nils-herrmann/clean_scirepeval huggingface dataset:
Qualitative research in health care. Analysing qualitative data.

Contrary to popular perception, qualitative research can produce vast amounts of data. These may include verbatim notes or transcribed recordings of interviews or focus groups, jotted notes and more detailed “fieldnotes” of observational research, a diary or chronological account, and the researcher’s reflective notes made during the research. These data are not necessarily small scale: transcribing a typical single interview takes several hours and can generate 20-40 pages of single spaced text. Transcripts and notes are the raw data of the research. They provide a descriptive record of the research, but they cannot provide explanations. The researcher has to make sense of the data by sifting and interpreting them.
CMOS current mode analog multiplier

This paper presents two different implementations of four Quadrant CMOS Analog Multiplier Circuits. The Multipliers are designed in current mode. Current squarer and translinear loops are the basic blocks for both the structures in realization of mathematical equations. The structures have simplicity in implementation. The proposed multiplier structures are designed in implementing in 180 nm CMOS technology with a supply of 1.8 V & 1.2 V resp. The structures have frequency bandwidth of 493 MHz & 75 MHz with a power consumption of 146.78μW & 36.08μW respectively.
Title-Guided Encoding for Keyphrase Generation

Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoderdecoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a titleguided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-ofthe-art models with a large margin, especially for documents with either very low or very high title length ratios.
Massive MIMO Systems With Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits

The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain. Recent works in the field of massive multiple-input multiple-output (MIMO) show that the user channels decorrelate when the number of antennas at the base stations (BSs) increases, thus strong signal gains are achievable with little interuser interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are reasonable in this asymptotic regime. This paper considers a new system model that incorporates general transceiver hardware impairments at both the BSs (equipped with large antenna arrays) and the single-antenna user equipments (UEs). As opposed to the conventional case of ideal hardware, we show that hardware impairments create finite ceilings on the channel estimation accuracy and on the downlink/uplink capacity of each UE. Surprisingly, the capacity is mainly limited by the hardware at the UE, while the impact of impairments in the large-scale arrays vanishes asymptotically and interuser interference (in particular, pilot contamination) becomes negligible. Furthermore, we prove that the huge degrees of freedom offered by massive MIMO can be used to reduce the transmit power and/or to tolerate larger hardware impairments, which allows for the use of inexpensive and energy-efficient antenna elements.
Late-summer phytoplankton in western Lake Erie (Laurentian Great Lakes): bloom distributions, toxicity, and environmental influences

Phytoplankton abundance and composition and the cyanotoxin, microcystin, were examined relative to environmental parameters in western Lake Erie during late-summer (2003–2005). Spatially explicit distributions of phytoplankton occurred on an annual basis, with the greatest chlorophyll (Chl) a concentrations occurring in waters impacted by Maumee River inflows and in Sandusky Bay. Chlorophytes, bacillariophytes, and cyanobacteria contributed the majority of phylogenetic-group Chl a basin-wide in 2003, 2004, and 2005, respectively. Water clarity, pH, and specific conductance delineated patterns of group Chl a, signifying that water mass movements and mixing were primary determinants of phytoplankton accumulations and distributions. Water temperature, irradiance, and phosphorus availability delineated patterns of cyanobacterial biovolumes, suggesting that biotic processes (most likely, resource-based competition) controlled cyanobacterial abundance and composition. Intracellular microcystin concentrations corresponded to Microcystis abundance and environmental parameters indicative of conditions coincident with biomass accumulations. It appears that environmental parameters regulate microcystin indirectly, via control of cyanobacterial abundance and distribution.
Mining Location-based Social Networks: A Predictive Perspective

With the development of location-based social networks, an increasing amount of individual mobility data accumulate over time. The more mobility data are collected, the better we can understand the mobility patterns of users. At the same time, we know a great deal about online social relationships between users, providing new opportunities for mobility prediction. This paper introduces a noveltyseeking driven predictive framework for mining location-based social networks that embraces not only a bunch of Markov-based predictors but also a series of location recommendation algorithms. The core of this predictive framework is the cooperation mechanism between these two distinct models, determining the propensity of seeking novel and interesting locations.
Airborne transmission of influenza A/H5N1 virus between ferrets.

