nils-herrmann/clean_scirepeval huggingface dataset:
Erik Erikson: critical times, critical theory.

The work and legacy of Erik Erikson are described in this brief outline of his career, his theories, and his impact on psychoanalysis, psychology, history, and the broader culture. His conception of the adolescent task-weaving internal tastes, talents, and values together with elements of one's life history and the demands of one's culture into a coherent identity-has had profound effects on developmental psychology and the way in which sophisticated youth construct and describe their lives. His extension of development through adulthood and old age established the field of life course development. His emphasis on the impact of history and culture on development was a critical element in the developing field of ego psychology. Many of his major contributions can be fruitfully understood in the context of his personal history and individual qualities.
StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks.
Animal welfare and international trade.

Globalisation is becoming a force that is revolutionising international trade, particularly that of animals and animal products. There is increasing interest in animal welfare worldwide, and as part of its 2001-2005 Strategic Plan the World Organisation for Animal Health (OIE) identified the development of international standards on animal welfare as a priority. The OIE's scientific approach to standard-setting provides the foundation for the development, and acceptance by all OIE Member Countries, of these animal welfare guidelines. The paper discusses how these guidelines on animal welfare can be implemented, both within the provisions of World Trade Organization (WTO) agreements and within the framework of voluntary codes of conduct. Even if animal welfare guidelines are not covered by any WTO agreements in the future, bi- and multilateral agreements, voluntary corporate codes, and transparent labelling of products should result in a progressive acceptance of OIE guidelines. Ultimately, consumer demands and demonstrable gains in animal production will result in an incremental evolution in animal welfare consciousness and adherence to international standards.
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations

One conventional tool for interpolating surfaces over scattered data, the thin-plate spline, has an elegant algebra expressing the dependence of the physical bending energy of a thin metal plate on point constraints. For interpolation of a surface over a fixed set of nodes in the plane, the bending energy is a quadratic form in the heights assigned to the surface. The spline is the superposition of eigenvectors of the bending energy matrix, of successively larger physical scales, over a tilted flat plane having no bending energy at all. When these splines are paired, one representing the x-coordinate of another form and the other the y-coordinate, they aid greatly in the modeling of biological shape change as deformation. In this context, the pair becomes an interpolation map from RZ to R' relating two sets of landmark points. The spline maps decompose, in the same way as the spline surfaces, into a linear part (an affine transformation) together with the superposition of principal warps, which are geometrically independent, affine-free deformations of progressively smaller geometrical scales. The warps decompose an empirical deformation into orthogonal features more or less as a conventional orthogonal functional analysis decomposes the single scene. This paper demonstrates the decomposition of deformations by principal warps, extends the method to deal with curving edges between landmarks, relates this formalism to other applications of splines current in computer vision, and indicates how they might aid in the extraction of features for analysis, comparison, and diagnosis of biological and medical images.
Indoor Scene Parsing with Instance Segmentation, Semantic Labeling and Support Relationship Inference

Over the years, indoor scene parsing has attracted a growing interest in the computer vision community. Existing methods have typically focused on diverse subtasks of this challenging problem. In particular, while some of them aim at segmenting the image into regions, such as object or surface instances, others aim at inferring the semantic labels of given regions, or their support relationships. These different tasks are typically treated as separate ones. However, they bear strong connections: good regions should respect the semantic labels, support can only be defined for meaningful regions, support relationships strongly depend on semantics. In this paper, we therefore introduce an approach to jointly segment the instances and infer their semantic labels and support relationships from a single input image. By exploiting a hierarchical segmentation, we formulate our problem as that of jointly finding the regions in the hierarchy that correspond to instances and estimating their class labels and pairwise support relationships. We express this via a Markov Random Field, which allows us to further encode links between the different types of variables. Inference in this model can be done exactly via integer linear programming, and we learn its parameters in a structural SVM framework. Our experiments on NYUv2 demonstrate the benefits of reasoning jointly about all these subtasks of indoor scene parsing.
LSB: A Lightweight Scalable BlockChain for IoT Security and Privacy

