nils-herrmann/clean_scirepeval huggingface dataset:
From Predictive Models to Instructional Policies

At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way to quantify whether the student has mastered a skill. A large amount of work has been done on building student models that can predict student performance on the next question. In this paper, we leverage this prior work with a new whento-stop policy that is compatible with any such predictive student model. Our results suggest that, when employed as part of our new predictive similarity policy, student models with similar predictive accuracies can suggest that substantially different amounts of practice are necessary. This suggests that predictive accuracy may not be a sufficient metric by itself when choosing which student model to use in intelligent tutoring systems.
Murder-suicides involving children: a 29-year study.

Review was undertaken from February 1969 to January 1998 at the State forensic science center (Forensic Science) in Adelaide, South Australia, of all cases of murder-suicide involving children <16 years of age. A total of 13 separate cases were identified involving 30 victims, all of whom were related to the perpetrators. There were 7 male and 6 female perpetrators (age range, 23-41 years; average, 31 years) consisting of 6 mothers, 6 father/husbands, and 1 uncle/son-in-law. The 30 victims consisted of 11 daughters, 11 sons, 1 niece, 1 mother-in-law, and 6 wives of the assailants. The 23 children were aged from 10 months to 15 years (average, 6.0 years). The 6 mothers murdered 9 children and no spouses, with 3 child survivors. The 6 fathers murdered 13 children and 6 wives, with 1 child survivor. This study has demonstrated a higher percentage of female perpetrators than other studies of murder-suicide. The methods of homicide and suicide used were generally less violent among the female perpetrators compared with male perpetrators. Fathers killed not only their children but also their wives, whereas mothers murdered only their children. These results suggest differences between murder-suicides that involve children and adult-only cases, and between cases in which the mother rather than the father is the perpetrator.
Equivalences for Free ! Univalent

Homotopy Type Theory promises a unification of the concepts of equality and equivalence in Type Theory, through the introduction of the univalence principle. However, existing proof assistants based on type theory treat this principle as an axiom, and it is not yet clear how to extend them to handle univalence internally. In this paper, we propose a construction grounded on a univalent version of parametricity to bring the benefits of univalence to the programmer and prover, that can be used on top of existing type theories. In particular, univalent parametricity strengthens parametricity to ensure preservation of type equivalences. We present a lightweight framework implemented in the Coq proof assistant that allows the user to transparently transfer definitions and theorems for a type to an equivalent one, as if they were equal. Our approach handles both type and term dependency. We study how to maximize the effectiveness of these transports in terms of computational behavior, and identify a fragment useful for certified programming on which univalent transport is guaranteed to be effective.
A Semantic Middleware Architecture Focused on Data and Heterogeneity Management within the Smart Grid

There is an increasing tendency of turning the current power grid, essentially unaware of variations in electricity demand and scattered energy sources, into something capable of bringing a degree of intelligence by using tools strongly related to information and communication technologies, thus turning into the so-called Smart Grid. In fact, it could be considered that the Smart Grid is an extensive smart system that spreads throughout any area where power is required, providing a significant optimization in energy generation, storage and consumption. However, the information that must be treated to accomplish these tasks is challenging both in terms of complexity (semantic features, distributed systems, suitable hardware) and quantity (consumption data, generation data, forecasting functionalities, service reporting), since the different energy beneficiaries are prone to be heterogeneous, as the nature of their own activities is. This paper presents a proposal on how to deal with these issues by using a semantic middleware architecture that integrates different components focused on specific tasks, and how it is used to handle information at every level and satisfy end user requests.
Removing Rain from Single Images via a Deep Detail Network

We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input to output, which makes the learning process easier. To further improve the de-rained result, we use a priori image domain knowledge by focusing on high frequency detail during training, which removes background interference and focuses the model on the structure of rain in images. This demonstrates that a deep architecture not only has benefits for high-level vision tasks but also can be used to solve low-level imaging problems. Though we train the network on synthetic data, we find that the learned network generalizes well to real-world test images. Experiments show that the proposed method significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. We discuss applications of this structure to denoising and JPEG artifact reduction at the end of the paper.
RF performance of 28nm PolySiON and HKMG CMOS devices

The impact of scaling in advanced RF/MS-CMOS has been extensively discussed but there has not been a publication that compares the RF characteristics of 28nm high-K metal gate HKMG and PolySiON technologies fabricated in the same facility. In this work, we show that HKMG improves transistor fT and increases varactor tunning range. However, it can actually decrease fmax. We examine how process features made to optimize cost and digital performance impact the RF performance. Process features which improve DC current and gm, including HKMG also give higher fT. However, fmax is sensitive to gate resistance and PolySiON has an advantage here.
Discovering Knowledge in Data: An Introduction to Data Mining

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever–increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today s big data world. The author demonstrates how to leverage a company s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will learn data mining by doing data mining . By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining .
Competitive Strategies for Brick-and-Mortar Stores to Counter "Showrooming"

