nils-herrmann/clean_scirepeval huggingface dataset:
A Survey of Procedural Content Generation Techniques Suitable to Game Development

The development of a complex game is a time consuming task that requires a significant amount of content generation, including terrains, objects, characters, etc that requires a lot of effort from the a designing team. The quality of such content impacts the project costs and budget. One of the biggest challenges concerning the content is how to improve its details and at the same time lower the creation costs. In this context procedural content generation techniques can help to reduce the costs associated with content creation. This paper presents a survey of classical and modern techniques focused on procedural content generation suitable for game development. They can be used to produce terrains, coastlines, rivers, roads and cities. All techniques are classified as assisted (require human intervention/guidance in order to produce results) or non-assisted (require few or no human intervention/guidance to produce the desired results).
Intent-Aware Multi-Agent Reinforcement Learning

This paper proposes an intent-aware multi-agent planning framework as well as a learning algorithm. Under this framework, an agent plans in the goal space to maximize the expected utility. The planning process takes the belief of other agents' intents into consideration. Instead of formulating the learning problem as a partially observable Markov decision process (POMDP), we propose a simple but effective linear function approximation of the utility function. It is based on the observation that for humans, other people's intents will pose an influence on our utility for a goal. The proposed framework has several major advantages: i) it is computationally feasible and guaranteed to converge. ii) It can easily integrate existing intent prediction and low-level planning algorithms. iii) It does not suffer from sparse feedbacks in the action space. We experiment our algorithm in a real-world problem that is non-episodic, and the number of agents and goals can vary over time. Our algorithm is trained in a scene in which aerial robots and humans interact, and tested in a novel scene with a different environment. Experimental results show that our algorithm achieves the best performance and human-like behaviors emerge during the dynamic process.
Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.
Story Generators : Models and Approaches for the Generation of Literary Artefacts

T his session is concerned with the automated creation of fiction or "literary artefacts" that might take the form of prose, poetry or drama. Special focus is placed upon those approaches that include the generation of narrative structures and therefore use some kind of story model. First attempts in automated story generation date back to the 1970s, with the implementation of Meehan's TALE-SPIN (1977) based on the achievement of character plans and Klein's automatic novel writer (1973/1979) that simulates the effects of generated events in the narrative universe. Currently, story generators enjoy a phase of revival, both as stand-alone systems or embedded components. Most of them make reference to an explicit model of narrative, but the approaches used are diverse: they range from story grammars in the generative vein to the conceptually inspired engagement-reflection cycle. Real-life applications include the generation of a set of plot plans for screen writers in a commercial entertainment environment, who could use the automatically created story pool as a source of inspiration, and the generation of new kinds of interactive dramas (video games).
Occupational burnout, retention and health outcomes in nephrology nurses.

Occupational burnout can have serious implications on productivity, nurses'health, service usage, and health care costs. This study examined the effect of burnout on nurses' mental and physical health outcomes and job retention. Randomly selected Canadian nephrology nurses completed surveys consisting of the Maslach Burnout Inventory and the Pressure Management Indicator. The nurses also completed questions related to job retention. After controlling for age and years of nephrology nursing experience, the multivariate results demonstrated that almost 40% of mental health symptoms experienced by nephrology nurses could be explained by burnout and 27.5% of physical symptoms could be explained by burnout. Twenty-three per cent of the sample had plans to leave their current position and retention was significantly associated with burnout, mental, and physical symptoms. Organizational strategies aimed at reducing perceptions of burnout are important, as a means to keep nurses healthy and working to their fullest potential.
Learning to Play Computer Games with Deep Learning and Reinforcement Learning Interim Report

With the integration of deep learning into the traditional field of reinforcement learning in the recent decades, the spectrum of applications that artificial intelligence caters is currently very broad. As using AI to play games is a traditional application of reinforcement learning, the project’s objective is to implement a deep reinforcement learning agent that can defeat a video game. Since it is often difficult to determine which algorithms are appropriate given the wide selection of state-of-the-art techniques in the discipline, proper comparisons and investigations of the algorithms are a prerequisite to implementing such an agent. As a result, this paper serves as a platform for exploring the possibility and effectiveness of using conventional state-of-the-art methods, such as Deep Q Networks and its variants, such as Double Deep Q Networks, are appropriate for game playing, with Deep Q Networks successful in playing a randomized map, further work in this project is needed in order for a comprehensive view of the discipline. Such work in the near future includes the investigation of the use of deep reinforcement learning on games unreported in the literature, or potential improvement to existing deep reinforcement learning techniques. In spite of the technical difficulties encountered and minor amendments to the project schedule, the project is still currently on schedule, ie. approximately 50% complete.
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention

Image denoising is always a challenging task in the field of computer vision and image processing. In this paper we have proposed an encoder-decoder model with direct attention, which is capable of denoising and reconstruct highly corrupted images. Our model is consisted of an encoder and a decoder, where encoder is a convolutional neural network and decoder is a multilayer Long Short-Term memory network. In the proposed model, the encoder reads an image and catches the abstraction of that image in a vector, where decoder takes that vector as well as the corrupted image to reconstruct a clean image. We have trained our model on MNIST handwritten digit database after making lower half of every image as black as well as adding noise top of that. After a massive destruction of the images where it is hard for a human to understand the content of those images, our model can retrieve that image with minimal error. Our proposed model has been compared with convolutional encoder-decoder, where our model has performed better at generating missing part of the images than convolutional auto encoder.
Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation

Recent deep CNNs contain forward shortcut connections; i.e. skip connections from low to high layers. Reusing features from lower layers that have higher resolution (location information) benefit higher layers to recover lost details and mitigate information degradation. However, during inference the lower layers do not know about high layer features, although they contain contextual high semantics that benefit low layers to adaptively extract informative features for later layers. In this paper, we study the influence of backward skip connections which are in the opposite direction to forward shortcuts, i.e. paths from high layers to low layers. To achieve this – which indeed runs counter to the nature of feed-forward networks – we propose a new fully convolutional model that consists of a pair of networks. A ‘Slave’ network is dedicated to provide the backward connections from its top layers to the ‘Master’ network’s bottom layers. The Master network is used to produce the final label predictions. In our experiments we validate the proposed FCN model on ADE20K (ImageNet scene parsing), PASCAL-Context, and PASCAL VOC 2011 datasets.
Stacked Generative Adversarial Networks

In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, leveraging the powerful discriminative representations to guide the generative model. In addition, we introduce a conditional loss that encourages the use of conditional information from the layer above, and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs. We first train each stack independently, and then train the whole model end-to-end. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Based on visual inspection, Inception scores and visual Turing test, we demonstrate that SGAN is able to generate images of much higher quality than GANs without stacking.
Domain Separation Networks

The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach circumventing this cost is training models on synthetic data where annotations are provided automatically. Despite their appeal, such models often fail to generalize from synthetic to real images, necessitating domain adaptation algorithms to manipulate these models before they can be successfully applied. Existing approaches focus either on mapping representations from one domain to the other, or on learning to extract features that are invariant to the domain from which they were extracted. However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain. We hypothesize that explicitly modeling what is unique to each domain can improve a model’s ability to extract domain-invariant features. Inspired by work on private-shared component analysis, we explicitly learn to extract image representations that are partitioned into two subspaces: one component which is private to each domain and one which is shared across domains. Our model is trained to not only perform the task we care about in the source domain, but also to use the partitioned representation to reconstruct the images from both domains. Our novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process.
Actions of melatonin in the reduction of oxidative stress. A review.

Melatonin was discovered to be a direct free radical scavenger less than 10 years ago. Besides its ability to directly neutralize a number of free radicals and reactive oxygen and nitrogen species, it stimulates several antioxidative enzymes which increase its efficiency as an antioxidant. In terms of direct free radical scavenging, melatonin interacts with the highly toxic hydroxyl radical with a rate constant equivalent to that of other highly efficient hydroxyl radical scavengers. Additionally, melatonin reportedly neutralizes hydrogen peroxide, singlet oxygen, peroxynitrite anion, nitric oxide and hypochlorous acid. The following antioxidative enzymes are also stimulated by melatonin: superoxide dismutase, glutathione peroxidase and glutathione reductase. Melatonin has been widely used as a protective agent against a wide variety of processes and agents that damage tissues via free radical mechanisms.
RETAIN: Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism

Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are important. We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention. RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.
Design Inspiration from Generative Networks

Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include: (i) different Generative Adversarial Networks architectures that start from noise vectors to generate fashion items, (ii) novel loss functions that encourage creativity, inspired from Sharma-Mittal divergence, a generalized mutual information measure for the widely used relative entropies such as Kullback-Leibler, and (iii) a generation process following the key elements of fashion design (disentangling shape and texture components). A key challenge of this study is the evaluation of generated designs and the retrieval of best ones, hence we put together an evaluation protocol associating automatic metrics and human experimental studies that we hope will help ease future research. We show that our proposed creativity losses yield better overall appreciation than the one employed in  In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelty and likability.  Training generative adversarial models with appropriate losses leads to realistic and creative 512× 512 fashion images.
vCorfu: A Cloud-Scale Object Store on a Shared Log

This paper presents vCorfu, a strongly consistent cloudscale object store built over a shared log. vCorfu augments the traditional replication scheme of a shared log to provide fast reads and leverages a new technique, composable state machine replication, to compose large state machines from smaller ones, enabling the use of state machine replication to be used to efficiently in huge data stores. We show that vCorfu outperforms Cassandra, a popular state-of-the art NOSQL stores while providing strong consistency (opacity, read-own-writes), efficient transactions, and global snapshots at cloud scale.
Estimating Embedding Vectors for Queries

