nils-herrmann/clean_scirepeval huggingface dataset:
Early Visual Concept Learning with Unsupervised Deep Learning
Venue: ArXiv

Automated discovery of early visual concepts from raw image data is a major open challenge in AI research. Addressing this problem, we propose an unsupervised approach for learning disentangled representations of the underlying factors of variation. We draw inspiration from neuroscience, and show how this can be achieved in an unsupervised generative model by applying the same learning pressures as have been suggested to act in the ventral visual stream in the brain. By enforcing redundancy reduction, encouraging statistical independence, and exposure to data with transform continuities analogous to those to which human infants are exposed, we obtain a variational autoencoder (VAE) framework capable of learning disentangled factors. Our approach makes few assumptions and works well across a wide variety of datasets. Furthermore, our solution has useful emergent properties, such as zero shot inference and an intuitive understanding of "objectness"
Globally Normalized Transition Based Neural Networks
Venue: ACL

We introduce a globally normalized transition based neural network model that achieves state of the art part of speech tagging, dependency parsing and sentence compression results. Our model is a simple feed forward neural network that operates on a task specific transition system, yet achieves comparable or better accuracies than recurrent models. We discuss the importance of global as opposed to local normalization: a key insight is that the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models.
TES Based X ray Microcalorimeter Performances Under AC Bias and FDM for Athena

Athena is a European X ray observatory, scheduled for launch in \sim ~2028. Athena will employ a high resolution imaging spectrometer called X ray integral field unit (X IFU) consisting of an array of \sim ~4000 transition edge sensor (TES) microcalorimeter pixels. For the readout of X IFU, we are developing frequency domain multiplexing, which is the baseline readout system. In this paper, we report on the performance of a TES X ray calorimeter array fabricated at Goddard Space Flight Center (GSFC) at MHz frequencies for the baseline of X IFU detector. During single pixel AC bias characterization, we measured X ray energy resolutions (at 6 keV) of about 2.9 eV at both 2.3 and 3.7 MHz. Furthermore, in the multiplexing mode, we measured X ray energy resolutions of about 2.9 eV at 1.3 and 1.7 MHz.
Complex Embeddings for Simple Link Prediction
Venue: ICML

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state of the art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.
Inflammation and its role in age related macular degeneration
Venue: Cellular and Molecular Life Sciences

Inflammation is a cellular response to factors that challenge the homeostasis of cells and tissues. Cell associated and soluble pattern recognition receptors, e.g. Toll like receptors, inflammasome receptors, and complement components initiate complex cellular cascades by recognizing or sensing different pathogen and damage associated molecular patterns, respectively. Cytokines and chemokines represent alarm messages for leukocytes and once activated, these cells travel long distances to targeted inflamed tissues. Although it is a crucial survival mechanism, prolonged inflammation is detrimental and participates in numerous chronic age related diseases. This article will review the onset of inflammation and link its functions to the pathogenesis of age related macular degeneration (AMD) which is the leading cause of severe vision loss in aged individuals in the developed countries. In this progressive disease, degeneration of the retinal pigment epithelium (RPE) results in the death of photoreceptors, leading to a loss of central vision. The RPE is prone to oxidative stress, a factor that together with deteriorating functionality, e.g. decreased intracellular recycling and degradation due to attenuated heterophagy/autophagy, induces inflammation. In the early phases, accumulation of intracellular lipofuscin in the RPE and extracellular drusen between RPE cells and Bruch's membrane can be clinically detected. Subsequently, in dry (atrophic) AMD there is geographic atrophy with discrete areas of RPE loss whereas in the wet (exudative) form there is neovascularization penetrating from the choroid to retinal layers. Elevations in levels of local and systemic biomarkers indicate that chronic inflammation is involved in the pathogenesis of both disease forms.
The NLRP3 Inflammasome and its Role in Age Related Macular Degeneration.
Venue: Advances in experimental medicine and biology

