nils-herrmann/clean_scirepeval huggingface dataset:
Toward Sustainable Insights, or Why Polygamy is Bad for You

Have you ever been in a sauna? If yes, according to our recent survey conducted on Amazon Mechanical Turk, people who go to saunas are more likely to know that Mike Stonebraker is not a character in “The Simpsons”. While this result clearly makes no sense, recently proposed tools to automatically suggest visualizations, correlations, or perform visual data exploration, significantly increase the chance that a user makes a false discovery like this one. In this paper, we first show how current tools mislead users to consider random fluctuations as significant discoveries. We then describe our vision and early results for QUDE, a new system for automatically controlling the various risk factors during the data exploration process.
Genesis: cluster analysis of microarray data

A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms.
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models

We investigate the integration of a planning mechanism into sequence-to-sequence models using attention. We develop a model which can plan ahead in the future when it computes its alignments between input and output sequences, constructing a matrix of proposed future alignments and a commitment vector that governs whether to follow or recompute the plan. This mechanism is inspired by the recently proposed strategic attentive reader and writer (STRAW) model for Reinforcement Learning. Our proposed model is end-to-end trainable using primarily differentiable operations. We show that it outperforms a strong baseline on character-level translation tasks from WMT’15, the algorithmic task of finding Eulerian circuits of graphs, and question generation from the text. Our analysis demonstrates that the model computes qualitatively intuitive alignments, converges faster than the baselines, and achieves superior performance with fewer parameters.
Parasol and GreenSwitch: managing datacenters powered by renewable energy

Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing nearby plant. Besides reducing carbon footprints, renewable energy can potentially reduce energy costs, reduce peak power costs, or both. However, certain renewable fuels are intermittent, which requires approaches for tackling the energy supply variability. One approach is to use batteries and/or the electrical grid as a backup for the renewable energy. It may also be possible to adapt the workload to match the renewable energy supply. For highest benefits, green datacenter operators must intelligently manage their workloads and the sources of energy at their disposal.
 In this paper, we first discuss the tradeoffs involved in building green datacenters today and in the future. Second, we present Parasol, a prototype green datacenter that we have built as a research platform. Parasol comprises a small container, a set of solar panels, a battery bank, and a grid-tie. Third, we describe GreenSwitch, our model-based approach for dynamically scheduling the workload and selecting the source of energy to use. Our real experiments with Parasol, GreenSwitch, and MapReduce workloads demonstrate that intelligent workload and energy source management can produce significant cost reductions. Our results also isolate the cost implications of peak power management, storing energy on the grid, and the ability to delay the MapReduce jobs. Finally, our results demonstrate that careful workload and energy source management can minimize the negative impact of electrical grid outages.
Getting the Message?: A Study of Explanation Interfaces for Microblog Data Analysis

In many of today's online applications that facilitate data exploration, results from information filters such as recommender systems are displayed alongside traditional search tools. However, the effect of prediction algorithms on users who are performing open-ended data exploration tasks through a search interface is not well understood. This paper describes a study of three interface variations of a tool for analyzing commuter traffic anomalies in the San Francisco Bay Area. The system supports novel interaction between a prediction algorithm and a human analyst, and is designed to explore the boundaries, limitations and synergies of both. The degree of explanation of underlying data and algorithmic process was varied experimentally across each interface. The experiment (N=197) was performed to assess the impact of algorithm transparency/explanation on data analysis tasks in terms of search success, general insight into the underlying data set and user experience. Results show that 1) presence of recommendations in the user interface produced a significant improvement in recall of anomalies, 2) participants were able to detect anomalies in the data that were missed by the algorithm, 3) participants who used the prediction algorithm performed significantly better when estimating quantities in the data, and 4) participants in the most explanatory condition were the least biased by the algorithm's predictions when estimating quantities.
Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms
Chinese Song Iambics Generation with Neural Attention-based Model

Learning and generating Chinese poems is a charming yet challenging task. Traditional approaches involve various language modeling and machine translation techniques, however, they perform not as well when generating poems with complex pattern constraints, for example Song iambics, a famous type of poems that involve variable-length sentences and strict rhythmic patterns. This paper applies the attention-based sequence-tosequence model to generate Chinese Song iambics. Specifically, we encode the cue sentences by a bi-directional Long-Short Term Memory (LSTM) model and then predict the entire iambic with the information provided by the encoder, in the form of an attention-based LSTM that can regularize the generation process by the fine structure of the input cues. Several techniques are investigated to improve the model, including global context integration, hybrid style training, character vector initialization and adaptation. Both the automatic and subjective evaluation results show that our model indeed can learn the complex structural and rhythmic patterns of Song iambics, and the generation is rather successful.
A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission.

A short battery of physical performance tests was used to assess lower extremity function in more than 5,000 persons age 71 years and older in three communities.



