Miracle-dz/UniFAR-FTUs huggingface dataset:
Rooted order on minimal generators of powers of some cover ideals

We deﬁne a total order, which we call rooted order, on minimal generating set of J ( P n ) s where J ( P n ) is the cover ideal of a path graph on n vertices. We show that each power of a cover ideal of a path has linear quotients with respect to the rooted order. Along the way, we characterize minimal generating set of J ( P n ) s for s ≥ 3 in terms of minimal generating set of J ( P n ) 2 . We also discuss the extension of the concept of rooted order to chordal graphs. Computational examples suggest that such order gives linear quotients for powers of cover ideals of chordal graphs as well.
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLP-related augmentation methods cannot directly produce available data required for this task. To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. On the one hand, our approach is knowledge guided, which can leverage existing knowledge bases to generate well-formed new sentences. On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework, and can interactively adjust the generation process to generate task-related sentences. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related training data for ECI; 2) our method outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.5 and +2.1 points on F1 value respectively).
Lifetime policy reuse and the importance of task capacity

A long-standing challenge in artificial intelligence is lifelong reinforcement learning, where learners are given many tasks in sequence and must transfer knowledge between tasks while avoiding catastrophic forgetting. Policy reuse and other multi-policy reinforcement learning techniques can learn multiple tasks but may generate many policies. This paper presents two novel contributions, namely 1) Lifetime Policy Reuse, a model-agnostic policy reuse algorithm that avoids generating many policies by optimising a fixed number of near-optimal policies through a combination of policy optimisation and adaptive policy selection; and 2) the task capacity, a measure for the maximal number of tasks that a policy can accurately solve. Comparing two state-of-the-art base-learners, the results demonstrate the importance of Lifetime Policy Reuse and task capacity based pre-selection on an 18-task partially observable Pacman domain and a Cartpole domain of up to 125 tasks.
Subset Node Representation Learning over Large Dynamic Graphs

Dynamic graph representation learning is a task to learn node embeddings over dynamic networks, and has many important applications, including knowledge graphs, citation networks to social networks. Graphs of this type are usually large-scale but only a small subset of vertices are related in downstream tasks. Current methods are too expensive to this setting as the complexity is at best linear-dependent on both the number of nodes and edges. In this paper, we propose a new method, namely Dynamic Personalized PageRank Embedding (DynamicPPE) for learning a target subset of node representations over large-scale dynamic networks. Based on recent advances in local node embedding and a novel computation of dynamic personalized PageRank vector (PPV), DynamicPPE has two key ingredients: 1) the per-PPV complexity is O (m d / ε) where m, d, and ε are the number of edges received, average degree, global precision error respectively. Thus, the per-edge event update of a single node is only dependent on d in average; and 2) by using these high quality PPVs and hash kernels, the learned embeddings have properties of both locality and global consistency. These two make it possible to capture the evolution of graph structure effectively. Experimental results demonstrate both the effectiveness and efficiency of the proposed method over large-scale dynamic networks. We apply DynamicPPE to capture the embedding change of Chinese cities in the Wikipedia graph during this ongoing COVID-19 pandemic. https://en.wikipedia. org/wiki/COVID-19_pandemic. Our results show that these representations successfully encode the dynamics of the Wikipedia graph.
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations

The field of few-shot learning has made remarkable strides in developing powerful models that can operate in the small data regime. Nearly all of these methods assume every unlabeled instance encountered will belong to a handful of known classes for which one has examples. This can be problematic for real-world use cases where one routinely finds 'none-of-the-above' examples. In this paper we describe this challenge of identifying what we term 'out-of-support' (OOS) examples. We describe how this problem is subtly different from out-of-distribution detection and describe a new method of identifying OOS examples within the Prototypical Networks framework using a fixed point which we call the generic representation. We show that our method outperforms other existing approaches in the literature as well as other approaches that we propose in this paper. Finally, we investigate how the use of such a generic point affects the geometry of a model's feature space.
Modeling Influencer Marketing Campaigns in Social Networks

