nils-herrmann/clean_scirepeval huggingface dataset:
Diversity is All You Need: Learning Skills without a Reward Function

Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose “Diversity is All You Need”(DIAYN), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy. On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping. In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward. We show how pretrained skills can provide a good parameter initialization for downstream tasks, and can be composed hierarchically to solve complex, sparse reward tasks. Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning.
Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing

With the developments and applications of the new information technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, a smart manufacturing era is coming. At the same time, various national manufacturing development strategies have been put forward, such as Industry 4.0, Industrial Internet, manufacturing based on Cyber-Physical System, and Made in China 2025. However, one of specific challenges to achieve smart manufacturing with these strategies is how to converge the manufacturing physical world and the virtual world, so as to realize a series of smart operations in the manufacturing process, including smart interconnection, smart interaction, smart control and management, etc. In this context, as a basic unit of manufacturing, shop-floor is required to reach the interaction and convergence between physical and virtual spaces, which is not only the imperative demand of smart manufacturing, but also the evolving trend of itself. Accordingly, a novel concept of digital twin shop-floor (DTS) based on digital twin is explored and its four key components are discussed, including physical shop-floor, virtual shop-floor, shop-floor service system, and shop-floor digital twin data. What is more, the operation mechanisms and implementing methods for DTS are studied and key technologies as well as challenges ahead are investigated, respectively.
A Rumor Events Detection Method Based on Deep Bidirectional GRU Neural Network

Traditional rumors detection methods often rely on statistical analysis to manually select features to construct classifiers. Not only is the message feature selection difficult, but the gap, between the representation space where the shallow statistical features of information exist and the representation space where the highly abstract features including semantics and emotion of information exist, is very big. Thus, the result of traditional classifiers based on the shallow or middle features is not so good. Due to this problem, a rumors deteciton method based on Deep Bidirectional Gated Recurrent Unit (D-Bi-GRU) is presented. To capture the evolution of group response information of microblog events over time, we consider the forward and backward sequences of microblog flow of group response information along time line simultaneously. The evolution representations of deep latent space including semantic and emotion learned by stack multi layers Bi-GRUs to rumor detection. Experimental results on a real world data set showed that rumor events detection by considering bidirectional sequence of group response information simultaneously can obtain a better performance, and stack multi-layers Bi-GRUs can better detect rumor events in microblog.
MR Image Classification Using Adaboost for Brain Tumor Type

In medical diagnostic application, early defect detection is a crucial task as it provides critical insight into diagnosis. Medical imaging technique is actively developing field inengineering. Magnetic Resonance imaging (MRI) is one those reliable imaging techniques on which medical diagnostic is based upon. Manual inspection of those images is a tedious job as the amount of data and minute details are hard to recognize by the human. For this automating those techniques are very crucial. In this paper, we are proposing a method which can be utilized to make tumor detection easier. The MRI deals with the complicated problem of brain tumor detection. Due to its complexity and variance getting better accuracy is a challenge. Using Adaboost machine learning algorithm we can improve over accuracy issue. The proposed system consists of three parts such as Preprocessing, Feature extraction and Classification. Preprocessing has removed noise in the raw data, for feature extraction we used GLCM (Gray Level Co- occurrence Matrix) and for classification boosting technique used (Adaboost).
Humanitarian aid logistics: supply chain management in high gear

This paper builds on the idea that private sector logistics can and should be applied to improve the performance of disaster logistics but that before embarking on this the private sector needs to understand the core capabilities of humanitarian logistics. With this in mind, the paper walks us through the complexities of managing supply chains in humanitarian settings. It pinpoints the cross learning potential for both the humanitarian and private sectors in emergency relief operations as well as possibilities of getting involved through corporate social responsibility. It also outlines strategies for better preparedness and the need for supply chains to be agile, adaptable and aligned—a core competency of many humanitarian organizations involved in disaster relief and an area which the private sector could draw on to improve their own competitive edge. Finally, the article states the case for closer collaboration between humanitarians, businesses and academics to achieve better and more effective supply chains to respond to the complexities of today’s logistics be it the private sector or relieving the lives of those blighted by disaster.  (2006) 57, 475–489. doi:10.1057/palgrave.jors.2602125 Published online 14 December 2005
Domain Adaptation for CNN Based Iris Segmentation

