nils-herrmann/clean_scirepeval huggingface dataset:
Multiresolution Consensus Clustering in Networks

Networks often exhibit structure at disparate scales. We propose a method for identifying community structure at different scales based on multiresolution modularity and consensus clustering. Our contribution consists of two parts. First, we propose a strategy for sampling the entire range of possible resolutions for the multiresolution modularity quality function. Our approach is directly based on the properties of modularity and, in particular, provides a natural way of avoiding the need to increase the resolution parameter by several orders of magnitude to break a few remaining small communities, necessitating the introduction of ad-hoc limits to the resolution range with standard sampling approaches. Second, we propose a hierarchical consensus clustering procedure, based on a modified modularity, that allows one to construct a hierarchical consensus structure given a set of input partitions. While here we are interested in its application to partitions sampled using multiresolution modularity, this consensus clustering procedure can be applied to the output of any clustering algorithm. As such, we see many potential applications of the individual parts of our multiresolution consensus clustering procedure in addition to using the procedure itself to identify hierarchical structure in networks.
Extended object and group tracking with Elliptic Random Hypersurface Models

This paper provides new results and insights for tracking an extended target object modeled with an Elliptic Random Hypersurface Model (RHM). An Elliptic RHM specifies the relative squared Mahalanobis distance of a measurement source to the center of the target object by means of a one-dimensional random scaling factor. It is shown that uniformly distributed measurement sources on an ellipse lead to a uniformly distributed squared scaling factor. Furthermore, a Bayesian inference mechanisms tailored to elliptic shapes is introduced, which is also suitable for scenarios with high measurement noise. Closed-form expressions for the measurement update in case of Gaussian and uniformly distributed squared scaling factors are derived.
Microfluidics technology for drug delivery: A review.

Microfluidics is undoubtedly an influential technology that is currently revolutionizing the chemical and biological studies by replicating laboratory bench-top technology on a miniature chip-scale device. In the area of drug delivery science, microfluidics offers advantages, such as precise dosage, ideal delivery, target-precise delivery, sustainable and controlled release, multiple dosing, and slight side effects. These advantages bring significant assets to the drug delivery systems. Microfluidic technology has been progressively used for fabrication of drug carriers, direct drug delivery systems, high-throughput screening, and formulation and immobilization of drugs. This review discusses the recent technological progress, outcomes and available opportunities for the usage of microfluidics systems in drug delivery systems.
Trapdoors for hard lattices and new cryptographic constructions

We show how to construct a variety of "trapdoor" cryptographic tools assuming the worst-case hardness of standard lattice problems (such as approximating the length of the shortest nonzero vector to within certain polynomial factors). Our contributions include a new notion of trapdoor function with preimage sampling, simple and efficient "hash-and-sign" digital signature schemes, and identity-based encryption. A core technical component of our constructions is an efficient algorithm that, given a basis of an arbitrary lattice, samples lattice points from a discrete Gaussian probability distribution whose standard deviation is essentially the length of the longest Gram-Schmidt vector of the basis. A crucial security property is that the output distribution of the algorithm is oblivious to the particular geometry of the given basis.
Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j

NoSQL and especially graph databases are constantly gaining popularity among developers of Web 2.0 applications as they promise to deliver superior performance when handling highly interconnected data compared to traditional relational databases. Apache Shindig is the reference implementation for OpenSocial with its highly interconnected data model. However, the default back-end is based on a relational database. In this paper we describe our experiences with a different back-end based on the graph database Neo4j and compare the alternatives for querying data with each other and the JPA-based sample back-end running on MySQL. Moreover, we analyze why the different approaches often may yield such diverging results concerning throughput. The results show that the graph-based back-end can match and even outperform the traditional JPA implementation and that Cypher is a promising candidate for a standard graph query language, but still leaves room for improvements.
Nanonization of megestrol acetate by laser fragmentation in aqueous milieu.