Highly pathogenic avian influenza A/H5N1 virus can cause morbidity and mortality in humans but thus far has not acquired the ability to be transmitted by aerosol or respiratory droplet ("airborne transmission") between humans. To address the concern that the virus could acquire this ability under natural conditions, we genetically modified A/H5N1 virus by site-directed mutagenesis and subsequent serial passage in ferrets. The genetically modified A/H5N1 virus acquired mutations during passage in ferrets, ultimately becoming airborne transmissible in ferrets. None of the recipient ferrets died after airborne infection with the mutant A/H5N1 viruses. Four amino acid substitutions in the host receptor-binding protein hemagglutinin, and one in the polymerase complex protein basic polymerase 2, were consistently present in airborne-transmitted viruses. The transmissible viruses were sensitive to the antiviral drug oseltamivir and reacted well with antisera raised against H5 influenza vaccine strains. Thus, avian A/H5N1 influenza viruses can acquire the capacity for airborne transmission between mammals without recombination in an intermediate host and therefore constitute a risk for human pandemic influenza.
NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity

We present in this paper our system developed for SemEval 2015 Shared Task 2 (2a English Semantic Textual Similarity, STS, and 2c Interpretable Similarity) and the results of the submitted runs. For the English STS subtask, we used regression models combining a wide array of features including semantic similarity scores obtained from various methods. One of our runs achieved weighted mean correlation score of 0.784 for sentence similarity subtask (i.e., English STS) and was ranked tenth among 74 runs submitted by 29 teams. For the interpretable similarity pilot task, we employed a rule-based approach blended with chunk alignment labeling and scoring based on semantic similarity features. Our system for interpretable text similarity was among the top three best performing systems.
A Comparison Study between Data Mining Tools over some Classification Methods

Nowadays, huge amount of data and information are available for everyone, Data can now be stored in many different kinds of databases and information repositories, besides being available on the Internet or in printed form. With such amount of data, there is a need for powerful techniques for better interpretation of these data that exceeds the human's ability for comprehension and making decision in a better way. In order to reveal the best tools for dealing with the classification task that helps in decision making, this paper has conducted a comparative study between a number of some of the free available data mining and knowledge discovery tools and software packages. Results have showed that the performance of the tools for the classification task is affected by the kind of dataset used and by the way the classification algorithms were implemented within the toolkits. For the applicability issue, the WEKA toolkit has achieved the highest applicability followed by Orange, Tanagra, and KNIME respectively. Finally; WEKA toolkit has achieved the highest improvement in classification performance; when moving from the percentage split test mode to the Cross Validation test mode, followed by Orange, KNIME and finally Tanagra respectively. Keywords-component; data mining tools; data classification; Wekak; Orange; Tanagra; KNIME.
Towards Pervasive Augmented Reality: Context-Awareness in Augmented Reality

Augmented Reality is a technique that enables users to interact with their physical environment through the overlay of digital information. While being researched for decades, more recently, Augmented Reality moved out of the research labs and into the field. While most of the applications are used sporadically and for one particular task only, current and future scenarios will provide a continuous and multi-purpose user experience. Therefore, in this paper, we present the concept of Pervasive Augmented Reality, aiming to provide such an experience by sensing the user’s current context and adapting the AR system based on the changing requirements and constraints. We present a taxonomy for Pervasive Augmented Reality and context-aware Augmented Reality, which classifies context sources and context targets relevant for implementing such a context-aware, continuous Augmented Reality experience. We further summarize existing approaches that contribute towards Pervasive Augmented Reality. Based our taxonomy and survey, we identify challenges for future research directions in Pervasive Augmented Reality.
The Sling Lift: An Ancillary Procedure for Malar Rejuvenation

The malar region has been a crucial target in many facial rejuvenation techniques because the beauty and youthful contour of a convex midface and a smooth eyelid–cheek sulcus are key features of a pleasing face-lift result. The full midface subperiosteal lift has helped to address these issues. However, the desire of patients currently for a rapid recovery and return to work with a natural-looking result has influenced procedural selection. Another concern is for safer procedures with reduced potential risk. Progressively fewer invasive techniques, such as the minimal access cranial suspension (MACS) lift, have been a response to these core concerns. After 3 years of performing the conventional three purse-string suture MACS lift, the author developed a practical procedural modification. For a total of 17 patients, the author combined limited regional subperiosteal lift and periosteal fixation with a simple sling approach to the more fully released malar tissue mass to make a single-point suspension just above the lateral orbital rim. The percutaneous sling lift appears to offer a degree and naturalness of rejuvenation of the malar region similar to those of the MACS lift and the full subperiosteal midface lift, but with fewer suspension points and less undermining. Also, the author observed less ecchymosis and edema than with the full midface subperiosteal lift, as would be expected. In all 17 cases, the need for the second and third purse-string sutures was eliminated. The early results for the percutaneous sling lift indicate that it offers promising results, rapid recovery, and reduced risk of serious complications.
Aerial 3D Building Detection and Modeling From Airborne LiDAR Point Clouds