Customers often evaluate products at brick-and-mortar stores to identify their “best fit” product but buy it for a lower price at a competing online retailer. This free-riding behavior by customers is referred to as “showrooming” and we show that this is detrimental to the profits of the brick-and-mortar stores. We first analyze price matching as a short-term strategy to counter showrooming. Since customers purchase from the store at lower than store posted price when they ask for price-matching, one would expect the price matching strategy to be less effective as the fraction of customers who seek the matching increases. However, our results show that with an increase in the fraction of customers who seek price matching, the stores profits initially decrease and then increase. While price-matching could be used even when customers do not exhibit showrooming behavior, we find that it is more effective when customers do showrooming. We then study exclusivity of product assortments as a long-term strategy to counter showrooming. This strategy can be implemented in two different ways. One, by arranging for exclusivity of known brands (e.g. Macy’s has such an arrangement with Tommy Hilfiger), or, two, through creation of store brands at the brick-and-mortar store (T.J.Maxx uses a large number of store brands). Our analysis suggests that implementing exclusivity through store brands is better than exclusivity through known brands when the product category has few digital attributes. However, when customers do not showroom, the known brand strategy dominates the store brand strategy.
Apollonian circle packings : number theory

Apollonian circle packings arise by repeatedly filling the interstices between mutually tangent circles with further tangent circles. It is possible for every circle in such a packing to have integer radius of curvature, and we call such a packing an integral Apollonian circle packing. This paper studies number-theoretic properties of the set of integer curvatures appearing in such packings. Each Descartes quadruple of four tangent circles in the packing gives an integer solution to the Descartes equation, which relates the radii of curvature of four mutually tangent circles: x þ y þ z þ w 1⁄4 1 2 ðx þ y þ z þ wÞ: Each integral Apollonian circle packing is classified by a certain root quadruple of integers that satisfies the Descartes equation, and that corresponds to a particular quadruple of circles appearing in the packing. We express the number of root quadruples with fixed minimal element n as a class number, and give an exact formula for it. We study which integers occur in a given integer packing, and determine congruence restrictions which sometimes apply. We present evidence suggesting that the set of integer radii of curvatures that appear in an integral Apollonian circle packing has positive density, and in fact represents all sufficiently large integers not excluded by Corresponding author. E-mail addresses: graham@ucsd.edu (R.L. Graham), jcl@research.att.com (J.C. Lagarias), colinm@ research.avayalabs.com (C.L. Mallows), ). 1 Current address: Department of Computer Science, University of California at San Diego, La Jolla, CA 92093, USA. 2 Work partly done during a visit to the Institute for Advanced Study. 3 Current address: Avaya Labs, Basking Ridge, NJ 07920, USA. 0022-314X/03/$ see front matter r 2003 Elsevier Science (USA). All rights reserved.  Finally, we discuss asymptotic properties of the set of curvatures obtained as the packing is recursively constructed from a root quadruple. r 2003 Elsevier Science (USA). All rights reserved.
Compiling path queries in software-defined networks

Monitoring the flow of traffic along network paths is essential for SDN programming and troubleshooting. For example, traffic engineering requires measuring the ingress-egress traffic matrix; debugging a congested link requires determining the set of sources sending traffic through that link; and locating a faulty device might involve detecting how far along a path the traffic makes progress. Past path-based monitoring systems operate by diverting packets to collectors that perform "after-the-fact" analysis, at the expense of large data-collection overhead. In this paper, we show how to do more efficient "during-the-fact" analysis. We introduce a query language that allows each SDN application to specify queries independently of the forwarding state or the queries of other applications. The queries use a regular-expression-based path language that includes SQL-like "groupby" constructs for count aggregation. We track the packet trajectory directly on the data plane by converting the regular expressions into an automaton, and tagging the automaton state (i.e., the path prefix) in each packet as it progresses through the network. The SDN policies that implement the path queries can be combined with arbitrary packet-forwarding policies supplied by other elements of the SDN platform. A preliminary evaluation of our prototype shows that our "during-the-fact" strategy reduces data-collection overhead over "after-the-fact" strategies.
CRISPR-Cas: From the Bacterial Adaptive Immune System to a Versatile Tool for Genome Engineering.

The field of biology has been revolutionized by the recent advancement of an adaptive bacterial immune system as a universal genome engineering tool. Bacteria and archaea use repetitive genomic elements termed clustered regularly interspaced short palindromic repeats (CRISPR) in combination with an RNA-guided nuclease (CRISPR-associated nuclease: Cas) to target and destroy invading DNA. By choosing the appropriate sequence of the guide RNA, this two-component system can be used to efficiently modify, target, and edit genomic loci of interest in plants, insects, fungi, mammalian cells, and whole organisms. This has opened up new frontiers in genome engineering, including the potential to treat or cure human genetic disorders. Now the potential risks as well as the ethical, social, and legal implications of this powerful new technique move into the limelight.
Fall Classification by Machine Learning Using Mobile Phones

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls-left and right lateral, forward trips, and backward slips-while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.
Poultry No . 1573 The replacement value of betaine for DL-methionine and Choline in broiler diets

Especially in times of high raw material prices as a result of limited availability of feed ingredients nutritionist look for ways to keep feed cost as low as possible. Part of this discussion is whether certain ingredients can be replaced by others while avoiding impairments of performance. This discussion sometimes includes the question whether supplemental methionine can be replaced by betaine. Use of supplemental methionine, choline and betaine is common in broiler diets. Biochemically, all three compounds can act as methyl group donors.  1 illustrates metabolic pathways connecting choline, betaine and methionine. This chart shows that choline is transformed to betaine which can then deliver a CH3-group for methylation reactions. One of those reactions is the methylation of homocysteine to methionine. This reaction occurs as part of the homocysteine cycle, which continues by transferring the methyl group further and yielding homocysteine again. Thus, there is no net yield of methionine from this cycle, since it only functions to transport a methyl group.
Comparison of a carbohydrate-free diet vs. fasting on plasma glucose, insulin and glucagon in type 2 diabetes.