Balance, gait, strength, and endurance were evaluated by examining ability to stand with the feet together in the side-by-side, semi-tandem, and tandem positions, time to walk 8 feet, and time to rise from a chair and return to the seated position 5 times.



A wide distribution of performance was observed for each test. Each test and a summary performance scale, created by summing categorical rankings of performance on each test, were strongly associated with self-report of disability. Both self-report items and performance tests were independent predictors of short-term mortality and nursing home admission in multivariate analyses. However, evidence is presented that the performance tests provide information not available from self-report items. Of particular importance is the finding that in those at the high end of the functional spectrum, who reported almost no disability, the performance test scores distinguished a gradient of risk for mortality and nursing home admission. Additionally, within subgroups with identical self-report profiles, there were systematic differences in physical performance related to age and sex.



This study provides evidence that performance measures can validly characterize older persons across a broad spectrum of lower extremity function. Performance and self-report measures may complement each other in providing useful information about functional status.
A Minimax Approach to Supervised Learning

Given a task of predicting Y from X , a loss function L, and a set of probability distributions Γ on (X,Y ), what is the optimal decision rule minimizing the worstcase expected loss over Γ? In this paper, we address this question by introducing a generalization of the maximum entropy principle. Applying this principle to sets of distributions with marginal on X constrained to be the empirical marginal, we provide a minimax interpretation of the maximum likelihood problem over generalized linear models, which connects the minimax problem for each loss function to a generalized linear model. While in some cases such as quadratic and logarithmic loss functions we revisit well-known linear and logistic regression models, our approach reveals novel models for other loss functions. In particular, for the 0-1 loss we derive a classification approach which we call the minimax SVM. The minimax SVM minimizes the worst-case expected 0-1 loss over the proposed Γ by solving a tractable optimization problem. Moreover, applying the minimax approach to Brier loss function we derive a new classification model called the minimax Brier. The maximum likelihood problem for this model uses the Huber penalty function. We perform several numerical experiments to show the power of the minimax SVM and the minimax Brier.
Performance Comparison and Evaluation of Web Development Technologies in PHP, Python, and Node.js

Large scale, high concurrency, and vast amount of data are important trends for the new generation of website. Node.js becomes popular and successful to build data-intensive web applications. To study and compare the performance of Node.js, Python-Web and PHP, we used benchmark tests and scenario tests. The experimental results yield some valuable performance data, showing that PHP and Python-Web handle much less requests than that of Node.js in a certain time. In conclusion, our results clearly demonstrate that Node.js is quite lightweight and efficient, which is an idea fit for I/O intensive websites among the three, while PHP is only suitable for small and middle scale applications, and Python-Web is developer friendly and good for large web architectures. To the best of our knowledge, this is the first paper to evaluate these Web programming technologies with both objective systematic tests (benchmark) and realistic user behavior tests (scenario), especially taking Node.js as the main topic to discuss.
Modeling of an electric vehicle charging station for fast DC charging

The proposed model of an electric vehicle charging station is suitable for the fast DC charging of multiple electric vehicles. The station consists of a single grid-connected inverter with a DC bus where the electric vehicles are connected. The control of the individual electric vehicle charging processes is decentralized, while a separate central control deals with the power transfer from the AC grid to the DC bus. The electric power exchange does not rely on communication links between the station and vehicles, and a smooth transition to vehicle-to-grid mode is also possible. Design guidelines and modeling are explained in an educational way to support implementation in Matlab/Simulink. Simulations are performed in Matlab/Simulink to illustrate the behavior of the station. The results show the feasibility of the model proposed and the capability of the control system for fast DC charging and also vehicle-to-grid.
Anatomy of the long tail: ordinary people with extraordinary tastes

The success of "infinite-inventory" retailers such as Amazon.com and Netflix has been ascribed to a "long tail" phenomenon. To wit, while the majority of their inventory is not in high demand, in aggregate these "worst sellers," unavailable at limited-inventory competitors, generate a significant fraction of total revenue. The long tail phenomenon, however, is in principle consistent with two fundamentally different theories. The first, and more popular hypothesis, is that a majority of consumers consistently follow the crowds and only a minority have any interest in niche content; the second hypothesis is that everyone is a bit eccentric, consuming both popular and specialty products. Based on examining extensive data on user preferences for movies, music, Web search, and Web browsing, we find overwhelming support for the latter theory. However, the observed eccentricity is much less than what is predicted by a fully random model whereby every consumer makes his product choices independently and proportional to product popularity; so consumers do indeed exhibit at least some a priori propensity toward either the popular or the exotic.
 Our findings thus suggest an additional factor in the success of infinite-inventory retailers, namely, that tail availability may boost head sales by offering consumers the convenience of "one-stop shopping" for both their mainstream and niche interests. This hypothesis is further supported by our theoretical analysis that presents a simple model in which shared inventory stores, such as Amazon Marketplace, gain a clear advantage by satisfying tail demand, helping to explain the emergence and increasing popularity of such retail arrangements. Hence, we believe that the return-on-investment (ROI) of niche products goes beyond direct revenue, extending to second-order gains associated with increased consumer satisfaction and repeat patronage. More generally, our findings call into question the conventional wisdom that specialty products only appeal to a minority of consumers.
Groundhog day: near-duplicate detection on Twitter

