nils-herrmann/clean_scirepeval huggingface dataset:
Pragmatics and Natural Language Generation

This paper addresses the question "why and how is it that we say the same thing differently to different people, or even to the same person in different circumstances?" We vary the content and form of our text in order to convey more information than is contained in the literal meanings of our words. This information expresses the speaker's interpersonal goals toward the hearer and, in general, his or her perception of the pragmatic aspects of the conversation. This paper discusses two insights that arise when one studies this question: the existence of a level of organization that mediates between communicative goals and generator decisions, and the interleaved planningrealization regime and associated monitoring required for generation. To illustrate these ideas, a computer program is described which contains plans and strategies to produce stylistically appropriate texts from a single representation under various settings that model pragmatic circumstances.
More Accurate Question Answering on Freebase

Real-world factoid or list questions often have a simple structure, yet are hard to match to facts in a given knowledge base due to high representational and linguistic variability. For example, to answer "who is the ceo of apple" on Freebase requires a match to an abstract "leadership" entity with three relations "role", "organization" and "person", and two other entities "apple inc" and "managing director". Recent years have seen a surge of research activity on learning-based solutions for this method. We further advance the state of the art by adopting learning-to-rank methodology and by fully addressing the inherent entity recognition problem, which was neglected in recent works.
 We evaluate our system, called Aqqu, on two standard benchmarks, Free917 and WebQuestions, improving the previous best result for each benchmark considerably. These two benchmarks exhibit quite different challenges, and many of the existing approaches were evaluated (and work well) only for one of them. We also consider efficiency aspects and take care that all questions can be answered interactively (that is, within a second). Materials for full reproducibility are available on our website: http://ad.informatik.uni-freiburg.de/publications.
Control of double inverted pendulum (DIP) using fuzzy hybrid adaptive neuro controller

This paper presents a new methodological approach for selection of appropriate type and number of Membership function (MF's) for the effective control of Double Inverted Pendulum (DIP). A Matlab-Simulink model of the system is built using governing mathematical equations. The relation between error tolerance of successive approximations and the number of MF's for controllers is also shown. Stabilization is done using Fuzzy and Adaptive Neuro Fuzzy Inference System (ANFIS) controllers having triangular and gbell MF's respectively. The proposed ANFIS and fuzzy controller stabilizes DIP system within 2.5 and 3.0 seconds respectively. All the three controllers have shown almost zero amount of steady state error. Both the controllers gives excellent result which proves the validity of the proposed model. ANFIS controller provides better results as compared to fuzzy controller. Results for Settling time (s), Steady state error and Maximum overshoot (degrees) for each input and output are elaborated with the help of graphs and tables.
DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data

DIYABC is a software package for a comprehensive analysis of population history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 implements a number of new features and analytical methods. It allows (i) the analysis of single nucleotide polymorphism data at large number of loci, apart from microsatellite and DNA sequence data, (ii) efficient Bayesian model choice using linear discriminant analysis on summary statistics and (iii) the serial launching of multiple post-processing analyses. DIYABC v2.0 also includes a user-friendly graphical interface with various new options. It can be run on three operating systems: GNU/Linux, Microsoft Windows and Apple Os X.


AVAILABILITY
Freely available with a detailed notice document and example projects to academic users at http://www1.montpellier.inra.fr/CBGP/diyabc CONTACT: estoup@supagro.inra.fr Supplementary information: Supplementary data are available at Bioinformatics online.
A Multi-Batch L-BFGS Method for Machine Learning

