nils-herrmann/clean_scirepeval huggingface dataset:
Controllable Neural Story Generation via Reinforcement Learning

Open story generation is the problem of automatically creating a story for any domain without retraining. Neural language models can be trained on large corpora across many domains and then used to generate stories. However, stories generated via language models tend to lack direction and coherence. We introduce a policy gradient reinforcement learning approach to open story generation that learns to achieve a given narrative goal state. In this work, the goal is for a story to end with a specific type of event, given in advance. However, a reward based on achieving the given goal is too sparse for effective learning. We use reward shaping to provide the reinforcement learner with a partial reward at every step. We show that our technique can train a model that generates a story that reaches the goal 94% of the time and reduces model perplexity. A human subject evaluation shows that stories generated by our technique are perceived to have significantly higher plausible event ordering and plot coherence over a baseline language modeling technique without perceived degradation of overall quality, enjoyability, or local causality.
Steps to Strengthen Ethics in Organizations: Research Findings, Ethics Placebos, and What Works

Research shows that many organizations overlook needs and opportunities to strengthen ethics. Barriers can make it hard to see the need for stronger ethics and even harder to take effective action. These barriers include the organization's misleading use of language, misuse of an ethics code, culture of silence, strategies of justification, institutional betrayal, and ethical fallacies. Ethics placebos tend to take the place of steps to see, solve, and prevent problems. This article reviews relevant research and specific steps that create change.
RGCNN: Regularized Graph CNN for Point Cloud Segmentation

Point cloud, an efficient 3D object representation, has become popular with the development of depth sensing and 3D laser scanning techniques. It has attracted attention in various applications such as 3D tele-presence, navigation for unmanned vehicles and heritage reconstruction. The understanding of point clouds, such as point cloud segmentation, is crucial in exploiting the informative value of point clouds for such applications. Due to the irregularity of the data format, previous deep learning works often convert point clouds to regular 3D voxel grids or collections of images before feeding them into neural networks, which leads to voluminous data and quantization artifacts. In this paper, we instead propose a regularized graph convolutional neural network (RGCNN) that directly consumes point clouds. Leveraging on spectral graph theory, we treat features of points in a point cloud as signals on graph, and define the convolution over graph by Chebyshev polynomial approximation. In particular, we update the graph Laplacian matrix that describes the connectivity of features in each layer according to the corresponding learned features, which adaptively captures the structure of dynamic graphs. Further, we deploy a graph-signal smoothness prior in the loss function, thus regularizing the learning process. Experimental results on the ShapeNet part dataset show that the proposed approach significantly reduces the computational complexity while achieving competitive performance with the state of the art. Also, experiments show RGCNN is much more robust to both noise and point cloud density in comparison with other methods. We further apply RGCNN to point cloud classification and achieve competitive results on ModelNet40 dataset.
Future of Cannabis and Cannabinoids in Therapeutics

This study reviews human clinical experience to date with several synthetic cannabinoids, including nabilone, levonantradol, ajulemic acid (CT3), dexanabinol (HU-211), HU-308, and SR141716 (Rimonabant®). Additionally, the concept of “clinical endogenous cannabinoid deficiency” is explored as a possible factor in migraine, idiopathic bowel disease, fibromyalgia and other clinical pain states. The concept of analgesic synergy of cannabinoids and opioids is addressed. A cannabinoid-mediated improvement in night vision at the retinal level is discussed, as well as its potential application to treatment of retinitis pigmentosa and other conditions. Additionally noted is the role of cannabinoid treatment in neuroprotection and its application to closed head injury, cerebrovascular accidents, and CNS degenerative diseases including Alzheimer, Huntington, Parkinson diseases and ALS. Excellent clinical results employing cannabis based medicine extracts (CBME) in spasticity and spasms of MS suggests extension of such treatment to other spasmodic and dystonic conditions. Finally, controversial areas of cannabinoid treatment in obstetrics, gynecology and pediatrics are addressed along with a rationale for such interventions.
mlpy: Machine Learning Python

mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3 at the website http://mlpy.fbk.eu.
eSport and the Human Body: foundations for a popular aesthetics

Notable philosophers of sport have rejected connections between videogames and sports throughout the past decade. This paper argues against an arbitrary identification of sporting activity with exhaustion and resistance. It reviews work in cognitive science and sociology that locates sporting potential in distributed cognition and team-based cooperation. Then it connects that work to an expanded discussion of the aesthetic issues raised by Henry Lowood's talk “Players Are Artists, too” at the Art History of Games Symposium. This introduction to the application of sports aesthetics to eSports revisits Lowood's major theoretical sources to expand upon the problems they raise for videogame performance. It argues that there are obstacles to the popular appreciation of eSporting aesthetics separate from the particular interests of archivists and expert spectators. And it suggests that different eSports present different sets of problems, producing multiple avenues for the advancement of aesthetic refinement in eSports broadcasts.
A survey on sentiment detection of reviews