BlockChain (BC) has attracted tremendous attention due to its immutable nature and the associated security and privacy benefits. BC has the potential to overcome security and privacy challenges of Internet of Things (IoT). However, BC is computationally expensive, has limited scalability and incurs significant bandwidth overheads and delays which are not suited to the IoT context. We propose a tiered Lightweight Scalable BC (LSB) that is optimized for IoT requirements. We explore LSB in a smart home setting as a representative example for broader IoT applications. Low resource devices in a smart home benefit from a centralized manager that establishes shared keys for communication and processes all incoming and outgoing requests. LSB achieves decentralization by forming an overlay network where high resource devices jointly manage a public BC that ensures end-to-end privacy and security. The overlay is organized as distinct clusters to reduce overheads and the cluster heads are responsible for managing the public BC. LSB incorporates several optimizations which include algorithms for lightweight consensus, distributed trust and throughput management. Qualitative arguments demonstrate that LSB is resilient to several security attacks. Extensive simulations show that LSB decreases packet overhead and delay and increases BC scalability compared to relevant baselines.
Deep Painterly Harmonization

Copying an element from a photo and pasting it into a painting is a challenging task. Applying photo compositing techniques in this context yields subpar results that look like a collage — and existing painterly stylization algorithms, which are global, perform poorly when applied locally. We address these issues with a dedicated algorithm that carefully determines the local statistics to be transferred. We ensure both spatial and inter-scale statistical consistency and demonstrate that both aspects are key to generating quality results. To cope with the diversity of abstraction levels and types of paintings, we introduce a technique to adjust the parameters of the transfer depending on the painting. We show that our algorithm produces significantly better results than photo compositing or global stylization techniques and that it enables creative painterly edits that would be otherwise difficult to achieve. CCS Concepts •Computing methodologies → Image processing;
A improved Network Security Situation Awareness Model

Fangwei Li Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China lifw@cqupt.edu.cn Xinyue Zhang Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China zhangxinyue159@163.com Jiang Zhu Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China zhujiang@cqupt.edu.cn Yan Wang Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China wangyan2250@sina.com ABSTRACT In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS) was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP) algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.
Liquid biopsy: monitoring cancer-genetics in the blood

Cancer is associated with mutated genes, and analysis of tumour-linked genetic alterations is increasingly used for diagnostic, prognostic and treatment purposes. The genetic profile of solid tumours is currently obtained from surgical or biopsy specimens; however, the latter procedure cannot always be performed routinely owing to its invasive nature. Information acquired from a single biopsy provides a spatially and temporally limited snap-shot of a tumour and might fail to reflect its heterogeneity. Tumour cells release circulating free DNA (cfDNA) into the blood, but the majority of circulating DNA is often not of cancerous origin, and detection of cancer-associated alleles in the blood has long been impossible to achieve. Technological advances have overcome these restrictions, making it possible to identify both genetic and epigenetic aberrations. A liquid biopsy, or blood sample, can provide the genetic landscape of all cancerous lesions (primary and metastases) as well as offering the opportunity to systematically track genomic evolution. This Review will explore how tumour-associated mutations detectable in the blood can be used in the clinic after diagnosis, including the assessment of prognosis, early detection of disease recurrence, and as surrogates for traditional biopsies with the purpose of predicting response to treatments and the development of acquired resistance.
CNN Is All You Need

CNNs have been successfully used in audio, image and text classification, analysis and generation [12,17,18], whereas the RNNs with LSTM cells [5,6] have been widely adopted for solving sequence transduction problems such as language modeling and machine translation [19,3,5]. The RNN models typically align the element positions of the input and output sequences to steps in computation time for generating the sequenced hidden states, with each depending on the current element and the previous hidden state. Such operations are inherently sequential which precludes parallelization and becomes the performance bottleneck. This situation has motivated researchers to extend the easily parallelizable CNN models for more efficient sequence-to-sequence mapping. Once such efforts can deliver satisfactory quality, the usage of CNN in deep learning would be significantly broadened.
Deep Binary Reconstruction for Cross-Modal Hashing

