nils-herrmann/clean_scirepeval huggingface dataset:
Logic-Based Approach to Semantic Query Optimization

The purpose of semantic query optimization is to use semantic knowledge (e.g., integrity constraints) for transforming a query into a form that may be answered more efficiently than the original version. In several previous papers we described and proved the correctness of a method for semantic query optimization in deductive databases couched in first-order logic. This paper consolidates the major results of these papers emphasizing the techniques and their applicability for optimizing relational queries. Additionally, we show how this method subsumes and generalizes earlier work on semantic query optimization. We also indicate how semantic query optimization techniques can be extended to databases that support recursion and integrity constraints that contain disjunction, negation, and recursion.
Hybrid Hexagonal/Classical Tiling for GPUs

Time-tiling is necessary for the efficient execution of iterative stencil computations. Classical hyper-rectangular tiles cannot be used due to the combination of backward and forward dependences along space dimensions. Existing techniques trade temporal data reuse for inefficiencies in other areas, such as load imbalance, redundant computations, or increased control flow overhead, therefore making it challenging for use with GPUs.
 We propose a time-tiling method for iterative stencil computations on GPUs. Our method does not involve redundant computations. It favors coalesced global-memory accesses, data reuse in local/shared-memory or cache, avoidance of thread divergence, and concurrency, combining hexagonal tile shapes along the time and one spatial dimension with classical tiling along the other spatial dimensions. Hexagonal tiles expose multi-level parallelism as well as data reuse. Experimental results demonstrate significant performance improvements over existing stencil compilers.
Parallel Computation of Skyline and Reverse Skyline Queries Using MapReduce

The skyline operator and its variants such as dynamic skyline and reverse skyline operators have attracted considerable attention recently due to their broad applications. However, computations of such operators are challenging today since there is an increasing trend of applications to deal with big data. For such data-intensive applications, the MapReduce framework has been widely used recently. In this paper, we propose efficient parallel algorithms for processing the skyline and its variants using MapReduce. We first build histograms to effectively prune out non-skyline (non-reverse skyline) points in advance. We next partition data based on the regions divided by the histograms and compute candidate (reverse) skyline points for each region independently using MapReduce. Finally, we check whether each candidate point is actually a (reverse) skyline point in every region independently. Our performance study confirms the effectiveness and scalability of the proposed algorithms.
Short Synthetic Terminators for Assembly of Transcription Units in Vitro and Stable Chromosomal Integration in Yeast S. cerevisiae.

Assembly of synthetic genetic circuits is central to synthetic biology. Yeast S. cerevisiae, in particular, has proven to be an ideal chassis for synthetic genome assemblies by exploiting its efficient homologous recombination. However, this property of efficient homologous recombination poses a problem for multigene assemblies in yeast, since repeated usage of standard parts, such as transcriptional terminators, can lead to rearrangements of the repeats in assembled DNA constructs in vivo. To address this issue in developing a library of orthogonal genetic components for yeast, we designed a set of short synthetic terminators based on a consensus sequence with random linkers to avoid repetitive sequences. We constructed a series of expression vectors with these synthetic terminators for efficient assembly of synthetic genes using Gateway recombination reactions. We also constructed two BAC (bacterial artificial chromosome) vectors for assembling multiple transcription units with the synthetic terminators in vitro and their integration in the yeast genome. The tandem array of synthetic genes integrated in the genome by this method is highly stable because there are few homologous segments in the synthetic constructs. Using this system of assembly and genomic integration of transcription units, we tested the synthetic terminators and their influence on the proximal transcription units. Although all the synthetic terminators have the common consensus with the identical length, they showed different activities and impacts on the neighboring transcription units.
Image reconstruction from projections : the fundamentals of computerized tomography

image reconstruction from projections the fundamentals of computerized tomography computer ebook, image reconstruction from projections the fundamentals of computerized tomography computer pdf, image reconstruction from projections the fundamentals of computerized tomography computer doc and image reconstruction from projections the fundamentals of computerized tomography computer epub for image reconstruction from projections the fundamentals of computerized tomography computer read online or image reconstruction from projections the fundamentals of computerized tomography computer download if want read offline.
Nivolumab Monotherapy for First-Line Treatment of Advanced Non-Small-Cell Lung Cancer.