We describe a batch method that uses a sizeable fraction of the training set at each iteration, and that employs secondorder information. • To improve the learning process, we follow a multi-batch approach in which the batch changes at each iteration. • This inherently gives the algorithm a stochastic flavor that can cause instability in L-BFGS. • We show how to perform stable quasi-Newton updating in the multi-batch setting, illustrate the behavior of the algorithm in a distributed computing platform, and study its convergence properties for both the convex and nonconvex cases. Introduction min w∈Rd F (w) = 1 n n ∑ i=1 f (w;x, y) Idea: select a sizeable sample Sk ⊂ {1, . . . , n} at every iteration and perform quasi-Newton steps 1. Distributed computing setting: distributed gradient computation (with faults) 2. Multi-Batch setting: samples are changed at every iteration to accelerate learning Goal: show that stable quasi-Newton updating can be achieved in both settings without incurring extra computational cost, or special synchronization Issue: samples used at the beginning and at the end of every iteration are different • potentially harmful for quasi-Newton methods Key: controlled sampling • consecutive samples overlap Sk ∩ Sk+1 = Ok 6= ∅ • gradient differences based on this overlap – stable quasi-Newton updates Multi-Batch L-BFGS  At the k-th iteration: • sample Sk ⊂ {1, . . . , n} chosen, and iterates updated via wk+1 = wk − αkHkgk k where gk k is the batch gradient g Sk k = 1 |Sk| ∑ i∈Sk ∇f ( wk;x , y ) and Hk is the inverse BFGS Hessian approximation Hk+1 =V T k HkVk + ρksks T k ρk = 1 yT k sk , Vk = 1− ρkyksk • to ensure consistent curvature pair updates sk+1 = wk+1 − wk, yk+1 = gk k+1 − g Ok k where gk k+1 and g Ok k are gradients based on the overlapping samples only Ok = Sk ∩ Sk+1 Sample selection:
Named Entity Recognition with a Maximum Entropy Approach

The named entity recognition (NER) task involves identifying noun phrases that are names, and assigning a class to each name. This task has its origin from the Message Understanding Conferences (MUC) in the 1990s, a series of conferences aimed at evaluating systems that extract information from natural language texts. It became evident that in order to achieve good performance in information extraction, a system needs to be able to recognize names. A separate subtask on NER was created in MUC6 and MUC-7 (Chinchor, 1998).
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection

Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from POLITIFACT.COM, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate metadata with text. We show that this hybrid approach can improve a text-only deep learning model.
Radiofrequency Volume Reduction of Gastrocnemius Muscle Hypertrophy for Cosmetic Purposes

Muscularly prominent calves, caused mainly by hypertrophy of the gastrocnemius muscle (GCM), are prevalent among Asian women, and this condition can be a significant factor leading to psychological stress. The authors have devised a method for contouring the calf using radiofrequency (RF) applications to the GCMs to correct thick, muscular legs. This study was performed to investigate the effects of RF energy in reducing enlarged GCMs for 250 patients (249 women and 1 man) who sought aesthetic consultation for problems such as thick, muscular, asymmetric, or bowed calves. The operations were performed from June 2004 to April 2006. The patients first received a local anesthetic and sedation. After application of RF current, the prominent muscular contours improved, and the GCMs were contoured to an appropriate proportional volume. The range of the reductions in the calf circumferences at their thickest levels was 1 to 6 cm (mean, 2.5 cm) during the follow-up visits 6 months after the procedures. Most of the patients could return to their activities of daily living, except for exercise, after 1 to 7 days, and they were satisfied with the improved aesthetic contour lines of their lower legs. Clinical photography and ultrasonic examination were performed, and the leg circumferences were measured. Radiofrequency-induced coagulation tissue necrosis of the muscles caused no functional disabilities, and the clinical improvement was well maintained after the treatments for up to 17 months of follow-up evaluation.
Scalability of write-ahead logging on multicore and multisocket hardware