Social media are extensively used in today’s world, and facilitate quick and easy sharing of information, which makes them a good way to advertise products. Influencers of a social media network, owing to their massive popularity, provide a huge potential customer base. However, it is not straightforward to decide which influencers should be selected for an advertising campaign that can generate high returns with low investment. In this work, we present an agent-based model (ABM) that can simulate the dynamics of influencer advertising campaigns in a variety of scenarios and can help discover the best influencer marketing strategy. Our system is a probabilistic graph-based model that provides the additional advantage to incorporate real-world factors such as customers’ interest in a product, customer behavior, the willingness to pay, a brand’s investment cap, influencers’ engagement with influence diffusion, and the nature of the product being advertised, viz. luxury and non-luxury. Using customer acquisition cost and conversion ratio as a unit economic, we evaluate the performance of different kinds of influencers under a variety of circumstances that are simulated by varying the nature of the product and the customers’ interest. Our results exemplify the circumstance-dependent nature of influencer marketing and provide insights into which kinds of influencers would be a better strategy under respective circumstances. For instance, we show that as the nature of the product varies from luxury to non-luxury, the performance of celebrities declines, whereas the performance of nano-influencers improves. In terms of the customers’ interest, we find that the performance of nano-influencers declines with the decrease in customers’ interest, whereas the performance of celebrities improves.
Implicit gradients based novel finite volume scheme for compressible single and multi-component flows

This paper introduces a novel approach to compute the numerical fluxes at the cell boundaries in the finite volume approach. Explicit gradients used in deriving the reconstruction polynomials are replaced by high-order gradients computed by compact finite differences, referred to as implicit gradients in this paper. The new finite volume scheme has superior dispersion and dissipation properties in comparison to the compact reconstruction approach. These implicit gradients are re-used in viscous flux computation and post-processing, which further improves efficiency. A problem-independent shock capturing approach via Boundary Variation Diminishing (BVD) algorithm is used to suppress oscillations for the simulation of flows with shocks and material interfaces. Several numerical test cases are carried out to verify the proposed finite volume method's capability using the implicit gradient method for single and multicomponent flows. Significant improvements are observed by computing the gradients implicitly for the viscous flows.
Multiplierless MP-Kernel Machine for Energy-Efficient Edge Devices

We present a novel framework for designing multiplierless kernel machines that can be used on resource-constrained platforms such as intelligent edge devices. The framework uses a piecewise linear (PWL) approximation based on a margin propagation (MP) technique and uses only addition/subtraction, shift, comparison, and register underflow/overflow operations. We propose a hardware-friendly MP-based inference and online training algorithm that has been optimized for a field-programmable gate array (FPGA) platform. Our FPGA implementation eliminates the need for digital signal processor (DSP) units and reduces the number of Look-Up Tables (LUTs). By reusing the same hardware for inference and training, we show that the platform can overcome classification errors and local minima artifacts that result from MP approximation. The implementation of this proposed multiplierless MP-kernel machine on FPGA results in an estimated energy consumption of 13.4 pJ and power consumption of 107 mW with ~9 k LUTs and Flip Flops (FFs) each for a 256 $\times $ 32 sized kernel making it superior in terms of power, performance, and area compared with other comparable implementations.
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack

State-of-the-art deep neural networks are sensitive to small input perturbations. Since the discovery of this intriguing vulnerability, many defence methods have been proposed that attempt to improve robustness to adversarial noise. Fast and accurate attacks are required to compare various defence methods. However, evaluating adversarial robustness has proven to be extremely challenging. Existing norm minimisation adversarial attacks require thousands of iterations (e.g. Carlini&Wagner attack), are limited to the specific norms (e.g. Fast Adaptive Boundary), or produce sub-optimal results (e.g. Brendel&Bethge attack). On the other hand, PGD attack, which is fast, general and accurate, ignores the norm minimisation penalty and solves a simpler perturbation-constrained problem. In this work, we introduce a fast, general and accurate adversarial attack that optimises the original non-convex constrained minimisation problem. We interpret optimising the Lagrangian of the adversarial attack optimisation problem as a two-player game: the first player minimises the Lagrangian wrt the adversarial noise; the second player maximises the Lagrangian wrt the regularisation penalty. Our attack algorithm simultaneously optimises primal and dual variables to find the minimal adversarial perturbation. In addition, for non-smooth $l_p$-norm minimisation, such as $l_{\infty}$-, $l_1$-, and $l_0$-norms, we introduce primal-dual proximal gradient descent attack. We show in the experiments that our attack outperforms current state-of-the-art $l_{\infty}$-, $l_2$-, $l_1$-, and $l_0$-attacks on MNIST, CIFAR-10 and Restricted ImageNet datasets against unregularised and adversarially trained models.
Niffler: A Reference Architecture and System Implementation for View Discovery over Pathless Table Collections by Example

