nils-herrmann/clean_scirepeval huggingface dataset:
Why Is it So Cold in Here? Explaining the Cold Temperatures Retrieved from Transmission Spectra of Exoplanet Atmospheres
Year: 2020

Transmission spectroscopy is a powerful technique widely used to probe exoplanet terminators. Atmospheric retrievals of transmission spectra are enabling comparative studies of exoplanet atmospheres. However, the atmospheric properties inferred by retrieval techniques display a significant anomaly: most retrieved temperatures are far colder than expected. In some cases, retrieved temperatures are ~1000 K colder than T_eq. Here, we provide an explanation for this conundrum. We demonstrate that erroneously cold temperatures result when 1D atmospheric models are applied to spectra of planets with differing morning evening terminator compositions. Despite providing an acceptable fit, 1D retrieval techniques artificially tune atmospheric parameters away from terminator averaged properties. Retrieved temperature profiles are hundreds of degrees cooler and have weaker temperature gradients than reality. Retrieved abundances are mostly biased by 1$\sigma$ and sometimes by 3$\sigma$ with the most extreme biases for ultra hot Jupiters. When morning evening compositional differences manifest for prominent opacity sources, H$_2$O abundances retrieved by 1D models can be biased by over an order of magnitude. Finally, we demonstrate that these biases provide an explanation for the cold retrieved temperatures reported for WASP 17b and WASP 12b. To overcome biases associated with 1D atmospheric models, there is an urgent need to develop multidimensional retrieval techniques.
An interval state estimation for electricity gas urban energy systems
Venue: 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2)
Year: 2017

Increasing interdependency between urban electricity and gas networks requires an integrated analysis for both networks. We propose an interval state estimation framework, which can provide the comprehensive interval information for the urban energy network real time monitoring and control. An op timization based approach is employed to produce the upper and lower bounds of state variables. Moreover, we introduce an un certainty budget for pseudo measurements considering the fact that pseudo measurements cannot be simultaneously valued at their upper/lower bounds Test results on the IEEE 13 node sys tem and a 9 node gas system indicate that the proposed interval state estimation approach produces an exact interval result. In the meanwhile, the consideration of measurement uncertainty budget contributes to providing a tighter interval solution.
Singing and companionship in the Hawthorn University of the Third Age Choir, Australia
Year: 2015

The international University of the Third Age (U3A) embodies the principles of lifelong learning and personal fulfilment amongst members. The research reported in this article focused on the Choir of the U3A Hawthorn, Melbourne, Australia and the benefits perceived by members undertaking this active music engagement in non competitive choral singing. This small scale phenomenological qualitative case study is part of a wider study of active arts engagement by older people that began in 2008. This study was undertaken in 2013 and revealed that participants decided to join the group for a range of factors including a positive attitude to singing, convenience and a desire for social connectedness. Those interviewed considered ongoing choir membership an effective use of leisure time that also provided opportunities for shared learning and personal validation. The data were analysed using Interpretative Phenomenological Analysis (IPA) and are reported under two themes: music engagement and social connections. Membership of the Hawthorn U3A choir provided participants opportunities for friendship, companionship, happiness, a sense of belonging and acceptance.
The Heckscher Ohlin Samuelson Theory of International Trade*
Year: 2016

This paper builds up a neo classical trade model to explain the 'product cycle' hypothesis originally proposed by Raymond Vernon. As the skill intensity of a product falls over time, the more capital abundant North tends to export 'new' goods and the less developed South exports 'old' goods. The trade pattern remains invariant over time although the product mix changes as the 'new' goods become old, and this exhibits the product cycle type phenomenon. Thus, it is shown that, with reasonable assumpitons, the traditional factor abundance model is sufficient to generate a product cycle type trade pattern.
Galerkin Approximations for the Stochastic Burgers Equation
Venue: SIAM J. Numer. Anal.
Year: 2013

