nils-herrmann/clean_scirepeval huggingface dataset:
Time-slicing spiral galaxies with SDSS-IV MaNGA

Spectra of galaxies contain a wealth of information about the stellar populations from which they are made. With integral field unit (IFU) surveys, such data can be used to map out stellar population properties across the face of a galaxy, allowing one to go beyond simple radial profiles and study details of non-axisymmetric structure. To-date, however, such studies have been limited by the quality of available data and the power of spectral analysis tools. We now take the next step and study the barred spiral galaxy MCG + 07-28-064 from observations obtained as part of the SDSS-IV MaNGA project. We find that we can decompose this galaxy into ‘time slices,’ which reveal the varying contributions that stars of differing ages make to its bar and spiral structure, offering new insight into the evolution of these features. We find evidence for the ongoing growth of the bar, including the most recent star formation on its leading edge, and for the underlying density wave responsible for spiral structure. This pilot study indicates that there is a wealth of untapped information on the spatial distribution of star formation histories available in the current generation of IFU galaxy surveys.
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks

Hardware-friendly network quantization (e.g., binary/uniform quantization) can efﬁciently accelerate the inference and meanwhile reduce memory consumption of the deep neural networks, which is crucial for model deployment on resource-limited devices like mobile phones. However, due to the discreteness of low-bit quantization, existing quantization methods often face the unstable training process and severe performance degradation. To address this problem, in this paper we propose Differentiable Soft Quantization (DSQ) to bridge the gap between the full-precision and low-bit networks. DSQ can automatically evolve during training to gradually approximate the standard quantization. Owing to its differentiable property, DSQ can help pursue the accurate gradients in backward propagation, and reduce the quantization loss in forward process with an appropriate clipping range. Extensive experiments over several popular network structures show that training low-bit neural networks with DSQ can consistently outperform state-of-the-art quantization methods. Besides, our ﬁrst efﬁcient implementation for deploying 2 to 4-bit DSQ on devices with ARM architecture achieves up to 1.7× speed up, compared with the open-source 8-bit high-performance inference framework NCNN .
Local and global applications of the Minimal Model Program for co-rank one foliations on threefolds

We show that the Minimal Model Program for co-rank one foliations on threefolds terminates by proving foliation flips terminate. Moreover, we recover a full suite of powerful results on the birational structure of co-rank one foliations on threefolds (Connectedness theorem, Inversion of adjunction, Non-vanishing theorem), despite the well-known failure of the foliated analogue of the vanishing results which are (classically) used to prove these statements in the Minimal Model Program in the case of log pairs. 
We then turn to several applications of the MMP to the analysis of local and global properties of foliations on threefolds. In particular, we study foliation singularities proving the existence of first integrals for isolated canonical foliation singularities (an extension of Malgrange's theorem) and derive a complete classification of terminal foliated threefolds singularities. We show the existence of separatrices for log canonical singularities. We also prove some hyperbolicity properties of foliations, showing that the failure of the canonical bundle to be nef implies the existence of entire holomorphic curves contained in the open strata of a natural stratification of the singular locus of the foliation.
Understanding CMB physics through the exploration of exotic cosmological models: a classroom study using CLASS

Every cosmology lecturer these days is confronted with teaching the modern cosmological standard model ΛCDM, and there are many approaches to do this. However, the danger is imminent that it is presented to students as something set into stone, merely to be accepted as a fact based on the plenty of evidences we have. This is even more critical, given that the standard model of cosmology confronts us with entities not yet fully understood, namely a cosmological constant Λ and cold dark matter. In this article, we report on an advanced cosmology course exercise, conducted in computer lab, which was conceived as a means to have students experience first-hand why the ΛCDM model has become so prevalent in the interpretation of modern cosmological data. To this end, we focused on the cosmic microwave background radiation (CMB) and calculated theoretical temperature and matter power spectra, using the modern Boltzmann code CLASS. By comparing and analyzing the outcome for ΛCDM, as well as for three other exotic cosmological models, the students were able to grasp the impact of cosmological parameters on CMB observables, and also to understand some of the complicated CMB physics in a direct way. Our chosen examples are not exhaustive and can be easily modified or expanded, so we express the hope that this article will serve as a valuable resource for interested students and lecturers.
Optical Black-hole Analog Created by Topological Phase Transition with a Long-lived Horizon.