—Identifying a project-join view (PJ-view) over collections of tables is the ﬁrst step of many data management projects, e.g., assembling a dataset for a business intelligence tool, creating a training dataset to ﬁt a machine learning model, and more. Query by example (QBE) systems identify relevant data when join information is available, but do not work in pathless scenarios that arise when combining databases or navigating data lakes, i.e., when no join information is available. We present a reference architecture that explicitly divides the end-to-end problem of discovering PJ-views over pathless table collections into a human and a technical problem. We then present Nifﬂer, a system built to address the technical problem. We introduce algorithms for the main components of Nifﬂer, including a SIGNAL - GENERATION component that helps reduce the size of the candidate views that may be large due to errors and ambiguity in both the data and input queries. We demonstrate that our approach scales to larger problems than other QBE systems over pathless table collections setting.
Traversing Steep and Granular Martian Analog Slopes With a Dynamic Quadrupedal Robot

Celestial bodies such as the Moon and Mars are mainly covered by loose, granular soil, a notoriously challenging terrain to traverse with (wheeled) robotic systems. Here, we present experimental work on traversing steep, granular slopes with the dynamically walking quadrupedal robot SpaceBok. To adapt to the challenging environment, we developed passive-adaptive planar feet and optimized grouser pads to reduce sinkage and increase traction on planar and inclined granular soil. Single-foot experiments revealed that a large surface area of 110cm2 per foot reduces sinkage to an acceptable level even on highly collapsible soil (ES-1). Implementing several 12mm grouser blades increases traction by 22% to 66% on granular media compared to grouser-less designs. Together with a terrain-adapting walking controller, we validate - for the first time - static and dynamic locomotion on Mars analog slopes of up to 25°(the maximum of the testbed). We evaluated the performance between point- and planar feet and static and dynamic gaits regarding stability (safety), velocity, and energy consumption. We show that dynamic gaits are energetically more efficient than static gaits but are riskier on steep slopes. Our tests also revealed that planar feet's energy consumption drastically increases when the slope inclination approaches the soil's angle of internal friction due to shearing. Point feet are less affected by slippage due to their excessive sinkage, but in turn, are prone to instabilities and tripping. We present and discuss safe and energy-efficient global path-planning strategies for accessing steep topography on Mars based on our findings.
Raman spectroscopic characteristics of Mg-Fe-Ca pyroxenes

Abstract Raman spectra of several compositions of (Mg, Fe, Ca)SiO3 pyroxenes were collected at ambient conditions. More than 10 Raman vibrational modes were observed for these pyroxenes in the wavenumber range between 200 and 1200 cm-1. In general, these pyroxenes are characterized by (1) the Si-O stretching modes above 800 cm-1; (2) the Si-O bending modes between 500 and 760 cm-1; (3) SiO4 rotation and metal-oxygen translation modes below 500 cm-1. For a constant Ca content, frequencies of the Raman modes in the enstatite-ferrosilite (opx) and diopside-hedenbergite (cpx) series generally decrease with an increase in Fe content. This phenomenon is attributed to an increase in both the bonding lengths and the reduced mass as Fe2+ is substituted for Mg. However, two modes at ~900 cm-1 in the enstatite-ferrosilite series increase in frequencies as Fe content increases. A possible explanation is to the shortening in the Si-O-Si bridging bonding bonds when the M2 sites are preferentially occupied by the iron cation. The effect of Fe substituting for Mg on the frequency shift in cpx is less profound than opx because the larger M2 was occupied by calcium and the substitution of iron and magnesium in the M1 site results in a less significant change in the bond length. The major-element composition of the (Mg, Fe, Ca)-pyroxenes, especially the orthopyroxene series, can be semi-quantitatively determined on the basis of the peak positions of their characteristic Raman modes.
Impact of Hypernova νp-process Nucleosynthesis on the Galactic Chemical Evolution of Mo and Ru

We calculate the Galactic Chemical Evolution of Mo and Ru by taking into account the contribution from ν p-process nucleosynthesis. We estimate yields of p-nuclei such as 92,94Mo and 96,98Ru through the ν p-process in various supernova progenitors based upon recent models. In particular, the ν p-process in energetic hypernovae produces a large amount of p-nuclei compared to the yield in ordinary core-collapse SNe. Because of this, the abundances of 92,94Mo and 96,98Ru in the Galaxy are significantly enhanced at [Fe/H] = 0 by the ν p-process. We find that the ν p-process in hypernovae is the main contributor to the elemental abundance of 92Mo at low metallicity [Fe/H] < −2. Our theoretical prediction of the elemental abundances in metal-poor stars becomes more consistent with observational data when the ν p-process in hypernovae is taken into account.
Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization

This paper considers stochastic first-order algorithms for convex-concave minimax problems of the form $\min_{\bf x}\max_{\bf y}f(\bf x, \bf y)$, where $f$ can be presented by the average of $n$ individual components which are $L$-average smooth. For $\mu_x$-strongly-convex-$\mu_y$-strongly-concave setting, we propose a new method which could find a $\varepsilon$-saddle point of the problem in $\tilde{\mathcal O} \big(\sqrt{n(\sqrt{n}+\kappa_x)(\sqrt{n}+\kappa_y)}\log(1/\varepsilon)\big)$ stochastic first-order complexity, where $\kappa_x\triangleq L/\mu_x$ and $\kappa_y\triangleq L/\mu_y$. This upper bound is near optimal with respect to $\varepsilon$, $n$, $\kappa_x$ and $\kappa_y$ simultaneously. In addition, the algorithm is easily implemented and works well in practical. Our methods can be extended to solve more general unbalanced convex-concave minimax problems and the corresponding upper complexity bounds are also near optimal.
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability

In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires (or “mindreading”). We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating. We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems. To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children’s performance on tests of mindreading, consisting of 10,320 question-answer pairs. We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points. Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems. The task-specific augmentations generally outperform task-agnostic augmentations. Automatic augmentations based on vectors (GloVe, FastText) perform the worst. We find that systems trained on MIND-CA generalize well to UK-MIND-20. We demonstrate that data augmentation strategies also improve the performance on unseen data.
Axionlike dark matter clouds around rotating black holes

Numerical analysis of dark matter axion-like cloud in the vicinity of rotating black hole has been performed. The model where axion-like scalar field is non-trivially coupled to Maxwell field is studied in the spacetime of Kerr black hole in uniform magnetic field and in Kerr-Newman one. The dependence of scalar mass and black hole angular momentum on accumulation of axion dark matter cloud was given. It was revealed that condensation of the dark matter clouds is preferable for a very small mass of axion. ∗ bkiczek@kft.umcs. lublin.pl † rogat@kft.umcs. lublin.pl 1 ar X iv :2 10 6. 01 56 5v 1 [ he pth ] 3 J un 2 02 1
Component stability in low-space massively parallel computation

In this paper, we study the power and limitations of component-stable algorithms in the low-space model of Massively Parallel Computation (MPC). Recently Ghaffari, Kuhn and Uitto (FOCS 2019) introduced the class of component-stable low-space MPC algorithms, which are, informally, defined as algorithms for which the outputs reported by the nodes in different connected components are required to be independent. This very natural notion was introduced to capture most (if not all) of the known efficient MPC algorithms to date, and it was the first general class of MPC algorithms for which one can show non-trivial conditional lower bounds. In this paper we enhance the framework of component-stable algorithms and investigate its effect on the complexity of randomized and deterministic low-space MPC. Our key contributions include: 1) We revise and formalize the lifting approach of Ghaffari, Kuhn and Uitto. This requires a very delicate amendment of the notion of component stability, which allows us to fill in gaps in the earlier arguments. 2) We also extend the framework to obtain conditional lower bounds for deterministic algorithms and fine-grained lower bounds that depend on the maximum degree Δ. 3) We demonstrate a collection of natural graph problems for which non-component-stable algorithms break the conditional lower bound obtained for component-stable algorithms. This implies that, for both deterministic and randomized algorithms, component-stable algorithms are conditionally weaker than the non-component-stable ones. Altogether our results imply that component-stability might limit the computational power of the low-space MPC model, paving the way for improved upper bounds that escape the conditional lower bound setting of Ghaffari, Kuhn, and Uitto.
Template-Based Named Entity Recognition Using BART

There is a recent interest in investigating few-shot NER, where the low-resource target domain has different label sets compared with a resource-rich source domain. Existing methods use a similarity-based metric. However, they cannot make full use of knowledge transfer in NER model parameters. To address the issue, we propose a template-based method for NER, treating NER as a language model ranking problem in a sequence-to-sequence framework, where original sentences and statement templates filled by candidate named entity span are regarded as the source sequence and the target sequence, respectively. For inference, the model is required to classify each candidate span based on the corresponding template scores. Our experiments demonstrate that the proposed method achieves 92.55% F1 score on the CoNLL03 (rich-resource task), and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 score on the MIT Movie, the MIT Restaurant, and the ATIS (low-resource task), respectively.