With more than 340~million messages that are posted on Twitter every day, the amount of duplicate content as well as the demand for appropriate duplicate detection mechanisms is increasing tremendously. Yet there exists little research that aims at detecting near-duplicate content on microblogging platforms. We investigate the problem of near-duplicate detection on Twitter and introduce a framework that analyzes the tweets by comparing (i) syntactical characteristics, (ii) semantic similarity, and (iii) contextual information. Our framework provides different duplicate detection strategies that, among others, make use of external Web resources which are referenced from microposts. Machine learning is exploited in order to learn patterns that help identifying duplicate content. We put our duplicate detection framework into practice by integrating it into Twinder, a search engine for Twitter streams. An in-depth analysis shows that it allows Twinder to diversify search results and improve the quality of Twitter search. We conduct extensive experiments in which we (1) evaluate the quality of different strategies for detecting duplicates, (2) analyze the impact of various features on duplicate detection, (3) investigate the quality of strategies that classify to what exact level two microposts can be considered as duplicates and (4) optimize the process of identifying duplicate content on Twitter. Our results prove that semantic features which are extracted by our framework can boost the performance of detecting duplicates.
Do Cascades Recur?

Cascades of information-sharing are a primary mechanism by which content reaches its audience on social media, and an active line of research has studied how such cascades, which form as content is reshared from person to person, develop and subside. In this paper, we perform a large-scale analysis of cascades on Facebook over significantly longer time scales, and find that a more complex picture emerges, in which many large cascades recur, exhibiting multiple bursts of popularity with periods of quiescence in between. We characterize recurrence by measuring the time elapsed between bursts, their overlap and proximity in the social network, and the diversity in the demographics of individuals participating in each peak. We discover that content virality, as revealed by its initial popularity, is a main driver of recurrence, with the availability of multiple copies of that content helping to spark new bursts. Still, beyond a certain popularity of content, the rate of recurrence drops as cascades start exhausting the population of interested individuals. We reproduce these observed patterns in a simple model of content recurrence simulated on a real social network. Using only characteristics of a cascade’s initial burst, we demonstrate strong performance in predicting whether it will recur in the future.
Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP

Since a company can only perform as well as it is allowed to by its suppliers, the importance of supplier selection in supply chain management has been increasingly recognized. Supplier selection can best be described as a highly complex process, due to the involvement of many, sometimes conflicting, qualitative and quantitative criteria. The objective is to select the most suitable supplier(s) that meet a company’s specific needs. This paper investigates supplier selection in the airline retail industry. We discuss a number of issues that make airline retail complex and distinguish it from conventional retail. The supplier selection problem is solved by means of a two-phased methodology. In the first phase, a conjunctive screening method is used, which aims to reduce the initial set of potential suppliers prior to the comprehensive final choice phase. In the second phase, a fuzzy analytic hierarchy process (AHP) is used, in which suppliers are evaluated against the main criteria and sub-criteria. By combining the decision-maker’s preferences, using the developed methodology will eventually result in a ranking of suppliers that makes it possible to select the most suitable supplier(s). The proposed methodology is applied to one of the largest airlines in Europe, the Royal Dutch Airlines (KLM), and the results are discussed extensively in this paper. We conclude by proposing avenues for future research regarding the general applicability and further extensions.
Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects

Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.
Neural networks and differential dynamic programming for reinforcement learning problems

We explore a model-based approach to reinforcement learning where partially or totally unknown dynamics are learned and explicit planning is performed. We learn dynamics with neural networks, and plan behaviors with differential dynamic programming (DDP). In order to handle complicated dynamics, such as manipulating liquids (pouring), we consider temporally decomposed dynamics. We start from our recent work  where we used locally weighted regression (LWR) to model dynamics. The major contribution of this paper is making use of deep learning in the form of neural networks with stochastic DDP, and showing the advantages of neural networks over LWR. For this purpose, we extend neural networks for: (1) modeling prediction error and output noise, (2) computing an output probability distribution for a given input distribution, and (3) computing gradients of output expectation with respect to an input. Since neural networks have nonlinear activation functions, these extensions were not easy. We provide an analytic solution for these extensions using some simplifying assumptions. We verified this method in pouring simulation experiments. The learning performance with neural networks was better than that of LWR. The amount of spilled materials was reduced. We also present early results of robot experiments using a PR2.