nils-herrmann/clean_scirepeval huggingface dataset:
Few-Shot Sequence Labeling with Label Dependency Transfer

Few-shot sequence labeling faces a unique challenge compared with the other fewshot classification problems, owing to the necessity for modeling the dependencies between labels. Different domains often have different label sets, which makes it difficult to directly utilize the label dependencies learned from one domain in another domain. In this paper, we introduce the dependency transfer mechanism that addresses such label-discrepancy problem. The dependency transfer mechanism learns the abstract label transition patterns from the source domains and generalizes such patterns in the target domain to benefit the prediction of a label sequence. We also develop the sequence matching network by adapting the matching network to sequence labeling case. Moreover, we propose a CRF-based few-shot sequence labeling framework to integrate both the dependency transfer mechanism and the sequence matching network. Experiments on slot tagging (ST) and named entity recognition (NER) datasets show that our model significantly outperforms the strongest few-shot learning baseline by 7.96 and 11.70 F1 scores respectively in the 1-shot setting.
Clustering and Classification Networks

In this paper, we will describe a network architecture that demonstrates high performance on various sizes of datasets. To do this, we will perform an architecture search by dividing the fully connected layer into three levels in the existing network architecture. The first step is to learn existing CNN layer and existing fully connected layer for 1 epoch. The second step is clustering similar classes by applying L1 distance to the result of Softmax. The third step is to reclassify using clustering class masks. We accomplished the result of state-of-the-art by performing the above three steps sequentially or recursively. The technology recorded an error of 11.56% on Cifar-100.
Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas. In addition, by utilizing an embedded platform and deep learning UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. To this end, this paper focuses on the automated aerial scene classification of disaster events from on-board a UAV. Specifically, a dedicated Aerial Image Database for Emergency Response (AIDER) applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network (CNN) architecture is developed, capable of running efficiently on an embedded platform achieving ~3x higher performance compared to existing models with minimal memory requirements with less than 2% accuracy drop compared to the state-of-the-art. These preliminary results provide a solid basis for further experimentation towards real-time aerial image classification for emergency response applications using UAVs.
Generic Ontology Design Patterns at Work

Generic Ontology Design Patterns, GODPs, are defined in Generic DOL, an extension of DOL, the Distributed Ontology, Model and Specification Language, and implemented using Heterogeneous Tool Set. 
Parameters such as classes, properties, individuals, or whole ontologies may be instantiated with arguments in a host ontology. The potential of Generic DOL is illustrated with GODPs for an example from the literature, namely the Role design pattern. We also discuss how larger GODPs may be composed by instantiating smaller GODPs.
Hybrid Quantum/Classical Derivative Theory: Analytical Gradients and Excited-State Dynamics for the Multistate Contracted Variational Quantum Eigensolver

The maturation of analytical derivative theory over the past few decades has enabled classical electronic structure theory to provide accurate and efficient predictions of a wide variety of observable properties. However, classical implementations of analytical derivative theory take advantage of explicit computational access to the approximate electronic wavefunctions in question, which is not possible for the emerging case of hybrid quantum/classical methods. Here, we develop an efficient Lagrangian-based approach for analytical first derivatives of hybrid quantum/classical methods using only observable quantities from the quantum portion of the algorithm. Specifically, we construct the key first-derivative property of the nuclear energy gradient for the recently-developed multistate, contracted variant of the variational quantum eigensolver (MC-VQE) within the context of the ab initio exciton model (AIEM). We show that a clean separation between the quantum and classical parts of the problem is enabled by the definition of an appropriate set of relaxed density matrices, and show how the wavefunction response equations in the quantum part of the algorithm (coupled-perturbed MC-VQE or CP-MC-VQE equations) are decoupled from the wavefunction response equations and and gradient perturbations in the classical part of the algorithm. We explore the magnitudes of the Hellmann-Feynman and response contributions to the gradients in quantum circuit simulations of MC-VQE+AIEM and demonstrate a quantum circuit simulator implementation of adiabatic excited state dynamics with MC-VQE+AIEM.
Autonomous Haiku Generation

Artificial Intelligence is an excellent tool to improve efficiency and lower cost in many quantitative real world applications, but what if the task is not easily defined? What if the task is generating creativity? Poetry is a creative endeavor that is highly difficult to both grasp and achieve with any level of competence. As Rita Dove, a famous American poet and author states, "Poetry is language at its most distilled and most powerful." Taking Doves quote as an inspiration, our task was to generate high quality haikus using artificial intelligence and deep learning.
Denseness conditions, morphisms and equivalences of toposes.