Existence and uniqueness for semilinear stochastic evolution equations with additive noise by means of finite dimensional Galerkin approximations is established and the convergence rate of the Galerkin approximations to the solution of the stochastic evolution equation is estimated. These abstract results are applied to several examples of stochastic partial differential equations (SPDEs) of evolutionary type including a stochastic heat equation, a stochastic reaction diffusion equation, and a stochastic Burgers equation. The estimated convergence rates are illustrated by numerical simulations. The main novelty in this article is the estimation of the difference of the finite dimensional Galerkin approximations and of the solution of the infinite dimensional SPDE uniformly in space, i.e. in the $L^\infty$ topology, instead of the usual Hilbert space estimates in the $L^2$ topology, that were shown before.
Deep Cascade Generation on Point Sets
Venue: IJCAI
Year: 2019

This paper proposes a deep cascade network to generate 3D geometry of an object on a point cloud, consisting of a set of permutation insensitive points. Such a surface representation is easy to learn from, but inhibits exploiting rich low dimensional topological manifolds of the object shape due to lack of geometric connectivity. For benefiting from its simple structure yet utilizing rich neighborhood information across points, this paper proposes a two stage cascade model on point sets. Specifically, our method adopts the state of the art point set autoencoder to generate a sparsely coarse shape first, and then locally refines it by encoding neighborhood connectivity on a graph representation. An ensemble of sparse refined surface is designed to alleviate the suffering from local minima caused by modeling complex geometric manifolds. Moreover, our model develops a dynamically weighted loss function for jointly penalizing the generation output of cascade levels at different training stages in a coarse to fine manner. Comparative evaluation on the publicly benchmarking ShapeNet dataset demonstrates superior performance of the proposed model to the state of the art methods on both single view shape reconstruction and shape autoencoding applications.
A Daily Box Office Prediction Model with LSTM
Venue: 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)
Year: 2019

The prediction model of daily box office plays an important role in optimizing the layout of cinema. However, there are many factors affecting the daily box office, and the life cycle of the box office sequence is short. These problems bring great challenges to the construction of the model. In view of the above problems, this paper proposes a box office forecasting model based on LSTM. In the case of using the same characteristics, the model designed in this paper has better prediction effect, and the prediction error is 30.2%
Insights on representational similarity in neural networks with canonical correlation
Venue: NeurIPS
Year: 2018

Comparing different neural network representations and determining how representations evolve over time remain challenging open questions in our understanding of the function of neural networks. Comparing representations in neural networks is fundamentally difficult as the structure of representations varies greatly, even across groups of networks trained on identical tasks, and over the course of training. Here, we develop projection weighted CCA (Canonical Correlation Analysis) as a tool for understanding neural networks, building off of SVCCA, a recently proposed method (Raghu et al. 2017) We first improve the core method, showing how to differentiate between signal and noise, and then apply this technique to compare across a group of CNNs, demonstrating that networks which generalize converge to more similar representations than networks which memorize, that wider networks converge to more similar solutions than narrow networks, and that trained networks with identical topology but different learning rates converge to distinct clusters with diverse representations. We also investigate the representational dynamics of RNNs, across both training and sequential timesteps, finding that RNNs converge in a bottom up pattern over the course of training and that the hidden state is highly variable over the course of a sequence, even when accounting for linear transforms. Together, these results provide new insights into the function of CNNs and RNNs, and demonstrate the utility of using CCA to understand representations.
Fault Diagnosis Framework of Rolling Bearing Using Adaptive Sparse Contrative Auto Encoder With Optimized Unsupervised Extreme Learning Machine
Venue: IEEE Access
Year: 2020

