and temperature sensing from a thermal imaging camera to infer a variety of gestures and touches for controlling a natural user interface. The system, coined Dante, is capable of (1) inferring multiple touch points from multiple users (92.6% accuracy), (2) detecting and classifying each user using their depth and thermal footprint (87.7% accuracy), and (3) detecting touches on objects placed upon the table top (91.7% accuracy). The system can also classify the pressure of chording motions. The system is real time, with an average processing delay of 40 ms.
Algorithms for Evolving No-Limit Texas Hold'em Poker Playing Agents

Computers have difficulty learning how to play Texas Hold’em Poker. The game contains a high degree of stochasticity, hidden information, and opponents that are deliberately trying to mis-represent their current state. Poker has a much larger game space than classic parlour games such as Chess and Backgammon. Evolutionary methods have been shown to find relatively good results in large state spaces, and neural networks have been shown to be able to find solutions to non-linear search problems. In this paper, we present several algorithms for teaching agents how to play No-Limit Texas Hold’em Poker using a hybrid method known as evolving neural networks. Furthermore, we adapt heuristics such as halls of fame and co-evolution to be able to handle populations of Poker agents, which can sometimes contain several hundred opponents, instead of a single opponent. Our agents were evaluated against several benchmark agents. Experimental results show the overall best performance was obtained by an agent evolved from a single population (i.e., with no co-evolution) using a large hall of fame. These results demonstrate the effectiveness of our algorithms in creating competitive No-Limit Texas Hold’em Poker agents.
An ultra-wideband vertical transition from microstrip to stripline in PCB technology

An ultra-wideband transition from microstrip to stripline in PCB technology is presented applying only through via holes for simple fabrication. The design is optimized using full-wave EM simulations. A prototype is manufactured and measured achieving a return loss better than 8.7dB and an insertion loss better than 1.2 dB in the FCC frequency range. A meander-shaped delay line in stripline technique is presented as an example of application.
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos

Conversational Memory Network: To classify emotion of utterance ui, corresponding histories (hista and histb) are taken. Each history, histλ, contains the preceding K utterances by person Pλ. Histories are modeled into memories and utilized as follows, Memory Representation: Memory representation Mλ = [mλ, ...,mλ ] for histλ is generated using a GRU, λ ∈ {a, b}.  Attention mechanism is used to read Mλ. Relevance of each memory mλ’s context with ui is computed using a match operation, pλ = softmax(q i .mλ) , qi = B.ui (1) Memory Output: Weighted combination of memories is calculated using attention scores. oλ = M ′ λ.pλ (2)
High performance of dual band/dual polarization compact OMT

The development of a compact circular polarization Orthomode Trasducer (OMT) working in two frequency bands with dual circular polarization (RHCP & LHCP) is presented. The device covers the complete communication spectrum allocated at C-band. At the same time, the device presents high power handling capability and very low mass and envelope size. The OMT plus a feed horn are used to illuminate a Reflector antenna, the surface of which is shaped to provide domestic or regional coverage from geostationary orbit. The full band operation increases the earth-satellite communication capability. The paper will show the OMT selected architecture, the RF performances at unit level and at component level. RF power aspects like multipaction and PIM are addressed. This development was performed under European Space Agency ESA ARTES-4 program.
Template detection via data mining and its applications

We formulate and propose the template detection problem, and suggest a practical solution for it based on counting frequent item sets. We show that the use of templates is pervasive on the web. We describe three principles, which characterize the assumptions made by hypertext information retrieval (IR) and data mining (DM) systems, and show that templates are a major source of violation of these principles. As a consequence, basic "pure" implementations of simple search algorithms coupled with template detection and elimination show surprising increases in precision at all levels of recall.
Mobility Increases Localizability: A Survey on Wireless Indoor Localization using Inertial Sensors

Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.
A wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment

We present a wavelet-based, high performance, hierarchical scheme for image matching which includes (1) dynamic detection of interesting points as feature points at different levels of subband images via the wavelet transform, (2) adaptive thresholding selection based on compactness measures of fuzzy sets in image feature space, and (3) a guided searching strategy for the best matching from coarse level to fine level. In contrast to the traditional parallel approaches which rely on specialized parallel machines, we explored the potential of distributed systems for parallelism. The proposed image matching algorithms were implemented on a network of workstation clusters using parallel virtual machine (PVM). The results show that our wavelet-based hierarchical image matching scheme is efficient and effective for object recognition.
Modified T-shaped planar monopole antennas for multiband operation

In this paper, we propose a novel modified T-shaped planar monopole antenna in that two asymmetric horizontal strips are used as additional resonators to produce the lower and upper resonant modes. As a result, a dual-band antenna for covering 2.4- and 5-GHz wireless local area network (WLAN) bands is implemented. In order to expand the lower band, a multiband antenna for covering the digital communications systems, personal communications systems, Universal Mobile Telecommunications Systems, and 2.4/5-GHz WLAN bands is also developed. Prototypes of the multiband antenna have been successfully implemented. Good omnidirectional radiation in the desired frequency bands has been achieved. The proposed multiband antenna with relatively low profile is very suitable for multiband mobile communication systems
A gene expression database for the molecular pharmacology of cancer