The dense vector representation of vocabulary terms, also known as word embeddings, have been shown to be highly effective in many natural language processing tasks. Word embeddings have recently begun to be studied in a number of information retrieval (IR) tasks. One of the main steps in leveraging word embeddings for IR tasks is to estimate the embedding vectors of queries. This is a challenging task, since queries are not always available during the training phase of word embedding vectors. Previous work has considered the average or sum of embedding vectors of all query terms (AWE) to model the query embedding vectors, but no theoretical justification has been presented for such a model. In this paper, we propose a theoretical framework for estimating query embedding vectors based on the individual embedding vectors of vocabulary terms. We then provide a number of different implementations of this framework and show that the AWE method is a special case of the proposed framework. We also introduce pseudo query vectors, the query embedding vectors estimated using pseudo-relevant documents. We further extrinsically evaluate the proposed methods using two well-known IR tasks: query expansion and query classification. The estimated query embedding vectors are evaluated via query expansion experiments over three newswire and web TREC collections as well as query classification experiments over the KDD Cup 2005 test set. The experiments show that the introduced pseudo query vectors significantly outperform the AWE method.
MOND effects in the inner solar system

I pinpoint a previously unrecognized MOND effect that may act in the inner solar system, and is due to the galactic acceleration, g g = ηa 0 : a byproduct of the MOND external-field effect. Predictions of the effect are not generic to the MOND paradigm, but depend on the particular MOND formulation at hand. However, the modified-Poisson formulation, on which I concentrate, uniquely predicts a subtle anomaly that may be detected in planetary and spacecraft motions (and perhaps in other precision systems, such as binary pulsars), despite their very high accelerations, and even if the MOND interpolating function is arbitrarily close to unity at high accelerations. Near the sun, this anomaly appears as a quadrupole field, with the acceleration at position u from the sun being g a i (u) = −q a ij u j , with q a ij diagonal, axisymmetric, and traceless: −2q a xx = −2q a yy = q a zz = q(η)(a 0 /R M), where R M = (M ⊙ G/a 0) 1/2 ≈ 8 · 10 3 a.u. is the MOND radius of the sun. The anomaly is described and analyzed as the Newtonian field of the fictitious cloud of " phantom matter " that hovers around the sun. I find, for the relevant range of η values, and for a range of interpolating functions, µ(x), values of 10 −2 −q 0.3, which turn out to be sensitive to the form of µ(x) around the MOND-to-Newtonian transition. This range verges on the present bounds from solar system measurements. There might thus exist favorable prospects for either measuring the effect, or constraining the theory and the relevant parameters. Probing this anomaly may also help distinguish between modified inertia and modified gravity formulations of MOND. I also discuss briefly an anomaly that is generic to MOND in all its formulations, and competes with the quadrupole anomaly in the special case that 1 − µ(x) vanishes as x −3/2 as x → ∞.
A Novel Approach of Pseudorandomly sorted list-based Steganography

We propose a new model of steganography based on a list of pseudo-randomly sorted sequences of characters. Given a list L of m columns containing n distinct strings each, with low or no semantic relationship between columns taken two by two, and a secret message s ∈ {0, 1}∗, our model embeds s in L block by block, by generating, for each column of L, a permutation number and by reordering strings contained in it according to that number. Where, letting l be average bit length of a string, the embedding capacity is given by [(m − 1) ∗ log2(n! − 1)/n ∗ l]. We’ve shown that optimal efficiency of the method can be obtained with the condition that (n >> l). The results which has been obtained by experiments, show that our model performs a better hiding process than some of the important existing methods, in terms of hiding capacity.
Mechatronic Systems for Machine Tools

This paper reviews current developments in mechatronic systems for metal cutting and forming machine tools. The integration of mechatronic modules to the machine tool and their interaction with manufacturing processes are presented. Sample mechatronic components for precision positioning and compensation of static, dynamic and thermal errors are presented as examples. The effect of modular integration of mechatronic system on the reconfigurability and reliability of the machine tools is discussed along with intervention strategies during machine tool operation. The performance and functionality aspects are discussed through active and passive intervention methods. A special emphasis was placed on active and passive damping of vibrations through piezo, magnetic and electro-hydraulic actuators. The modular integration of mechatronic components to the machine tool structure, electronic unit and CNC software system is presented. The paper concludes with the current research challenges required to expand the application of mechatronics in machine tools and manufacturing systems.
Isovists as a Means to Predict Spatial Experience and Behavior

Two experiments are presented studying interrelations between spatial properties of environments and both experience and spatial behavior. In order to systematically study such interrelations, a generic description of space is required that provides comparability between arbitrarily shaped environments and captures behaviorally relevant properties of space. In this study the suitability of isovist derived measurands for this purpose was explored. Isovist-based descriptions of 16 virtual indoor scenes were correlated with behavioral data from two experimental tasks. For both tasks, an active navigation task and a rating of experiential qualities, strong correlations between subjects’ behavior and measurands derived from isovist analysis were found. The general outcomes suggest that isovist measurands are indeed a promising means to predict the experience of space and spatial behavior for the chosen experimental tasks.