Nowadays, the intelligent fault diagnosis based on deep learning have achieved remarkable results in the fields of the industrial equipment health monitoring and management. To implement the adaptive feature extraction and fault isolation for key components of rotating machinery (rolling bearings, etc. two new algorithms, Adaptive Sparse Contrative Auto encoder (ASCAE) algorithm and Optimized Unsupervised Extreme Learning Machine (OUSELM) classifier by Cuckoo Search Algorithm (CSA) can be firstly designed in this paper, respectively. Furthermore, a new rolling bearing fault diagnosis framework based on ASCAE combined with OUSELM is first of all proposed in this paper. Accordingly, this designed fault diagnosis framework can be divided into three main steps: i) Firstly, the vibration signals of rolling bearings can be collected and processed on the key components of rotating machinery, and then the collected vibration signals can be accordingly converted into frequency signals; ii) Secondly, the transformed spectral signals can be entered into the constructed ASCAE for feature learning to exploit the multi layer sensitive features from the hidden raw data; iii) Thirdly, the extracted multi layer sensitive features can be flowed into the trained OUSELM classifier for unsupervised fault state separation and diagnosis. More specifically, our designed fault diagnosis framework (ASCAE OUSELM) can employ the homotopy regularization theory, sparse theory, intelligent optimization algorithm and other tools to optimize the parameters and improve the performance of the original Contrative Auto encoder (CAE) algorithm and Unsupervised Extreme Learning Machine (USLEM) algorithm, respectively. At the same time, the proposed fault diagnosis framework can achieve effective sparse and sensitive feature information extraction in the feature extraction stage (ASCAE) to avoid over fitting. In fault isolation stage, the issue of the supervised and low training efficiency caused by traditional deep learning model can be perfectly addressed by OUSELM. Eventually, the experimental data of rolling bearings validated the effectiveness of the proposed fault diagnosis framework and two deigned algorithms.
'Everybody's a Sleuth Nowadays' The Effect the Fictionalisation of Oscar Wilde, Sir Arthur Conan Doyle, Charles Dickens, and the Bronte sisters as Detectives has on their authorial and cultural identity in Contemporary Detective Biofiction
Year: 2020

Deze thesis onderzoekt de fictionalisatie van Sir Arthur Conan Doyle, Oscar Wilde, Charles Dickens en de Bronte sisters als detectives in zes geselecteerde biofictie boeken in het detective genre. Daarvoor is de volgende onderzoeksvraag opgesteld: wat voor effect heeft de fictionalisering van Sir Arthur Conan Doyle, Oscar Wilde, Charles Dickens en de Bronte sisters als detectives in 21ste eeuwse Britse en Amerikaanse biofictie op de manier waarop hun culturele identiteit, auteursidentiteit en hun werken worden vormgegeven? Om deze onderzoeksvraag te beantwoorden zal ik gebruik maken van verscheidene theorieen binnen life writing en biofictie, specifiek gericht op concepten zoals "fictionalisering" en "biographemes" om te analyseren hoe deze auteurs neergezet worden in de boeken. Deze analyse zal gebaseerd zijn op mijn eigen geselecteerde boeken. Het hoofddoel is om te onderzoeken hoe deze historische auteurs neergezet worden in biofictie wanneer ze een andere rol aannemen dan hun auteurschap namelijk de rol van een detective en hoe hun werken, persoonlijkheid, en culturele en auteursidentiteit hiermee verweven zijn en het verhaal als een geheel.
Computational redesign of endonuclease DNA binding and cleavage specificity
Venue: Nature
Year: 2006

The reprogramming of DNA binding specificity is an important challenge for computational protein design that tests current understanding of protein DNA recognition, and has considerable practical relevance for biotechnology and medicine. Here we describe the computational redesign of the cleavage specificity of the intron encoded homing endonuclease I MsoI using a physically realistic atomic level forcefield. Using an in silico screen, we identified single base pair substitutions predicted to disrupt binding by the wild type enzyme, and then optimized the identities and conformations of clusters of amino acids around each of these unfavourable substitutions using Monte Carlo sampling. A redesigned enzyme that was predicted to display altered target site specificity, while maintaining wild type binding affinity, was experimentally characterized. The redesigned enzyme binds and cleaves the redesigned recognition site ~10,000 times more effectively than does the wild type enzyme, with a level of target discrimination comparable to the original endonuclease. Determination of the structure of the redesigned nuclease recognition site complex by X ray crystallography confirms the accuracy of the computationally predicted interface. These results suggest that computational protein design methods can have an important role in the creation of novel highly specific endonucleases for gene therapy and other applications.
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Venue: EMNLP
Year: 2018