With the increasing demand of massive multimodal data storage and organization, cross-modal retrieval based on hashing technique has drawn much attention nowadays. It takes the binary codes of one modality as the query to retrieve the relevant hashing codes of another modality. However, the existing binary constraint makes it difficult to find the optimal cross-modal hashing function. Most approaches choose to relax the constraint and perform thresholding strategy on the real-value representation instead of directly solving the original objective. In this paper, we first provide a concrete analysis about the effectiveness of multimodal networks in preserving the inter- and intra-modal consistency. Based on the analysis, we provide a so-called Deep Binary Reconstruction (DBRC) network that can directly learn the binary hashing codes in an unsupervised fashion. The superiority comes from a proposed simple but efficient activation function, named as Adaptive Tanh (ATanh). The ATanh function can adaptively learn the binary codes and be trained via back-propagation. Extensive experiments on three benchmark datasets demonstrate that DBRC outperforms several state-of-the-art methods in both image2text and text2image retrieval task.
Giving computers a nose for news: exploring the limits of story detection and verification

The use of social media as a source of news is entering a new phase as computer algorithms are developed and deployed to detect, rank, and verify news. The efficacy and ethics of such technology are the subject of this article, which examines the SocialSensor application, a tool developed by a multidisciplinary EU research project. The results suggest that computer software can be used successfully to identify trending news stories, allow journalists to search within a social media corpus, and help verify social media contributors and content. However, such software also raises questions about accountability as social media is algorithmically filtered for use by journalists and others. Our analysis of the inputs SocialSensor relies on shows biases towards those who are vocal and have an audience, many of whom are men in the media. We also reveal some of the technology's temporal and topic preferences. The conclusion discusses whether such biases are necessary for systems like SocialSensor to be effective. The article also suggests that academic research has failed to fully recognise the changes to journalists' sourcing practices brought about by social media, particularly Twitter, and provides some countervailing evidence and an explanation for this failure.  The ubiquity of computing in contemporary culture has resulted in human decision-making being augmented, and even partially replaced, by computational processes or algorithms using artificial intelligence and information-retrieval techniques. Such augmentation and substitution is already common, and even predominates, in some industries, such as financial trading and legal research. Frey and Osborne (2013) have attempted to predict the extent to which a wide spectrum of jobs is susceptible to computerisation. Although journalists were not included in their analysis, some of the activities undertaken by journalists—for example those carried out by interviewers, proofreaders, and copy markers—were, and had a greater than 50 per cent probability of being computerised. It is that potential for the automation of journalistic work that is explored in this article. Frey and Osborne remind us of how automation can be aggressively resisted by workers, giving the example of William Lee who, they say, was driven out of Britain by the guild of hosiers for inventing a machine that knitted stockings. Such resistance also exists in the context of journalistic automation. For example, the German Federation of Journalists have said they " don't think it is … desirable that journalism is done with algorithms " (Konstantin Dörr, personal communication, 6 February …
Facebook sentiment: Reactions and Emojis

Emojis are used frequently in social media. A widely assumed view is that emojis express the emotional state of the user, which has led to research focusing on the expressiveness of emojis independent from the linguistic context. We argue that emojis and the linguistic texts can modify the meaning of each other. The overall communicated meaning is not a simple sum of the two channels. In order to study the meaning interplay, we need data indicating the overall sentiment of the entire message as well as the sentiment of the emojis stand-alone. We propose that Facebook Reactions are a good data source for such a purpose. FB reactions (e.g. “Love” and “Angry”) indicate the readers’ overall sentiment, against which we can investigate the types of emojis used the comments under different reaction profiles. We present a data set of 21,000 FB posts (57 million reactions and 8 million comments) from public media pages across four countries.
Functional magnetic resonance imaging of reward prediction.

Technical and conceptual advances in functional magnetic resonance imaging now allow visualization of real-time changes in oxygenation of deep subcortical regions, leading to rapid advances in scientific characterization of the neural substrates that underlie reward prediction in humans.



