nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Extract Conditional Knowledge for Question Answering using Dialogue

Knowledge based question answering (KBQA) has attracted much attention from both academia and industry in the field of Artificial Intelligence. However, many existing knowledge bases (KBs) are built by static triples. It is hard to answer user questions with different conditions, which will lead to significant answer variances in questions with similar intent. In this work, we propose to extract conditional knowledge base (CKB) from user question-answer pairs for answering user questions with different conditions through dialogue. Given a subject, we first learn user question patterns and conditions. Then we propose an embedding based co-clustering algorithm to simultaneously group the patterns and conditions by leveraging the answers as supervisor information. After that, we extract the answers to questions conditioned on both question pattern clusters and condition clusters as a CKB. As a result, when users ask a question without clearly specifying the conditions, we use dialogues in natural language to chat with users for question specification and answer retrieval. Experiments on real question answering (QA) data show that the dialogue model using automatically extracted CKB can more accurately answer user questions and significantly improve user satisfaction for questions with missing conditions.
1 Do Institutions Cause Growth ?

We revisit the debate over whether political institutions cause economic growth, or whether, alternatively, growth and human capital accumulation lead to institutional improvement. We find that most indicators of institutional quality used to establish the proposition that institutions cause growth are constructed to be conceptually unsuitable for that purpose. We also find that some of the instrumental variable techniques used in the literature are flawed. Basic OLS results, as well as a variety of additional evidence, suggest that a) human capital is a more basic source of growth than are the institutions, b) poor countries get out of poverty through good policies, often pursued by dictators, and c) subsequently improve their political institutions.  Harvard University, Dartmouth College, Yale University, and Harvard University, respectively. We thank Philippe Aghion, Olivier Blanchard, Oded Galor, Simon Johnson, Aart Kraay, Ross Levine, Torsten Persson, Dani Rodrik and an anonymous referee for helpful comments, and Sebastian Brown for research assistance.
Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading

This paper aims at improving how machines can answer questions directly from text, with the focus of having models that can answer correctly multiple types of questions and from various types of texts, documents or even from large collections of them. To that end, we introduce the Weaver model that uses a new way to relate a question to a textual context by weaving layers of recurrent networks, with the goal of making as few assumptions as possible as to how the information from both question and context should be combined to form the answer. We show empirically on six datasets that Weaver performs well in multiple conditions. For instance, it produces solid results on the very popular SQuAD dataset (Rajpurkar et al., 2016), solves almost all bAbI tasks (Weston et al., 2015) and greatly outperforms state-of-the-art methods for open domain question answering from text (Chen et al., 2017).
Mixed-Integer Linear Programming Solution to Multi-Robot Task Allocation Problem

Multi-robot systems require efficient and accurate planning in order to perform mission-critical tasks. This paper introduces a mixed-integer linear programming solution to coordinate multiple heterogenenous robots for detecting and controlling multiple regions of interest in an unknown environment. The objective function contains four basic requirements of a multi-robot system serving this purpose: control regions of interest, provide communication between robots, control maximum area and detect regions of interest. Our solution defines optimum locations of robots in order to maximize the objective function while efficiently satisfying some constraints such as avoiding obstacles and staying within the speed capabilities of the robots. We implemented and tested our approach under realistic scenarios. We showed various extensions to objective function and constraints to show the flexibility of mixed-integer linear programming formulation.  atay@cse.wustl.edu Burchan Bayazit Department of Computer Science and Engineering Washington University in St. Louis Email: bayazit@cse.wustl.edu Abstract— Multi-robot systems require efficient and accurate planning in order to perform mission-critical tasks. This paper introduces a mixed-integer linear programming solution to coordinate multiple heterogenenous robots for detecting and controlling multiple regions of interest in an unknown environment. The objective function contains four basic requirements of a multi-robot system serving this purpose: control regions of interest, provide communication between robots, control maximum area and detect regions of interest. Our solution defines optimum locations of robots in order to maximize the objective function while efficiently satisfying some constraints such as avoiding obstacles and staying within the speed capabilities of the robots. We implemented and tested our approach under realistic scenarios. We showed various extensions to objective function and constraints to show the flexibility of mixed-integer linear programming formulation. Multi-robot systems require efficient and accurate planning in order to perform mission-critical tasks. This paper introduces a mixed-integer linear programming solution to coordinate multiple heterogenenous robots for detecting and controlling multiple regions of interest in an unknown environment. The objective function contains four basic requirements of a multi-robot system serving this purpose: control regions of interest, provide communication between robots, control maximum area and detect regions of interest. Our solution defines optimum locations of robots in order to maximize the objective function while efficiently satisfying some constraints such as avoiding obstacles and staying within the speed capabilities of the robots. We implemented and tested our approach under realistic scenarios. We showed various extensions to objective function and constraints to show the flexibility of mixed-integer linear programming formulation.
Real-time 3D reconstruction at scale using voxel hashing

