nils-herrmann/clean_scirepeval huggingface dataset:
A Joint Model for Multimodal Document Quality Assessment

The quality of a document is affected by various factors, including grammaticality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text - such as images, font choices, and visual layout - we propose a joint model that combines the text content with a visual rendering of the document for document quality assessment. Experimental results over a Wikipedia dataset reveal that textual and visual features are complementary, achieving state-of-the-art results. Further experiments on a Peer Review dataset verify the general applicability of our proposed model.
Diffusive nuclear burning in cooling simulations and application to new temperature data of the Cassiopeia A neutron star

A critical relation in the study of neutron star cooling is the one between surface temperature and interior temperature. This relation is determined by the composition of the neutron star envelope and can be affected by the process of diffusive nuclear burning (DNB), which occurs when elements diffuse to depths where the density and temperature are sufficiently high to ignite nuclear burning. We calculate models of H-He and He-C envelopes that include DNB and obtain analytic temperature relations that can be used in neutron star cooling simulations. We find that DNB can lead to a rapidly changing envelope composition and prevents the build-up of thermally stable hydrogen columns yH ≳ 107 g cm-2, while DNB can make helium envelopes more transparent to heat flux for surface temperatures Ts ≳ 2x106 K. We perform neutron star cooling simulations in which we evolve temperature and envelope composition, with the latter due to DNB and accretion from the interstellar medium. We find that a time-dependent envelope composition can be relevant for understanding the long-term cooling behaviour of isolated neutron stars. We also report on the latest Chandra observations of the young neutron star in the Cassiopeia A supernova remnant; the resulting 13 temperature measurements over more than 18 years yield a ten-year cooling rate of ~2%. Finally, we fit the observed cooling trend of the Cassiopeia A neutron star with a model that includes DNB in the envelope.
Behind the Standard Model

These lectures provide a concise introduction to the so-called "Beyond the Standard Model"' physics, with particular emphasis on the problem of the microscopic origin of the Higgs mass term and of the Electro-Weak symmetry breaking scale in connection with Naturalness. The standard scenarios of Supersymmetry and Composite Higgs are shortly reviewed. An attempt is made to summarise the implications of the LHC run-$1$ results on what we expect to lie beyond (or behind) the Standard Model.
An Insight into the Dynamics and State Space Modelling of a 3-D Quadrotor

Drones have gained popularity in a wide range of field ranging from aerial photography, aerial mapping, and investigation of electric power lines. Every drone that we know today is carrying out some kind of control algorithm at the low level in order to manoeuvre itself around. For the quadrotor to either control itself autonomously or to develop a high-level user interface for us to control it, we need to understand the basic mathematics behind how it functions. This paper aims to explain the mathematical modelling of the dynamics of a 3 Dimensional quadrotor. As it may seem like a trivial task, it plays a vital role in how we control the drone. Also, additional effort has been taken to explain the transformations of the drone's frame of reference to the inertial frame of reference.
Swimmer Suspensions on Substrates: Anomalous Stability and Long-Range Order.

We present a comprehensive theory of the dynamics and fluctuations of a two-dimensional suspension of polar active particles in an incompressible fluid confined to a substrate. We show that, depending on the sign of a single parameter, a state with polar orientational order is anomalously stable (or anomalously unstable), with a nonzero relaxation (or growth) rate for angular fluctuations, not parallel to the ordering direction, at zero wave number. This screening of the broken-symmetry mode in the stable state does lead to conventional rather than giant number fluctuations as argued by Bricard et al.,  (2013), but their bend instability in a splay-stable flock does not exist and the polar phase has long-range order in two dimensions. Our theory also describes confined three-dimensional thin-film suspensions of active polar particles as well as dense compressible active polar rods, and predicts a flocking transition without a banding instability.
Adaptive Density Estimation for Generative Models

Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models. GANs have dramatically improved sample quality, but suffer from two drawbacks: (i) they mode-drop, \ie, do not cover the full support of the train data, and (ii) they do not allow for likelihood evaluations on held-out data. In contrast likelihood-based training encourages models to cover the full support of the train data, but yields poorer samples. These mutual shortcomings can in principle be addressed by training generative latent variable models in a hybrid adversarial-likelihood manner. However, we show that commonly made parametric assumptions create a conflict between them, making successful hybrid models non trivial. As a solution, we propose the use of deep invertible transformations in the latent variable decoder. This approach allows for likelihood computations in image space, is more efficient than fully invertible models, and can take full advantage of adversarial training. We show that our model significantly improves over existing hybrid models: offering GAN-like samples, IS and FID scores that are competitive with fully adversarial models and improved likelihood scores.
Dealing with Limited Backhaul Capacity in Millimeter-Wave Systems: A Deep Reinforcement Learning Approach

Millimeter-wave (mmWave) communication is a key technology of fifth generation wireless systems to achieve the expected 1000x data rate. With large bandwidth at the mmWave band, the link capacity between users and base stations (BSs) can be much higher compared to sub-6 GHz wireless systems. Meanwhile, due to the high cost of infrastructure upgrade, it would be difficult for operators to drastically enhance the capacity of backhaul links between mmWave BSs and the core network. As a result, the data rate provided by backhaul may not be sufficient to support all mmWave links; hence, the backhaul connection becomes the new bottleneck. On the other hand, as mmWave channels are subject to random blockage, the data rates of mmWave users significantly vary over time. With limited backhaul capacity and highly dynamic data rates of users, how to allocate backhaul resource to each user remains a challenge for mmWave systems. In this article, we present a deep reinforcement learning (DRL) approach to address this challenge. By learning the blockage pattern, the system dynamics can be captured and predicted, resulting in efficient utilization of backhaul resource. We begin with a discussion on DRL and its application in wireless systems. We then investigate the problem of backhaul resource allocation and present the DRL-based solution. Finally, we discuss open problems for future research and conclude this article.
Machine Translation: A Literature Review

Machine translation (MT) plays an important role in benefiting linguists, sociologists, computer scientists, etc. by processing natural language to translate it into some other natural language. And this demand has grown exponentially over past couple of years, considering the enormous exchange of information between different regions with different regional languages. Machine Translation poses numerous challenges, some of which are: a) Not all words in one language has equivalent word in another language b) Two given languages may have completely different structures c) Words can have more than one meaning. Owing to these challenges, along with many others, MT has been active area of research for more than five decades. Numerous methods have been proposed in the past which either aim at improving the quality of the translations generated by them, or study the robustness of these systems by measuring their performance on many different languages. In this literature review, we discuss statistical approaches (in particular word-based and phrase-based) and neural approaches which have gained widespread prominence owing to their state-of-the-art results across multiple major languages.
Information Measure Similarity Theory: Message Importance Measure via Shannon Entropy

Rare events attract more attention and interests in many scenarios of big data such as anomaly detection and security systems. To characterize the rare events importance from probabilistic perspective, the message importance measure (MIM) is proposed as a kind of semantics analysis tool. Similar to Shannon entropy, the MIM has its special functional on information processing, in which the parameter $\varpi$ of MIM plays a vital role. Actually, the parameter $\varpi$ dominates the properties of MIM, based on which the MIM has three work regions where the corresponding parameters satisfy $ 0 \le \varpi \le 2/\max\{p(x_i)\}$, $\varpi > 2/\max\{p(x_i)\}$ and $\varpi < 0$ respectively. Furthermore, in the case $ 0 \le \varpi \le 2/\max\{p(x_i)\}$, there are some similarity between the MIM and Shannon entropy in the information compression and transmission, which provide a new viewpoint for information theory. This paper first constructs a system model with message importance measure and proposes the message importance loss to enrich the information processing strategies. Moreover, we propose the message importance loss capacity to measure the information importance harvest in a transmission. Furthermore, the message importance distortion function is presented to give an upper bound of information compression based on message importance measure. Additionally, the bitrate transmission constrained by the message importance loss is investigated to broaden the scope for Shannon information theory.
A Unified Framework of Epidemic Spreading Prediction by Empirical Mode Decomposition- Based Ensemble Learning Techniques