Age related macular degeneration (AMD) is the most common cause of blindness among people of 65 years and older in developed countries (Klein and Klein, Invest Ophthalmol Vis Sci 54:7395 7401, 2013) Recent advances in dry AMD research points towards an important role of the inflammatory response in the development of the disease. The presence of inflammatory cells, antibodies, complement factors and pro inflammatory cytokines in AMD retinas and drusen indicates that the immune system could be an important driving force in dry AMD. The NLRP3 inflammasome has been proposed as an integrator of process associated with AMD and the induction of inflammation. Herein we summarize the most recent studies that attempt to understand the role of the NLRP3 inflammasome in AMD.
Preparation of superhydrophobic coating with excellent abrasion resistance and durability using nanofibrillated cellulose

Nanofibrillated cellulose (NFC) is a renewable and environmentally friendly material. Its large ratio of length to diameter and its pliability are very helpful for forming a three dimensional network structure that could contribute to suitable roughness for superhydrophobic coatings. At present, superhydrophobic coatings have limited applications, mainly due to their poor abrasion resistance and durability. Therefore, in this study, a semi translucent NFC superhydrophobic coating was prepared by spraying NFC ethanol suspension onto a wood surface that had been previously covered with a commercial spray paint as adhesive, followed by modification via phase chemical vapor deposition (CVD) to reduce surface free energy. The results showed that in addition to good superhydrophobic and self cleaning properties, the coating also possessed excellent abrasion resistance and durability and was able to resist sandpaper abrasion, knife scratch, finger wipe, long time impregnation in water, UV radiation, and low temperatures, demonstrating good potential for development and application.
Encode, Review, and Decode: Reviewer Module for Caption Generation
Venue: ArXiv

We propose a novel module, the reviewer module, to improve the encoder decoder learning framework. The reviewer module is generic, and can be plugged into an existing encoder decoder model. The reviewer module performs a number of review steps with attention mechanism on the encoder hidden states, and outputs a fact vector after each review step; the fact vectors are used as the input of the attention mechanism in the decoder. We show that the conventional encoder decoders are a special case of our framework. Empirically, we show that our framework can improve over state of the art encoder decoder systems on the tasks of image captioning and source code captioning.
Sentence learning on deep convolutional networks for image Caption Generation
Venue: 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

We propose a method to train image representation to improve the performance of image Caption Generation. We use transfer learning CNN on sentences, and extract image representation with deep Fisher kernel. With this representation, we generate sentence with gLSTM and show the improvements in performance. In the experiment, we validate each steps of our method by comparing with previous state of the art models.
Deciphering the origin of giant magnetic anisotropy and fast quantum tunnelling in Rhenium(IV) single molecule magnets
Venue: Nature communications

Single molecule magnets represent a promising route to achieve potential applications such as high density information storage and spintronics devices. Among others, 4d/5d elements such as Re(IV) ion are found to exhibit very large magnetic anisotropy, and inclusion of this ion aggregated clusters yields several attractive molecular magnets. Here, using ab intio calculations, we unravel the source of giant magnetic anisotropy associated with the Re(IV) ions by studying a series of mononuclear Re(IV) six coordinate complexes. The low lying doublet states are found to be responsible for large magnetic anisotropy and the sign of the axial zero field splitting parameter (D) can be categorically predicted based on the position of the ligand coordination. Large transverse anisotropy along with large hyperfine interactions opens up multiple relaxation channels leading to a fast quantum tunnelling of the magnetization (QTM) process. Enhancing the Re ligand covalency is found to significantly quench the QTM process.
Evidence of rapid changes in Lyme disease awareness in Canada.
Venue: Ticks and tick borne diseases

Lyme disease (LD) is emerging in Canada. A key preventive strategy is promoting the adoption by the general public of personal preventive behaviors regarding tick bites. The aim of this study was to measure the changes in public awareness toward ticks and LD before and after the launch of a national communication campaign in Canada using data from two surveys conducted in March and December 2014. The results show a significant increase in awareness of LD after compared to before the campaign, but also suggest that the importance of this increase is not equal amongst Canadian regions. Moreover, respondents whose level of awareness increased most significantly were those who lived in regions with low entomologic risk. The findings underline the importance of risk communications for emerging diseases and reinforce the need to understand the specific characteristics of the targeted populations before the implementation of communication campaigns to increase their efficacy.
Using Deep Learning for Image Based Plant Disease Detection
Venue: Front. Plant Sci.