Neuroimaging research over the past year has focused on determining the necessary neural substrates for reward prediction.



While the orbitofrontal cortex has long been implicated in modality-specific reward representation, the ventral striatum (particularly the nucleus accumbens) may play a role in modality-independent representations of predicted reward. On the other hand, the mesial prefrontal cortex appears to play a role in representing reward prediction error and the dorsal caudate in linking reward to behavior. Theoretically, future studies will need to establish the specificity of these responses to reward versus punishment and anticipation versus outcome. Clinically, current findings suggest that patients can predict reward without a prefrontal cortex, but should experience difficulty correcting their behavior when reward predictions are violated.
Learning Dilation Factors for Semantic Segmentation of Street Scenes

Contextual information is crucial for semantic segmentation. However, finding the optimal trade-off between keeping desired fine details and at the same time providing sufficiently large receptive fields is non trivial. This is even more so, when objects or classes present in an image significantly vary in size. Dilated convolutions have proven valuable for semantic segmentation, because they allow to increase the size of the receptive field without sacrificing image resolution. However, in current state-of-the-art methods, dilation parameters are hand-tuned and fixed. In this paper, we present an approach for learning dilation parameters adaptively per channel, consistently improving semantic segmentation results on street-scene datasets like Cityscapes and Camvid.
A Machine Learning Approach to Predict Chemical Reactions

Being able to predict the course of arbitrary chemical react ions is essential to the theory and applications of organic chemistry. Previous app roaches are not highthroughput, are not generalizable or scalable, or lack suffi cient data to be effective. We describe single mechanistic reactions as concerted elec tron movements from an electron orbital source to an electron orbital sink. We us e an existing rule-based expert system to derive a dataset consisting of 2,989 productive mechanistic steps and6.14 million non-productive mechanistic steps. We then pose ide nt fying productive mechanistic steps as a ranking problem: rank potent ial orbital interactions such that the top ranked interactions yield the major produc ts. The machine learning implementation follows a two-stage approach, in which w e first train atom level reactivity filters to prune94.0% of non-productive reactions with less than a 0.1% false negative rate. Then, we train an ensemble of ranking mo dels n pairs of interacting orbitals to learn a relative productivity func tion over single mechanistic reactions in a given system. Without the use of explicit t ransformation patterns, the ensemble perfectly ranks the productive mechanisms at t he top89.1% of the time, rising to99.9% of the time when top ranked lists with at most four nonproductive reactions are considered. The final system allow s multi-step reaction prediction. Furthermore, it is generalizable, making reas on ble predictions over reactants and conditions which the rule-based expert syste m does not handle.
Hindsight Experience Replay

Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering. It can be combined with an arbitrary off-policy RL algorithm and may be seen as a form of implicit curriculum. We demonstrate our approach on the task of manipulating objects with a robotic arm. In particular, we run experiments on three different tasks: pushing, sliding, and pick-and-place, in each case using only binary rewards indicating whether or not the task is completed. Our ablation studies show that Hindsight Experience Replay is a crucial ingredient which makes training possible in these challenging environments. We show that our policies trained on a physics simulation can be deployed on a physical robot and successfully complete the task. The video presenting our experiments is available at https://goo.gl/SMrQnI.
EFFECTS OF INTERNET USE AND SOCIAL RESOURCES ON CHANGES IN DEPRESSION

We examine how people’s different uses of the Internet predict their later scores on a standard measure of depression, and how their existing social resources moderate these effects. In a longitudinal US survey conducted in 2001 and 2002, almost all respondents reported using the Internet for information, and entertainment and escape; these uses of the Internet had no impact on changes in respondents’ level of depression. Almost all respondents also used the Internet for communicating with friends and family, and they showed lower depression scores six months later. Only about 20 percent of this sample reported using the Internet to meet new people and talk in online groups. Doing so changed their depression scores depending on their initial levels of social support. Those having high or medium levels of social support showed higher depression scores; those with low levels of social support did not experience these increases in depression. Our results suggest that individual differences in social resources and people’s choices of how they use the Internet may account for the different outcomes reported in the literature.