In this paper, a unified susceptible-exposed-infected-susceptible-aware (SEIS-A) framework is proposed to combine the epidemic spreading process with individuals’ self-query behaviors on the Internet. An epidemic spreading prediction model that contains two phases is established based on the SEIS-A framework. To deal with the nonstationary complex characteristic of the time series data of disease density, it is decomposed through the empirical mode decomposition (EMD) method to obtain the intrinsic mode functions (IMFs) in phase I. To enhance the prediction performance, the ensemble learning techniques that use the self-query data as an external input are applied to these IMFs in phase II. Finally, an empirical study on the prediction of weekly consultation rates of hand-foot-and-mouth disease (HFMD) in Hong Kong is conducted to validate the effectiveness of the proposed method. The main advantage of this method is that it outperforms other learning methods on fluctuating complex epidemic spreading data.
The H i morphology and stellar properties of strongly barred galaxies: support for bar quenching in massive spirals

Galactic bars are able to affect the evolution of galaxies by redistributing their gas, possibly contributing to the cessation of star formation. Several recent works point to ‘bar quenching’ playing an important role in massive disc galaxies. We construct a sample of six gas-rich and strongly barred disc galaxies with resolved H i observations. This sample of galaxies, which we call H i-rich barred galaxies, was identified with the help of Galaxy Zoo to find galaxies hosting a strong bar, and the Arecibo Legacy Fast Arecibo L-band Feed Array blind H i survey to identify galaxies with a high H i content. The combination of strong bar and high gas fraction is rare, so this set of six galaxies is the largest sample of its type with resolved H i observations. We measure the gas fractions, H i morphology and kinematics, and use archival optical data from the Sloan Digital Sky Survey to reveal star formation histories and bar properties. The galaxies with the lowest gas fractions (still very high for their mass) show clear H i holes, dynamically advanced bars, and low star formation rates, while those with the highest gas fractions show little impact from their bar on the H i morphology, and are still actively star-forming. These galaxies support a picture in which the movement of gas by bars can lead to star formation quenching. How these unusual galaxies came to be is an open question.
Building connections: How scientists meet each other during a conference

We present the results of two studies on how individuals interact with each other during a international, interdisciplinary scientific conference. We first show that contact activity is highly variable across the two conferences and between different socio-demographic groups. However, we found one consistent phenomenon: Professors connect and interact significantly less than the other participants. We interpret this effect as non-tenured researchers using conferences to accumulate social capital, while established researchers already have such capital. We then show that groups mix well during conferences, but note that a language-based homophily is always present. Finally, we show that the dynamics of the contacts across days is also similar between conferences. First day connections are established, then filtering occurs during the following days. The connection turnover between consecutive days proves to be large ( ), and related to the intensity of interactions.
Aspect Category Detection via Topic-Attention Network

The e-commerce has started a new trend in natural language processing through sentiment analysis of user-generated reviews. Different consumers have different concerns about various aspects of a specific product or service. Aspect category detection, as a subtask of aspect-based sentiment analysis, tackles the problem of categorizing a given review sentence into a set of pre-defined aspect categories. In recent years, deep learning approaches have brought revolutionary advances in multiple branches of natural language processing including sentiment analysis. In this paper, we propose a deep neural network method based on attention mechanism to identify different aspect categories of a given review sentence. Our model utilizes several attentions with different topic contexts, enabling it to attend to different parts of a review sentence based on different topics. Experimental results on two datasets in the restaurant domain released by SemEval workshop demonstrates that our approach outperforms existing methods on both datasets. Visualization of the topic attention weights shows the effectiveness of our model in identifying words related to different topics.
Learning Sound Event Classifiers from Web Audio with Noisy Labels

As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little research into the impact of these errors. To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42.5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. We characterize the label noise empirically, and provide a CNN baseline system. Experiments suggest that training with large amounts of noisy data can outperform training with smaller amounts of carefully-labeled data. We also show that noise-robust loss functions can be effective in improving performance in presence of corrupted labels.
Facial and Meridional Isomers of Tris(bidentate) Ir(III) Complexes: Unravelling Their Different Excited State Reactivity.

