Miracle-dz/UniFAR-FTUs huggingface dataset:
DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles

Quotation extraction and attribution are challenging tasks, aiming at determining the spans containing quotations and attributing each quotation to the original speaker. Applying this task to news data is highly related to fact-checking, media monitoring and news tracking. Direct quotations are more traceable and informative, and therefore of great significance among different types of quotations. Therefore, this paper introduces DirectQuote, a corpus containing 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. To the best of our knowledge, this is the largest and most complete corpus that focuses on direct quotations in news texts. We ensure that each speaker in the annotation can be linked to a specific named entity on Wikidata, benefiting various downstream tasks. In addition, for the first time, we propose several sequence labeling models as baseline methods to extract and attribute quotations simultaneously in an end-to-end manner.
Mobility Aware Edge Computing Segmentation Towards Localized Orchestration

The current trend in end-user device’s advancements in computing and communication capabilities makes edge computing an attractive solution to pave the way for the coveted ultra-low latency services. The success of the edge computing networking paradigm depends on the proper orchestration of the edge servers. Several Edge applications and services are intolerant to latency, especially in 5G and beyond networks, such as intelligent video surveillance, E-health, Internet of Vehicles, and augmented reality applications. The edge devices underwent rapid growth in both capabilities and size to cope with the service demands. Orchestrating it on the cloud was a prominent trend during the past decade. However, the increasing number of edge devices poses a significant burden on the orchestration delay. In addition to the growth in edge devices, the high mobility of users renders traditional orchestration schemes impractical for contemporary edge networks. Proper segmentation of the edge space becomes necessary to adapt these schemes to address these challenges. In this paper, we introduce a segmentation technique employing lax clustering and segregated mobility-based clustering. We then apply latency mapping to these clusters. The proposed scheme’s main objective is to create subspaces (segments) that enable light and efficient edge orchestration by-reducing the processing time and the core cloud communication overhead. A bench-marking simulation is conducted with the results showing decreased mobility-related failures and reduced orchestration delay.
Deep transfer learning for blended source identification in galaxy survey data

We present BlendHunter, a proof-of-concept deep-transfer-learning-based approach for the automated and robust identification of blended sources in galaxy survey data. We take the VGG-16 network with pre-trained convolutional layers and train the fully connected layers on parametric models of COSMOS images. We test the efficacy of the transfer learning by taking the weights learned on the parametric models and using them to identify blends in more realistic Canada-France Imaging Survey (CFIS)-like images. We compare the performance of this method to SEP (a Python implementation of SExtractor) as a function of noise level and the separation between sources. We find that BlendHunter outperforms SEP by ∼ 15% in terms of classification accuracy for close blends (< 10 pixel separation between sources) regardless of the noise level used for training. Additionally, the method provides consistent results to SEP for distant blends (≥ 10 pixel separation between sources) provided the network is trained on data with noise that has a relatively close standard deviation to that of the target images. The code and data have been made publicly available to ensure the reproducibility of the results.
Tensor-to-Image: Image-to-Image Translation with Vision Transformers

Transformers gain huge attention since they are first introduced and have a wide range of applications. Transformers start to take over all areas of deep learning and the Vision transformers paper also proved that they can be used for computer vision tasks. In this paper, we utilized a vision transformerbased custom-designed model, tensor-to-image, for the image to image translation. With the help of self-attention, our model was able to generalize and apply to different problems without a single modification.
The extremal function for structured sparse minors

Let c(H) be the smallest value for which e(G)/|G| > c(H) implies H is a minor of G. We show a new upper bound on c(H), which improves previous bounds for graphs with a vertex partition where some pairs of parts have many more edges than others — for instance a complete bipartite graph with a small number of edges placed inside one class. We also show a tight matching lower bound for almost all such graphs. We apply these results to show c(Kft/ log t,t) = (0.638 . . .+ of (1))t √ f , for f = o(log t) = ω(1).
Compact representation for matrices of bounded twin-width

For every fixed $d \in \mathbb{N}$, we design a data structure that represents a binary $n \times n$ matrix that is $d$-twin-ordered. The data structure occupies $O_d(n)$ bits, which is the least one could hope for, and can be queried for entries of the matrix in time $O_d(\log \log n)$ per query.
Phase Field Models for Thermal Fracturing and Their Variational Structures