Dependency trees help relation extraction models capture long range relations between words. However, existing dependency based models either neglect crucial information (e.g. negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state of the art performance on the large scale TACRED dataset, outperforming existing sequence and dependency based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art.
PERUBAHAN KONDISI FISIK BUAH KOPI (Coffea sp) SETELAH DISIMPAN SELAMA 1 BULAN
Year: 2020

umatera Barat merupakan provinsi penghasil kopi di Indonesia. Luas areal perkebunan kopi pada tahun 2016 sampai 2018 yakni 38.365 Ha; 33.276 Ha; dan 34.024 ha dengan produksi 22.721 ton; 17.553 ton; dan 18.026 ton. Buah kopi harus ditangani secara cepat menjadi bentuk yang lebih stabil agar aman untuk disimpan dalam jangka waktu tertentu. Kriteria mutu biji yang meliputi aspek fisik, cita rasa dan kebersihan serta aspek keseragaman dan konsistensi sangat ditentukan oleh perlakuan pada setiap tahapan proses produksinya. Penelitian ini dilaksanakan di Laboratorium Teknologi Benih Fakulltas Pertanian Universitas Andalas dari bulan Agustus 2020. Pelaksanaan penelitian ini digunakan bahan antara lain benih kopi robusta yang diambil di daerah solok, Alat yang digunakan dalam penelitian ini adalah meteran, botol botol plastik, pisau, timbangan analitik, beaker glass, gunting, karung goni, ember, kamera, sarung tangan, masker dan alat tulis. Penelitian ini dilakukan secara deskriptif yakni mengambil beberapa sampel buah tanaman kopi robusta dan mengamati perubahan fisik dari buah yang diambil secara random disuatu areal perkebunan kopi robusta. Berdasarkan pengamatan yang dilakukan terkait dengan kondisi penyimpanan buah kopi selama sebulan didapatkan perubahannya yakni, waktu setelah dipanen didapatkan kondisi buah kopi merah terang, kulit segar mulus dan lunak, lalu disimpan selama 7 hari didapatkan kondisi merah gelap kulit sedikit keriput, disimpan selama 14 hari dengan kondisi merah gelap dan kulit keriput jelas, disimpan selama 21 hari didapat warna hitam kecoklatan, kulit kaku dan keras dan setelah disimpan selama 30 hari didapat warna kulit buah kopi hitam, kulit kaku dan keras.  Respirasi, Kulit Buah, Warna Buah
Notes from the Field: Unintentional Drug Overdose Deaths with Kratom Detected 27 States, July 2016 December 2017
Venue: MMWR. Morbidity and mortality weekly report
Year: 2019

Kratom (Mitragyna speciosa) a plant native to Southeast Asia, contains the alkaloid mitragynine, which can produce stimulant effects in low doses and some opioid like effects at higher doses when consumed (1) Use of kratom has recently increased in popularity in the United States, where it is usually marketed as a dietary or herbal supplement (1) Some studies suggest kratom has potential for dependence and abuse (1,2) As of April 2019, kratom was not scheduled as a controlled substance. However, since 2012, the Food and Drug Administration has taken a number of actions related to kratom, and in November 2017 issued a public health advisory* in addition, the Drug Enforcement Administration has identified kratom as a drug of concern. During 2011 2017, the national poison center reporting database documented 1,807 calls concerning reported exposure to kratom (3) To assess the impact of kratom, CDC analyzed data from the State Unintentional Drug Overdose Reporting System (SUDORS) CDC funds 32 states and the District of Columbia to abstract into SUDORS detailed data on unintentional and undetermined intent opioid overdose deaths from death certificates and medical examiner and coroner reports, including postmortem toxicology results. Although kratom is not an opioid, overdose deaths involving kratom (including nonopioid overdose deaths) are included in SUDORS.SS Although postmortem toxicology testing varies in scope among medical examiners and coroners, SUDORS records all substances detected on postmortem toxicology testing, along with overdose specific circumstances. CDC analyzed overdose deaths in which kratom was detected on postmortem toxicology testing and deaths in which kratom was determined by a medical examiner or coroner to be a cause
An 850 nm pure near infrared emitting iridium complex for solution processed organic light emitting diodes
Year: 2020

High performance pure near infrared organic light emitting materials and devices with full emission over 700 nm are still rare, though urgently needed. Iridium(III) complexes have been proven to be an important class of NIR emitters. Herein, we report a new NIR homoleptic Ir(III) complex, tris[1,4 di(5 n octylthiophen 2 yl)benzo[g]phthalazine] iridium(III) (Ir(dotbpa)3) with full emission beyond 700 nm with an iridium mass content as low as 10 wt% Thanks to the introduction of flexible n octyl groups, Ir(dotbpa)3 shows greatly improved solubility and excellent solution processability in comparison with its counterpart Ir(dtbpa)3. By utilizing a multifunctional host, solution processed NIR OLEDs were fabricated with a simple structure of only three organic layers and they demonstrated a pure NIR emission starting from 725 nm and a peak at 850 nm with the maximum external quantum efficiency of 0.17% which makes them the first example of pure NIR OLEDs based on Ir(III) emitters.
Robust detection of shady and highlighted roads for monocular camera based navigation of UGV
Venue: 2011 IEEE International Conference on Robotics and Automation
Year: 2011

This paper addresses the problem of UGV navigation in various environments and lightning conditions. Previous approaches use a combination of different sensors, or work well, only in scenarios with noticeable road marking or borders. Our robot is used for chemical, nuclear and biological contamination measurement. Thus, to avoid complications with decontamination, only a monocular camera serves as a sensor since it is already equipped. In this paper, we propose a novel approach a fusion of frequency based vanishing point estimation and probabilistically based color segmentation. Detection of a vanishing point, is based on the estimation of a texture flow, produced by a bank of Gabor wavelets and a voting function. Next, the vanishing point defines the training area, which is used for self supervised learning of color models. Finally, road patches are selected by measuring of the roadness score. A few rules deal with dark cast shadows, overexposed highlights and adaptivity speed. In addition to the robustness of our system, it is easy to use since no calibration is needed.
Anomalous Event Detection and Localization Using Stacked Autoencoder
Venue: CVIP
Year: 2019

Anomalous event detection and localization from the crowd is a challenging problem to the computer vision community. It is an important aspect of intelligent video surveillance. Surveillance cameras are set up to monitor anomalous or unusual events. But, the majority of video data, related to normal or usual events, is accessible. Thus, analysis and recognition of anomalous events from huge data are very difficult. In this work, an automated system is proposed to identify and localize anomalies at local level. The proposed work is divided into four steps, namely preprocessing, feature extraction, training of stacked autoencoder and anomaly detection and localization. Preprocessing step removes background from video frames. To capture the dynamic nature of foreground objects, magnitude of optical flow is computed. Deep feature representation is obtained over the raw magnitude of optical flow using stacked autoencoder. Autoencoder extracts high level structural information from motion magnitudes to distinguish between normal and anomalous behaviors. The performance of proposed approach is experimentally evaluated on standard UCSD and UMN dataset developed for anomaly detection. Result of the proposed system demonstrate its usefulness in anomaly detection and localization compared to existing methods.