The use of tris(bidentate) Ir(III) complexes as light active components in phosphorescent organic light-emitting diodes (PhOLEDs) is currently the state-of-the-art technology to attain long-lasting and highly performing devices. Still, further improvements of their operational lifetimes are required for their practical use in lighting and displays. Facial/meridional stereoisomerism of the tris(bidentate) Ir(III) architectures strongly influences their emissive properties and thereto their PhOLEDs performances and operational device stabilities. This work underpins at the first-principles level the different excited state reactivities of facial and meridional stereoisomers of a series of tris(bidentate) Ir(III) complexes, which is found to originate in the presence of distinct triplet metal-centered (3MC) deactivation pathways. These deactivation pathways are herein presented for the first time for the meridional isomers. Finally, we propose some phosphor design strategies.
Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans

We present the first attempt to perform short glass fiber semantic segmentation from X-ray computed tomography volumetric datasets at medium (3.9 {\mu}m isotropic) and low (8.3 {\mu}m isotropic) resolution using deep learning architectures. We performed experiments on both synthetic and real CT scans and evaluated deep fully convolutional architectures with both 2D and 3D kernels. Our artificial neural networks outperform existing methods at both medium and low resolution scans.
Transfer learning from language models to image caption generators: Better models may not transfer better

When designing a neural caption generator, a convolutional neural network can be used to extract image features. Is it possible to also use a neural language model to extract sentence prefix features? We answer this question by trying different ways to transfer the recurrent neural network and embedding layer from a neural language model to an image caption generator. We find that image caption generators with transferred parameters perform better than those trained from scratch, even when simply pre-training them on the text of the same captions dataset it will later be trained on. We also find that the best language models (in terms of perplexity) do not result in the best caption generators after transfer learning.
Adversarial Examples versus Cloud-Based Detectors: A Black-Box Empirical Study

Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100 percent, and the semantic segmentation based attacks have a success rate over 90 percent among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation.
Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times

A new mechanism for efficiently solving the Markov decision processes (MDPs) is proposed in this paper. We introduce the notion of reachability landscape where we use the Mean First Passage Time (MFPT) as a means to characterize the reachability of every state in the state space. We show that such reachability characterization very well assesses the importance of states and thus provides a natural basis for effectively prioritizing states and approximating policies. Built on such a novel observation, we design two new algorithms - Mean First Passage Time based Value Iteration (MFPT-VI) and Mean First Passage Time based Policy Iteration (MFPT-PI) - that have been modified from the state-of-the-art solution methods. To validate our design, we have performed numerical evaluations in robotic decision-making scenarios, by comparing the proposed new methods with corresponding classic baseline mechanisms. The evaluation results showed that MFPT-VI and MFPT-PI have outperformed the state-of-the-art solutions in terms of both practical runtime and number of iterations. Aside from the advantage of fast convergence, this new solution method is intuitively easy to understand and practically simple to implement.
Speaker Adaptation for End-to-End CTC Models

We propose two approaches for speaker adaptation in end-to-end (E2E) automatic speech recognition systems. One is Kullback-Leibler divergence (KLD) regularization and the other is multi-task learning (MTL). Both approaches aim to address the data sparsity especially output target sparsity issue of speaker adaptation in E2E systems. The KLD regularization adapts a model by forcing the output distribution from the adapted model to be close to the unadapted one. The MTL utilizes a jointly trained auxiliary task to improve the performance of the main task. We investigated our approaches on E2E connectionist temporal classification (CTC) models with three different types of output units. Experiments on the Microsoft short message dictation task demonstrated that MTL outperforms KLD regularization. In particular, the MTL adaptation obtained 8.8% and 4.0% relative word error rate reductions (WERRs) for supervised and unsupervised adaptations for the word CTC model, and 9.6% and 3.8% relative WERRs for the mix-unit CTC model, respectively.