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof) The trained model achieves an accuracy of 99.35% on a held out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone assisted crop disease diagnosis on a massive global scale.
Fast Algorithms for Convolutional Neural Networks
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Deep convolutional neural networks take GPU days of computation to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3 3 filters. We introduce a new class of fast algorithms for convolutional neural networks using Winograd's minimal filtering algorithms. The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes. We benchmark a GPU implementation of our algorithm with the VGG network and show state of the art throughput at batch sizes from 1 to 64.
Factors Influencing Student Engagement

The purpose of this study was to assess and outline major factors influencing student engagement in the middle school context. In order to contribute to the existing body of research and to benefit students by outlining the particular aspects of learning experience that are related to engagement, a case study has been carried out, which sought to analyze the students' schooling experience as a source of valuable data. Qualitative methods of data collection were applied during the study. The survey, focus group interview, and observations allowed classifying the factors influencing the levels of engagement among the study participants into five major clusters: 1) communication, collaboration, active involvement into learning activities, and enriching educational experiences; 2) interactions between students and teachers; 3) levels of academic challenge; 4) supporting classroom environment; and 5) supporting family environment. These clusters unite factors that were found to produce the greatest influence on students' eagerness to participate in in class activities and on students' perception of the importance of education, as well as desire to succeed academically.
Efficient Numerical Frameworks for Multi objective Cyber Security Planning
Venue: ESORICS

We consider the problem of optimal investment in cyber security by an enterprise. Optimality is measured with respect to the overall (1) monetary cost of implementation, (2) negative side effects of cyber security controls (indirect costs) and (3) mitigation of the cyber security risk. We consider "passive" and "reactive" threats, the former representing the case where attack attempts are independent of the defender's plan, the latter, where attackers can adapt and react to an implemented cyber security defense. Moreover, we model in three different ways the combined effect of multiple cyber security controls, depending on their degree of complementarity and correlation. We also consider multi stage attacks and the potential correlations in the success of different stages. First, we formalize the problem as a non linear multi objective integer programming. We then convert them into Mixed Integer Linear Programs (MILP) that very efficiently solve for the exact Pareto optimal solutions even when the number of available controls is large. In our case study, we consider 27 of the most typical security controls, each with multiple intensity levels of implementation, and 37 common vulnerabilities facing a typical SME. We compare our findings against expert recommended critical controls. We then investigate the effect of the security models on the resulting optimal plan and contrast the merits of different security metrics. In particular, we show the superior robustness of the security measures based on the "reactive" threat model, and the significance of the hitherto overlooked role of correlations.
A Novel Approach to Design Hybrid Vigenere Caesar Cipher Encryption with Genetic Algorithm (HVCEGA) for Data Security in Cloud Computing

In present scenario cloud computing is a large pool of easily and accessible virtualized resources, such as hardware, development platforms and software services. Cloud computing is a broad solution that delivers IT as a service. Cloud is an internet based technology which uses the Internet& central remote servers to support data and applications. In cloud computing, the data will be stored in data storage provided by cloud service providers. But still many business companies are not willing to adopt cloud computing technology due to lack of proper security control policy which lead to many vulnerability in cloud computing. Cryptography is mostly used technology to protect data within cloud environment. It is an art and science of hiding existing data from the external environment. It allows information to be sent in a secure form in such a manner that only authorized person is able to retrieve the original information. The paper presents a secure cloud data encryption technology which encrypts the data using Hybrid Algorithm (Vigenere and Caesar Cipher Encryption Algorithm and Genetic AlgorithmHVCEGA) A hybrid approach of more than one technique is more reliable and strong enough to provide security of data while processing and storage by using secured non repeatable key (generated by one time pad) Computation time, avalanche effect and throughput are the parameters for which performance has been compared with existing encryption approach. The proposed HVCEGA algorithm provides more security by multi layer encryption and reduces computation complexity using genetic algorithm.
Comparison of coaxial magnetic gears with parallel and series magnetic circuits
Venue: 2016 Eleventh International Conference on Ecological Vehicles and Renewable Energies (EVER)