Online 3D reconstruction is gaining newfound interest due to the availability of real-time consumer depth cameras. The basic problem takes live overlapping depth maps as input and incrementally fuses these into a single 3D model. This is challenging particularly when real-time performance is desired without trading quality or scale. We contribute an online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure. Our system uses a simple spatial hashing scheme that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure. Surface data is only stored densely where measurements are observed. Additionally, data can be streamed efficiently in or out of the hash table, allowing for further scalability during sensor motion. We show interactive reconstructions of a variety of scenes, reconstructing both fine-grained details and large scale environments. We illustrate how all parts of our pipeline from depth map pre-processing, camera pose estimation, depth map fusion, and surface rendering are performed at real-time rates on commodity graphics hardware. We conclude with a comparison to current state-of-the-art online systems, illustrating improved performance and reconstruction quality.
Effect of probiotic supplements in women with gestational diabetes mellitus on inflammation and oxidative stress biomarkers: a randomized clinical trial.

Very little is known about the use of probiotics among pregnant women with gestational diabetes mellitus (GDM) especially its effect on oxidative stress and inflammatory indices. The aim of present study was to measure the effect of a probiotic supplement capsule on inflammation and oxidative stress biomarkers in women with newly-diagnosed GDM.



64 pregnant women with GDM were enrolled in a double-blind placebo controlled randomized clinical trial in the spring and summer of 2014. They were randomly assigned to receive either a probiotic containing four bacterial strains of Lactobacillus acidophilus LA-5, Bifidobacterium BB-12, Streptococcus Thermophilus STY-31 and Lactobacillus delbrueckii bulgaricus LBY-27 or placebo capsule for 8 consecutive weeks. Blood samples were taken pre- and post-treatment and serum indices of inflammation and oxidative stress were assayed. The measured mean response scales were then analyzed using mixed effects model. All statistical analysis was performed using Statistical Package for Social Sciences (SPSS) software (version 16).



Serum high-sensitivity C-reactive protein and tumor necrosis factor-α levels improved in the probiotic group to a statistically significant level over the placebo group. Serum interleukin-6 levels decreased in both groups after intervention; however, neither within group nor between group differences interleukin-6 serum levels was statistically significant. Malondialdehyde, glutathione reductase and erythrocyte glutathione peroxidase levels improved significantly with the use of probiotics when compared with the placebo.



The probiotic supplement containing L.acidophilus LA- 5, Bifidobacterium BB- 12, S.thermophilus STY-31 and L.delbrueckii bulgaricus LBY-2 appears to improve several inflammation and oxidative stress biomarkers in women with GDM.
GOAL-CONDITIONED POLICIES

Representation learning is a central challenge across a range of machine learning areas. In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems. Most prior work on representation learning has focused on generative approaches, learning representations that capture all underlying factors of variation in the observation space in a more disentangled or well-ordered manner. In this paper, we instead aim to learn functionally salient representations: representations that are not necessarily complete in terms of capturing all factors of variation in the observation space, but rather aim to capture those factors of variation that are important for decision making – that are “actionable.” These representations are aware of the dynamics of the environment, and capture only the elements of the observation that are necessary for decision making rather than all factors of variation, without explicit reconstruction of the observation. We show how these representations can be useful to improve exploration for sparse reward problems, to enable long horizon hierarchical reinforcement learning, and as a state representation for learning policies for downstream tasks. We evaluate our method on a number of simulated environments, and compare it to prior methods for representation learning, exploration, and hierarchical reinforcement learning.
A survey on forecasting of time series data