The shift to multi-core and multi-socket hardware brings new challenges to database systems, as the software parallelism determines performance. Even though database systems traditionally accommodate simultaneous requests, a multitude of synchronization barriers serialize execution. Write-ahead logging is a fundamental, omnipresent component in ARIES-style concurrency and recovery, and one of the most important yet-to-be addressed potential bottlenecks, especially in OLTP workloads making frequent small changes to data. In this paper, we identify four logging-related impediments to database system scalability. Each issue challenges different level in the software architecture: (a) the high volume of small-sized I/O requests may saturate the disk, (b) transactions hold locks while waiting for the log flush, (c) extensive context switching overwhelms the OS scheduler with threads executing log I/Os, and (d) contention appears as transactions serialize accesses to in-memory log data structures. We demonstrate these problems and address them with techniques that, when combined, comprise a holistic, scalable approach to logging. Our solution achieves a 20–69% speedup over a modern database system when running log-intensive workloads, such as the TPC-B and TATP benchmarks, in a single-socket multiprocessor server. Moreover, it achieves log insert throughput over 2.2 GB/s for small log records on the single-socket server, roughly 20 times higher than the traditional way of accessing the log using a single mutex. Furthermore, we investigate techniques on scaling the performance of logging to multi-socket servers. We present a set of optimizations which partly ameliorate the latency penalty that comes with multi-socket hardware, and then we investigate the feasibility of applying a distributed log buffer design at the socket level.
Neuro-dynamic programming: an overview

Neuro-dynamic programming (NDP for short) is a relatively new class of dynamic programming methods for control and sequential decision making under uncertainty. These methods have the potential of dealing with problems that for a long time were thought to be intractable due to either a large state space or the lack of an accurate model. They combine ideas from the fields of neural networks, artificial intelligence, cognitive science, simulation, and approximation theory. We will delineate the major conceptual issues, survey a number of recent developments, describe some computational experience, and address a number of open questions. We consider systems where decisions are made in stages. The outcome of each decision is not fully predictable but can be anticipated to some extent before the next decision is made. Each decision results in some immediate cost but also affects the context in which future decisions are to be made and therefore affects the cost incurred in future stages. Dynamic programming (DP for short) provides a mathematical formalization of the tradeoff between immediate and future costs. Generally, in DP formulations there is a discrete-time dynamic system whose state evolves according to given transition probabilities that depend on a decision/control u. In particular, if we are in state i and we choose decision u, we move to state j with given probability pij(u). Simultaneously with this transition, we incur a cost g(i, u, j). In comparing, however, the available decisions u, it is not enough to look at the magnitude of the cost g(i, u, j); we must also take into account how desirable the next state j is. We thus need a way to rank or rate states j. This is done by using the optimal cost (over all remaining stages) starting from state j, which is denoted by J∗(j). These costs can be shown to
Flood flow forecasting using ANN, ANFIS and regression models

Flood prediction is an important for the design, planning and management of water resources systems. This study presents the use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), multiple linear regression (MLR) and multiple nonlinear regression (MNLR) for forecasting maximum daily flow at the outlet of the Khosrow Shirin watershed, located in the Fars Province of Iran. Precipitation data from four meteorological stations were used to develop a multilayer perceptron topology model. Input vectors for simulations included the original precipitation data, an area-weighted average precipitation and antecedent flows with one- and two-day time lags. Performances of the models were evaluated with the RMSE and the R 2. The results showed that the area-weighted precipitation as an input to ANNs and MNLR and the spatially distributed precipitation input to ANFIS and MLR lead to more accurate predictions (e.g., in ANNs up to 2.0 m3 s−1 reduction in RMSE). Overall, the MNLR was shown to be superior (R 2 = 0.81 and RMSE = 0.145 m3 s−1) to ANNs, ANFIS and MLR for prediction of maximum daily flow. Furthermore, models including antecedent flow with one- and two-day time lags significantly improve flow prediction. We conclude that nonlinear regression can be applied as a simple method for predicting the maximum daily flow.
Robust inference for visual-inertial sensor fusion

Inference of three-dimensional motion from the fusion of inertial and visual sensory data has to contend with the preponderance of outliers in the latter. Robust filtering deals with the joint inference and classification task of selecting which data fits the model, and estimating its state. We derive the optimal discriminant and propose several approximations, some used in the literature, others new. We compare them analytically, by pointing to the assumptions underlying their approximations, and empirically. We show that the best performing method improves the performance of state-of-the-art visual-inertial sensor fusion systems, while retaining the same computational complexity.
AEG: Automatic Exploit Generation