Hyperglycemia improves when patients with type 2 diabetes are placed on a weight-loss diet. Improvement typically occurs soon after diet implementation. This rapid response could result from low fuel supply (calories), lower carbohydrate content of the weight-loss diet, and/or weight loss per se. To differentiate these effects, glucose, insulin, C-peptide and glucagon were determined during the last 24 h of a 3-day period without food (severe calorie restriction) and a calorie-sufficient, carbohydrate-free diet.



Seven subjects with untreated type 2 diabetes were studied. A randomized-crossover design with a 4-week washout period between arms was used.



Results from both the calorie-sufficient, carbohydrate-free diet and the 3-day fast were compared with the initial standard diet consisting of 55% carbohydrate, 15% protein and 30% fat.



The overnight fasting glucose concentration decreased from 196 (standard diet) to 160 (carbohydrate-free diet) to 127 mg/dl (fasting). The 24 h glucose and insulin area responses decreased by 35% and 48% on day 3 of the carbohydrate-free diet, and by 49% and 69% after fasting. Overnight basal insulin and glucagon remained unchanged.



Short-term fasting dramatically lowered overnight fasting and 24 h integrated glucose concentrations. Carbohydrate restriction per se could account for 71% of the reduction. Insulin could not entirely explain the glucose responses. In the absence of carbohydrate, the net insulin response was 28% of the standard diet. Glucagon did not contribute to the metabolic adaptations observed.
Single-Trial Classification of Event-Related Potentials in Rapid Serial Visual Presentation Tasks Using Supervised Spatial Filtering

Accurate detection of single-trial event-related potentials (ERPs) in the electroencephalogram (EEG) is a difficult problem that requires efficient signal processing and machine learning techniques. Supervised spatial filtering methods that enhance the discriminative information in EEG data are commonly used to improve single-trial ERP detection. We propose a convolutional neural network (CNN) with a layer dedicated to spatial filtering for the detection of ERPs and with training based on the maximization of the area under the receiver operating characteristic curve (AUC). The CNN is compared with three common classifiers: 1) Bayesian linear discriminant analysis; 2) multilayer perceptron (MLP); and 3) support vector machines. Prior to classification, the data were spatially filtered with xDAWN (for the maximization of the signal-to-signal-plus-noise ratio), common spatial pattern, or not spatially filtered. The 12 analytical techniques were tested on EEG data recorded in three rapid serial visual presentation experiments that required the observer to discriminate rare target stimuli from frequent nontarget stimuli. Classification performance discriminating targets from nontargets depended on both the spatial filtering method and the classifier. In addition, the nonlinear classifier MLP outperformed the linear methods. Finally, training based AUC maximization provided better performance than training based on the minimization of the mean square error. The results support the conclusion that the choice of the systems architecture is critical and both spatial filtering and classification must be considered together.
Echo Chambers: Emotional Contagion and Group Polarization on Facebook

Recent findings showed that users on Facebook tend to select information that adhere to their system of beliefs and to form polarized groups - i.e., echo chambers. Such a tendency dominates information cascades and might affect public debates on social relevant issues. In this work we explore the structural evolution of communities of interest by accounting for users emotions and engagement. Focusing on the Facebook pages reporting on scientific and conspiracy content, we characterize the evolution of the size of the two communities by fitting daily resolution data with three growth models - i.e. the Gompertz model, the Logistic model, and the Log-logistic model. Although all the models appropriately describe the data structure, the Logistic one shows the best fit. Then, we explore the interplay between emotional state and engagement of users in the group dynamics. Our findings show that communities' emotional behavior is affected by the users' involvement inside the echo chamber. Indeed, to an higher involvement corresponds a more negative approach. Moreover, we observe that, on average, more active users show a faster shift towards the negativity than less active ones.
IBM PAIRS curated big data service for accelerated geospatial data analytics and discovery

IBM's Physical Analytics Integrated Data Repository and Services (PAIRS) is a geospatial Big Data service. PAIRS contains a massive amount of curated geospatial (or more precisely spatio-temporal) data from a large number of public and private data resources, and also supports user contributed data layers. PAIRS offers an easy-to-use platform for both rapid assembly and retrieval of geospatial datasets or performing complex analytics, lowering time-to-discovery significantly by reducing the data curation and management burden. In this paper, we review recent progress with PAIRS and showcase a few exemplary analytical applications which the authors are able to build with relative ease leveraging this technology.