Convolutional Neural Networks (CNNs) have shown great success in solving key artificial vision challenges such as image segmentation. Training these networks, however, normally requires plenty of labeled data, while data labeling is an expensive and time-consuming task, due to the significant human effort involved. In this paper we propose two pixel-level domain adaptation methods, introducing a training model for CNN based iris segmentation. Based on our experiments, the proposed methods can effectively transfer the domains of source databases to those of the targets, producing new adapted databases. The adapted databases then are used to train CNNs for segmentation of iris texture in the target databases, eliminating the need for the target labeled data. We also indicate that training a specific CNN for a new iris segmentation task, maintaining optimal segmentation scores, is possible using a very low number of training samples.
Resilience : The emergence of a perspective for social – ecological systems analyses

The resilience perspective is increasingly used as an approach for understanding the dynamics of social–ecological systems. This article presents the origin of the resilience perspective and provides an overview of its development to date. With roots in one branch of ecology and the discovery of multiple basins of attraction in ecosystems in the 1960–1970s, it inspired social and environmental scientists to challenge the dominant stable equilibrium view. The resilience approach emphasizes non-linear dynamics, thresholds, uncertainty and surprise, how periods of gradual change interplay with periods of rapid change and how such dynamics interact across temporal and spatial scales. The history was dominated by empirical observations of ecosystem dynamics interpreted in mathematical models, developing into the adaptive management approach for responding to ecosystem change. Serious attempts to integrate the social dimension is currently taking place in resilience work reflected in the large numbers of sciences involved in explorative studies and new discoveries of linked social–ecological systems. Recent advances include understanding of social processes like, social learning and social memory, mental models and knowledge–system integration, visioning and scenario building, leadership, agents and actor groups, social networks, institutional and organizational inertia and change, adaptive capacity, transformability and systems of adaptive governance that allow for management of essential ecosystem services. r 2006
Tense and Aspect Assignment in Narrative Discourse

We describe a method for assigning English tense and aspect in a system that realizes surface text for symbolically encoded narratives. Our testbed is an encoding interface in which propositions that are attached to a timeline must be realized from several temporal viewpoints. This involves a mapping from a semantic encoding of time to a set of tense/aspect permutations. The encoding tool realizes each permutation to give a readable, precise description of the narrative so that users can check whether they have correctly encoded actions and statives in the formal representation. Our method selects tenses and aspects for individual event intervals as well as subintervals (with multiple reference points), quoted and unquoted speech (which reassign the temporal focus), and modal events such as conditionals.
Controlling Popularity Bias in Learning-to-Rank Recommendation

Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable recommendations. In this paper, we introduce a flexible regularization-based framework to enhance the long-tail coverage of recommendation lists in a learning-to-rank algorithm. We show that regularization provides a tunable mechanism for controlling the trade-off between accuracy and coverage. Moreover, the experimental results using two data sets show that it is possible to improve coverage of long tail items without substantial loss of ranking performance.
CNN-based small object detection and visualization with feature activation mapping

Object detection is a well-studied topic, however detection of small objects still lacks attention. Detecting small objects has been difficult due to small sizes, occlusion and complex backgrounds. Small objects detection is important in a number of applications including detection of small insects. One application is spider detection and removal. Spiders are frequently found on grapes and broccolis sold at supermarkets and this poses a significant safety issue and generates negative publicity for the industry. In this paper, we present a fine-tuned VGG16 network for detection of small objects such as spiders. Furthermore, we introduce a simple technique called “feature activation mapping” for object visualization from VGG16 feature maps. The testing accuracy of our network on tiny spiders with various backgrounds is 84%, as compared to 72% using fined-tuned Faster R-CNN and 95.32% using CAM. Even though our feature activation mapping technique has a mid-range of test accuracy, it provides more detailed shape and size of spiders than using CAM which is important for the application area. A data set for spider detection is made available online.
FASTNORM: IMPROVING NUMERICAL STABILITY OF NEURAL NETWORK TRAINING WITH EFFICIENT NORMALIZATION

We propose a modification to weight normalization techniques that provides the same convergence benefits but requires fewer computational operations. The proposed method, FastNorm, exploits the low-rank properties of weight updates and infers the norms without explicitly calculating them, replacing anO(n) computation with an O(n) one for a fully-connected layer. It improves numerical stability and reduces accuracy variance enabling higher learning rate and offering better convergence. We report experimental results that illustrate the advantage of the proposed method.
Context-Based Few-Shot Word Representation Learning

Word representation learning methods have mostly been designed for and evaluated on frequent words. However, in real-world settings, deep neural architectures are often expected to accept a large vocabulary of possible input words. In this paper, we investigate context-based techniques for few-shot learning of representations for infrequent words. We first adapt word2vec to account for expanded contexts and subsequently introduce an additional smoothing procedure. Experiments on similarity benchmarks show significant improvements for rare words.
XGSN: An Open-source Semantic Sensing Middleware for the Web of Things

We present XGSN, an open-source system that relies on semantic representations of sensor metadata and observations, to guide the process of annotating and publishing sensor data on the Web. XGSN is able to handle the data acquisition process of a wide number of devices and protocols, and is designed as a highly extensible platform, leveraging on the existing capabilities of the Global Sensor Networks (GSN) middleware. Going beyond traditional sensor management systems, XGSN is capable of enriching virtual sensor descriptions with semantically annotated content using standard vocabularies. In the proposed approach, sensor data and observations are annotated using an ontology network based on the SSN ontology, providing a standardized queryable representation that makes it easier to share, discover, integrate and interpret the data. XGSN manages the annotation process for the incoming sensor observations, producing RDF streams that are sent to the cloud-enabled Linked Sensor Middleware, which can internally store the data or perform continuous query processing. The distributed nature of XGSN allows deploying different remote instances that can interchange observation data, so that virtual sensors can be aggregated and consume data from other remote virtual sensors. In this paper we show how this approach has been implemented in XGSN, and incorporated to the wider OpenIoT platform, providing a highly flexible and scalable system for managing the life-cycle of sensor data, from acquisition to publishing, in the context of the semantic Web of Things.
Plant phenotypic plasticity in a changing climate.

Climate change is altering the availability of resources and the conditions that are crucial to plant performance. One way plants will respond to these changes is through environmentally induced shifts in phenotype (phenotypic plasticity). Understanding plastic responses is crucial for predicting and managing the effects of climate change on native species as well as crop plants. Here, we provide a toolbox with definitions of key theoretical elements and a synthesis of the current understanding of the molecular and genetic mechanisms underlying plasticity relevant to climate change. By bringing ecological, evolutionary, physiological and molecular perspectives together, we hope to provide clear directives for future research and stimulate cross-disciplinary dialogue on the relevance of phenotypic plasticity under climate change.
Long range physical cell-to-cell signalling via mitochondria inside membrane nanotubes: a hypothesis

Coordinated interaction of single cells by cell-to-cell communication (signalling) enables complex behaviour necessary for the functioning of multicellular organisms. A quite newly discovered cell-to-cell signalling mechanism relies on nanotubular cell-co-cell connections, termed “membrane nanotubes” (MNTs). The present paper presents the hypothesis that mitochondria inside MNTs can form a connected structure (mitochondrial network) which enables the exchange of energy and signals between cells. It is proposed that two modes of energy and signal transmission may occur: electrical/electrochemical and electromagnetic (optical). Experimental work supporting the hypothesis is reviewed, and suggestions for future research regarding the discussed topic are given.
TripleBit: a Fast and Compact System for Large Scale RDF Data

The volume of RDF data continues to grow over the past decade and many known RDF datasets have billions of triples. A grant challenge of managing this huge RDF data is how to access this big RDF data efficiently. A popular approach to addressing the problem is to build a full set of permutations of (S, P, O) indexes. Although this approach has shown to accelerate joins by orders of magnitude, the large space overhead limits the scalability of this approach and makes it heavyweight. In this paper, we present TripleBit, a fast and compact system for storing and accessing RDF data. The design of TripleBit has three salient features. First, the compact design of TripleBit reduces both the size of stored RDF data and the size of its indexes. Second, TripleBit introduces two auxiliary index structures, ID-Chunk matrix and ID-Predicate bit matrix, to minimize the number of index selection during query evaluation. Third, its query processor dynamically generates an optimal execution ordering for join queries, leading to fast query execution and effective reduction on the size of intermediate results. Our experiments show that TripleBit outperforms RDF-3X, MonetDB, BitMat on LUBM, UniProt and BTC 2012 benchmark queries and it offers orders of mangnitude performance improvement for some complex join queries.
A Ka-band broadband traveling-wave power divider/combiner based on low-loss septum unsymmetrical E-plane T-junction series

A Ka-band broadband traveling-wave power divider based on low-loss septum unsymmetrical E-plane T-junction series has been designed and fabricated. The high isolation, which guarantees the graceful degradation of a modular solid-state device system, is realized by the septum T-junction series. The attractive features of the proposed structure are easy fabrication and low loss. Moreover, a wide operation band of 30%. The simulated isolation and reflection of the outputs are better than 20 dB and 15 dB respectively from 28 GHz to 38 GHz. The measured return loss of input port is better than 19 dB and a maximum transmission coefficient amplitude imbalance of ±1dB is achieved.
A methodology for mapping Instagram hashtags

While social media research has provided detailed cumulative analyses of selected social media platforms and content, especially Twitter, newer platforms, apps, and visual content have been less extensively studied so far. This paper proposes a methodology for studying Instagram activity, building on established methods for Twitter research by initially examining hashtags, as common structural features to both platforms. In doing so, we outline methodological challenges to studying Instagram, especially in comparison to Twitter. Finally, we address critical questions around ethics and privacy for social media users and researchers alike, setting out key considerations for future social media research.
How the brain creates the mind.

A s the millennium draws to a close, it is apparent that one question towers above all others in the life sciences: How does the set of processes we call mind emerge from the activity of the organ we call brain? The question is hardly new. It has been formulated in one way or another for centuries. Once it became possible to pose the question and not be burned at the stake, it has been asked openly and insistently. Recently the question has preoccupied both the experts—neuroscientists, cognitive scientists and philosophers—and others who wonder about the origin of the mind, specifically the conscious mind. The question of consciousness now occupies center stage because biology in general and neuroscience in particular have been so remarkably successful at unraveling a great many of life’s secrets. More may have been learned about the brain and the mind in the 1990s—the so-called decade of the brain—than during the entire previous history of psychology and neuroscience. Elucidating the neurobiological basis of the conscious mind—a version of the classic mind-body problem—has become almost a residual challenge. Contemplation of the mind may induce timidity in the contemplator, especially when consciousness becomes the focus of the inquiry. Some thinkers, expert and amateur alike, believe the question may be unanswerable in principle. For others, the relentless and exponential increase in new knowledge may give rise to a vertiginous feeling that no problem can resist the assault of science if only the theory is right and the techniques are powerful enough. The debate is intriguing and even unexpected, as no comparable doubts have been raised over the likelihood of explaining how the brain is responsible for processes such as vision or memory, which are obvious components of the larger process of the conscious mind. The multimedia mind-show occurs constantly as the brain processes external and internal sensory events. As the brain answers the unasked question of who is experiencing the mindshow, the sense of self emerges. by Antonio R. Damasio
Data Poisoning Attacks on Multi-Task Relationship Learning

Multi-task learning (MTL) is a machine learning paradigm that improves the performance of each task by exploiting useful information contained in multiple related tasks. However, the relatedness of tasks can be exploited by attackers to launch data poisoning attacks, which has been demonstrated a big threat to single-task learning. In this paper, we provide the first study on the vulnerability of MTL. Specifically, we focus on multi-task relationship learning (MTRL) models, a popular subclass of MTL models where task relationships are quantized and are learned directly from training data. We formulate the problem of computing optimal poisoning attacks on MTRL as a bilevel program that is adaptive to arbitrary choice of target tasks and attacking tasks. We propose an efficient algorithm called PATOM for computing optimal attack strategies. PATOM leverages the optimality conditions of the subproblem of MTRL to compute the implicit gradients of the upper level objective function. Experimental results on realworld datasets show that MTRL models are very sensitive to poisoning attacks and the attacker can significantly degrade the performance of target tasks, by either directly poisoning the target tasks or indirectly poisoning the related tasks exploiting the task relatedness. We also found that the tasks being attacked are always strongly correlated, which provides a clue for defending against such attacks.
Step-by-step guide to critiquing research. Part 1: quantitative research.

When caring for patients, it is essential that nurses are using the current best practice. To determine what this is, nurses must be able to read research critically. But for many qualified and student nurses, the terminology used in research can be difficult to understand, thus making critical reading even more daunting. It is imperative in nursing that care has its foundations in sound research, and it is essential that all nurses have the ability to critically appraise research to identify what is best practice. This article is a step-by-step approach to critiquing quantitative research to help nurses demystify the process and decode the terminology.