The automatic exploit generation challenge is given a program, automatically find vulnerabilities and generate exploits for them. In this paper we present AEG, the first end-to-end system for fully automatic exploit generation. We used AEG to analyze 14 open-source projects and successfully generated 16 control flow hijacking exploits. Two of the generated exploits (expect-5.43 and htget-0.93) are zero-day exploits against unknown vulnerabilities. Our contributions are: 1) we show how exploit generation for control flow hijack attacks can be modeled as a formal verification problem, 2) we propose preconditioned symbolic execution, a novel technique for targeting symbolic execution, 3) we present a general approach for generating working exploits once a bug is found, and 4) we build the first end-to-end system that automatically finds vulnerabilities and generates exploits that produce a shell.
Biologically-plausible learning algorithms can scale to large datasets

The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this “weight transport problem” (Grossberg, 1987), two more biologically plausible algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP’s weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study by Bartunov et al. (2018) evaluate variants of target-propagation (TP) and feedback alignment (FA) on MINIST, CIFAR, and ImageNet datasets, and find that although many of the proposed algorithms perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry algorithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights share signs but not magnitudes. We examine the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet, RetinaNet for MS COCO). Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks. These results complement the study by Bartunov et al. (2018), and establish a new benchmark for future biologically plausible learning algorithms on more difficult datasets and more complex architectures. This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. ar X iv :1 81 1. 03 56 7v 2 [ cs .L G ] 2 5 N ov 2 01 8 BIOLOGICALLY-PLAUSIBLE LEARNING ALGORITHMS CAN SCALE TO LARGE DATASETS
A planar wideband wide-scan phased array: Connected array loaded with artificial dielectric layers

We present a novel concept for wideband, wide-scan phased array applications. The array is composed by connected-slot elements loaded with artificial dielectric superstrates. The proposed solution consists of a single multi-layer planar printed circuit board (PCB) and does not require the typically employed vertical arrangement of multiple PCBs. This offers advantages in terms of complexity of the assembly and cost of the array. We developed an analytical method for the prediction of the array performance, in terms of active input impedance. This method allows to estimate the relevant parameters of the array with a negligible computational cost. A design example with a bandwidth exceeding one octave (VSWR<;2 from 6.5 to 14.3) and scanning up to 50 degrees for all azimuth planes is presented.
Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search

Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, planning optimally in the face of uncertainty is notoriously taxing, since the search space is enormous. In this paper we introduce a tractable, sample-based method for approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our approach avoids expensive applications of Bayes rule within the search tree by sampling models from current beliefs, and furthermore performs this sampling in a lazy manner. This enables it to outperform previous Bayesian model-based reinforcement learning algorithms by a significant margin on several well-known benchmark problems. As we show, our approach can even work in problems with an infinite state space that lie qualitatively out of reach of almost all previous work in Bayesian exploration.
Building Text Classifiers Using Positive and Unlabeled Examples

This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. In this paper, we first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques.
Investigation of Galvanic-Coupled Intrabody Communication Using the Human Body Circuit Model

Intrabody Communication (IBC) is a technique that uses the human body as a transmission medium for electrical signals to connect wearable electronic sensors and devices. Understanding the human body as the transmission medium in IBC paves way for practical implementation of IBC in body sensor networks. In this study, we propose a model for galvanic coupling-type IBC based on a simplified equivalent circuit representation of the human upper arm. We propose a new way to calculate the electrode-skin contact impedance. Based on the model and human experimental results, we discuss important characteristics of galvanic coupling-type IBC, namely, the effect of tissues, anthropometry of subjects, and electrode configuration on signal propagation. We found that the dielectric properties of the muscle primarily characterize the received signal when receiver electrodes are located close to transmitter electrodes. When receiver and transmitter electrodes are far apart, the skin dielectric property affects the received signal.
The emotional arcs of stories are dominated by six basic shapes

Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture’s evolution through its texts using a ‘big data’ lens. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories and forming patterns that are meaningful to us. Here, by classifying the emotional arcs for a filtered subset of 1,327 stories from Project Gutenberg’s fiction collection, we find a set of six core emotional arcs which form the essential building blocks of complex emotional trajectories. We strengthen our findings by separately applying matrix decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads.