nils-herrmann/clean_scirepeval huggingface dataset:
Building machines that learn and think like people
Venue: Behavioral and Brain Sciences

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end to end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning to learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.
Optimization of Nested Queries using the NF2 Algebra
Venue: SIGMOD Conference

A key promise of SQL is that the optimizer will find the most efficient execution plan, regardless of how the query is formulated. In general, query optimizers of modern database systems are able to keep this promise, with the notable exception of nested queries. While several optimization techniques for nested queries have been proposed, their adoption in practice has been limited. In this paper, we argue that the NF2 (non first normal form) algebra, which was originally designed to process nested tables, is a better approach to nested query optimization as it fulfills two key requirements. First, the NF2 algebra can represent all types of nested queries as well as both existing and novel optimization techniques based on its equivalences. Second, performance benefits can be achieved with little changes to existing transformation based query optimizers as the NF2 algebra is an extension of the relational algebra.
Efficient processing of multiple nested event pattern queries over multi dimensional event streams based on a triaxial hierarchical model
Venue: Artif. Intell. Medicine

For efficient and sophisticated analysis of complex event patterns that appear in streams of big data from health care information systems and support for decision making, a triaxial hierarchical model is proposed in this paper.  Our triaxial hierarchical model is developed by focusing on hierarchies among nested event pattern queries with an event concept hierarchy, thereby allowing us to identify the relationships among the expressions and sub expressions of the queries extensively. We devise a cost based heuristic by means of the triaxial hierarchical model to find an optimised query execution plan in terms of the costs of both the operators and the communications between them. According to the triaxial hierarchical model, we can also calculate how to reuse the results of the common sub expressions in multiple queries. By integrating the optimised query execution plan with the reuse schemes, a multi query optimisation strategy is developed to accomplish efficient processing of multiple nested event pattern queries.  We present empirical studies in which the performance of multi query optimisation strategy was examined under various stream input rates and workloads. Specifically, the workloads of pattern queries can be used for supporting monitoring patients' conditions. On the other hand, experiments with varying input rates of streams can correspond to changes of the numbers of patients that a system should manage, whereas burst input rates can correspond to changes of rushes of patients to be taken care of. The experimental results have shown that, in Workload 1, our proposal can improve about 4 and 2 times throughput comparing with the relative works, respectively; in Workload 2, our proposal can improve about 3 and 2 times throughput comparing with the relative works, respectively; in Workload 3, our proposal can improve about 6 times throughput comparing with the relative work.  The experimental results demonstrated that our proposal was able to process complex queries efficiently which can support health information systems and further decision making.
Incremental Stream Processing of Nested Relational Queries
Venue: DEXA

Current work on stream processing is focused on approximation techniques that calculate approximate answers to simple queries by focusing on a fixed or sliding window that contains the most recent tuples from an input stream and by using condensed synopses to summarize the state. It is widely believed that without using approximation techniques, most interesting queries would be blocking i.e. they would have to wait for the end of stream to release their results or unbounded i.e. their memory requirements would grow proportionally to the stream size, which may be infinite. The goal of this paper is to convert nested relational queries to incremental stream processing programs automatically. In contrast to most current stream processing systems that calculate approximate answers, our system derives incremental programs that return accurate results. This is accomplished by retaining a state during the query evaluation lifetime and by using incremental evaluation techniques to return an accurate snapshot answer at each time interval that depends on the current state and the data in the current fixed window. Our methods can handle most forms of declarative queries on nested data collections, including arbitrarily nested queries, group by with aggregation, and equi joins. We report on a prototype system implementation and we show some preliminary results on evaluating queries on a small computer cluster running Spark.
High Intensity Interval Training Reduces the Levels of Serum Inflammatory Cytokine on Women with Metabolic Syndrome.
Venue: Experimental and clinical endocrinology diabetes official journal, German Society of Endocrinology [and] German Diabetes Association