We used cDNA microarrays to assess gene expression profiles in 60 human cancer cell lines used in a drug discovery screen by the National Cancer Institute. Using these data, we linked bioinformatics and chemoinformatics by correlating gene expression and drug activity patterns in the NCI60 lines. Clustering the cell lines on the basis of gene expression yielded relationships very different from those obtained by clustering the cell lines on the basis of their response to drugs. Gene-drug relationships for the clinical agents 5-fluorouracil and L-asparaginase exemplify how variations in the transcript levels of particular genes relate to mechanisms of drug sensitivity and resistance. This is the first study to integrate large databases on gene expression and molecular pharmacology.
Continuous User Authentication on Mobile Devices

Recent developments in sensing and communication technologies have led to an explosion in the use of mobile devices such as smartphones and tablets. With the increase in the use of mobile devices, one has to constantly worry about the security and privacy as the loss of a mobile device could compromise personal information of the user. To deal with this problem, continuous authentication (also known as active authentication) systems have been proposed in which users are continuously monitored after the initial access to the mobile device. In this paper, we provide an overview of different continuous authentication methods on mobile devices. We discuss the merits and drawbacks of available approaches and identify promising avenues of research in this rapidly evolving field.
Hard-x-ray lensless imaging of extended objects.

We demonstrate a hard-x-ray microscope that does not use a lens and is not limited to a small field of view or an object of finite size. The method does not suffer any of the physical constraints, convergence problems, or defocus ambiguities that often arise in conventional phase-retrieval diffractive imaging techniques. Calculation times are about a thousand times shorter than in current iterative algorithms. We need no a priori knowledge about the object, which can be a transmission function with both modulus and phase components. The technique has revolutionary implications for x-ray imaging of all classes of specimen.
Understanding Process Behaviours in a Large Insurance Company in Australia: A Case Study

Having a reliable understanding about the behaviours, problems, and performance of existing processes is important in enabling a targeted process improvement initiative. Recently, there has been an increase in the application of innovative process mining techniques to facilitate evidence-based understanding about organizations’ business processes. Nevertheless, the application of these techniques in the domain of finance in Australia is, at best, scarce. This paper details a 6-month case study on the application of process mining in one of the largest insurance companies in Australia. In particular, the challenges encountered, the lessons learned, and the results obtained from this case study are detailed. Through this case study, we not only validated existing ‘lessons learned’ from other similar case studies, but also added new insights that can be beneficial to other practitioners in applying process mining in their respective fields.
Computer networks as social networks.

Computer networks are inherently social networks, linking people, organizations, and knowledge. They are social institutions that should not be studied in isolation but as integrated into everyday lives. The proliferation of computer networks has facilitated a deemphasis on group solidarities at work and in the community and afforded a turn to networked societies that are loosely bounded and sparsely knit. The Internet increases people's social capital, increasing contact with friends and relatives who live nearby and far away. New tools must be developed to help people navigate and find knowledge in complex, fragmented, networked societies.
Formal worst-case timing analysis of Ethernet TSN's time-aware and peristaltic shapers

Ethernet is considered as a future communication standard for distributed embedded systems in the automotive and industrial domains. A key challenge is the deterministic low-latency transport of Ethernet frames, as many safety-critical real-time applications in these domains have tight timing requirements. Time-sensitive networking (TSN) is an upcoming set of Ethernet standards, which (among other things) address these requirements by specifying new quality of service mechanisms in the form of different traffic shapers. In this paper, we consider TSN's time-aware and peristaltic shapers and evaluate whether these shapers are able to fulfill these strict timing requirements. We present a formal timing analysis, which is a key requirement for the adoption of Ethernet in safety-critical real-time systems, to derive worst-case latency bounds for each shaper. We use a realistic automotive Ethernet setup to compare these shapers to each other and against Ethernet following IEEE 802.1Q.
Measuring diversity on a low-altitude UAV in a ground-to-air wireless 802.11 mesh network

We consider the problem of mitigating a highly varying wireless channel between a transmitting ground node and receivers on a small, low-altitude unmanned aerial vehicle (UAV) in a 802.11 wireless mesh network. One approach is to use multiple transmitter and receiver nodes that exploit the channel's spatial/temporal diversity and that cooperate to improve overall packet reception. We present a series of measurement results from a real-world testbed that characterize the resulting wireless channel. We show that the correlation between receiver nodes on the airplane is poor at small time scales so receiver diversity can be exploited. Our measurements suggest that using several receiver nodes simultaneously can boost packet delivery rates substantially. Lastly, we show that similar results apply to transmitter selection diversity as well.