We establish a general theorem providing necessary and sufficient explicit conditions for a morphism of sites to induce an equivalence of toposes. This results from a detailed analysis of arrows in Grothendieck toposes and denseness conditions, which yields results of independent interest. We also derive site characterizations of the property of a geometric morphism to be an inclusion (resp. a surjection, hyperconnected, localic), as well as site-level descriptions of the surjection-inclusion and hyperconnected-localic factorizations.
We Need No Pixels: Video Manipulation Detection Using Stream Descriptors

Manipulating video content is easier than ever. Due to the misuse potential of manipulated content, multiple detection techniques that analyze the pixel data from the videos have been proposed. However, clever manipulators should also carefully forge the metadata and auxiliary header information, which is harder to do for videos than images. In this paper, we propose to identify forged videos by analyzing their multimedia stream descriptors with simple binary classifiers, completely avoiding the pixel space. Using well-known datasets, our results show that this scalable approach can achieve a high manipulation detection score if the manipulators have not done a careful data sanitization of the multimedia stream descriptors.
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors

Deep neural networks as image priors have been recently introduced for problems such as denoising, super-resolution and inpainting with promising performance gains over hand-crafted image priors such as sparsity and low-rank. Unlike learned generative priors they do not require any training over large datasets. However, few theoretical guarantees exist in the scope of using untrained neural network priors for inverse imaging problems. We explore new applications and theory for untrained neural network priors. Specifically, we consider the problem of solving linear inverse problems, such as compressive sensing, as well as non-linear problems, such as compressive phase retrieval. We model images to lie in the range of an untrained deep generative network with a fixed seed. We further present a projected gradient descent scheme that can be used for both compressive sensing and phase retrieval and provide rigorous theoretical guarantees for its convergence. We also show both theoretically as well as empirically that with deep network priors, one can achieve better compression rates for the same image quality compared to hand crafted priors.
Human vs Machine Attention in Neural Networks: A Comparative Study

Human visual system can selectively attend to parts of a scene for quick perception, a biological mechanism known as Human attention. Inspired by this, recent deep learning models encode attention mechanisms to focus on the most task-relevant parts of the input signal for further processing, which is called Machine/Neural/Artificial attention. Understanding the relation between human and machine attention is important for interpreting and designing neural networks. Many works claim that the attention mechanism offers an extra dimension of interpretability by explaining where the neural networks look. However, recent studies demonstrate that artificial attention maps do not always coincide with common intuition. In view of these conflicting evidence, here we make a systematic study on using artificial attention and human attention in neural network design. With three example computer vision tasks, diverse representative backbones, and famous architectures, corresponding real human gaze data, and systematically conducted large-scale quantitative studies, we quantify the consistency between artificial attention and human visual attention and offer novel insights into existing artificial attention mechanisms by giving preliminary answers to several key questions related to human and artificial attention mechanisms. Overall results demonstrate that human attention can benchmark the meaningful `ground-truth' in attention-driven tasks, where the more the artificial attention is close to human attention, the better the performance; for higher-level vision tasks, it is case-by-case. It would be advisable for attention-driven tasks to explicitly force a better alignment between artificial and human attention to boost the performance; such alignment would also improve the network explainability for higher-level computer vision tasks.
Extra-factorial sum: a graph-theoretic parameter in Hamiltonian cycles of complete weighted graphs

A graph-theoretic parameter, in a form of a function, called the extra-factorial sum is discussed. The main results are presented in ref.  (Nastou et al., Optim Lett, 10, 1203-1220, 2016) and the reader is strongly advised to study the aforementioned paper. The current work presents subject matter in a tutorial form with proofs and some newer unpublished results towards the end (lemma six extension and lemma seven). The extra-factorial sum is relevant to Hamiltonian cycles of complete weighted graphs $WH_n$ with $n$ vertices and is obtained for each edge of $WH_n$. If this sum is multiplied by $1 / (n - 2)$ then it gives directly the arithmetic mean of the sum of lengths $l_i$ of all Hamiltonian cycles that traverse a selected edge $e_q$. The number of terms in this sum is a factorial proven to be $(n - 2)!$ which signifies that its value depends on $n$. Using the extra-factorial sum, the arithmetic mean of the sum of the squared lengths of $(n - 1)! / 2$ Hamiltonian cycles of $WH_{n}$ can be obtained as well.
Submodular Batch Selection for Training Deep Neural Networks