This study investigate the effects of a high intensity interval training (HIIT) and 2 weeks of detraining in functional and body composition parameters, lipoproteins, glucose metabolismand inflammation markers in postmenopausal women with metabolic syndrome (MS) Design: 17 untrained women with MS underwent a HIIT program for 12 weeks.  The training was performed in treadmills, 3 days per week, with intensity ranging from 70 90% of the maximum heart rate (HRmax) and 2 weeks untrained (inactive) Functional and body composition parameters were evaluated before and after the training, while maximal oxygen uptake, lipoprotein and inflammation markers were analyzed before, after training and also in detraining.  The HITT program resulted in changesparameters as glucose, HbA1cand NOx after training. In addition, a reduction in pro inflammatory interleukins and an increase in IL 10 after the HIIT program were found. However, an increase in plasma levels of lipoprotein was found and body composition parameters remain unaltered.Besides, only 2 weeks of detraining are able to revert the effects on inflammatory parameters afforded by the HIIT program.  The HIIT program used here positively affected inflammatory profile and other parameters, as glucose, HbA1cand NOx, on postmenopausal women with MS. Moreover, 2 weeks of detraining can reverse the beneficial effects of HIIT program. Our results point out the necessity to aply acontinuous HITT program, in order maintain the benefits detected, to post menopausal women with MS.
Linking Data, Services and Human Know How
Venue: ESWC

An increasing number of everyday tasks involve a mixture of human actions and machine computation. This paper presents the first framework that allows non programmer users to create and execute workflows where each task can be completed by a human or a machine. In this framework, humans and machines interact through a shared knowledge base which is both human and machine understandable. This knowledge base is based on the prohow Linked Data vocabulary that can represent human instructions and link them to machine functionalities. Our hypothesis is that non programmer users can describe how to achieve certain tasks at a level of abstraction which is both human and machine understandable. This paper presents the prohow vocabulary and describes its usage within the proposed framework. We substantiate our claim with a concrete implementation of our framework and by experimental evidence.
Deep Learning over Multi field Categorical Data A Case Study on User Response Prediction
Venue: ECIR

Predicting user responses, such as click through rate and conversion rate, are critical in many web applications including web search, personalised recommendation, and online advertising. Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi field and are mostly discrete and categorical while their dependencies are little known. Major user response prediction models have to either limit themselves to linear models or require manually building up high order combination features. The former loses the ability of exploring feature interactions, while the latter results in a heavy computation in the large feature space. To tackle the issue, we propose two novel models using deep neural networks (DNNs) to automatically learn effective patterns from categorical feature interactions and make predictions of users' ad clicks. To get our DNNs efficiently work, we propose to leverage three feature transformation methods, i.e. factorisation machines (FMs) restricted Boltzmann machines (RBMs) and denoising auto encoders (DAEs) This paper presents the structure of our models and their efficient training algorithms. The large scale experiments with real world data demonstrate that our methods work better than major state of the art models.
Effective Multimodality Fusion Framework for Cross Media Topic Detection
Venue: IEEE Transactions on Circuits and Systems for Video Technology

Due to the prevalence of We Media, information is quickly published and received in various forms anywhere and anytime through the Internet. The rich cross media information carried by the multimodal data in multiple media has a wide audience, deeply reflects the social realities, and brings about much greater social impact than any single media information. Therefore, automatically detecting topics from cross media is of great benefit for the organizations (i.e. advertising agencies and governments) that care about the social opinions. However, cross media topic detection is challenging from the following aspects: 1) the multimodal data from different media often involve distinct characteristics and 2) topics are presented in an arbitrary manner among the noisy web data. In this paper, we propose a multimodality fusion framework and a topic recovery (TR) approach to effectively detect topics from cross media data. The multimodality fusion framework flexibly incorporates the heterogeneous multimodal data into a multimodality graph, which takes full advantage from the rich cross media information to effectively detect topic candidates (T.C. The TR approach solidly improves the entirety and purity of detected topics by: 1) merging the T.C. that are highly relevant themes of the same real topic and 2) filtering out the less relevant noise data in the merged T.C. Extensive experiments on both single media and cross media data sets demonstrate the promising flexibility and effectiveness of our method in detecting topics from cross media.
Long Short Range Context Neural Networks for Language Modeling
Venue: EMNLP