This paper quantitatively compares two coaxial magnetic gears (CMGs) with parallel magnetic circuit and series magnetic circuit, which are artfully integrated into a permanent magnet (PM) brushless DC motor. By using the finite element method, the performances of both CMGs are compared and evaluated. Analysis results show that the CMG series magnetic circuit can offer higher pull out torque and back EMF than the CMG parallel magnetic circuit, while the CMG parallel magnetic circuit has lower PM eddy current loss, iron losses and lower distortion of back EMF.
High order implicit time integration schemes on multiresolution adaptive grids for stiff PDEs

We consider high order, implicit Runge Kutta schemes to solve time dependent stiff PDEs on dynamically adapted grids generated by multiresolution analysis for unsteady problems disclosing localized fronts. The multiresolution finite volume scheme yields highly compressed representations within a user defined accuracy tolerance, hence strong reductions of computational requirements to solve large, coupled nonlinear systems of equations. SDIRK and RadauIIA Runge Kutta schemes are implemented with particular interest in those with L stability properties and accuracy based time stepping capabilities. Numerical evidence is provided of the computational efficiency of the numerical strategy to cope with highly unsteady problems modeling various physical scenarios with a broad spectrum of time and space scales.
Comparing the Consumer Price Index with the Gross Domestic Product Price Index and Gross Domestic Product Implicit Price Deflator

The Consumer Price Index (CPI) and the gross domestic product (GDP) price index and implicit price deflator are measures of inflation in the U.S. economy. The CPI measures price changes in goods and services purchased out of pocket by urban consumers, whereas the GDP price index and implicit price deflator measure price changes in goods and services purchased by consumers, businesses, government, and foreigners, but not importers. Thus, which one to use in a given scenario depends on one's purpose.
Can consumer price index predict gold price returns

In this paper using data for 54 countries we test whether consumer price index (CPI) predicts gold price returns. Our test for predictability is based on a recently developed flexible generalised least squares estimator, which most importantly accommodates the endogeneity of CPI, its persistency and any heteroskedasticity in the model. We find limited evidence that CPI predicts gold price returns in in sample tests; however, out of sample tests reveal relatively strong evidence that CPI predicts gold returns. These results are robust to different forecasting horizons. On the whole, we discover reasonable evidence that consumer prices predict gold price returns.
Towards Supremum Sum Subdifferential Calculus Free of Qualification Conditions
Venue: SIAM J. Optim.

We give a formula for the subdifferential of the sum of two convex functions where one of them is the supremum of an arbitrary family of convex functions. This is carried out under a weak assumption expressing a natural relationship between the lower semicontinuous envelopes of the data functions in the domain of the sum function. We also provide a new rule for the subdifferential of the sum of two convex functions, which uses a strategy of augmenting the involved functions. The main feature of our analysis is that no continuity type condition is required. Our approach allows us to unify, recover, and extend different results in the recent literature.
Motivating students through providing a middle school STEM rotation class
Venue: 2016 IEEE Integrated STEM Education Conference (ISEC)

Today, STEM is taught, perceived, and practiced in many different ways. There is a need to design an embedded curriculum that represents all of the disciplines of STEM. "The reason behind all of this is simple: if we do not design lessons and units that will strengthen students' commitment to learn, then we cannot expect them to take an active or in depth approach to learning" (Silver Perini, 2010, ) This will empower students to become conscientious and well informed global citizens. All children, regardless of academic level, should be exposed to a STEM education in order to increase the diversity of students in STEM careers. "Lack of motivation is commonly recognized as a significant problem in American education" (Brophy, 2010,  A classroom's social climate, the students' own expectations of their performance, and the value in which the student places on his education are the main issues that deter motivation in students. A dynamic classroom should be full of engaged students who are collaborating, communicating, and performing to solve relevant problems in which students can relate. This can be achieved when teachers reflect on how things work, or do not work, in the classroom. Gathering data and analyzing the results will help teachers to improve students' level of motivation. The teacher is the key component in creating an environment using appropriate strategies that will engage students to attain a growth mindset. In this paper, I will share how the results of a climate survey can empower me to regulate my learning activities and styles to provide my students with a STEM environment that is favorable to engagement and motivation.