Mini-batch gradient descent based methods are the de facto algorithms for training neural network architectures today.We introduce a mini-batch selection strategy based on submodular function maximization. Our novel submodular formulation captures the informativeness of each sample and diversity of the whole subset. We design an efficient, greedy algorithm which can give high-quality solutions to this NP-hard combinatorial optimization problem. Our extensive experiments on standard datasets show that the deep models trained using the proposed batch selection strategy provide better generalization than Stochastic Gradient Descent as well as a popular baseline sampling strategy across different learning rates, batch sizes, and distance metrics.
Understanding Filter Bubbles and Polarization in Social Networks

Recent studies suggest that social media usage -- while linked to an increased diversity of information and perspectives for users -- has exacerbated user polarization on many issues. A popular theory for this phenomenon centers on the concept of "filter bubbles": by automatically recommending content that a user is likely to agree with, social network algorithms create echo chambers of similarly-minded users that would not have arisen otherwise. However, while echo chambers have been observed in real-world networks, the evidence for filter bubbles is largely post-hoc. 
In this work, we develop a mathematical framework to study the filter bubble theory. We modify the classic Friedkin-Johnsen opinion dynamics model by introducing another actor, the network administrator, who filters content for users by making small changes to the edge weights of a social network (for example, adjusting a news feed algorithm to change the level of interaction between users). 
On real-world networks from Reddit and Twitter, we show that when the network administrator is incentivized to reduce disagreement among users, even relatively small edge changes can result in the formation of echo chambers in the network and increase user polarization. We theoretically support this observed sensitivity of social networks to outside intervention by analyzing synthetic graphs generated from the stochastic block model. Finally, we show that a slight modification to the incentives of the network administrator can mitigate the filter bubble effect while minimally affecting the administrator's target objective, user disagreement.
Latent Distribution Assumption for Unbiased and Consistent Consensus Modelling

We study the problem of aggregation noisy labels. Usually, it is solved by proposing a stochastic model for the process of generating noisy labels and then estimating the model parameters using the observed noisy labels. A traditional assumption underlying previously introduced generative models is that each object has one latent true label. In contrast, we introduce a novel latent distribution assumption, implying that a unique true label for an object might not exist, but rather each object might have a specific distribution generating a latent subjective label each time the object is observed. Our experiments showed that the novel assumption is more suitable for difficult tasks, when there is an ambiguity in choosing a "true" label for certain objects.
Low-cost ultrasonic distance measurement in a mechanical resonance experiment

We present a low-cost, dual-probe position sensor in a mechanical resonance experiment suitable for deployment in large lab courses with multiple stations. The motion of the two ends of a driven, damped spring oscillator is recorded with US-100 ultrasonic distance sensors and ESP8266 microcontrollers. Sensor lag is compensated via a modified Savitzky-Golay filter. Data is downloaded to a computer via Wi-Fi in a format suitable for analysis in Logger Pro. Due to the simple and fast data acquisition process, students can gather sufficient data to plot curves of the amplitude and phase lag as a function of driving frequency.
EMPIRE: The IRAM 30 m Dense Gas Survey of Nearby Galaxies

We present EMPIRE, an IRAM 30 m large program that mapped λ = 3–4 mm dense gas tracers at ∼1–2 kpc resolution across the whole star-forming disk of nine nearby massive spiral galaxies. We describe the EMPIRE observing and reduction strategies and show new whole-galaxy maps of HCN(1−0), HCO+(1−0), HNC(1−0), and CO(1−0). We explore how the HCN-to-CO and IR-to-HCN ratios, observational proxies for the dense gas fraction and dense gas star formation efficiency, depend on host galaxy and local environment. We find that the fraction of dense gas correlates with stellar surface density, gas surface density, molecular-to-atomic gas ratio, and dynamical equilibrium pressure. In EMPIRE, the star formation rate per unit dense gas is anticorrelated with these same environmental parameters. Thus, although dense gas appears abundant in the central regions of many spiral galaxies, this gas appears relatively inefficient at forming stars. These results qualitatively agree with previous work on nearby galaxies and the Milky Way’s Central Molecular Zone. To first order, EMPIRE demonstrates that the conditions in a galaxy disk set the gas density distribution and that the dense gas traced by HCN shows an environment-dependent relation to star formation. However, our results also show significant (±0.2 dex) galaxy-to-galaxy variations. We suggest that gas structure below the scale of our observations and dynamical effects likely also play an important role.
Deep-CEE I: fishing for galaxy clusters with deep neural nets

We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for photometric catalogues. This technique is complementary to traditional methods and could also be used in combination with them to confirm existing galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm, adapted to localize and classify galaxy clusters from other astronomical objects in Sloan Digital Sky Survey imaging. As there is an abundance of labelled data for galaxy clusters from previous classifications in publicly available catalogues, we do not need to rely on simulated data. This means we keep our training data as realistic as possible, which is advantageous when training a deep learning algorithm. Ultimately, we will apply our model to surveys such as Large Synoptic Survey Telescope and Euclid to probe wider and deeper into unexplored regions of the Universe. This will produce large samples of both high-redshift and low-mass clusters, which can be utilized to constrain both environment-driven galaxy evolution and cosmology.
Numerical binary black hole collisions in dynamical Chern-Simons gravity

We produce the first numerical relativity binary black hole gravitational waveforms in a higher-curvature theory beyond general relativity. In particular, we study head-on collisions of binary black holes in order-reduced dynamical Chern-Simons gravity. This is a precursor to producing beyond-general-relativity waveforms for inspiraling binary black hole systems that are useful for gravitational wave detection. Head-on collisions are interesting in their own right, however, as they cleanly probe the quasi-normal mode spectrum of the final black hole. We thus compute the leading-order dynamical Chern-Simons modifications to the complex frequencies of the post-merger gravitational radiation. We consider equal-mass systems, with equal spins oriented along the axis of collision, resulting in remnant black holes with spin. We find modifications to the complex frequencies of the quasi-normal mode spectrum that behave as a power law with the spin of the remnant, and that are not degenerate with the frequencies associated with a Kerr black hole of any mass and spin. We discuss these results in the context of testing general relativity with gravitational wave observations.
When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Framework

Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resources in order to maximize either the average or the worst-case performance. However, joint consideration of both metrics is often neglected as they are competing in nature. In this article, a mechanism for radio resource management using multi-agent deep reinforcement learning (RL) is proposed, which strikes the right trade-off between maximizing the average and the $5^{th}$ percentile user throughput. Each transmitter in the network is equipped with a deep RL agent, receiving partial observations from the network (e.g., channel quality, interference level, etc.) and deciding whether to be active or inactive at each scheduling interval for given radio resources, a process referred to as link scheduling. Based on the actions of all agents, the network emits a reward to the agents, indicating how good their joint decisions were. The proposed framework enables the agents to make decisions in a distributed manner, and the reward is designed in such a way that the agents strive to guarantee a minimum performance, leading to a fair resource allocation among all users across the network. Simulation results demonstrate the superiority of our approach compared to decentralized baselines in terms of average and $5^{th}$ percentile user throughput, while achieving performance close to that of a centralized exhaustive search approach. Moreover, the proposed framework is robust to mismatches between training and testing scenarios. In particular, it is shown that an agent trained on a network with low transmitter density maintains its performance and outperforms the baselines when deployed in a network with a higher transmitter density.
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning

Detection of malicious behavior is a fundamental problem in security. One of the major challenges in using detection systems in practice is in dealing with an overwhelming number of alerts that are triggered by normal behavior (the so-called false positives), obscuring alerts resulting from actual malicious activities. We introduce a novel approach for computing a policy for prioritizing alerts using adversarial reinforcement learning. Our approach assumes that the attacker knows the full state of the detection system and the defender's alert prioritization policy, and will dynamically choose an optimal attack. The first step of our approach is to capture the interaction between the defender and attacker in a game theoretic model. To tackle the computational complexity of solving this game to obtain a dynamic stochastic alert prioritization policy, we propose an adversarial reinforcement learning framework. In this framework, we use neural reinforcement learning to compute best response policies for both the defender and the adversary to an arbitrary stochastic policy of the other. We then use these in a double-oracle framework to obtain an approximate equilibrium of the game, which in turn yields a robust stochastic policy for the defender. We use case studies in network intrusion and fraud detection to demonstrate that our approach is effective in creating robust alert prioritization