The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learning of short range dependencies, which generally model the syntactic properties of a language and/or long range dependencies, which are semantic in nature. We propose in this paper a new multi span architecture, which separately models the short and long context information while it dynamically merges them to perform the language modeling task. This is done through a novel recurrent Long Short Range Context (LSRC) network, which explicitly models the local (short) and global (long) context using two separate hidden states that evolve in time. This new architecture is an adaptation of the Long Short Term Memory network (LSTM) to take into account the linguistic properties. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state of the art language modeling techniques.
Recurrent Memory Networks for Language Modeling
Venue: NAACL

Recurrent Neural Networks (RNN) have obtained excellent result in many natural language processing (NLP) tasks. However, understanding and interpreting the source of this success remains a challenge. In this paper, we propose Recurrent Memory Network (RMN) a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data. We demonstrate the power of RMN on language modeling and sentence completion tasks. On language modeling, RMN outperforms Long Short Term Memory (LSTM) network on three large German, Italian, and English dataset. Additionally we perform in depth analysis of various linguistic dimensions that RMN captures. On Sentence Completion Challenge, for which it is essential to capture sentence coherence, our RMN obtains 69.2% accuracy, surpassing the previous state of the art by a large margin.
Adaptive Optimal Control of MapReduce Performance, Availability and Costs

MapReduce is a popular programming model for distributed data processing and Big Data applications running on clouds. Extensive research has been conducted either to improve the dependability or to increase performance of MapReduce, ranging from adaptive and on demand fault tolerance solutions, adaptive task scheduling techniques to optimized job execution mechanisms. This paper investigates an optimization based solution to control MapReduce systems in order to provide guarantees in terms of both performance and availability while reducing utilization costs. We follow a control theoretical approach for MapReduce cluster scaling and admission control. Moreover, we aim to be robust to changes in MapRe duce and in it's environment by adapting the controller online to those changes. This paper highlights the major challenges of combining system adaptation and optimal control to take the best of both approaches. CCS Concepts Networks Cloud computing; Software and its engineering Software configuration management and version control systems; Computer systems organization Dependable and fault tolerant systems and networks;
Adaptive model predictive control of autonomic distributed parallel computations with variable horizons and switching costs
Venue: Concurr. Comput. Pract. Exp.

Autonomic computing is a paradigm for building systems capable of adapting their operation when external changes occur, such as workload variations, load surges and changes in the resource availability. The optimal configuration in terms of the number of computing resources assigned to each component must be automatically adjusted to the new environmental conditions. To accomplish the execution goals with the desired Quality of Service, decision making strategies should be in charge of selecting the best reconfigurations by taking into account metrics like performance, efficiency (avoiding wasting resources) number and frequency of reconfigurations, and their amplitude (performing minimal modifications of the current configuration) This paper presents a decision making strategy that merges the potential of Model Predictive Control with a cooperative optimization framework. After a description of our approach, we investigate the effect of different switching costs to model the resource allocation problem. We use a control method in which our proactive decision making strategy (designed to use future prediction horizons) is made adaptive itself by dynamically changing the horizon length on the basis of the prediction errors. Simulations have been used to exemplify our approach and to discuss the effectiveness of the variable horizon strategy in achieving the best trade offs between reconfiguration metrics.
Functional Behavioral Assessments and Behavior Support Plans for Work Based Learning

