nils-herrmann/clean_scirepeval huggingface dataset:
Resnet in Resnet: Generalizing Residual Architectures
Venue: ArXiv

Residual networks (ResNets) have recently achieved state of the art on challenging computer vision tasks. We introduce Resnet in Resnet (RiR) a deep dual stream architecture that generalizes ResNets and standard CNNs and is easily implemented with no computational overhead. RiR consistently improves performance over ResNets, outperforms architectures with similar amounts of augmentation on CIFAR 10, and establishes a new state of the art on CIFAR 100.
Applying Machine Reasoning and Learning in Real World Applications
Venue: Reasoning Web

Knowledge discovery, as an area focusing upon methodologies for extracting knowledge through deduction (a priori) or from data (a posteriori) has been largely studied in Database and Artificial Intelligence. Deductive reasoning such as logic reasoning gains logically knowledge from pre established (certain) knowledge statements, while inductive inference such as data mining or learning discovers knowledge by generalising from initial information. While deductive reasoning and inductive learning are conceptually addressing knowledge discovery problems from different perspectives, they are inference techniques that nicely complement each other in real world applications. In this chapter we will present how techniques from machine learning and reasoning can be reconciled and integrated to address large scale problems in the context of (i) transportation in cities of Bologna, Dublin, Miami, Rio and (ii) spend optimisation in finance.
Feature Based Automatic Multiview Image Registration

Automatic image registration is a vital yet challenging task, particularly for remote sensing images. A fully automatic registration approach which is accurate, robust, and fast is required. The registration process is divided into six main steps: feature detection, feature extraction, feature matching, outlier detection and removal, transform model estimation and resampling. In the feature detection, keypoints (PCA SIFT) blob (SURF) region (MSER) features are detected. The features are then matched to produce point correspondences. The alignment process uses the RANSAC algorithm to estimate an outlier free transformation using point correspondences. The target image is transformed by using transformation function, which results in automatic image registration. Mutual information is used for fine tuning registration. In this thesis, feature descriptors used are SURF (Speeded Up Robust Features) MSER (Maximally Stable Extremal Regions) and PCA SIFT. They are invariant to zoom, noise, scale, rotation and illumination, hence very useful. The analysis is conducted for various image transformations such as image rotation, scaling and change in illumination. For all these transformations, various quality checking parameters such as recall and RMSE error are evaluated to analyse the performance of registration. In the registration of multiview images, the affine transformation model applied is not suitable. So a more appropriate transformation models such as thin plate spline is subtitled the affine model in the proposed work.
A Short History Of Financial Euphoria

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Unsupervised Deep Embedding for Clustering Analysis
Venue: ICML

Clustering is central to many data driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC) a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state of the art methods.
Language switching but not foreign language use per se reduces the framing effect.
Venue: Journal of experimental psychology. Learning, memory, and cognition

Recent studies reported reductions of well established biases in decision making under risk, such as the framing effect, during foreign language (FL) use. These modulations were attributed to the use of FL itself, which putatively entails an increase in emotional distance. A reduced framing effect in this setting, however, might also result from enhanced cognitive control associated with language switching in mixed language contexts, an account that has not been tested yet. Here we assess predictions of the 2 accounts in 2 experiments with over 1,500 participants. In Experiment 1, we tested a central prediction of the emotional distance account, namely that the framing effect would be reduced at low, but not high, FL proficiency levels. We found a strong framing effect in the native language, and surprisingly also in the foreign language, independent of proficiency. In Experiment 2, we orthogonally manipulated foreign language use and language switching to concurrently test the validity of both accounts. As in Experiment 1, foreign language use per se had no effect on framing. Crucially, the framing effect was reduced following a language switch, both when switching into the foreign and the native language. Thus, our results suggest that reduced framing effects are not mediated by increased emotional distance in a foreign language, but by transient enhancement of cognitive control, putting the interplay of bilingualism and decision making in a new light.
Intelligent industrial measurement instruments with silicon piezoresistive MEMS pressure sensors
Venue: 2016 4th International Symposium on Environmental Friendly Energies and Applications (EFEA)