The sentiment detection of texts has been witnessed a booming interest in recent years, due to the increased availability of online reviews in digital form and the ensuing need to organize them. Till to now, there are mainly four different problems predominating in this research community, namely, subjectivity classification, word sentiment classification, document sentiment classification and opinion extraction. In fact, there are inherent relations between them. Subjectivity classification can prevent the sentiment classifier from considering irrelevant or even potentially misleading text. Document sentiment classification and opinion extraction have often involved word sentiment classification techniques. This survey discusses related issues and main approaches to these problems.
Object recognition in images using convolutional neural network

Object detection from repository of images is challenging task in the area of computer vision and image processing in this work we present object classification and detection using cifar-10 data set with intended classification and detection of airplain images. So we used convolutional neural network on keras with tensorflow support the experimental results shows the time required to train, test and create the model in limited computing system. We train the system with 60,000 images with 25 epochs each epoch is taking 722to760 seconds in training step on tensorflow cpu system. At the end of 25 epochs the training accuracy is 96 percentage and the system can recognition input images based on train model and the output is respective label of images.
A Meaning-based Statistical English Math Word Problem Solver

We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.
A Survey Paper on Video Steganography

Now a days, it is very risky to handle the data in internet against intruders. Data is generally in the form of text, audio , video and image. Steganography is one of the best method to share the data secretly and securely. Steganography algorithm can be applied to audio, video and image file. Secret data may in the form of text, image or even in the form of video and audio. Hiding secret information in video file is known as video steganography. In this paper, a review on various video steganography techniques has been presented. Various spatial domain and transform domain techniques of video steganography have been discussed in this paper.  Steganography, Discrete wavelet transform, Discrete Cosine transform, cover image.
Getting past yes: negotiating as if implementation mattered.

Many deals that look good on paper never materialize into value-creating endeavors. Often, the problem begins at the negotiating table. In fact, the very person everyone thinks is pivotal to a deal's success--the negotiator--is often the one who undermines it. That's because most negotiators have a deal maker mind-set: They see the signed contract as the final destination rather than the start of a cooperative venture. What's worse, most companies reward negotiators on the basis of the number and size of the deals they're signing, giving them no incentive to change. The author asserts that organizations and negotiators must transition from a deal maker mentality--which involves squeezing your counterpart for everything you can get--to an implementation mind-set--which sets the stage for a healthy working relationship long after the ink has dried. Achieving an implementation mind-set demands five new approaches. First, start with the end in mind: Negotiation teams should carry out a "benefit of hindsight" exercise to imagine what sorts of problems they'll have encountered 12 months down the road. Second, help your counterpart prepare. Surprise confers advantage only because the other side has no time to think through all the implications of a proposal. If they agree to something they can't deliver, it will affect you both. Third, treat alignment as a shared responsibility. After all, if the other side's interests aren't aligned, it's your problem, too. Fourth, send one unified message. Negotiators should brief implementation teams on both sides together so everyone has the same information. And fifth, manage the negotiation like a business exercise: Combine disciplined negotiation preparation with post-negotiation reviews. Above all, companies must remember that the best deals don't end at the negotiating table--they begin there.
A solution to the single-question crowd wisdom problem

Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard ‘most popular’ or ‘most confident’ principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions.
Time-Domain Analysis of Intermodulation Distortion of Closed-Loop Class-D Amplifiers

This paper presents a time-domain analysis of the intermodulation distortion (IMD) of a closed-loop Class-D amplifier (amp) with either first- or second-order loop filter. The derived expression for the IMD indicates that there exist significant third-order intermodulation products (3rd-IMPs) within the output spectrum, which may lead to even greater distortion than the intrinsic harmonic components. In addition, the output expressions are compact, precise, and suitable for hand calculation so that the parametric relationships between the IMD and the magnitude and frequency of the input signals, as well as the effect of the loop filter design are straightforwardly investigated. In order to accurately represent the IMD performance of class-D amp, a modified testing setup is introduced to account for the dominantly large 3rd-IMPs when the International Telecommunication Union Radiocommunication Sector (ITU-R) standard is applied.
Study on Government Anti-Corruption Based on Game Theory

It is a well-established fact that corruption is a widespread phenomenon and it is widely acknowledged because of negative impact on economy and society. An important aspect of corruption is that two parties act separately or jointly in order to further their own interests at the expense of society. To strengthen prevent corruption, most of countries have construct special organization. The paper presents a new measure based on introducing game theory as an analytical tool for analyzing the relation between anti-corruption and corruption. Firstly, the paper introduces the corruption situation in China, gives the definition of the game theory and studies government anti-corruption activity through constructing the game theoretic models between anti-corruption and corruption. The relation between supervisor and the anti-corruption will be explained next. A thorough analysis of the mechanism of informant system has been made accordingly in the third part. At last, some suggestions for preventing and fight corruption are put forward.
AI Characters and Directors for Interactive Computer Games