Work experiences are linked to positive post school outcomes for youth and young adults with disabilities. Unfortunately, students who struggle to manage conflict and challenges in work settings have a difficult time maintaining employment. Though ecological assessments are used to create supported work plans surrounding socially inappropriate behavior, some students may benefit from a more targeted function based approach to addressing these behaviors in work settings. This article provides information about how functional behavioral assessments and behavior support plans, tools routinely used in schools, can be adapted for use in employment settings.
A high directional LED system to form holographic light patterns

Holograms are recordings of light formed by the interference fringe between object and reference light beams. They enable the display of fully 3D images, and have applications in 3D displays, computer memory, and microscopes. As part of our recent research into holographic head mounted 3D display and quantitative phase microscopy,1 3 we have produced a high directional LED lighting device known as HOLOLIGHT.4, 5 Currently, highdirectional lighting is achieved chiefly using laser technology. However, our approach offers a safer method of achieving highvisibility displays. Our device emits a high visibility light that appears parallel. We achieve this through the use of a combined high brightness LED and a lens, which are both enclosed in the same packaging. Light emitted by the LED is expanded and imaged by an optical system such that the expanded light behaves like parallel light  It is therefore possible to achieve the light intensity distribution of rectangular pseudo parallel light. Our LED system manipulates the expanded image with optics to enable the formation of light patterns as squares, circles, lines, and arches  These patterns, whose shape is determined by the shape of the LED emitter, can be used in inspection, on stage, and in architecture, road safety, art, and entertainment. HOLOLIGHT gives out light with high directivity, and enables the formation of arbitrary light patterns at a distance, in a way analogous to pixel art. The size of the image is proportional to the distance of the lens from the projection plane, and the divergence angle during imaging is narrow 1) Using such a system, it is possible to produce an image of 20cm2 at a distance of 10m from the projector. We call the resultant image a 'multipoint' light pattern. We demonstrated a multipoint lighting event with 300 devices at Mount Ookusa at Hamamatsu Kanzanji in 2009. On that occasion, the irradiation distance was 500m  Figure 1. The image expanding optical system used in HOLOLIGHT enables high directional lighting for holographic display.
Placental Abruption Revealed by Hemoperitoneum: A Case Report
Venue: AJP reports

Hemoperitoneum is a life threatening surgical emergency. Diagnosis of the cause is often difficult, in particular, during pregnancy when it may be either obstetric or nonobstetric. Case We report the case of a hemoperitoneum caused by the backflow of blood through a uterine tube, due to placental abruption.  Hemoperitoneum in pregnant women with no other signs can reveal placental abruption. The difficulty in identifying the cause may delay appropriate management.
A Glossary Of Literary Terms

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Temporal Attention Model for Neural Machine Translation
Venue: ArXiv

Attention based Neural Machine Translation (NMT) models suffer from attention deficiency issues as has been observed in recent research. We propose a novel mechanism to address some of these limitations and improve the NMT attention. Specifically, our approach memorizes the alignments temporally (within each sentence) and modulates the attention with the accumulated temporal memory, as the decoder generates the candidate translation. We compare our approach against the baseline NMT model and two other related approaches that address this issue either explicitly or implicitly. Large scale experiments on two language pairs show that our approach achieves better and robust gains over the baseline and related NMT approaches. Our model further outperforms strong SMT baselines in some settings even without using ensembles.
Pre Translation for Neural Machine Translation
Venue: COLING

Recently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical machine translation (SMT) based systems, in some cases, the NMT system produces translations that have a completely different meaning. This is especially the case when rare words occur. When using statistical machine translation, it has already been shown that significant gains can be achieved by simplifying the input in a preprocessing step. A commonly used example is the pre reordering approach. In this work, we used phrase based machine translation to pre translate the input into the target language. Then a neural machine translation system generates the final hypothesis using the pre translation. Thereby, we use either only the output of the phrase based machine translation (PBMT) system or a combination of the PBMT output and the source sentence. We evaluate the technique on the English to German translation task. Using this approach we are able to outperform the PBMT system as well as the baseline neural MT system by up to 2 BLEU points. We analyzed the influence of the quality of the initial system on the final result.