Hawking radiation, a manifestation of quantum field theory in curved spacetime, has stimulated extensive theoretical and experimental studies of various black-hole (BH) analogs. However, an undisputed confirmation of Hawking radiation remains elusive. One challenge is BH analog structures with long-lived horizons are difficult to achieve. Here, we theoretically demonstrate a new type of optical BH analog based on light cone evolution associated with topological phase transition of Dirac cones. The transition from a type-II to type-I Dirac/Weyl cone creates an analogous curved spacetime that crosses a type-III Dirac/Weyl cone, which affords a stationary configuration of long-lived event horizon. Photons tunneling through the horizon emit a spectrum of Hawking radiation. As an example, we design a laboratory version in an inhomogeneous two-dimensional graphyne-like topological photonic lattice with a Hawking temperature of 0.14 mK. Understanding Hawking-like radiation in this unique topological BH is not only of fundamental interest in its own right but may also provide new hints to gravitational physics.
Fusion of Detected Objects in Text for Visual Question Answering

To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The “Bounding Boxes in Text Transformer” (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark, achieving a new state-of-the-art with a 25% relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard (as of May 22, 2019). A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided.
Towards Explainable AI Planning as a Service

Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.
Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling

Visual question answering and visual dialogue tasks have been increasingly studied in the multimodal field towards more practical real-world scenarios. A more challenging task, audio visual scene-aware dialogue (AVSD), is proposed to further advance the technologies that connect audio, vision, and language, which introduces temporal video information and dialogue interactions between a questioner and an answerer. This paper proposes an intuitive mechanism that fuses features and attention in multiple stages in order to well integrate multimodal features, and the results demonstrate its capability in the experiments. Also, we apply several state-of-the-art models in other tasks to the AVSD task, and further analyze their generalization across different tasks.
Spin-Orbital Glass Transition in a Model of a Frustrated Pyrochlore Magnet without Quenched Disorder.

We show theoretically that spin and orbital degrees of freedom in the pyrochlore oxide Y_{2}Mo_{2}O_{7}, which is free of quenched disorder, can exhibit a simultaneous glass transition, working as dynamical disorder for each other. The interplay of spins and orbitals is mediated by the Jahn-Teller lattice distortion that selects the choice of orbitals, which then generates variant spin exchange interactions ranging from ferromagnetic to antiferromagnetic ones. Our Monte Carlo simulations detect the power-law divergence of the relaxation times and the negative divergence of both the magnetic and dielectric nonlinear susceptibilities, resolving the long-standing puzzle on the origin of the disorder-free spin glass.
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models

The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of network; and the proposed graph convolutional network called AdaGCN~(AdaBoosting Graph Convolutional Network) has the ability to efficiently extract knowledge from high-order neighbors and integrate knowledge from different hops of neighbors into the network in an AdaBoost way. We also present the architectural difference between AdaGCN and existing graph convolutional methods to show the benefits of our proposal. Finally, extensive experiments demonstrate the state-of-the-art prediction performance and the computational advantage of our approach AdaGCN.
A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN

The use of fingerprinting localization techniques in outdoor IoT settings has started gaining popularity over the recent years. Communication signals of Low Power Wide Area Networks (LPWAN), such as LoRaWAN, are used to estimate the location of low power mobile devices. In this study, a publicly available dataset of LoRaWAN RSSI measurements is utilized to compare different machine learning methods and their accuracy in producing location estimates. The tested methods are: the k Nearest Neighbours method, the Extra Trees method and a neural network approach using a Multilayer Perceptron. To facilitate the reproducibility of tests and the comparability of results, the code and the train/validation/test split of the dataset used in this study have become available. The neural network approach was the method with the highest accuracy, achieving a mean error of 357 meters and a median error of 206 meters.
Unsupervised Behavior Change Detection in Multidimensional Data Streams for Maritime Traffic Monitoring

The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities. In this work, we describe research gaps and challenges in machine learning for vessel behavior change and event detection, considering several constraints imposed by real-time data streams and the maritime monitoring domain. As a starting point, we investigate how unsupervised and semi-supervised change detection methods may be employed for identifying shifts in vessel behavior, aiming to detect and label unusual events.
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension

Although the vast majority of knowledge bases (KBs) are heavily biased towards English, Wikipedias do cover very different topics in different languages. Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts.
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension

Conversational machine comprehension requires deep understanding of the dialogue flow, and the prior work proposed FlowQA to implicitly model the context representations in reasoning for better understanding. This paper proposes to explicitly model the information gain through the dialogue reasoning in order to allow the model to focus on more informative cues. The proposed model achieves the state-of-the-art performance in a conversational QA dataset QuAC and sequential instruction understanding dataset SCONE, which shows the effectiveness of the proposed mechanism and demonstrate its capability of generalization to different QA models and tasks.
MemeFaceGenerator: Adversarial Synthesis of Chinese Meme-face from Natural Sentences

Chinese meme-face is a special kind of internet subculture widely spread in Chinese Social Community Networks. It usually consists of a template image modified by some amusing details and a text caption. In this paper, we present MemeFaceGenerator, a Generative Adversarial Network with the attention module and template information as supplementary signals, to automatically generate meme-faces from text inputs. We also develop a web service as system demonstration of meme-face synthesis. MemeFaceGenerator has been shown to be capable of generating high-quality meme-faces from random text inputs.
Directional TSDF: Modeling Surface Orientation for Coherent Meshes

Real-time 3D reconstruction from RGB-D sensor data plays an important role in many robotic applications, such as object modeling and mapping. The popular method of fusing depth information into a truncated signed distance function (TSDF) and applying the marching cubes algorithm for mesh extraction has severe issues with thin structures: not only does it lead to loss of accuracy, but it can generate completely wrong surfaces. To address this, we propose the directional TSDF—a novel representation that stores opposite surfaces separate from each other. The marching cubes algorithm is modified accordingly to retrieve a coherent mesh representation. We further increase the accuracy by using surface gradient-based ray casting for fusing new measurements. We show that our method outperforms state-of-the-art TSDF reconstruction algorithms in mesh accuracy.
Aleph: Efficient Atomic Broadcast in Asynchronous Networks with Byzantine Nodes

The spectacular success of Bitcoin and Blockchain Technology in recent years has provided enough evidence that a widespread adoption of a common cryptocurrency system is not merely a distant vision, but a scenario that might come true in the near future. However, the presence of Bitcoin's obvious shortcomings such as excessive electricity consumption, unsatisfying transaction throughput, and large validation time (latency) makes it clear that a new, more efficient system is needed. We propose a protocol in which a set of nodes maintains and updates a linear ordering of transactions that are being submitted by users. Virtually every cryptocurrency system has such a protocol at its core, and it is the efficiency of this protocol that determines the overall throughput and latency of the system. We develop our protocol on the grounds of the well-established field of Asynchronous Byzantine Fault Tolerant (ABFT) systems. This allows us to formally reason about correctness, efficiency, and security in the strictest possible model, and thus convincingly prove the overall robustness of our solution. Our protocol improves upon the state-of-the-art HoneyBadgerBFT by Miller et al. by reducing the asymptotic latency while matching the optimal communication complexity. Furthermore, in contrast to the above, our protocol does not require a trusted dealer thanks to a novel implementation of a trustless ABFT Randomness Beacon.
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding

Recent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations - a process in which each word in sentence A attends to all words in sentence B and vice versa. As a result, computing the similarity between a query sentence and a set of candidate sentences, requires the propagation of all query-candidate sentence-pairs throughout a stack of cross-attention layers. This exhaustive process becomes computationally prohibitive when the number of candidate sentences is large. In contrast, sentence embedding techniques learn a sentence-to-vector mapping and compute the similarity between the sentence vectors via simple elementary operations. In this paper, we introduce Distilled Sentence Embedding (DSE) - a model that is based on knowledge distillation from cross-attentive models, focusing on sentence-pair tasks. The outline of DSE is as follows: Given a cross-attentive teacher model (e.g. a fine-tuned BERT), we train a sentence embedding based student model to reconstruct the sentence-pair scores obtained by the teacher model. We empirically demonstrate the effectiveness of DSE on five GLUE sentence-pair tasks. DSE significantly outperforms several ELMO variants and other sentence embedding methods, while accelerating computation of the query-candidate sentence-pairs similarities by several orders of magnitude, with an average relative degradation of 4.6% compared to BERT. Furthermore, we show that DSE produces sentence embeddings that reach state-of-the-art performance on universal sentence representation benchmarks. Our code is made publicly available at this https URL.
Temporal Analysis of Reddit Networks via Role Embeddings