In this paper we first describe the latest silicon piezoresistive MEMS pressure sensor developed and manufactured at the Center of Microelectronic Technologies (ICTM CMT) We introduce the concept of intelligent industrial instrumentation, and present a method that enables simultaneous high performance pressure and temperature measurement to be realized using the described sensor. Experimental verification of the method was performed using the intelligent industrial instrument platform developed at ICTM CMT. The obtained results have been discussed. They indicate that it is possible to meet or exceeded the measurement performance of existing industrial grade equipment by using the described approach. An important advantage of the method is that it can enable the temperature measurement capability to be added to some existing intelligent transmitters without hardware modifications. A new intelligent industrial liquid level transmitter, based on the described method and developed at IHTM CMT, is also presented.
A method enabling simultaneous pressure and temperature measurement using a single piezoresistive MEMS pressure sensor

In this paper we present a high performance, simple and low cost method for simultaneous measurement of pressure and temperature using a single piezoresistive MEMS pressure sensor. The proposed measurement method utilizes the parasitic temperature sensitivity of the sensing element for both pressure measurement correction and temperature measurement. A parametric mathematical model of the sensor was established and its parameters were calculated using the obtained characterization data. Based on the model, a real time sensor correction for both pressure and temperature measurements was implemented in a target measurement system. The proposed method was verified experimentally on a group of typical industrial grade piezoresistive sensors. The obtained results indicate that the method enables the pressure measurement performance to exceed that of typical digital industrial pressure transmitters, achieving at the same time the temperature measurement performance comparable to industrial grade platinum resistance temperature sensors. The presented work is directly applicable in industrial instrumentation, where it can add temperature measurement capability to the existing pressure measurement instruments, requiring little or no additional hardware, and without adverse effects on pressure measurement performance.
Supervised Classification of Very High Resolution Optical Images Using Wavelet Based Textural Features
Venue: IEEE Transactions on Geoscience and Remote Sensing

In this paper, we explore the potentialities of using wavelet based multivariate models for the classification of very high resolution optical images. A strategy is proposed to apply these models in a supervised classification framework. This strategy includes a content based image retrieval analysis applied on a texture database prior to the classification in order to identify which multivariate model performs the best in the context of application. Once identified, the best models are further applied in a supervised classification procedure by extracting texture features from a learning database and from regions obtained by a presegmentation of the image to classify. The classification is then operated according to the decision rules of the chosen classifier. The use of the proposed strategy is illustrated in two real case applications using Pleiades panchromatic images: the detection of vineyards and the detection of cultivated oyster fields. In both cases, at least one of the tested multivariate models displays higher classification accuracies than gray level cooccurrence matrix descriptors. Its high adaptability and the low number of parameters to be set are other advantages of the proposed approach.
Chemical Composition, Antioxidant, and Antibacterial Activity of Wood Vinegar from Litchi chinensis
Venue: Molecules

The antioxidant and antibacterial activities of wood vinegar from Litchi chinensis, and its components have been studied. The chemical compositions of wood vinegar were analyzed by gas chromatography mass spectrometry (GC MS) A total of 17 chemical compounds were identified, representing 83.96% of the compositions in the wood vinegar. Three major components, included 2,6 dimethoxyphenol (syringol, 29.54% 2 methoxyphenol (guaiacol, 12.36% and 3,5 dimethoxy 4 hydroxytoluene (11.07% were found in the wood vinegar. Antioxidant activities of the acids were investigated from the aspects of 1,1 Diphyl 2 picrylhydrazyl (DPPH) free radicals scavenging capacity, superoxide anion radical scavenging capacity, and reducing power. The pyroligneous acid exhibited high antioxidant activity which was comparable to the reference standards (vitamin C and butylated hydroxyl toluene) at the same dose with IC50 values of 36.5 ppm calculated by the DPPH radical scavenging assay, 38.38 g Trolox equivalent/100 g DW by the trolox equivalent antioxidant capacity (TEAC) assay, and 67.9 by the reducing power analysis. Antibacterial activity was evaluated using the disc diffusion and microdilution methods against a group of clinically antibiotic resistant isolates. The major components exhibited broad spectrum inhibition against all the bacterial strains with a range of disc inhibition zoon between 15 19 mm. The minimum inhibition concentration and minimum bactericide concentration against the test strains was ranging in 0.95 3.80 mL/100 mL and 1.90 3.80 mL/100 mL, respectively. Most of the antibiotic resistant strains were more susceptible to the wood vinegar than the non antibiotic resistant strain except the strain of ornithine resistant Staphylococcus aureus. Based on the chemical profile, it was considered that the strongest antioxidant and antibacterial activity of Litchi chinensis wood vinegar was due to its highly phenolic compositions. This study revealed that the Litchi chinensis wood vinegar is valuable to develop as alternative food antioxidant and antibiotics.
Optimal sensor network upgrade for fault detection using principal component analysis