It is often observed that thermal stress enhances crack propagation in materials, and, conversely, crack propagation can contribute to temperature shifts in materials. In this study, we first consider the thermoelasticity model proposed by M. A. Biot and study its energy dissipation property. The Biot thermoelasticity model takes into account the following effects. Thermal expansion and contraction are caused by temperature changes, and, conversely, temperatures decrease in expanding areas but increase in contracting areas. In addition, we examine its thermomechanical properties through several numerical examples and observe that the stress near a singular point is enhanced by the thermoelastic effect. In the second part, we propose two crack propagation models under thermal stress by coupling a phase field model for crack propagation and the Biot thermoelasticity model and show their variational structures. In our numerical experiments, we investigate how thermal coupling affects the crack speed and shape. In particular, we observe that the lowest temperature appears near the crack tip, and the crack propagation is accelerated by the enhanced thermal stress.
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem

We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to much larger sizes. Also, we show that NeuroLKH can be applied to other routing problems such as Capacitated Vehicle Routing Problem (CVRP), Pickup and Delivery Problem (PDP), and CVRP with Time Windows (CVRPTW).
FOLD-R++: A Toolset for Automated Inductive Learning of Default Theories from Mixed Data

FOLD-R is an automated inductive learning algorithm for learning default rules with exceptions for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for classification tasks. We present an improved FOLD-R algorithm, called FOLD-R++, that significantly increases the efficiency and scalability of FOLD-R. FOLD-R++ improves upon FOLD-R without compromising or losing information in the input training data during the encoding or feature selection phase. The FOLD-R++ algorithm is competitive in performance with the widely-used XGBoost algorithm, however, unlike XGBoost, the FOLD-R++ algorithm produces an explainable model. Next, we create a powerful tool-set by combining FOLD-R++ with s(CASP)—a goal-directed ASP execution engine— to make predictions on new data samples using the answer set program generated by FOLD-R++. The s(CASP) system also produces a justification for the prediction. Experiments presented in this paper show that our improved FOLD-R++ algorithm is a significant improvement over the original de-sign and that the s(CASP) system can make predictions in an efficient manner as well.
$N^3LO$ calculations for $2 \to 2$ processes using simplified differential equations

We present the computation of the massless three-loop ladder-box family with one external off-shell leg using the Simplified Differential Equations (SDE) approach. We also discuss the methods we used for finding a canonical differential equation for the two tennis-court families with one off-shell leg, and the application of the SDE approach on these two families.
DBI inflation and warped black holes

We study a possible amplification mechanism for the curvature perturbations generated during inflation by a Dirac-Born-Infeld (DBI) inflaton in the presence of a sharp feature in the warp factor. The large growth resulting in the scalar power spectrum is a consequence of the decreasing speed of sound. We obtain analytical approximate expressions for the relevant dynamical quantities both in the slow roll phase and during the transient phase which leads to the amplification. We finally compare our approximations to the exact numerical evolution and give, in such a context, the general rules for building a viable inflationary model leading to, during the subsequent radiation dominated era, an abundant primordial black holes formation.
Weak nonlinearity for strong non-normality

Abstract We propose a theoretical approach to derive amplitude equations governing the weakly nonlinear evolution of non-normal dynamical systems, when they experience transient growth or respond to harmonic forcing. This approach reconciles the non-modal nature of these growth mechanisms and the need for a centre manifold to project the leading-order dynamics. Under the hypothesis of strong non-normality, we take advantage of the fact that small operator perturbations suffice to make the inverse resolvent and the inverse propagator singular, which we encompass in a multiple-scale asymptotic expansion. The methodology is outlined for a generic nonlinear dynamical system, and four application cases highlight common non-normal mechanisms in hydrodynamics: the streamwise convective non-normal amplification in the flow past a backward-facing step, and the Orr and lift-up mechanisms in the plane Poiseuille flow.
Chunked-Cache: On-Demand and Scalable Cache Isolation for Security Architectures

Shared cache resources in multi-core processors are vulnerable to cache side-channel attacks. Recently proposed defenses have their own caveats: Randomization-based defenses are vulnerable to the evolving attack algorithms besides relying on weak cryptographic primitives, because they do not fundamentally address the root cause for cache side-channel attacks. Cache partitioning defenses, on the other hand, provide the strict resource partitioning and effectively block all side-channel threats. However, they usually rely on way-based partitioning which is not fine-grained and cannot scale to support a larger number of protection domains, e.g., in trusted execution environment (TEE) security architectures, besides degrading performance and often resulting in cache underutilization. To overcome the shortcomings of both approaches, we present a novel and flexible set-associative cache partitioning design for TEE architectures, called Chunked-Cache. Chunked-Cache enables an execution context to"carve"out an exclusive configurable chunk of the cache if the execution requires side-channel resilience. If side-channel resilience is not required, mainstream cache resources are freely utilized. Hence, our solution addresses the security-performance trade-off practically by enabling selective and on-demand utilization of side-channel-resilient caches, while providing well-grounded future-proof security guarantees. We show that Chunked-Cache provides side-channel-resilient cache utilization for sensitive code execution, with small hardware overhead, while incurring no performance overhead on the OS. We also show that it outperforms conventional way-based cache partitioning by 43%, while scaling significantly better to support a larger number of protection domains.
Linear Channel Estimation Based on a Low-Bandwidth Observation Channel with Unknown Response