A fast, completely automated method to create 3D watertight building models from airborne LiDAR point clouds is presented. The proposed method analyzes the scene content and produces multi-layer rooftops with complex boundaries and vertical walls that connect rooftops to the ground. A graph cuts based method is used to segment vegetative areas from the rest of scene content. The ground terrain and building rooftop patches are then extracted utilizing our technique, the hierarchical Euclidean clustering. Our method adopts a “divide-and-conquer” strategy. Once potential points on rooftops are segmented from terrain and vegetative areas, the whole scene is divided into individual pendent processing units which represent potential building footprints. For each individual building region, significant features on the rooftop are further detected using a specifically designed region growing algorithm with smoothness constraint. Boundaries for all of these features are refined in order to produce strict description. After this refinement, mesh models could be generated using an existing robust dual contouring method.
Achievement of More Than 25% Conversion Efficiency With Crystalline Silicon Heterojunction Solar Cell

The crystalline silicon heterojunction structure adopted in photovoltaic modules commercialized as Panasonic's HIT has significantly reduced recombination loss, resulting in greater conversion efficiency. The structure of an interdigitated back contact was adopted with our crystalline silicon heterojunction solar cells to reduce optical loss from a front grid electrode, a transparent conducting oxide (TCO) layer, and a-Si:H layers as an approach for exceeding the conversion efficiency of 25%. As a result of the improved short-circuit current (Jsc), we achieved the world's highest efficiency of 25.6% for crystalline silicon-based solar cells under 1-sun illumination (designated area: 143.7 cm2).
Dynamic Nonlinear Inverse-Model Based Control of a Twin Rotor System Using Adaptive Neuro-fuzzy Inference System

A dynamic control system design has been a great demand in the control engineering community, with many applications particularly in the field of flight control. This paper presents investigations into the development of a dynamic nonlinear inverse-model based control of a twin rotor multi-input multi-output system (TRMS). The TRMS is an aerodynamic test rig representing the control challenges of modern air vehicle. A model inversion control with the developed adaptive model is applied to the system. An adaptive neuro-fuzzy inference system (ANFIS) is augmented with the control system to improve the control response. To demonstrate the applicability of the methods, a simulated hovering motion of the TRMS, derived from experimental data is considered in order to evaluate the tracking properties and robustness capacities of the inverse- model control technique.
A multi-step outlier-based anomaly detection approach to network-wide traffic

Outlier detection is of considerable interest in fields such as physical sciences, medical diagnosis, surveillance detection, fraud detection and network anomaly detection. The data mining and network management research communities are interested in improving existing score-based network traffic anomaly detection techniques because of ample scopes to increase performance. In this paper, we present a multi-step outlier-based approach for detection of anomalies in network-wide traffic. We identify a subset of relevant traffic features and use it during clustering and anomaly detection. To support outlier-based network anomaly identification, we use the following modules: a mutual information and generalized entropy based feature selection technique to select a relevant non-redundant subset of features, a tree-based clustering technique to generate a set of reference points and an outlier score function to rank incoming network traffic to identify anomalies. We also design a fast distributed feature extraction and data preparation framework to extract features from raw network-wide traffic. We evaluate our approach in terms of detection rate, false positive rate, precision, recall and F -measure using several high dimensional synthetic and real-world datasets and find the performance superior in comparison to competing algorithms.
A no-regret generalization of hierarchical softmax to extreme multi-label classification

Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels. Large label spaces can be efficiently handled by organizing labels as a tree, like in the hierarchical softmax (HSM) approach commonly used for multi-class problems. In this paper, we investigate probabilistic label trees (PLTs) that have been recently devised for tackling XMLC problems. We show that PLTs are a no-regret multi-label generalization of HSM when precision@k is used as a model evaluation metric. Critically, we prove that pick-one-label heuristic—a reduction technique from multi-label to multi-class that is routinely used along with HSM—is not consistent in general. We also show that our implementation of PLTs, referred to as EXTREMETEXT (XT), obtains significantly better results than HSM with the pick-one-label heuristic and XML-CNN, a deep network specifically designed for XMLC problems. Moreover, XT is competitive to many state-of-the-art approaches in terms of statistical performance, model size and prediction time which makes it amenable to deploy in an online system.
Approximate earth mover’s distance in linear time

The earth moverpsilas distance (EMD) is an important perceptually meaningful metric for comparing histograms, but it suffers from high (O(N3 logN)) computational complexity. We present a novel linear time algorithm for approximating the EMD for low dimensional histograms using the sum of absolute values of the weighted wavelet coefficients of the difference histogram. EMD computation is a special case of the Kantorovich-Rubinstein transshipment problem, and we exploit the Holder continuity constraint in its dual form to convert it into a simple optimization problem with an explicit solution in the wavelet domain. We prove that the resulting wavelet EMD metric is equivalent to EMD, i.e. the ratio of the two is bounded. We also provide estimates for the bounds. The weighted wavelet transform can be computed in time linear in the number of histogram bins, while the comparison is about as fast as for normal Euclidean distance or chi2 statistic. We experimentally show that wavelet EMD is a good approximation to EMD, has similar performance, but requires much less computation.
Understanding and Designing Complex Systems: Response to "A framework for optimal high-level descriptions in science and engineering - preliminary report"

SFI Working Papers contain accounts of scienti5ic work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-­‐reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at SFI, inspired by an invited visit to or collaboration at SFI, or funded by an SFI grant.
Scalable Micro-planned Generation of Discourse from Structured Data

We present a framework for generating natural language description from structured data such as tables. Motivated by the need to approach this problem in a manner that is scalable and easily adaptable to newer domains, unlike existing related systems, our system does not require parallel data; it rather relies on monolingual corpora and basic NLP tools which are easily accessible. The system employs a 3-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixeddomain dataset curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other data types such as Knowledge-Graphs and Key-Value dictionaries.
Entrepreneurship, Frictions, and Wealth;

This paper constructs and calibrates a parsimonious model of occupational choice that allows for entrepreneurial entry, exit, and investment decisions in presence of borrowing constraints. The model fits very well a number of empirical observations, including the observed wealth distribution for entrepreneurs and workers. At the aggregate level, more restrictive borrowing constraints generate less wealth concentration, and reduce average firm size, aggregate capital, and the fraction of entrepreneurs. Voluntary bequests allow some high-ability workers to establish or enlarge an entrepreneurial activity. With accidental bequests only, there would be fewer very large firms, and less aggregate capital and wealth concentration.
Identifying cyber risk hotspots: A framework for measuring temporal variance in computer network risk

Modern computer networks generate significant volume of behavioural system logs on a daily basis. Such networks comprise many computers with Internet connectivity, and many users who access the Web and utilise Cloud services make use of numerous devices connected to the network on an ad-hoc basis. Measuring the risk of cyber attacks and identifying the most recent modus-operandi of cyber criminals on large computer networks can be difficult due to the wide range of services and applications running within the network, the multiple vulnerabilities associated with each application, the severity associated with each vulnerability, and the ever-changing attack vector of cyber criminals. In this paper we propose a framework to represent these features, enabling real-time network enumeration and traffic analysis to be carried out, in order to produce quantified measures of risk at specific points in time. We validate the approach using data from a University network, with a data collection consisting of 462,787 instances representing threats measured over a 144 hour period. Our analysis can be generalised to a variety of other contexts.
A COMPARATIVE STUDY OF FULL ADDER USING STATIC CMOS LOGIC STYLE

In this review paper 1-bit CMOS full adder cells are studied using standard static CMOS logic style. The comparison is carried out using several parameters like number of transistors, delay, power dissipation and power delay product (PDP).The circuits are designed at transistor level using180nm CMOS technology. Different full adders are studied in this paper like Conventional CMOS (C-CMOS), Complementary pass transistor logic (CPL), Double pass transistor logic (DPL), Transmission gate (TGA), Transmission function (TFA), New 14T, Hybrid CMOS, HPSC, Pseudo nMOS, GDI full adders.