nils-herrmann/clean_scirepeval huggingface dataset:
Coarse-to-Fine Decoding for Neural Semantic Parsing

Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.
Grounded cognition.

Grounded cognition rejects traditional views that cognition is computation on amodal symbols in a modular system, independent of the brain's modal systems for perception, action, and introspection. Instead, grounded cognition proposes that modal simulations, bodily states, and situated action underlie cognition. Accumulating behavioral and neural evidence supporting this view is reviewed from research on perception, memory, knowledge, language, thought, social cognition, and development. Theories of grounded cognition are also reviewed, as are origins of the area and common misperceptions of it. Theoretical, empirical, and methodological issues are raised whose future treatment is likely to affect the growth and impact of grounded cognition.
Microbial metabolites in nutrition, healthcare and agriculture

Microorganisms are a promising source of an enormous number of natural products, which have made significant contribution to almost each sphere of human, plant and veterinary life. Natural compounds obtained from microorganisms have proved their value in nutrition, agriculture and healthcare. Primary metabolites, such as amino acids, enzymes, vitamins, organic acids and alcohol are used as nutritional supplements as well as in the production of industrial commodities through biotransformation. Whereas, secondary metabolites are organic compounds that are largely obtained by extraction from plants or tissues. They are primarily used in the biopharmaceutical industry due to their capability to reduce infectious diseases in human beings and animals and thus increase the life expectancy. Additionally, microorganisms and their products inevitably play a significant role in sustainable agriculture development.
Content-based image retrieval in medical applications.

To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation.



Stepwise content analysis of medical images results in six layers of information modeling incorporating medical expert knowledge (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). A reference database with 10,000 images categorized according to the image modality, orientation, body region, and biological system is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. A distributed system designed with only three core elements is implemented: (i) the central database holds program sources, processing scheme descriptions, images, features, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine.



Leaving-one-out experiments were distributed by the scheduler and controlled via corresponding job lists offering transparency regarding the viewpoints of a distributed system and the user. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.
Visually Indicated Sounds

Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene. We present an algorithm that synthesizes sound from silent videos of people hitting and scratching objects with a drumstick. This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions.
Embedding Logical Queries on Knowledge Graphs

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict what drugs are likely to target proteins involved with both diseases X and Y?—a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries—a flexible but tractable subset of first-order logic—on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multiple labels densely over a video sequence. To study this problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new dataset of dense labels over unconstrained internet videos. Modeling multiple, dense labels benefits from temporal relations within and across classes. We define a novel variant of long short-term memory deep networks for modeling these temporal relations via multiple input and output connections. We show that this model improves action labeling accuracy and further enables deeper understanding tasks ranging from structured retrieval to action prediction.
Effects of dietary inclusion of high concentrations of crude glycerin on meat quality and fatty acid profile of feedlot fed Nellore bulls

Crude glycerin, the main by-product of biodiesel production, can replace dietary energy sources, such as corn. The objective of this study was to evaluate the inclusion of up to 30% of crude glycerin in dry matter (DM) of the total diets, and its effects on meat quality parameters of feedlot Nellore bulls. Thirty animals (227.7 ± 23.8 kg body weight; 18 months old) were housed in individual pens and fed 5 experimental diets, containing 0, 7.5, 15, 22.5 or 30% crude glycerin (DM basis). After 103 d (21 d adaptation) animals were slaughtered and the Longissimus muscle was collected. The characteristics assessed were chemical composition, fatty acid profile, cholesterol, shear force, pH, color, water-holding capacity, cooking loss and sensory properties. The increasing inclusion of crude glycerin in the diets did not affect the chemical composition of the Longissimus muscle (P > 0.10). A quadratic effect was observed when levels of crude glycerin were increased, on the concentration of pentadecanoic, palmitoleic and eicosenoic fatty acids in meat (P < 0.05), and on the activity of the delta-9 desaturase 16 and delta-9 desaturase 18 enzymes (P < 0.05). The addition of crude glycerin increased the gamma linolenic fatty acid concentration (P < 0.01), and altered the monounsaturated fatty acids in Longissimus muscle of animals (Pquad. < 0.05). Crude glycerin decreased cholesterol content in meat (P < 0.05), and promoted higher flavor score and greasy intensity perception of the meat (P < 0.01). The inclusion of up to 30% crude glycerin in Nellore cattle bulls`diets (DM basis) improves meat cholesterol and sensory attributes, such as flavor, without affecting significantly the physical traits, the main fatty acid concentrations and the chemical composition.
Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs

Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving. We build on recent work [32] that trained a convolutional neural net to predict instance labeling in local image patches, extracted exhaustively in a stride from an image. A simple Markov random field model using several heuristics was then proposed in [32] to derive a globally consistent instance labeling of the image. In this paper, we formulate the global labeling problem with a novel densely connected Markov random field and show how to encode various intuitive potentials in a way that is amenable to efficient mean field inference . Our potentials encode the compatibility between the global labeling and the patch-level predictions, contrast-sensitive smoothness as well as the fact that separate regions form different instances. Our experiments on the challenging KITTI benchmark [8] demonstrate that our method achieves a significant performance boost over the baseline [32].
Time in the mind: Using space to think about time

How do we construct abstract ideas like justice, mathematics, or time-travel? In this paper we investigate whether mental representations that result from physical experience underlie people's more abstract mental representations, using the domains of space and time as a testbed. People often talk about time using spatial language (e.g., a long vacation, a short concert). Do people also think about time using spatial representations, even when they are not using language? Results of six psychophysical experiments revealed that people are unable to ignore irrelevant spatial information when making judgments about duration, but not the converse. This pattern, which is predicted by the asymmetry between space and time in linguistic metaphors, was demonstrated here in tasks that do not involve any linguistic stimuli or responses. These findings provide evidence that the metaphorical relationship between space and time observed in language also exists in our more basic representations of distance and duration. Results suggest that our mental representations of things we can never see or touch may be built, in part, out of representations of physical experiences in perception and motor action.
Convolutional Networks on Graphs for Learning Molecular Fingerprints

Predicting properties of molecules requires functions that take graphs as inputs. Molecular graphs are usually preprocessed using hash-based functions to produce fixed-size fingerprint vectors, which are used as features for making predictions. We introduce a convolutional neural network that operates directly on graphs, allowing end-to-end learning of the feature pipeline. This architecture generalizes standard molecular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.
When Networks Disagree : Ensemble Methods for Hybrid Neural Networks

This paper presents a general theoretical framework for ensemble methods of constructing signiicantly improved regression estimates. Given a population of regression estimators, we construct a hybrid estimator which is as good or better in the MSE sense than any estimator in the population. We argue that the ensemble method presented has several properties: 1) It eeciently uses all the networks of a population-none of the networks need be discarded. 2) It eeciently uses all the available data for training without over-tting. 3) It inherently performs regularization by smoothing in functional space which helps to avoid over-tting. 4) It utilizes local minima to construct improved estimates whereas other neural network algorithms are hindered by local minima. 5) It is ideally suited for parallel computation. 6) It leads to a very useful and natural measure of the number of distinct estimators in a population. 7) The optimal parameters of the ensemble estimator are given in closed form. Experimental results are provided which show that the ensemble method dramatically improves neural network performance on diicult real-world optical character recognition tasks.
Image Generation from Caption

Generating images from a text description is as challenging as it is interesting. The Adversarial network performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS step-by-step in this paper that could be easily understood. This paper presents visual comparative study of other models attempting to generate image conditioned on the text description. One sentence can be related to many images. And to achieve this multi-modal characteristic, conditioning augmentation is also performed. The performance of Stack-GAN is better in generating images from captions due to its unique architecture. As it consists of two GANS instead of one, it first draws a rough sketch and then corrects the defects yielding a high-resolution image.
Control of octopus arm extension by a peripheral motor program.

For goal-directed arm movements, the nervous system generates a sequence of motor commands that bring the arm toward the target. Control of the octopus arm is especially complex because the arm can be moved in any direction, with a virtually infinite number of degrees of freedom. Here we show that arm extensions can be evoked mechanically or electrically in arms whose connection with the brain has been severed. These extensions show kinematic features that are almost identical to normal behavior, suggesting that the basic motor program for voluntary movement is embedded within the neural circuitry of the arm itself. Such peripheral motor programs represent considerable simplification in the motor control of this highly redundant appendage.
Tracking Phishing Attacks Over Time

The so-called “phishing” attacks are one of the important threats to individuals and corporations in today’s Internet. Combatting phishing is thus a top-priority, and has been the focus of much work, both on the academic and on the industry sides. In this paper, we look at this problem from a new angle. We have monitored a total of 19,066 phishing attacks over a period of ten months and found that over 90% of these attacks were actually replicas or variations of other attacks in the database. This provides several opportunities and insights for the fight against phishing: first, quickly and efficiently detecting replicas is a very effective prevention tool. We detail one such tool in this paper. Second, the widely held belief that phishing attacks are dealt with promptly is but an illusion. We have recorded numerous attacks that stay active throughout our observation period. This shows that the current prevention techniques are ineffective and need to be overhauled. We provide some suggestions in this direction. Third, our observation give a new perspective into the modus operandi of attackers. In particular, some of our observations suggest that a small group of attackers could be behind a large part of the current attacks. Taking down that group could potentially have a large impact on the phishing attacks observed today.
Potassium Solubilizing Microorganisms for Sustainable Agriculture

Increasing cost of the fertilizers with lesser nutrient use efficiency necessitates alternate means to fertilizers. Soil is a storehouse of nutrients and energy for living organisms under the soil-plant-microorganism system. These rhizospheric microorganisms are crucial components of sustainable agricultural ecosystems. They are involved in sustaining soil as well as crop productivity under organic matter decomposition, nutrient transformations, and biological nutrient cycling. The rhizospheric microorganisms regulate the nutrient flow in the soil through assimilating nutrients, producing biomass, and converting organically bound forms of nutrients. Soil microorganisms play a significant role in a number of chemical transformations of soils and thus, influence the availability of macroand micronutrients. Use of plant growth-promoting microorganisms (PGPMs) helps in increasing yields in addition to conventional plant protection. The most important PGPMs are Azospirillum, Azotobacter, Bacillus subtilis, B. mucilaginosus, B. edaphicus, B. circulans, Paenibacillus spp., Acidithiobacillus ferrooxidans, Pseudomonas, Burkholderia, potassium, phosphorous, zinc-solubilizing V.S. Meena (*) Department of Soil Science and Agricultural Chemistry, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India Indian Council of Agricultural Research – Vivekananda Institute of Hill Agriculture, Almora 263601, Uttarakhand, India e-mail: vijayssac.bhu@gmail.com; vijay.meena@icar.gov.in I. Bahadur • B.R. Maurya Department of Soil Science and Agricultural Chemistry, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India A. Kumar Department of Botany, MMV, Banaras Hindu University, Varanasi 221005, India R.K. Meena Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad 500046, TG, India S.K. Meena Division of Soil Science and Agricultural Chemistry, Indian Agriculture Research Institute, New Delhi 110012, India J.P. Verma Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 22100, Uttar Pradesh, India # Springer India 2016 V.S. Meena et al. (eds.), Potassium Solubilizing Microorganisms for Sustainable Agriculture, DOI 10.1007/978-81-322-2776-2_1 1 microorganisms, or SMART microbes; these are eco-friendly and environmentally safe. The rhizosphere is the important area of soil influenced by plant roots. It is composed of huge microbial populations that are somehow different from the rest of the soil population, generally denominated as the “rhizosphere effect.” The rhizosphere is the small region of soil that is immediately near to the root surface and also affected by root exudates.
Clustering by Authorship Within and Across Documents

The vast majority of previous studies in authorship attribution assume the existence of documents (or parts of documents) labeled by authorship to be used as training instances in either closed-set or open-set attribution. However, in several applications it is not easy or even possible to find such labeled data and it is necessary to build unsupervised attribution models that are able to estimate similarities/differences in personal style of authors. The shared tasks on author clustering and author diarization at PAN 2016 focus on such unsupervised authorship attribution problems. The former deals with single-author documents and aims at grouping documents by authorship and establishing authorship links between documents. The latter considers multi-author documents and attempts to segment a document into authorial components, a task strongly associated with intrinsic plagiarism detection. This paper presents an overview of the two tasks including evaluation datasets, measures, results, as well as a survey of a total of 10 submissions (8 for author clustering and 2 for author diarization).
Modern artificial intelligence ( AI ) techniques can aid forecasters on a wide variety of high-impact weather phenomena . USING ARTIFICIAL INTELLIGENCE TO IMPROVE REAL-TIME DECISION-MAKING FOR HIGH-IMPACT WEATHER

Weather significantly impacts society for better and for worse. For example, severe weather hazards caused over $7.9 billion of property damage in 2015 (National Oceanic and Atmospheric Administration/National Centers for Environmental Information 2016; CoreLogic 2016). The National Academies of Sciences, Engineering, and Medicine (2016) cites improving forecasting of such events as a critical priority, and the European Centre for Medium-Range Weather Forecasts (ECMWF) recently announced goals for 2025 (ECMWF 2016) that stress the importance of improving these forecasts. On the positive side, improvements in forecasting solar power, which increasingly impacts the electrical grid, are expected to save utility companies $455 million by 2040 (Haupt et al. 2016). Additional savings can be found through improved forecasting in other areas of computational sustainability. Computational sustainability is a new and growing interdisciplinary research area focusing on computational solutions for questions of Earth sustainability. In recent years, operational numerical weather prediction (NWP) models have significantly increased in resolution (e.g., Weygandt et al. 2009). At the same time, the number and quality of observational systems has grown, and new systems, such as Geostationary Operational Environmental Satellite R series (GOES-R), will generate high-quality data at fine spatial and temporal resolutions. These data contain valuable information, but their variety and volume can be overwhelming to forecasters, and this can hinder decision-making if not handled properly (Karstens et al. 2015, 2016). This data deluge is commonly termed “big data.” Artificial intelligence (AI) and related data science methods have been developed to work with big data across a variety of disciplines. Applying AI techniques in conjunction with a physical understanding of the environment can substantially improve prediction skill for multiple types of high-impact weather. This approach expands on traditional model output statistics (MOS) techniques (Glahn and Lowry 1972), which derive probabilistic, categorical, and deterministic forecasts from NWP model output. Because of their simplicity and longevity, forecasters have gained trust in MOS techniques. AI techniques provide a number of advantages, including easily generalizing spatially and temporally, handling large numbers of predictor variables, integrating physical understanding into the