We propose a novel system identification technique, based on a least-mean square algorithm, allowing for the estimation of a linear channel by using an unknown-response measurement channel. The key of the technique is a memoryless nonlinear function working as uncoupling block between the estimated and observation channels, conforming a Wiener-Hammerstein scheme. We prove that this estimation, only differing from the actual channel response by a scaling factor and a temporal shift, does not depend on the observation channel bandwidth. As a consequence, this technique enables the usage of low-cost measurement devices as feedback channel. We present numerical examples of the method, supporting the proposal and displaying excellent results.
Benchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve Optimization

Evolutionary transfer multiobjective optimization (ETMO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others. Besides, the potential for transfer optimization is deemed invaluable from the standpoint of human-like problem-solving capabilities where knowledge gather and reuse are instinctive. To promote the research on ETMO, benchmark problems are of great importance to ETMO algorithm analysis, which helps designers or practitioners to understand the merit and demerit better of ETMO algorithms. Therefore, a total number of 40 benchmark functions are proposed in this report, covering diverse types and properties in the case of knowledge transfer, such as various formulation models, various PS geometries and PF shapes, large-scale of variables, dynamically changed environment, and so on. All the benchmark functions have been implemented in JAVA code, which can be downloaded on the following website: https://github.com/songbai-liu/etmo.
Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction

Abstract In this paper we investigate a simple hypothesis for the Open Information Extraction (Ope-nIE) task, that it may be easier to extract some elements of an triple if the extraction is conditioned on prior extractions which may be easier to extract. We successfully exploit this and propose a neural multilingual OpenIE sys-tem that iteratively extracts triples by conditioning extractions on different elements of the triple leading to a rich set of extractions. The iterative nature of M I LIE also allows for seamlessly integrating rule based extraction systems with a neural end-to-end system leading to improved performance. M I LIE outperforms SOTA systems on multiple languages ranging from Chinese to Galician thanks to it’s ability of combining multiple extraction pathways. Our analysis conﬁrms that it is indeed true that certain elements of an extraction are easier to extract than others. Finally, we introduce OpenIE evaluation datasets for two low resource languages namely Japanese and Gali-cian.
Sharp L∞ estimates of HDG methods for Poisson equation II: 3D

In [SIAM J. Numer. Anal. , 59 (2), 720-745], we proved quasi-optimal $L^\infty$ estimates (up to logarithmic factors) for the solution of Poisson's equation by a hybridizable discontinuous Galerkin (HDG) method. However, the estimates only work in 2D. In this paper, we obtain sharp (without logarithmic factors) $L^\infty$ estimates for the HDG method in both 2D and 3D. Numerical experiments are presented to confirm our theoretical result.
Cross-Lingual Fine-Grained Entity Typing

The growth of cross-lingual pre-trained models has enabled NLP tools to rapidly generalize to new languages. While these models have been applied to tasks involving entities, their ability to explicitly predict typological features of these entities across languages has not been established. In this paper, we present a unified cross-lingual fine-grained entity typing model capable of handling over 100 languages and analyze this model's ability to generalize to languages and entities unseen during training. We train this model on cross-lingual training data collected from Wikipedia hyperlinks in multiple languages (training languages). During inference, our model takes an entity mention and context in a particular language (test language, possibly not in the training languages) and predicts fine-grained types for that entity. Generalizing to new languages and unseen entities are the fundamental challenges of this entity typing setup, so we focus our evaluation on these settings and compare against simple yet powerful string match baselines. Experimental results show that our approach outperforms the baselines on unseen languages such as Japanese, Tamil, Arabic, Serbian, and Persian. In addition, our approach substantially improves performance on unseen entities (even in unseen languages) over the baselines, and human evaluation shows a strong ability to predict relevant types in these settings.
No Slip gravity in light of LISA standard sirens

Standard sirens (SS) are the gravitational wave analog of the astronomical standard candles, and can provide powerful information about the dynamics of the Universe up to very high z values. In this work, we generate three mock SS catalogs based on the merger of massive black hole binaries which are expected to be observed in the LISA operating frequency band. Then, we perform an analysis to test modifications of general relativity (GR) inspired by the No Slip gravity framework. We find that in the best scenarios, we can constrain the free parameters which quantify deviations from GR to 21 per cent accuracy, while the Hubble parameter can be simultaneously fit to 6 per cent accuracy. In combination with CMB information, we find a 15 per cent accuracy on the modified gravity free parameters and 0.7 per cent accuracy on the Hubble parameter. The SS events at very large cosmological distances to be observed in LISA band will provide a unique way to test nature of gravity, but in the context of the analysis performed here, it will not be possible to distinguish the No Slip gravity from GR.
On the rising and sinking of granular bubbles and droplets

