nils-herrmann/clean_scirepeval huggingface dataset:
Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks

Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone's coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%.
Exploring connections among nature , biodiversity , ecosystem services , and human health and well-being : Opportunities to enhance health and biodiversity conservation

We are at a key juncture in history where biodiversity loss is occurring daily and accelerating in the face of population growth, climate change, and rampant development. Simultaneously, we are just beginning to appreciate the wealth of human health benefits that stem from experiencing nature and biodiversity. Here we assessed the state of knowledge on relationships between human health and nature and biodiversity, and prepared a comprehensive listing of reported health effects. We found strong evidence linking biodiversity with production of ecosystem services and between nature exposure and human health, but many of these studies were limited in rigor and often only correlative. Much less information is available to link biodiversity and health. However, some robust studies indicate that exposure to microbial biodiversity can improve health, specifically in reducing certain allergic and respiratory diseases. Overall, much more research is needed on mechanisms of causation. Also needed are a reenvisioning of land-use planning that places human well-being at the center and a new coalition of ecologists, health and social scientists and planners to conduct research and develop policies that promote human interaction with nature and biodiversity. Improvements in these areas should enhance human health and ecosystem, community, as well as human resilience.
Self-transcendent positive emotions increase spirituality through basic world assumptions.

Spirituality has mostly been studied in psychology as implied in the process of overcoming adversity, being triggered by negative experiences, and providing positive outcomes. By reversing this pathway, we investigated whether spirituality may also be triggered by self-transcendent positive emotions, which are elicited by stimuli appraised as demonstrating higher good and beauty. In two studies, elevation and/or admiration were induced using different methods. These emotions were compared to two control groups, a neutral state and a positive emotion (mirth). Self-transcendent positive emotions increased participants' spirituality (Studies 1 and 2), especially for the non-religious participants (Study 1). Two basic world assumptions, i.e., belief in life as meaningful (Study 1) and in the benevolence of others and the world (Study 2) mediated the effect of these emotions on spirituality. Spirituality should be understood not only as a coping strategy, but also as an upward spiralling pathway to and from self-transcendent positive emotions.
LexRank : Graph-based Centrality as Salience in Text Summarization

We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.
Self-organization, free energy minimization, and optimal grip on a field of affordances

In this paper, we set out to develop a theoretical and conceptual framework for the new field of Radical Embodied Cognitive Neuroscience. This framework should be able to integrate insights from several relevant disciplines: theory on embodied cognition, ecological psychology, phenomenology, dynamical systems theory, and neurodynamics. We suggest that the main task of Radical Embodied Cognitive Neuroscience is to investigate the phenomenon of skilled intentionality from the perspective of the self-organization of the brain-body-environment system, while doing justice to the phenomenology of skilled action. In previous work, we have characterized skilled intentionality as the organism's tendency toward an optimal grip on multiple relevant affordances simultaneously. Affordances are possibilities for action provided by the environment. In the first part of this paper, we introduce the notion of skilled intentionality and the phenomenon of responsiveness to a field of relevant affordances. Second, we use Friston's work on neurodynamics, but embed a very minimal version of his Free Energy Principle in the ecological niche of the animal. Thus amended, this principle is helpful for understanding the embeddedness of neurodynamics within the dynamics of the system "brain-body-landscape of affordances." Next, we show how we can use this adjusted principle to understand the neurodynamics of selective openness to the environment: interacting action-readiness patterns at multiple timescales contribute to the organism's selective openness to relevant affordances. In the final part of the paper, we emphasize the important role of metastable dynamics in both the brain and the brain-body-environment system for adequate affordance-responsiveness. We exemplify our integrative approach by presenting research on the impact of Deep Brain Stimulation on affordance responsiveness of OCD patients.
Bidirectional Attentional Encoder-Decoder Model and Bidirectional Beam Search for Abstractive Summarization

Sequence generative models with RNN variants, such as LSTM, GRU, show promising performance on abstractive document summarization. However, they still have some issues that limit their performance, especially while dealing with long sequences. One of the issues is that, to the best of our knowledge, all current models employ a unidirectional decoder, which reasons only about the past and still limited to retain future context while giving a prediction. This makes these models suffer on their own by generating unbalanced outputs. Moreover, unidirectional attention-based document summarization can only capture partial aspects of attentional regularities due to the inherited challenges in document summarization. To this end, we propose an end-to-end trainable bidirectional RNN model to tackle the aforementioned issues. The model has a bidirectional encoder-decoder architecture; in which the encoder and the decoder are bidirectional LSTMs. The forward decoder is initialized with the last hidden state of the backward encoder while the backward decoder is initialized with the last hidden state of the forward encoder. In addition, a bidirectional beam search mechanism is proposed as an approximate inference algorithm for generating the output summaries from the bidirectional model. This enables the model to reason about the past and future and to generate balanced outputs as a result. Experimental results on CNN / Daily Mail dataset show that the proposed model outperforms the current abstractive state-of-the-art models by a considerable margin.
FORGETTING BY MAXIMIZING TRANSFER AND MINIMIZING INTERFERENCE

Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely.1 We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller. 1 SOLVING THE CONTINUAL LEARNING PROBLEM A long-held goal of AI is to build agents capable of operating autonomously for long periods. Such agents must incrementally learn and adapt to a changing environment while maintaining memories of what they have learned before, a setting known as lifelong learning (Thrun, 1994; 1996). In this paper we explore a variant called continual learning (Ring, 1994). In continual learning we assume that the learner is exposed to a sequence of tasks, where each task is a sequence of experiences from the same distribution . We would like to develop a solution in this setting by discovering notions of tasks without supervision while learning incrementally after every experience. This is challenging because in standard offline single task and multi-task learning (Caruana, 1997) it is implicitly assumed that the data is drawn from an i.i.d. stationary distribution. Unfortunately, neural networks tend to struggle whenever this is not the case (Goodrich, 2015). Over the years, solutions to the continual learning problem have been largely driven by prominent conceptualizations of the issues faced by neural networks. One popular view is catastrophic forgetting (interference) (McCloskey & Cohen, 1989), in which the primary concern is the lack of stability in neural networks, and the main solution is to limit the extent of weight sharing across experiences by focusing on preserving past knowledge (Kirkpatrick et al., 2017; Zenke et al., 2017; Lee et al., 2017). Another popular and more complex conceptualization is the stability-plasticity dilemma (Carpenter & Grossberg, 1987). In this view, the primary concern is the balance between network We consider task agnostic future gradients, referring to gradients of the model parameters with respect to unseen data points. These can be drawn from tasks that have already been partially learned or unseen tasks.
A Broadband WR10 Turnstile Junction Orthomode Transducer

We present a broadband waveguide ortho-mode transducer for the WR10 band that was designed for CLOVER, an astrophysics experiment aiming to characterize the polarization of the cosmic microwave background radiation. The design, based on a turnstile junction, was manufactured and then tested using a millimeter-wave vector network analyzer. The average measured return loss and isolation were -22 dB and -45 dB, respectively, across the entire WR10 band
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models

Volatility prediction—an essential concept in financial markets—has recently been addressed using sentiment analysis methods. We investigate the sentiment of annual disclosures of companies in stock markets to forecast volatility. We specifically explore the use of recent Information Retrieval (IR) term weighting models that are effectively extended by related terms using word embeddings. In parallel to textual information, factual market data have been widely used as the mainstream approach to forecast market risk. We therefore study different fusion methods to combine text and market data resources. Our word embedding-based approach significantly outperforms state-ofthe-art methods. In addition, we investigate the characteristics of the reports of the companies in different financial sectors.
Extended sub-SMAS dissection and cheek elevation.

The extended SMAS facelift techniques gave plastic surgeons the ability to correct the nasolabial fold and medial cheek. Retensioning the SMAS transmits the benefit through the multilinked fibrous support system of the facial soft tissues. The effect is to provide a recontouring of the ptotic soft tissues, which fills out the cheeks as it reduces nasolabial fullness. Indirectly, dermal tightening occurs to a lesser but more natural degree than with traditional facelift surgery. Although details of current techniques may be superseded, the emerging surgical principles are becoming more clearly defined. This article presents these principles and describes the author's current surgical technique.
Dual Attention Network for Scene Segmentation

In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the selfattention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data. we make the code and trained models publicly available at https://github.com/junfu1115/DANet
Changing Teacher Beliefs PRIME Leadership Standards PLCs in Guatemala

The NCSM Journal of Mathematics Education Leadership is published at least twice yearly, in the spring and fall. Permission to photocopy material from the NCSM Journal of Mathematics Education Leadership is granted for instructional use when the material is to be distributed free of charge (or at cost only), provided that it is duplicated with the full credit given to the authors of the materials and the NCSM Journal of Mathematics Education Leadership. This permission does not apply to copyrighted articles reprinted in the NCSM Journal of Mathematics Education Leadership. The editors of the NCSM Journal of Mathematics Education Leadership are interested in manuscripts that address concerns of leadership in mathematics rather than those of content or delivery. Editors are interested in publishing articles from a broad spectrum of formal and informal leaders who practice at local, regional, national, and international levels. Categories for submittal include: Note: The last two categories are intended for short pieces of 2 to 3 pages in length. Submittal of items should be done electronically to the Journal editor. Do not put any author identification in the body of the item being submitted, but do include author information as you would like to see it in the Journal. Items submitted for publication will be reviewed by two members of the NCSM Review Panel and one editor with comments and suggested revisions sent back to the author at least six weeks before publication. Final copy must be agreed to at least three weeks before publication.  A spiral vortex generated with fractal algorithms • Strengthening mathematics education leadership through the dissemination of knowledge related to research, issues, trends, programs, policy, and practice in mathematics education • Fostering inquiry into key challenges of mathematics education leadership • Raising awareness about key challenges of mathematics education leadership, in order to influence research, programs, policy, and practice • Engaging the attention and support of other education stakeholders, and business and government, in order to broaden as well as strengthen mathematics education leadership E arlier this year, NCSM released a new mission and vision statement. Our mission speaks to our commitment to " support and sustain improved student achievement through the development of leadership skills and relationships among current and future mathematics leaders. " Our vision statement challenges us as the leaders in mathematics education to collaborate with all stakeholders and develop leadership skills that will lead to improved …
Biodegradable Metals for Cardiovascular Stent Application: Interests and New Opportunities

During the last decade, biodegradable metallic stents have been developed and investigated as alternatives for the currently-used permanent cardiovascular stents. Degradable metallic materials could potentially replace corrosion-resistant metals currently used for stent application as it has been shown that the role of stenting is temporary and limited to a period of 6-12 months after implantation during which arterial remodeling and healing occur. Although corrosion is generally considered as a failure in metallurgy, the corrodibility of certain metals can be an advantage for their application as degradable implants. The candidate materials for such application should have mechanical properties ideally close to those of 316L stainless steel which is the gold standard material for stent application in order to provide mechanical support to diseased arteries. Non-toxicity of the metal itself and its degradation products is another requirement as the material is absorbed by blood and cells. Based on the mentioned requirements, iron-based and magnesium-based alloys have been the investigated candidates for biodegradable stents. This article reviews the recent developments in the design and evaluation of metallic materials for biodegradable stents. It also introduces the new metallurgical processes which could be applied for the production of metallic biodegradable stents and their effect on the properties of the produced metals.
Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using backpropagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.
RGBD Human Action Recognition using Multi-Features Combination and K-Nearest Neighbors Classification

In this paper, we present a novel system to analyze human body motions for action recognition task from two sets of features using RGBD videos. The Bag-of-Features approach is used for recognizing human action by extracting local spatialtemporal features and shape invariant features from all video frames. These feature vectors are computed in four steps: Firstly, detecting all interest keypoints from RGB video frames using Speed-Up Robust Features and filters motion points using Motion History Image and Optical Flow, then aligned these motion points to the depth frame sequences. Secondly, using a Histogram of orientation gradient descriptor for computing the features vector around these points from both RGB and depth channels, then combined these feature values in one RGBD feature vector. Thirdly, computing Hu-Moment shape features from RGBD frames, fourthly, combining the HOG features with Hu-moments features in one feature vector for each video action. Finally, the k-means clustering and the multi-class K-Nearest Neighbor is used for the classification task. This system is invariant to scale, rotation, translation, and illumination. All tested are utilized on a dataset that is available to the public and used often in the community. By using this new feature combination method improves performance on actions with low movement and reach recognition rates superior to other publications of the dataset. RGBD Videos; Feature Extraction; k-means Clustering; KNN (K-Nearest Neighbor)