Nanonization is a simple and effective method to improve dissolution rate and oral bioavailability of drugs with poor water solubility. There is growing interest to downscale the nanocrystal production to enable early preclinical evaluation of new drug candidates when compound availability is scarce. The purpose of the present study was to investigate laser fragmentation to form nanosuspensions in aqueous solution of the insoluble model drug megestrol acetate (MA) using very little quantities of the drug. Laser fragmentation was obtained by focusing a femtosecond (fs) or nanosecond (ns) laser radiation on a magnetically stirred MA suspension in water or aqueous solution of a stabilizing agent. The size distribution and physicochemical properties of the drug nanoparticles were characterized, and the in vitro dissolution and in vivo oral pharmacokinetics of a laser fragmented formulation were evaluated. A MA nanosuspension was also prepared by media milling for comparison purpose. For both laser radiations, smaller particles were obtained as the laser power was increased, but at a cost of higher degradation. Significant nanonization was achieved after a 30-minfs laser treatment at 250mW and a 1-hns laser treatment at 2500mW. The degradation induced by the laser process of the drug was primarily oxidative in nature. The crystal phase of the drug was maintained, although partial loss of crystallinity was observed. The in vitro dissolution rate and in vivo bioavailability of the laser fragmented formulation were similar to those obtained with the nanosuspension prepared by media milling, and significantly improved compared to the coarse drug powder. It follows that this laser nanonization method has potential to be used for the preclinical evaluation of new drug candidates.
Automated SQL tuning through trial and (sometimes) error

SQL tuning---the attempt to improve a poorly-performing execution plan produced by the database query optimizer---is a critical aspect of database performance tuning. Ironically, as commercial databases strive to improve on the manageability front, SQL tuning is becoming more of a black art. It requires a high level of expertise in areas like (i) query optimization, run-time execution of query plan operators, configuration parameter settings, and other database internals; (ii) identification of missing indexes and other access structures; (iii) statistics maintained about the data; and (iv) characteristics of the underlying storage system. Since database systems, their workloads, and the data that they manage are not getting any simpler, database users and administrators often rely on trial and error for SQL tuning.
 In this paper, we take the position that the trial-and-error (or, experiment-driven) process of SQL tuning can be automated by the database system in an efficient manner; freeing the user or administrator from this burden in most cases. A number of current approaches to SQL tuning indeed take an experiment-driven approach. We are prototyping a tool, called zTuned, that automates experiment-driven SQL tuning. This paper describes the design choices in zTuned to address three nontrivial issues: (i) how is the SQL tuning logic integrated with the regular query optimizer, (ii) how to plan the experiments to conduct so that a satisfactory (new) plan can be found quickly, and (iii) how to conduct experiments with minimal impact on the user-facing production workload. We conclude with a preliminary empirical evaluation and outline promising new directions in automated SQL tuning.
SPICE simulation of coupled core fluxgate magnetometers

SPICE simulation is universally recognized as a powerful tool in the field of electronic engineering. These simulations are, particularly useful for dealing with a strong nonlinear behavior that cannot be easily handled analytically. Magnetic hysteresis is one example of a non-linearity that underpins many practical applications, especially in the field of magnetometers and magnetic sensors. The aim of this paper is to present a behavioral model of magnetic hysteresis applied to the numerical simulation of coupled core fluxgate magnetometers
An Alternating Current Electroosmotic Pump Based on Conical Nanopore Membranes.

Electroosmotic flow (EOF) is used to pump solutions through microfluidic devices and capillary electrophoresis columns. We describe here an EOF pump based on membrane EOF rectification, an electrokinetic phenomenon we recently described. EOF rectification requires membranes with asymmetrically shaped pores, and conical pores in a polymeric membrane were used here. We show here that solution flow through the membrane can be achieved by applying a symmetrical sinusoidal voltage waveform across the membrane. This is possible because the alternating current (AC) carried by ions through the pore is rectified, and we previously showed that rectified currents yield EOF rectification. We have investigated the effect of both the magnitude and frequency of the voltage waveform on flow rate through the membrane, and we have measured the maximum operating pressure. Finally, we show that operating in AC mode offers potential advantages relative to conventional DC-mode EOF pumps.
InterPro in 2017—beyond protein family and domain annotations

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.
Ravel: Programming IoT Applications as Distributed Models, Views, and Controllers

The embedded sensor networks are a promising technology to improve our life with home and industrial automation, health monitoring, and sensing and actuation in agriculture. Fitness trackers, thermostats, door locks are just a few examples of Internet of Things that have already become part of our everyday life. Despite advances in sensors, microcontrollers, signal processing, networking and programming languages, developing an Internet of Things application is a laborious task. Many of these complex distributed systems share a 3-tier architecture consisting of embedded nodes, gateways that connect an embedded network to the wider Internet and data services in servers or the cloud. Yet the IoT applications are developed for each tier separately. Consequently, the developer needs to amalgamate these distinct applications together. This paper proposes a novel approach for programming applications across 3-tiers using a distributed extension of the Model-View-Controller architecture. We add new primitive: a space - that contains properties and implementation of a particular tier. Writing applications in this architecture affords numerous advantages: automatic model synchronization, data transport, and energy efficiency.
Catalytic reduction of 4-nitrophenol using biogenic gold and silver nanoparticles derived from Breynia rhamnoides.