Abstract Recently, the existence of so-called granular bubbles and droplets has been demonstrated experimentally. Granular bubbles and droplets are clusters of particles that respectively rise and sink if submerged in an aerated and vibrated bed of another granular material of different size and/or density. However, currently, there is no model that explains the coherent motion of these clusters and predicts the transition between a rising and sinking motion. Here, we propose an analytical model predicting accurately the neutral buoyancy limit of a granular bubble/droplet. This model allows the compilation of a regime map identifying five distinct regimes of granular bubble/droplet motion.
LPRules: Rule Induction in Knowledge Graphs Using Linear Programming

We present a simple linear programming (LP) based method to learn compact and interpretable sets of rules encoding the facts in a knowledge graph (KG) and use these rules to solve the KG completion problem. Our LP model chooses a set of rules of bounded complexity from a list of candidate first-order logic rules and assigns weights to them. The complexity bound is enforced via explicit constraints. We combine simple rule generation heuristics with our rule selection LP to obtain predictions with accuracy comparable to state-of-the-art codes, even while generating much more compact rule sets. Furthermore, when we take as input rules generated by other codes, we often improve interpretability by reducing the number of chosen rules, while maintaining accuracy.
Hardware Architecture of Layered Decoders for PLDPC-Hadamard Codes

Protograph-based low-density parity-check Hadamard codes (PLDPC-HCs) are a new type of ultimate-Shannon-limit-approaching codes. In this paper, we propose a hardware architecture for the PLDPC-HC layered decoders. The decoders consist mainly of random address memories, Hadamard sub-decoders and control logics. Two types of pipelined structures are presented and the latency and throughput of these two structures are derived. Implementation of the decoder design on an FPGA board shows that a throughput of 1.48 Gbps is achieved with a bit error rate (BER) of <inline-formula> <tex-math notation="LaTeX">$10^{-5}$ </tex-math></inline-formula> at around <inline-formula> <tex-math notation="LaTeX">$E_{b}/N_{0}=-0.40$ </tex-math></inline-formula> dB. The decoder can also achieve the same BER at <inline-formula> <tex-math notation="LaTeX">$E_{b}/N_{0}=-1.14$ </tex-math></inline-formula> dB with a reduced throughput of 0.20 Gbps.
A Survey of Evolutionary Multi-Objective Clustering Approaches

This article presents how the studies of the evolutionary multi-objective clustering have been evolving over the years, based on a mapping of the indexed articles in the ACM, IEEE, and Scopus. We present the most relevant approaches considering the high-impact journals and conferences to provide an overview of this study field. We analyzed the algorithms based on the features and components presented in the proposed general architecture of the evolutionary multi-objective clustering. These algorithms were grouped considering common clustering strategies and applications. Furthermore, issues regarding the difficulty in defining appropriate clustering criteria applied to evolutionary multi-objective clustering and the importance of the evolutionary process evaluation to have a clear view of the optimization efficiency are discussed. It is essential to observe these aspects besides specific clustering properties when designing new approaches or selecting/using the existing ones. Finally, we present other potential subjects of future research, in which this article can contribute to newcomers or busy researchers who want to have a wide vision of the field.
Measurement of the Coherent Elastic Neutrino-Nucleus Scattering Cross Section on CsI by COHERENT.

We measured the cross section of coherent elastic neutrino-nucleus scattering (CEvNS) using a CsI[Na] scintillating crystal in a high flux of neutrinos produced at the Spallation Neutron Source at Oak Ridge National Laboratory. New data collected before detector decommissioning have more than doubled the dataset since the first observation of CEvNS, achieved with this detector. Systematic uncertainties have also been reduced with an updated quenching model, allowing for improved precision. With these analysis improvements, the COHERENT Collaboration determined the cross section to be (165_{-25}^{+30})×10^{-40} cm^{2}, consistent with the standard model, giving the most precise measurement of CEvNS yet. The timing structure of the neutrino beam has been exploited to compare the CEvNS cross section from scattering of different neutrino flavors. This result places leading constraints on neutrino nonstandard interactions while testing lepton flavor universality and measures the weak mixing angle as sin^{2}θ_{W}=0. 220_{-0.026}^{+0.028} at Q^{2}≈(50 MeV)^{2}.