Nivolumab, a programmed death-1 (PD-1) immune checkpoint inhibitor antibody, has demonstrated improved survival over docetaxel in previously treated advanced non-small-cell lung cancer (NSCLC). First-line monotherapy with nivolumab for advanced NSCLC was evaluated in the phase I, multicohort, Checkmate 012 trial.



Fifty-two patients received nivolumab 3 mg/kg intravenously every 2 weeks until progression or unacceptable toxicity; postprogression treatment was permitted per protocol. The primary objective was to assess safety; secondary objectives included objective response rate (ORR) and 24-week progression-free survival (PFS) rate; overall survival (OS) was an exploratory end point.



Any-grade treatment-related adverse events (AEs) occurred in 71% of patients, most commonly: fatigue (29%), rash (19%), nausea (14%), diarrhea (12%), pruritus (12%), and arthralgia (10%). Ten patients (19%) reported grade 3 to 4 treatment-related AEs; grade 3 rash was the only grade 3 to 4 event occurring in more than one patient (n = 2; 4%). Six patients (12%) discontinued because of a treatment-related AE. The confirmed ORR was 23% (12 of 52), including four ongoing complete responses. Nine of 12 responses (75%) occurred by first tumor assessment (week 11); eight (67%) were ongoing (range, 5.3+ to 25.8+ months) at the time of data lock. ORR was 28% (nine of 32) in patients with any degree of tumor PD-ligand 1 expression and 14% (two of 14) in patients with no PD-ligand 1 expression. Median PFS was 3.6 months, and the 24-week PFS rate was 41% (95% CI, 27 to 54). Median OS was 19.4 months, and the 1-year and 18-month OS rates were 73% (95% CI, 59 to 83) and 57% (95% CI, 42 to 70), respectively.



First-line nivolumab monotherapy demonstrated a tolerable safety profile and durable responses in first-line advanced NSCLC.
Enhanced Maintenance and Explanation of Expert Systems Through Explicit Models of Their Development

Principled development techniques could greatly enhance the understandability of expert systems for both users and system developers. Current systems have limited explanatory capabilities and present maintenance problems because of a failure to explicitly represent the knowledge and reasoning that went into their design. This paper describes a paradigm for constructing expert systems which attempts to identify that tacit knowledge, provide means for capturing it in the knowledge bases of expert systems, and, apply it towards more perspicuous machine-generated explanations and more consistent and maintainable system organization.
Comparison of data acquisition technique using logical extraction method on Unrooted Android Device

Acquisition data method on Mobile Device Forensics's activity divided into two method: physical and logical extraction where each of these method has their own advantages and disadvantages. On Unrooted Android Device that have limited privilege, Logical Extraction method is used to acquire data on the device. Technique on the Logical Extraction method to acquire the data include AFLogical, SDcard Imaging, Android Backup Analysis, and proprietary applications for mobile device forensics activity such as Oxygen-Forensics. These four technique has its own acquisition technique with different capability to capture the data on the device and different activity while the acquisition data is performed.  Because of that problem, it's necessary to analyze several techniques on Logical Extraction method by comparing quantity of data that can be acquired and how intrusive activities performed on the device to be able to determine better acquisition technique. Best technique is technique that can acquire enough data that needed as a digital evidence without damaging the integrity of the evidence itself. On the capability to capture the data, we found that Android Backup analysis is the best technique to capture application artifact, SDCard Imaging is the best technique to captured external memory data, and Commercial provider using Oxygen-forensics is the best technique to capture other system files data with limited privilege. Finally with proving the communication log on Steam apps, after comparing all technique in Logical Extraction in safetyness (rooting or install application activities) and data captured aspect, we found that using Android Backup Analysis technique is the best technique for unrooted without doing rooting activity or installing application because of Android Backup Analysis itself is analyze the backup that created from the phone from android debug bridge which the backup store some application data on the phone. We use Steam apps case study as our proof of concept.
Fast and robust multiframe super resolution

Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate approach using L/sub 1/ norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.
Ten Benefits of Testing and Their Applications to Educational Practice