The efficiency of a fault monitoring system critically depends on the structure of the plant instrumentation system. For processes monitored using principal component analysis, the multivariate statistical technique most used for fault diagnosis in industry, an existing strategy aims at selecting the set of instruments that satisfies the detection of a given set of faults at minimum cost. It is based on the minimum fault magnitude concept. Because that procedure discards lower cost feasible solutions, in this work, a new optimal selection methodology is proposed whose constraints are straightaway defined in terms of the principal component analysis's statistics. To solve the optimization problem, a level traversal search with cutting criteria is enhanced taking into account that the fault observability is a necessary condition for fault detection when statistical monitoring techniques are applied. Furthermore, observability and detection degree concepts are defined and considered as constraints of the opt.
Desired emotions across cultures: A value based account.
Venue: Journal of personality and social psychology

Values reflect how people want to experience the world; emotions reflect how people actually experience the world. Therefore, we propose that across cultures people desire emotions that are consistent with their values. Whereas prior research focused on the desirability of specific affective states or 1 or 2 target emotions, we offer a broader account of desired emotions. After reporting initial evidence for the potential causal effects of values on desired emotions in a preliminary study (N 200) we tested the predictions of our proposed model in 8 samples (N 2,328) from distinct world cultural regions. Across cultural samples, we found that people who endorsed values of self transcendence (e.g. benevolence) wanted to feel more empathy and compassion, people who endorsed values of self enhancement (e.g. power) wanted to feel more anger and pride, people who endorsed values of openness to change (e.g. self direction) wanted to feel more interest and excitement, and people who endorsed values of conservation (e.g. tradition) wanted to feel more calmness and less fear. These patterns were independent of differences in emotional experience. We discuss the implications of our value based account of desired emotions for understanding emotion regulation, culture, and other individual differences.
Evanescent wave analysis and experimental realization of refractive index sensor based on D shaped plastic optical fiber

A simple D shaped plastic multimode fiber optic sensor is proposed and demonstrated for refractive index testing. The D shaped fiber is fabricated using the wheel side polishing method. Experimental results exhibit a good linear relationship between the receiving optical power and the refractive index of the test medium. Different parameters, such as the depth, the length and the bending of the D shaped region, are taken into account to analyze and optimize the sensitivity of the sensor. Normalized transmittance intensity decreases 13.4% with the refractive index increasing from 1.333 to 1.455 when the depth and length are 500 mm and 2 cm with excurvature radius of 5 cm and optical source wavelength of 652 nm. For different refractive indices of the test medium, we simulate the energy distribution of the D type structure by using Finite Element Method, which achieves a good agreement between the experimental and theoretical results. Such a low cost and easily fabricated structure will be competitive for remote and continuous measurement of refractive index in chemical, biological, and biochemical sensing areas.
Comparing the Effects of E Government and Social Media Use on Trust in Government: Evidence from Seoul, South Korea

This study examines how citizens' use of government social media accounts and e government websites relate to perceptions of government trustworthiness. We hypothesize that greater use of e government websites for information by citizens is negatively associated with perceptions of government trustworthiness, whereas greater use of government social media accounts is positively associated with perceptions of government trustworthiness. We investigate these hypotheses using data from a 2012 sample of 1,100 Seoul citizens. In general, the findings offer support for our hypotheses yet also indicate that some dimensions of government trustworthiness are more intransigent than others. This paper concludes by discussing implications for theory and practice.
Social Representations of Social Media Use in Government

Social media use in government has gained a rapid growth in the last few years around the world. China has witnessed a boom in social media use in government, especially government microblogging. In this study, we employ social representation theory as the primary theoretical lens to investigate Chinese government microblogging. We hope the employment of social representation theory could help us overcome the limitation that previous studies focused on the technology and government side. Interviewing data are drawn from a number of Chinese citizens for eliciting their understandings of Chinese government microblogging, followed by structural analytical procedure on the collected data. The social representation elements are obtained and presented in a map. The findings indicate that (1) social media use in government in China is at the stage of fast growth but at low interaction level; (2) governance and social media user experiences are two new but significant concepts that might have been missed in literature; (3) government microblogging does improve citizens' perceptions toward government; however, the final outcomes like trust and accountability cannot be achieved by relying on social media use in government alone. Theoretical contributions and practitioner implications are then discussed, followed by the limitations. Finally, we present the concluding remarks for this study.
Plant hormone mediated regulation of stress responses
Venue: BMC Plant Biology

BackgroundBeing sessile organisms, plants are often exposed to a wide array of abiotic and biotic stresses. Abiotic stress conditions include drought, heat, cold and salinity, whereas biotic stress arises mainly from bacteria, fungi, viruses, nematodes and insects. To adapt to such adverse situations, plants have evolved well developed mechanisms that help to perceive the stress signal and enable optimal growth response. Phytohormones play critical roles in helping the plants to adapt to adverse environmental conditions. The elaborate hormone signaling networks and their ability to crosstalk make them ideal candidates for mediating defense responses.ResultsRecent research findings have helped to clarify the elaborate signaling networks and the sophisticated crosstalk occurring among the different hormone signaling pathways. In this review, we summarize the roles of the major plant hormones in regulating abiotic and biotic stress responses with special focus on the significance of crosstalk between different hormones in generating a sophisticated and efficient stress response. We divided the discussion into the roles of ABA, salicylic acid, jasmonates and ethylene separately at the start of the review. Subsequently, we have discussed the crosstalk among them, followed by crosstalk with growth promoting hormones (gibberellins, auxins and cytokinins) These have been illustrated with examples drawn from selected abiotic and biotic stress responses. The discussion on seed dormancy and germination serves to illustrate the fine balance that can be enforced by the two key hormones ABA and GA in regulating plant responses to environmental signals.ConclusionsThe intricate web of crosstalk among the often redundant multitudes of signaling intermediates is just beginning to be understood. Future research employing genome scale systems biology approaches to solve problems of such magnitude will undoubtedly lead to a better understanding of plant development. Therefore, discovering additional crosstalk mechanisms among various hormones in coordinating growth under stress will be an important theme in the field of abiotic stress research. Such efforts will help to reveal important points of genetic control that can be useful to engineer stress tolerant crops.
Global Plant Stress Signaling: Reactive Oxygen Species at the Cross Road
Venue: Front. Plant Sci.

Current technologies have changed biology into a data intensive field and significantly increased our understanding of signal transduction pathways in plants. However, global defense signaling networks in plants have not been established yet. Considering the apparent intricate nature of signaling mechanisms in plants (due to their sessile nature) studying the points at which different signaling pathways converge, rather than the branches, represents a good start to unravel global plant signaling networks. In this regard, growing evidence shows that the generation of reactive oxygen species (ROS) is one of the most common plant responses to different stresses, representing a point at which various signaling pathways come together. In this review, the complex nature of plant stress signaling networks will be discussed. An emphasis on different signaling players with a specific attention to ROS as the primary source of the signaling battery in plants will be presented. The interactions between ROS and other signaling components, e.g. calcium, redox homeostasis, membranes, G proteins, MAPKs, plant hormones, and transcription factors will be assessed. A better understanding of the vital roles ROS are playing in plant signaling would help innovate new strategies to improve plant productivity under the circumstances of the increasing severity of environmental conditions and the high demand of food and energy worldwide.
Recent Advances in Utilizing Transcription Factors to Improve Plant Abiotic Stress Tolerance by Transgenic Technology
Venue: Front. Plant Sci.

Agricultural production and quality are adversely affected by various abiotic stresses worldwide and this will be exacerbated by the deterioration of global climate. To feed a growing world population, it is very urgent to breed stress tolerant crops with higher yields and improved qualities against multiple environmental stresses. Since conventional breeding approaches had marginal success due to the complexity of stress tolerance traits, the transgenic approach is now being popularly used to breed stress tolerant crops. So identifying and characterizing the critical genes involved in plant stress responses is an essential prerequisite for engineering stress tolerant crops. Far beyond the manipulation of single functional gene, engineering certain regulatory genes has emerged as an effective strategy now for controlling the expression of many stress responsive genes. Transcription factors (TFs) are good candidates for genetic engineering to breed stress tolerant crop because of their role as master regulators of many stress responsive genes. Many TFs belonging to families AP2/EREBP, MYB, WRKY, NAC, bZIP have been found to be involved in various abiotic stresses and some TF genes have also been engineered to improve stress tolerance in model and crop plants. In this review, we take five large families of TFs as examples and review the recent progress of TFs involved in plant abiotic stress responses and their potential utilization to improve multiple stress tolerance of crops in the field conditions.