Time series analysis and forecasting future values has been a major research focus since years ago. Time series analysis and forecasting in time series data finds it significance in many applications such as business, stock market and exchange, weather, electricity demand, cost and usage of products such as fuels, electricity, etc. and in any kind of place that has specific seasonal or trendy changes with time. The forecasting of time series data provides the organization with useful information that is necessary for making important decisions. In this paper, a detailed survey of the various techniques applied for forecasting different types of time series dataset is provided. This survey covers the overall forecasting models, the algorithms used within the model and other optimization techniques used for better performance and accuracy. The various performance evaluation parameters used for evaluating the forecasting models are also discussed in this paper. This study gives the reader an idea about the various researches that take place within forecasting using the time series data.
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

As a basic task in computer vision, semantic segmentation can provide fundamental information for object detection and instance segmentation to help the artificial intelligence better understand real world. Since the proposal of fully convolutional neural network (FCNN), it has been widely used in semantic segmentation because of its high accuracy of pixel-wise classification as well as high precision of localization. In this paper, we apply several famous FCNN to brain tumor segmentation, making comparisons and adjusting network architectures to achieve better performance measured by metrics such as precision, recall, mean of intersection of union (mIoU) and dice score coefficient (DSC). The adjustments to the classic FCNN include adding more connections between convolutional layers, enlarging decoders after up sample layers and changing the way shallower layers’ information is reused. Besides the structure modification, we also propose a new classifier with a hierarchical dice loss. Inspired by the containing relationship between classes, the loss function converts multiple classification to multiple binary classification in order to counteract the negative effect caused by imbalance data set. Massive experiments have been done on the training set and testing set in order to assess our refined fully convolutional neural networks and new types of loss function. Competitive figures prove they are more effective than their predecessors.
THE PLATINUM-GROUP MINERALS IN THE UPPER SECTION OF THE KEIVITSANSARVI Ni – Cu – PGE DEPOSIT , NORTHERN FINLAND

The Keivitsansarvi deposit, in northern Finland, is a low-grade dissemination of Ni–Cu sulfides containing 1.3–26.6 g/t PGE. It occurs in the northeastern part of the 2.05 Ga Keivitsa intrusion and is hosted by olivine wehrlite and olivine websterite, metamorphosed at greenschist-facies conditions. The sulfide-mineralized area shows variable bulk S, Ni, Co, Cu, PGE, Au, As, Sb, Se, Te and Bi contents. S and Au tend to decrease irregularly from bottom to top of the deposit, whereas Ni, Ni/Co, PGE, As, Sb, Se, Te and Bi tend to increase. Thus, the upper section of the deposit has low S (<1.5 wt.%) and Au (160 ppb on average), but elevated levels of the PGE (2120 ppb Pt, 1855 ppb Pd on average). Sulfides consist of intergranular, highly disseminated aggregates mainly made up of pentlandite, pyrite, and chalcopyrite (all showing fine intergrowths), as well as nickeline, maucherite and gersdorffite in some samples. Most platinum-group minerals occur as single, minute grains included in silicates (57%) or attached to the grain boundaries of sulfides (36%). Only a few PGM grains (6%) are included in sulfides. Pt minerals (mainly moncheite and sperrylite) are the most abundant PGM, whereas Pd minerals (mainly merenskyite, Pd-rich melonite, kotulskite and sobolevskite) are relatively scarce, and most contain significant amounts of Pt. Whole-rock PGE analyses show a general Pd enrichment with respect to Pt. This discrepancy results from the fact that a major part of Pd is hidden in solid solution in the structure of gersdorffite, nickeline, maucherite and pentlandite. The mineral assemblages and textures of the upper section of the Keivitsansarvi deposit result from the combined effect of serpentinization, hydrothermal alteration and metamorphism of preexisting, low-grade disseminated Ni–Cu ore formed by the intercumulus crystallization of a small fraction of immiscible sulfide melt. Serpentinization caused Ni enrichment of sulfides and preserved the original PGE concentrations of the magmatic mineralization. Later, coeval with greenschist-facies metamorphism, PGE and some As (together with other semimetals) were leached out from other mineralized zones by hydrothermal fluids, probably transported in the form of chloride complexes, and precipitated in discrete Ni–Cu–PGE-rich horizons, as observed in the upper part of the deposit. Metamorphism also caused partial dissolution and redistribution of the sulfide (and arsenide) aggregates, contributing to a further Ni enrichment in the sulfide ores.
Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments

We present a practical approach to global motion planning and terrain assessment for ground robots in generic three-dimensional (3D) environments, including rough outdoor terrain, multilevel facilities, and more complex geometries. Our method computes optimized six-dimensional trajectories compliant with curvature and continuity constraints directly on unordered point cloud maps, omitting any kind of explicit surface reconstruction, discretization, or topology extraction. We assess terrain geometry and traversability on demand during motion planning, by fitting robot-sized planar patches to the map and analyzing the local distribution of map points. Our motion planning approach consists of sampling-based initial trajectory generation, followed by precise local optimization according to a custom cost measure, using a novel, constraint-aware trajectory optimization paradigm. We embed these methods in a complete autonomous navigation system based on localization and mapping by means of a 3D laser scanner and iterative closest point matching, suitable for both static and dynamic environments. The performance of the planning and terrain assessment algorithms is evaluated in offline experiments using recorded and simulated sensor data. Finally, we present the results of navigation experiments in three different environments—rough outdoor terrain, a two-level parking garage, and a dynamic environment, demonstrating how the proposed methods enable autonomous navigation in complex 3D terrain.
Security Incident Response Criteria: A Practitioner's Perspective

Industrial reports indicate that security incidents continue to inflict large financial losses on organizations. Researchers and industrial analysts contend that there are fundamental problems with existing security incident response process solutions. This paper presents the Security Incident Response Criteria (SIRC) which can be applied to a variety of security incident response approaches. The criteria are derived from empirical data based on in-depth interviews conducted within a Global Fortune 500 organization and supporting literature. The research contribution of this paper is twofold. First, the criteria presented in this paper can be used to evaluate existing security incident response solutions and second, as a guide, to support future security incident response improvement initiatives.
Mining on Manifolds: Metric Learning Without Labels

In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds. Both types of examples are revealed by disagreements between Euclidean and manifold similarities. The discovered examples can be used in training with any discriminative loss. The method is applied to unsupervised fine-tuning of pre-trained networks for fine-grained classification and particular object retrieval. Our models are on par or are outperforming prior models that are fully or partially supervised.
Car-driver cooperation in future vehicles

The two main research branches in intelligent vehicles field are Advanced Driver Assistance Systems (ADAS)  and autonomous driving . ADAS generally work on predefined enviroment and limited scenarios such as highway driving, low speed driving, night driving etc. In such situations this systems have sufficiently high performance and the main features that allow their large diffusion and that have enabled commercialization in this years are the low cost, the small size and the easy integration into the vehicle. Autonomous vehicle, on the other hand, should be ready to work over all-scenarios, all-terrain and all-wheather conditions, but nowadays autonomous vehicle are used in protected and structured enviroments or military applications [3], . Generally many differences between ADAS and autonomous vehicles, both hardware and software features, are related on cost and integration: ADAS are embedded into vehicles and might be low cost; on the other hand usually are not heavy limitations on cost and integration related to autonomous vehicles. Obviosly, the main difference is the presence/absence of the driver. Otherwise, most of the undelying ideas are shared, such as perception, planning, actuation needed in this kind of systems.
Key Factors for Selecting an Agile Method : A Systematic Literature Review

Agile methods have become popular in recent years because the success rate of project development using Agile methods is better than structured design methods. Nevertheless, less than 50 percent of projects implemented using Agile methods are considered successful, and selecting the wrong Agile method is one of the reasons for project failure. Selecting the most appropriate Agile method is a challenging task because there are so many to choose from. In addition, potential adopters believe that migrating to an Agile method involves taking a drastic risk. Therefore, to assist project managers and other decision makers, this study aims to identify the key factors that should be considered when selecting an appropriate Agile method. A systematic literature review was performed to elicit these factors in an unbiased manner, and then content analysis was used to analyze the resultant data. It was found that the nature of the project, development team skills, project constraints, customer involvement and organizational culture are the key factors that should guide decision makers in the selection of an appropriate Agile method based on the value these factors have for different organizations and/or different projects.  Agile method selection; factors of selecting Agile methods; SLR
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

We introduce a method which enables a recurrent dynamics model to be temporally abstract. Our approach, which we call Adaptive Skip Intervals (ASI), is based on the observation that in many sequential prediction tasks, the exact time at which events occur is irrelevant to the underlying objective. Moreover, in many situations, there exist prediction intervals which result in particularly easy-to-predict transitions. We show that there are prediction tasks for which we gain both computational efficiency and prediction accuracy by allowing the model to make predictions at a sampling rate which it can choose itself.