Testing in school is usually done for purposes of assessment, to assign students grades (from tests in classrooms) or rank them in terms of abilities (in standardized tests). Yet tests can serve other purposes in educational settings that greatly improve performance; this chapter reviews 10 other benefits of testing. Retrieval practice occurring during tests can greatly enhance retention of the retrieved information (relative to no testing or even to restudying). Furthermore, besides its durability, such repeated retrieval produces knowledge that can be retrieved flexibly and transferred to other situations. On open-ended assessments (such as essay tests), retrieval practice required by tests can help students organize information and form a coherent knowledge base. Retrieval of some information on a test can also lead to easier retrieval of related information, at least on PsychologyofLearningandMotivation, Volume 55  DOI 10.1016/B978-0-12-387691-1.00001-6 All rights reserved.
Large Scale Machine Learning

Cette thèse aborde de façon générale les algorithmes d'apprentissage, avec un intérêt tout particulier pour les grandes bases de données. Après avoir for-mulé leprobì eme de l'apprentissage demanì ere mathématique, nous présentons plusieurs algorithmes d'apprentissage importants, en particulier les Multi Layer Perceptrons, les Mixture d'Experts ainsi que les Support Vector Machines. Nous considérons ensuite une méthode d'entraˆınement pour les Support Vector Machines , adaptée aux ensembles de données de tailles raisonnables. Cepen-dant, l'entraˆınement d'un tel modèle reste irréalisable sur de très grande bases de données. Inspirés par la stratégie " diviser pour régner " , nous proposons alors un modèle de la famille des Mixture d'Experts, permettant de séparer le probì eme d' apprentissage en sous-probì emes plus simples , tout en gardant de bonnes performances en généralisation. Malgré de très bonnes performances en pratique , cet algorithme n ' en reste pas moins difficilè a utiliser , ` a cause de son nombre important d ' hyper-paramètres. Pour cette raison , nous préférons nous intéresser ensuitè a l ' amélioration de l ' entraˆınement des Multi Layer Percep-trons , bien plus facilesà utiliser , et plus adaptés aux grandes bases de données que les Support Vector Machines. Enfin , nous montrons que l ' idée de la marge qui fait la force des Support Vector Machines peutêtre appliquéè a une cer-taine classe de Multi Layer Perceptrons , ce qui nous m ` enè a un algorithme très rapide et ayant de très bonnes performances en généralisation.  This thesis aims to address machine learning in general , with a particular focus on large models and large databases. After introducing the learning problem in a formal way , we first review several important machine learning algorithms , particularly Multi Layer Perceptrons , Mixture of Experts and Support Vector Machines. We then present a training method for Support Vector Machines , adapted to reasonably large datasets. However the training of such a model is still intractable on very large databases. We thus propose a divide and conquer approach based on a kind of Mixture of Experts in order to break up the training problem into small pieces , while keeping good generalization performance. This mixture model can be applied to any kind of existing machine learning algorithm. Even though it performs well in practice the major drawback of this algorithm is the number of hyper-parameters to tune , which makes it …
Whats in a Country Name - Twitter Hashtag Analysis of #singapore

Twitter as a micro-blogging platform rose to instant fame mainly due to its minimalist features that allow seamless communication between users. As the conversations grew thick and faster, a placeholder feature called as Hashtags became important as it captured the themes behind the tweets. Prior studies have investigated the conversation dynamics, interplay with other media platforms and communication patterns between users for specific event-based hashtags such as the #Occupy movement. Commonplace hashtags which are used on a daily basis have been largely ignored due to their seemingly innocuous presence in tweets and also due to the lack of connection with real-world events. However, it can be postulated that utility of these hashtags is the main reason behind their continued usage. This study is aimed at understanding the rationale behind the usage of a particular type of commonplace hashtags:-location hashtags such as country and city name hashtags. Tweets with the hashtag #singapore were extracted for a week’s duration. Manual and automatic tweet classification was performed along with social network analysis, to identify the underlying themes. Seven themes were identified. Findings indicate that the hashtag is prominent in tweets about local events, local news, users’ current location and landmark related information sharing. Users who share content from social media sites such as Instagram make use of the hashtag in a more prominent way when compared to users who post textual content. News agencies, commercial bodies and celebrities make use of the hashtag more than common individuals. Overall, the results show the non-conversational nature of the hashtag. The findings are to be validated with other country names and crossvalidated with hashtag data from other social media platforms.
End-to-End Multi-Task Learning with Attention