A simple, green method is described for the synthesis of Gold (Au) and Silver (Ag) nanoparticles (NPs) from the stem extract of Breynia rhamnoides. Unlike other biological methods for NP synthesis, the uniqueness of our method lies in its fast synthesis rates (~7 min for AuNPs) and the ability to tune the nanoparticle size (and subsequently their catalytic activity) via the extract concentration used in the experiment. The phenolic glycosides and reducing sugars present in the extract are largely responsible for the rapid reduction rates of Au(3+) ions to AuNPs. Efficient reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP) in the presence of AuNPs (or AgNPs) and NaBH(4) was observed and was found to depend upon the nanoparticle size or the stem extract concentration used for synthesis.
Finding Actors and Actions in Movies

We address the problem of learning a joint model of actors and actions in movies using weak supervision provided by scripts. Specifically, we extract actor/action pairs from the script and use them as constraints in a discriminative clustering framework. The corresponding optimization problem is formulated as a quadratic program under linear constraints. People in video are represented by automatically extracted and tracked faces together with corresponding motion features. First, we apply the proposed framework to the task of learning names of characters in the movie and demonstrate significant improvements over previous methods used for this task. Second, we explore the joint actor/action constraint and show its advantage for weakly supervised action learning. We validate our method in the challenging setting of localizing and recognizing characters and their actions in feature length movies Casablanca and American Beauty.
Toward a broader view of social stereotyping.

Stereotyping effects are typically considered to be assimilative in nature: A member of a group stereotyped as having some attribute is judged to have more of that attribute than a member of some comparison group. This article highlights the fact that stereotyping effects can also occur in the direction of contrast--or even null effects-- depending on the nature and form of the outcome being assessed (from the researcher's perspective, the dependent variable of interest). Relying on theory and research from the shifting standards model (M. Biernat, M. Manis, & T. F. Nelson, 1991), this review highlights the different ways in which and the factors that determine how stereotypes influence judgment and behavior toward individual group members.
Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA

Classification is important problem in data mining. Given a data set, classifier generates meaningful description for each class. Decision trees are most effective and widely used classification methods. There are several algorithms for induction of decision trees. These trees are first induced and then prune subtrees with subsequent pruning phase to improve accuracy and prevent overfitting. In this paper, various pruning methods are discussed with their features and also effectiveness of pruning is evaluated. Accuracy is measured for diabetes and glass dataset with various pruning factors. The experiments are shown for this two datasets for measuring accuracy and size of the tree.
Normalization as a canonical neural computation

There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation.
Towards Decoding as Continuous Optimisation in Neural Machine Translation

We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We reformulate decoding, a discrete optimization problem, into a continuous problem, such that optimization can make use of efficient gradient-based techniques. Our powerful decoding framework allows for more accurate decoding for standard neural machine translation models, as well as enabling decoding in intractable models such as intersection of several different NMT models. Our empirical results show that our decoding framework is effective, and can leads to substantial improvements in translations, especially in situations where greedy search and beam search are not feasible. Finally, we show how the technique is highly competitive with, and complementary to, reranking.
Judgment Error in Pie Chart Variations

Pie charts and their variants are prevalent in business settings and many other uses, even if they are not popular with the academic community. In a recent study, we found that contrary to general belief, there is no clear evidence that these charts are read based on the central angle. Instead, area and arc length appear to be at least equally important. In this paper, we build on that study to test several pie chart variations that are popular in information graphics: exploded pie chart, pie with larger slice, elliptical pie, and square pie (in addition to a regular pie chart used as the baseline). We find that even variants that do not distort central angle cause greater error than regular pie charts. Charts that distort the shape show the highest error. Many of our predictions based on the previous study’s results are borne out by this study’s findings.
Pointwise Prediction for Robust, Adaptable Japanese Morphological Analysis