We are creating an environment for investigating the role of advanced AI in interactive, story-based computer games. This environment is based on the Unreal Tournament (UT) game engine and the Soar AI engine. Unreal provides a 3D virtual environment, while Soar provides a flexible architecture for developing complex AI characters. This paper describes our progress to date, starting with our game, Haunt 2, which is designed so that complex AI characters will be critical to the success (or failure) of the game. It addresses design issues with constructing a plot for an interactive storytelling environment, creating synthetic characters for that environment, and using a story director agent to tell the story with those characters.
Clustering in heterogeneous network and higher order structure

Cluster identification in large scale information network is a highly attractive issue in the network knowledge mining. Traditionally, community detection algorithms are designed to cluster object population based on minimizing the cutting edge number. Recently, researchers proposed the concept of higher-order clustering framework to segment network objects under the higher-order connectivity patterns. However, the essences of the numerous methodologies are focusing on mining the homogeneous networks to identify groups of objects which are closely related to each other, indicating that they ignore the heterogeneity of different types of objects and links in the networks. In this study, we propose an integrated framework of heterogeneous information network structure and higher-order clustering for mining the hidden relationship, which include three major steps: (1) Construct the heterogeneous network, (2) Convert HIN to Homogeneous network, and (3) Community detection.
Liquidity risk of corporate bond returns : conditional approach $ Viral

We study the exposure of the US corporate bond returns to liquidity shocks of stocks and Treasury bonds over the period 1973–2007 in a regime-switching model. In one regime, liquidity shocks have mostly insignificant effects on bond prices, whereas in another regime, a rise in illiquidity produces significant but conflicting effects: Prices of investment-grade bonds rise while prices of speculative-grade (junk) bonds fall substantially (relative to the market). Relating the probability of these regimes to macroeconomic conditions we find that the second regime can be predicted by economic conditions that are characterized as “stress.” These effects, which are robust to controlling for other systematic risks (term and default), suggest the existence of time-varying liquidity risk of corporate bond returns conditional on episodes of flight to liquidity. Our model can predict the out-of-sample bond returns for the stress years 2008–2009. We find a similar pattern for stocks classified by high or low book-to-market ratio, where again, liquidity shocks play a special role in periods characterized by adverse economic conditions. & 2013 Elsevier B.V. All rights reserved.
Towards Addressing the Winograd Schema Challenge - Building and Using a Semantic Parser and a Knowledge Hunting Module

Concerned about the Turing test’s ability to correctly evaluate if a system exhibits human-like intelligence, the Winograd Schema Challenge (WSC) has been proposed as an alternative. A Winograd Schema consists of a sentence and a question. The answers to the questions are intuitive for humans but are designed to be difficult for machines, as they require various forms of commonsense knowledge about the sentence. In this paper we demonstrate our progress towards addressing the WSC. We present an approach that identifies the knowledge needed to answer a challenge question, hunts down that knowledge from text repositories, and then reasons with them to come up with the answer. In the process we develop a semantic parser (www.kparser.org). We show that our approach works well with respect to a subset of Winograd schemas.
Spherical Mosaics with Quaternions and Dense Correlation

We describe an algorithm for generating spherical mosaics from a collection of images acquired from a common optical center. The algorithm takes as input an arbitrary number of partially overlapping images, an adjacency map relating the images, initial estimates of the rotations relating each image to a specified base image, and approximate internal calibration information for the camera. The algorithm's output is a rotation relating each image to the base image, and revised estimates of the camera's internal parameters. Our algorithm is novel in the following respects. First, it requires no user input. (Our image capture instrumentation provides both an adjacency map for the mosaic, and an initial rotation estimate for each image.) Second, it optimizes an objective function based on a global correlation of overlapping image regions. Third, our representation of rotations significantly increases the accuracy of the optimization. Finally, our representation and use of adjacency information guarantees globally consistent rotation estimates. The algorithm has proved effective on a collection of nearly four thousand images acquired from more than eighty distinct optical centers. The experimental results demonstrate that the described global optimization strategy is superior to non-global aggregation of pair-wise correlation terms, and that it successfully generates high-quality mosaics despite significant error in initial rotation estimates.
Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network

Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.
New development in CAR-T cell therapy

Chimeric antigen receptor (CAR)-engineered T cells (CAR-T cells) have yielded unprecedented efficacy in B cell malignancies, most remarkably in anti-CD19 CAR-T cells for B cell acute lymphoblastic leukemia (B-ALL) with up to a 90% complete remission rate. However, tumor antigen escape has emerged as a main challenge for the long-term disease control of this promising immunotherapy in B cell malignancies. In addition, this success has encountered significant hurdles in translation to solid tumors, and the safety of the on-target/off-tumor recognition of normal tissues is one of the main reasons. In this mini-review, we characterize some of the mechanisms for antigen loss relapse and new strategies to address this issue. In addition, we discuss some novel CAR designs that are being considered to enhance the safety of CAR-T cell therapy in solid tumors.
Matterport3D: Learning from RGB-D Data in Indoor Environments

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a restricted scale of spaces. In this paper, we introduce Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided with surface reconstructions, camera poses, and 2D and 3D semantic segmentations. The precise global alignment and comprehensive, diverse panoramic set of views over entire buildings enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification.