nils-herrmann/clean_scirepeval huggingface dataset:
Lightweight unmanned aerial vehicles will revolutionize spatial ecology

R emote-sensing techniques have transformed ecological research by providing both spatial and temporal perspectives on ecological phenomena that would otherwise be difficult to study (eg Kerr and Ostrovsky 2003; Running et al. 2004; Vierling et al. 2008). In particular, a strong focus has been placed on the use of data obtained from space-borne remote-sensing instruments because these provide regional-to global-scale observations and repeat time-series sampling of ecological indicators (eg Gould 2000). The main limitation of most of the research-focused satellite missions is the mismatch between the pixel resolution of many regional-extent sensors (eg Landsat [spatial resolution of ~30 m] to the Moderate Resolution Imaging Spectro-radiometer [spatial resolution of ~1 km]), the revisit period (eg 18 days for Landsat), and the scale of many ecological processes. Indeed, data provided by these platforms are often " too general to meet regional or local objectives " in ecology (Wulder et al. 2004). To address this limitation, a range of new (largely commercially operated) satellite sensors have become operational over the past decade, offering data at finer than 10-m spatial resolution with more responsive capabilities (eg Quickbird, IKONOS, GeoEye-1, OrbView-3, WorldView-2). Such data are useful for ecological studies (Fretwell et al. 2012), but there remain three operational constraints: (1) a high cost per scene; (2) suitable repeat times are often only possible if oblique view angles are used, distorting geometric and radiometric pixel properties; and (3) cloud contamination, which can obscure features of interest (Loarie et al. 2007). Imaging sensors on board civilian aircraft platforms may also be used; these can provide more scale-appropriate data for fine-scale ecological studies, including data from light detection and ranging (LiDAR) sensors (Vierling et al. 2008). In theory, these surveys can be made on demand, but in practice data acquisition is costly, meaning that regular time-series monitoring is operationally constrained. A new method for fine-scale remote sensing is now emerging that could address all of these operational issues and thus potentially revolutionize spatial ecology and environmental science. Unmanned aerial vehicles (UAVs) are lightweight, low-cost aircraft platforms operated from the ground that can carry imaging or non-imaging payloads. UAVs offer ecologists a promising route to responsive, timely, and cost-effective monitoring of environmental phenomena at spatial and temporal resolutions that are appropriate to the scales of many ecologically relevant variables. Emerging from a military background, there are now a growing number of civilian agencies and organizations that have recognized the …
A Framework for Collaborative, Content-Based and Demographic Filtering

We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants.
Web site accessibility, usability and security: a survey of government web sites in Kyrgyz Republic

The information in government web sites, which are widely adopted in many countries, must be accessible for all people, easy to use, accurate and secure. The main objective of this study is to investigate the usability, accessibility and security aspects of e-government web sites in Kyrgyz Republic. The analysis of web government pages covered 55 sites listed in the State Information Resources of the Kyrgyz Republic and five government web sites which were not included in the list. Analysis was conducted using several automatic evaluation tools. Results suggested that government web sites in Kyrgyz Republic have a usability error rate of 46.3 % and accessibility error rate of 69.38 %. The study also revealed security vulnerabilities in these web sites. Although the “Concept of Creation and Development of Information Network of the Kyrgyz Republic” was launched at September 23, 1994, government web sites in the Kyrgyz Republic have not been reviewed and still need great efforts to improve accessibility, usability and security.
BANK OF SAN FRANCISCO WORKING PAPER SERIES Uncertainty Shocks are Aggregate Demand Shocks

We show that to capture the empirical effects of uncertainty on the unemployment rate, it is crucial to study the interactions between search frictions and nominal rigidities. Our argument is guided by empirical evidence showing that an increase in uncertainty leads to a large increase in unemployment and a significant decline in inflation, suggesting that uncertainty partly operates via an aggregate demand channel. To understand the mechanism through which uncertainty generates these macroeconomic effects, we incorporate search frictions and nominal rigidities in a DSGE model. We show that an option-value channel that arises from search frictions interacts with a demand channel that arises from nominal rigidities, and such interactions magnify the effects of uncertainty to generate roughly 60 percent of the observed increase in unemployment following an uncer-
Thinking about intentions

In this fMRI study, we investigated the convergence of underlying neural networks in thinking about a scenario involving one's own intentional action and its consequences and setting up and holding in mind an intention to act. A factorial design was employed comprising two factors: i. Causality (intentional or physical events) and ii. Prospective Memory (present or absent). In each condition, subjects answered questions about various hypothetical scenarios, which related either to the link between the subject's own intentions and consequential actions (Intentional Causality) or to the link between a natural, physical event and its consequences (Physical Causality). A prospective memory task was embedded in half the blocks. In this task, subjects were required to keep in mind an intention (to press a key on seeing a red stimulus background) whilst carrying out the ongoing Causality task. Answering questions about intentional causality versus physical causality activated a network of regions that have traditionally been associated with Theory of Mind, including the medial prefrontal cortex (mPFC), the superior temporal sulcus and the temporal poles bilaterally. In addition, the precuneus bordering with posterior cingulate cortex, an area involved in self-awareness and self-related processing, was activated more when thinking about intentional causality. In the prospective memory task, activations were found in the right parietal cortex, frontopolar cortex (BA 10) and precuneus. Different subregions within the precuneus/posterior cingulate cortex were activated in both main effects of intentional causality and prospective memory. Therefore, the precuneus/posterior cingulate cortex subserves separately thinking about one's own intentions and consequent actions and bearing in mind an intention to make an action. Previous studies have shown that prospective memory, requiring the formation of an intention and the execution of a corresponding action, is associated with decreased activation in the dorsal mPFC, close to the region activated in Theory of Mind tasks. Here, we found that holding in mind an intention to act and at the same time thinking about an intentional action led to reduced activity in a dorsal section of the mPFC. This was a different region from a more anterior, inferior dorsal mPFC region that responded to intentional causality. This suggests that different regions of mPFC play different roles in thinking about intentions.
How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks

With the recent advances of neural models and natural language processing, automatic generation of classical Chinese poetry has drawn significant attention due to its artistic and cultural value. Previous works mainly focus on generating poetry given keywords or other text information, while visual inspirations for poetry have been rarely explored. Generating poetry from images is much more challenging than generating poetry from text, since images contain very rich visual information which cannot be described completely using several keywords, and a good poem should convey the image accurately. In this paper, we propose a memory based neural model which exploits images to generate poems. Specifically, an Encoder-Decoder model with a topic memory network is proposed to generate classical Chinese poetry from images. To the best of our knowledge, this is the first work attempting to generate classical Chinese poetry from images with neural networks. A comprehensive experimental investigation with both human evaluation and quantitative analysis demonstrates that the proposed model can generate poems which convey images accurately.
An integrated stress response regulates amino acid metabolism and resistance to oxidative stress.

Eukaryotic cells respond to unfolded proteins in their endoplasmic reticulum (ER stress), amino acid starvation, or oxidants by phosphorylating the alpha subunit of translation initiation factor 2 (eIF2alpha). This adaptation inhibits general protein synthesis while promoting translation and expression of the transcription factor ATF4. Atf4(-/-) cells are impaired in expressing genes involved in amino acid import, glutathione biosynthesis, and resistance to oxidative stress. Perk(-/-) cells, lacking an upstream ER stress-activated eIF2alpha kinase that activates Atf4, accumulate endogenous peroxides during ER stress, whereas interference with the ER oxidase ERO1 abrogates such accumulation. A signaling pathway initiated by eIF2alpha phosphorylation protects cells against metabolic consequences of ER oxidation by promoting the linked processes of amino acid sufficiency and resistance to oxidative stress.
Fluency and Comprehension 1 Running head : RELATIONSHIPS OF READING FLUENCY TO COMPREHENSION Relationships of Three Components of Reading Fluency to Reading Comprehension

This study examined the relationships of three levels of reading fluency, the individual word, the syntactic unit, and the whole passage, to reading comprehension among 278 fifth graders heterogeneous in reading ability. Hierarchical regression analyses revealed that reading fluency at each level related uniquely to performance on a standardized reading comprehension test in a model including inferencing skill and background knowledge. The study supported an automaticity effect for word recognition speed and an automaticity-like effect related to syntactic processing skill. Additionally, hierarchical regressions using longitudinal data suggested that fluency and reading comprehension had a bidirectional relationship. The discussion emphasizes the theoretical expansion of reading fluency to three levels of cognitive processes and the relations of these processes to reading comprehension.
An Extended Frank-Wolfe Method with "In-Face" Directions, and Its Application to Low-Rank Matrix Completion

Motivated principally by the low-rank matrix completion problem, we present an extension of the Frank–Wolfe method that is designed to induce near-optimal solutions on lowdimensional faces of the feasible region. This is accomplished by a new approach to generating “in-face” directions at each iteration, as well as through new choice rules for selecting between inface and “regular” Frank–Wolfe steps. Our framework for generating in-face directions generalizes the notion of away steps introduced by Wolfe. In particular, the in-face directions always keep the next iterate within the minimal face containing the current iterate. We present computational guarantees for the new method that trade off efficiency in computing near-optimal solutions with upper bounds on the dimension of minimal faces of iterates. We apply the new method to the matrix completion problem, where low-dimensional faces correspond to low-rank matrices. We present computational results that demonstrate the effectiveness of our methodological approach at producing nearly optimal solutions of very low rank. On both artificial and real datasets, we demonstrate significant speedups in computing very low rank nearly optimal solutions as compared to the Frank–Wolfe method (as well as several of its significant variants).
Recognizing Human Act ion in Time-Sequent ial Images using Hidden Markov Model

This paper proposes a new human action recognition method baaed on a Hidden Markov Model (HMM). We do not adopt model-based top-down approach becawe our purpose is not to reconstruct a geometric representation of the human body but to recognize human action. We use a feature based bottom up approach with HMMa that is characterized by its learning capability and time-scale invariability. To apply HMMd to our aim, one set of time-sequential images i d transformed into an image feature vector sequence, and the sequence is converted into a symbol sequence by vector quantization. I n learning human action categories, the parameters of the HMMs, one per category, are optimized do a4 to best describe the training sequenced from the category. To recognize an observed sequence, the HMM which best matches the sequence is chosen. Ezperimental results of real time-sequential images of sports scenes dhow recognition rates higher than 90%. We confirm that the recognition rate w improved by increasing the number of people wed to generate the training data. This indicates the posdiblity of establishing a person independent action recognizer. This method can be easily applied to other fields becawe no domain knowledge w used.
Multi-label classification of music by emotion

This work studies the task of automatic emotion detection in music. Music may evoke more than one different emotion at the same time. Single-label classification and regression cannot model this multiplicity. Therefore, this work focuses on multi-label classification approaches, where a piece of music may simultaneously belong to more than one class. Seven algorithms are experimentally compared for this task. Furthermore, the predictive power of several audio features is evaluated using a new multi-label feature selection method. Experiments are conducted on a set of 593 songs with six clusters of emotions based on the Tellegen-Watson-Clark model of affect. Results show that multi-label modeling is successful and provide interesting insights into the predictive quality of the algorithms and features.
Action Recognition with Stacked Fisher Vectors

Representation of video is a vital problem in action recognition. This paper proposes Stacked Fisher Vectors (SFV), a new representation with multi-layer nested Fisher vector encoding, for action recognition. In the first layer, we densely sample large subvolumes from input videos, extract local features, and encode them using Fisher vectors (FVs). The second layer compresses the FVs of subvolumes obtained in previous layer, and then encodes them again with Fisher vectors. Compared with standard FV, SFV allows refining the representation and abstracting semantic information in a hierarchical way. Compared with recent mid-level based action representations, SFV need not to mine discriminative action parts but can preserve mid-level information through Fisher vector encoding in higher layer. We evaluate the proposed methods on three challenging datasets, namely Youtube, J-HMDB, and HMDB51. Experimental results demonstrate the effectiveness of SFV, and the combination of the traditional FV and SFV outperforms stateof-the-art methods on these datasets with a large margin.
Performance Analysis of IEC 61850 Sampled Value Process Bus Networks

Process bus networks are the next stage in the evolution of substation design, bringing digital technology to the high-voltage switchyard. Benefits of process buses include facilitating the use of nonconventional instrument transformers, improved disturbance recording and phasor measurement, and the removal of costly, and potentially hazardous, copper cabling from substation switchyards and control rooms. This paper examines the role a process bus plays in an IEC 61850-based substation automation system. Measurements taken from a process bus substation are used to develop an understanding of the network characteristics of “whole of substation” process buses. The concept of “coherent transmission” is presented, and the impact of this on Ethernet switches is examined. Experiments based on substation observations are used to investigate in detail the behavior of Ethernet switches with sampled value traffic. Test methods that can be used to assess the adequacy of a network are proposed, and examples of the application and interpretation of these tests are provided. Once sampled value frames are queued by an Ethernet switch, the additional delay incurred by subsequent switches is minimal, and this allows their use in switchyards to further reduce communications cabling, without significantly impacting operation. The performance and reliability of a process bus network operating close to the theoretical maximum number of digital sampling units (merging units or electronic instrument transformers) was investigated with networking equipment from several vendors and has been demonstrated to be acceptable.
The Convergence of Machine Learning and Communications

The areas of machine learning and communication technology are converging. Today’s communications systems generate a huge amount of traffic data, which can help to significantly enhance the design and management of networks and communication components when combined with advanced machine learning methods. Furthermore, recently developed end-to-end training procedures offer new ways to jointly optimize the components of a communication system. Also in many emerging application fields of communication technology, e.g., smart cities or internet of things, machine learning methods are of central importance. This paper gives an overview over the use of machine learning in different areas of communications and discusses two exemplar applications in wireless networking. Furthermore, it identifies promising future research topics and discusses their potential impact.
Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold

Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top–down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy.
Delving Deeper into Convolutional Networks for Learning Video Representations

We propose an approach to learn spatio-temporal features in videos from intermediate visual representations we call “percepts” using Gated-Recurrent-Unit Recurrent Networks (GRUs). Our method relies on percepts that are extracted from all levels of a deep convolutional network trained on the large ImageNet dataset. While high-level percepts contain highly discriminative information, they tend to have a low-spatial resolution. Low-level percepts, on the other hand, preserve a higher spatial resolution from which we can model finer motion patterns. Using low-level percepts, however, can lead to high-dimensionality video representations. To mitigate this effect and control the number of parameters, we introduce a variant of the GRU model that leverages the convolution operations to enforce sparse connectivity of the model units and share parameters across the input spatial locations. We empirically validate our approach on both Human Action Recognition and Video Captioning tasks. In particular, we achieve results equivalent to state-of-art on the YouTube2Text dataset using a simpler caption-decoder model and without extra 3D CNN features.
Big data analytics for healthcare

Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. In this tutorial, we introduce the characteristics and related mining challenges on dealing with big medical data. Many of those insights come from medical informatics community, which is highly related to data mining but focuses on biomedical specifics. We survey various related papers from data mining venues as well as medical informatics venues to share with the audiences key problems and trends in healthcare analytics research, with different applications ranging from clinical text mining, predictive modeling, survival analysis, patient similarity, genetic data analysis, and public health. The tutorial will include several case studies dealing with some of the important healthcare applications.
Deep Convolutional Neural Network for Image Deconvolution

Many fundamental image-related problems involve deconvolution operators. Real blur degradation seldom complies with an ideal linear convolution model due to camera noise, saturation, image compression, to name a few. Instead of perfectly modeling outliers, which is rather challenging from a generative model perspective, we develop a deep convolutional neural network to capture the characteristics of degradation. We note directly applying existing deep neural networks does not produce reasonable results. Our solution is to establish the connection between traditional optimization-based schemes and a neural network architecture where a novel, separable structure is introduced as a reliable support for robust deconvolution against artifacts. Our network contains two submodules, both trained in a supervised manner with proper initialization. They yield decent performance on non-blind image deconvolution compared to previous generative-model based methods.