● Create a Japanese morphological analyzer (word segmentation + POS tagging) that is robust and adaptable to new domains ●Approach: Use pointwise prediction, which estimates all tags independently of other tags ●Pointwise prediction: ●Robust: does not rely on dictionaries as much as previous methods ●Adaptable: it can be learned from single annotated words, not full sentences ●Works with active learning: Single words to annotate can be chosen effectively ●Evaluation on Japanese morphological analysis shows improvement over traditional methods 1
Rhetorical Functions of Hashtag Forms Across Social Media Applications

This study analyzes the complex rhetorical practice of hashtag use across social media platforms and emphasizes the implications for UX designers and technical communicators working with social media. Specifically, we document and analyze the ways that users extend the function of hashtagging beyond findability toward meta-communication, effectively co-designing the hashtagging feature and helping social media designers develop new possibilities for hashtags as communicative tools. Qualitatively-collected data from five popular social media applications (Twitter, Facebook, Instagram, Tumblr, and Pinterest) are used to investigate how users implement hashtags in different contexts in order to achieve particular rhetorical purposes. Using a grounded approach, we identify 5 primary categories of "metacommunicative" hashtags and suggest how experience architects might incorporate them into future social media designs.
Considerations for testing and simulation of MIL-STD-1760E/HS1760 avionics interfaces

MIL-STD-1760, a Department of Defense Interface Standard, was developed to reduce the proliferation of interfaces between aircraft and their stores, and instead to promote interoperability between weapons and aircraft platforms. The original version of the MIL-STD-1760 defined a standardized electrical interface and connector that included both digital and analog databuses, a standardized message protocol (MIL-STD-1553), power, and discrete signals. In 2007, the latest version of MIL-STD-1760 was released. MIL-STD-1760E, employed the previously unused High Bandwidth 2 and High Bandwidth 4 pins of the standardized MIL-STD-1760 connector. These pins were utilized to carry a Fibre Channel based high speed digital databus, FC-AE-1553 which is an adaptation of MIL-STD-1553 for Fibre Channel. This paper provides a brief technical overview of the HS1760 databus and related standardized protocols and also provides an overview of the key technical considerations that must be accounted for when designing test and simulation equipment used to test various aircraft systems and stores which employ the MIL-STD-1760E interface.
The Best of Supply Chain Management Review 7 Principles of Supply Chain ManagementThe

years later. Managers increasingly fi nd themselves assigned the role of the rope in a very real tug of war—pulled one way by customers’ mounting demands and the opposite way by the company’s need for growth and profi tability. Many have discovered that they can keep the rope from snapping and, in fact, achieve profi table growth by treating supply chain management as a strategic variable. These savvy managers recognize two important things. First, they think about the supply chain as a whole—all the links involved in managing the fl ow of products, services, and information from their suppliers’ suppliers to their customers’ customers (that is, channel customers, such as distributors and retailers). Second, they pursue tangible outcomes—focused on revenue growth, asset utilization, and cost. Rejecting the traditional view of a company and its component parts as distinct functional entities, these managers realize that the real measure of success is how well activities coordinate across the supply chain to create value for customers, while increasing the profi tability of every link in the chain. Our analysis of initiatives to improve supply chain management by more than 100 manufacturers, distributors, and retailers shows many making great progress, while others fail dismally. The successful initiatives that have contributed to profi table growth share several themes. They are typically broad efforts, combining both strategic and tactical change. They also refl ect a holistic approach, viewing the supply chain from end to end and orchestrating efforts so that the whole improvement achieved—in revenue, costs, and asset utilization—is greater than the sum of its parts. 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 1
Cloning parallel simulations

We present a cloning mechanism that enables the evaluation of multiple simulated futures. Performance of the mechanism is analyzed and evaluated experimentally on a shared memory multiprocessor. A running parallel discrete event simulation is dynamically cloned at decision points to explore different execution paths concurrently. In this way, what-if and alternative scenario analysis can be performed in applications such as gaming or tactical and strategic battle management. A construct called virtual logical processes avoids repeating common computations among clones and improves efficiency. The advantages of cloning are preserved regardless of the number of clones (or execution paths). Our performance results with a commercial air traffic control simulation demonstrate that cloning can significantly reduce the time required to compute multiple alternate futures.