Inspired by diachronic word analysis from the field of natural language processing, we propose an approach for uncovering temporal insights regarding user roles from social networks using graph embedding methods. Specifically, we apply the role embedding algorithm, struc2vec, to a collection of social networks exhibiting either "loyal" or "vagrant" characteristics derived from the popular online social news aggregation website Reddit. For each subreddit, we extract nine months of data and create network role embeddings on consecutive time windows. We are then able to compare and contrast how user roles change over time by aligning the resulting temporal embeddings spaces. In particular, we analyse temporal role embeddings from an individual and a community-level perspective for both loyal and vagrant communities present on Reddit.
On The Evaluation of Machine Translation SystemsTrained With Back-Translation

Back-translation is a widely used data augmentation technique which leverages target monolingual data. However, its effectiveness has been challenged since automatic metrics such as BLEU only show significant improvements for test examples where the source itself is a translation, or translationese. This is believed to be due to translationese inputs better matching the back-translated training data. In this work, we show that this conjecture is not empirically supported and that back-translation improves translation quality of both naturally occurring text as well as translationese according to professional human translators. We provide empirical evidence to support the view that back-translation is preferred by humans because it produces more fluent outputs. BLEU cannot capture human preferences because references are translationese when source sentences are natural text. We recommend complementing BLEU with a language model score to measure fluency.
Agent‐based simulator of dynamic flood‐people interactions

This article presents a simulator for the modelling of the two‐way interactions between flooding and people. The simulator links a hydrodynamic model to a pedestrian model in a single agent‐based modelling platform, Flexible Large‐scale Agent Modelling Environment for the Graphical Processing Unit (FLAMEGPU). Dynamic coupling is achieved by the simultaneous update and exchange of information across multiple agent types. Behavioural rules and states for the pedestrian agents are proposed to account for the pedestrians' presence/actions in/to floodwater. These are based on a commonly used hazard rate (HR) metric to evaluate the risk states of people in floodwater, and by considering two roles for the pedestrians: evacuees or responders for action during or before the flood event, respectively. The potential of the simulator is demonstrated in a case study of a flooded and busy shopping centre for two scenarios: (a) during a flood evacuation and (b) pre‐flood intervention to deploy a sandbag barrier. The evacuation scenario points to changes in floodwater hydrodynamics around congested areas, which either worsen (by 5–8%) or lessen (by 25%) the HR. The intervention scenario demonstrates the utility of the simulator to select an optimal barrier height and number of responders for safe and effective deployment. Accompanying details for software accessibility are provided.
Epistemological approach in immersive virtual environments and the neurophysiology learning process

Currently virtual reality (VR) usage in training processes is increasing due to their usefulness in the learning processes based on visual information empowered. The information in virtual environments is perceived by sight, sound and touch, but the relationship or impact that these stimuli can have on the oscillatory activity of the brain such as the processing, propagation and synchronization of information still needs to be established in relation to the cognitive load of attention. Therefore, this study seeks to identify the suggested epistemological basis through literature review and current research agendas in the relationship that exists between the immersive virtual environment and the neurophysiology of learning processes by means of the analysis of visual information. The suggested dimensional modeling of this research is composed by the theory of information processing which allows the incorporation of learning through stimuli with the use of attention, perception and storage by means of information management and the Kolb's learning model which defines the perception and processing of information as dimensions of learning. Regarding to the neurophysiology of learning, the literature has established he links between the prefrontal cortex and working memory within the process of information management. The challenges and advances discussed in this research are based in the relationship between the identified constructs (Income Stimuli, Information Management and Cognitive Processing) and the establishment of a research agenda on how to identify the necessary indicators to measure memory and attention in the virtual immersion environments.