In this paper, we propose a novel multi-task learning architecture, which incorporates recent advances in attention mechanisms. Our approach, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with task-specific soft-attention modules, which are trainable in an end-to-end manner. These attention modules allow for learning of task-specific features from the global pool, whilst simultaneously allowing for features to be shared across different tasks. The architecture can be built upon any feed-forward neural network, is simple to implement, and is parameter efficient. Experiments on the CityScapes dataset show that our method outperforms several baselines in both single-task and multi-task learning, and is also more robust to the various weighting schemes in the multitask loss function. We further explore the effectiveness of our method through experiments over a range of task complexities, and show how our method scales well with task complexity compared to baselines.
The use of Multi-Attribute Trade-Off Analysis in Strategic Planning For an Electric Distribution utility: An Analysis of Abu Dhabi Distribution Company

Multi-Attribute Trade-Off Analysis (MATA) provides decision-makers with an analytical tool to identify Pareto Superior options for solving a problem with conflicting objectives or attributes. This technique is ideally suited to electric distribution systems, where decision-makers must choose investments that will ensure reliable service at reasonable cost. This paper describes the application of MATA to an electric distribution system facing dramatic growth, the Abu Dhabi Distribution Company (ADDC) in the United Arab Emirates. ADDC has a range of distribution system design options from which to choose in order to meet this growth. The distribution system design options have different levels of service quality (i.e., reliability) and service cost. Management can use MATA to calculate, summarize and compare the service quality and service cost attributes of the various design options. The Pareto frontier diagrams present management with clear, simple pictures of the trade-offs between service cost and service quality.
The ethics of nurse poaching from the developing world.

Recruiting nurses from other countries is a long-standing practice. In recent years many countries in the developed world have more frequently recruited nurses from the developing world, causing an imbalance in the health services in often already impoverished countries. Despite guidelines and promises by developed countries that the practice should cease, it has largely failed to do so. A consortium of authors from countries that have experienced significant nurse poaching consider the ethical aspects behind this continuing practice.
Deep Learning of Turbulent Scalar Mixing

Based on recent developments in physics-informed deep learning and deep hidden physics models, we put forth a framework for discovering turbulence models from scattered and potentially noisy spatio-temporal measurements of the probability density function (PDF). The models are for the conditional expected diffusion and the conditional expected dissipation of a Fickian scalar described by its transported single-point PDF equation. The discovered model are appraised against exact solution derived by the amplitude mapping closure ( model of binary scalar mixing in homogeneous turbulence.
Phenological variation within and among populations of Plathymenia reticulata in Brazilian Cerrado, the Atlantic Forest and transitional sites.

Plathymenia reticulata (Leguminosae) is a Brazilian tree that occurs in two biomes: Cerrado, a woody savanna vegetation, and the Atlantic Forest, a tropical forest. In this study, phenological patterns and their variability within and among populations located in these biomes and in transitional zones between them were assessed.



During a 15-month period, individuals from two populations in Cerrado, two in the Atlantic Forest, and six in transitional zones (three in a cerrado-like environment and three in forest fragments) were evaluated in Minas Gerais State, Brazil. The individuals were evaluated monthly according to the proportion of the canopy in each vegetative phenophase (leaf fall, leaf flush and mature leaves) and each reproductive phenophase (floral buds, flowers, immature fruits and mature fruit/seed dispersal). In order to assess the phenological variability within and among populations, habitats and biomes, the Shannon-Wiener diversity index, the Morisita-Horn similarity index and genetic population approach of partitioning diversity were used.



Populations of P. reticulata, in general, showed similar phenology; the main differences were related to leaf fall, a process that starts months earlier in the Cerrado than in transitional sites, and even later in forest areas. Considerable synchrony was observed for reproductive phenology among populations and between biomes. Most phenological diversity was due to differences among individuals within populations.



In spite of environmental differences, P. reticulata from the Atlantic Forest and Cerrado showed similar phenological behavior with only about 10% of the total diversity being attributed to differences between biomes.
SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.
Intrinsic motivations and open-ended development in animals, humans, and robots: an overview

1 Laboratory of Computational Embodied Neuroscience, Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy 2 Department of Psychology, University of Sheffield, Sheffield, UK 3 Department of Clinical and Social Sciences in Psychology, University of Rochester, River, New York, USA 4 Department of Computer Science, University of Massachusetts Amherst, Massachusetts, USA *Correspondence: gianluca.baldassarre@istc.cnr.it