Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English

Currently, natural language processing (NLP) models proliferate language discrimination leading to potentially harmful societal impacts as a result of biased outcomes. For example, part-of-speech taggers trained on Mainstream American English (MAE) produce non-interpretable results when applied to African American English (AAE) as a result of language features not seen during training. In this work, we incorporate a human-in-the-loop paradigm to gain a better understanding of AAE speakers’ behavior and their language use, and highlight the need for dialectal language inclusivity so that native AAE speakers can extensively interact with NLP systems while reducing feelings of disenfranchisement.
Understanding RowHammer Under Reduced Wordline Voltage: An Experimental Study Using Real DRAM Devices

RowHammer is a circuit-level DRAM vulnerability, where repeatedly activating and precharging a DRAM row, and thus alternating the voltage of a row’s wordline between low and high voltage levels, can cause bit flips in physically nearby rows. Recent DRAM chips are more vulnerable to RowHammer: with technology node scaling, the minimum number of activate-precharge cycles to induce a RowHammer bit flip reduces and the RowHammer bit error rate increases. Therefore, it is critical to develop effective and scalable approaches to protect modern DRAM systems against RowHammer. To enable such solutions, it is essential to develop a deeper understanding of the RowHammer vulnerability of modern DRAM chips. However, even though the voltage toggling on a wordline is a key determinant of RowHammer vulnerability, no prior work experimentally demonstrates the effect of wordline voltage (VPP) on the RowHammer vulnerability. Our work closes this gap in understanding.This is the first work to experimentally demonstrate on 272 real DRAM chips that lowering VPP reduces a DRAM chip’s RowHammer vulnerability. We show that lowering VPP 1) increases the number of activate-precharge cycles needed to induce a RowHammer bit flip by up to 85.8 % with an average of 7.4 % across all tested chips and 2) decreases the RowHammer bit error rate by up to 66.9 % with an average of 15.2 % across all tested chips. At the same time, reducing VPP marginally worsens a DRAM cell’s access latency, charge restoration, and data retention time within the guardbands of system-level nominal timing parameters for 208 out of 272 tested chips. We conclude that reducing VPP is a promising strategy for reducing a DRAM chip’s RowHammer vulnerability without requiring modifications to DRAM chips.
Lightcurves and Rotations of Trans-Neptunian Objects in the 2:1 Mean Motion Resonance with Neptune

We report the rotational lightcurves of 21 trans-Neptunian objects (TNOs) in Neptune’s 2:1 mean motion resonance obtained with the 6.5 m Magellan-Baade telescope and the 4.3 m Lowell Discovery Telescope. The main survey’s goal is to find objects displaying a large lightcurve amplitude that is indicative of contact binaries or highly elongated objects. In our sample, two 2:1 resonant TNOs showed a significant short-term lightcurve amplitude: 2002 VD130 and (531074) 2012 DX98. The full lightcurve of 2012 DX98 infers a periodicity of 20.80 ± 0.06 hr and amplitude of 0.56 ± 0.03 mag, whereas 2002 VD130 rotates in 9.85 ± 0.07 hr with a 0.31 ± 0.04 mag lightcurve amplitude. Based on lightcurve morphology, we classify (531074) 2012 DX98 as a likely contact binary but 2002 VD130 as a likely single elongated object. Based on our sample and the lightcurves reported in the literature, we estimate the lower percentage of nearly equal-sized contact binaries at only 7%–14% in the 2:1 resonance, which is comparable to the low fraction reported for the dynamically cold classical TNOs. This low contact binary fraction in the 2:1 Neptune resonance is consistent with the lower estimate of the recent numerical modeling. We report the Sloan g′, r′, and i′ surface colors of 2002 VD130, which is an ultra-red TNO whereas 2012 DX98 is a very red object based on published surface colors.
Finding Good Proofs for Answers to Conjunctive Queries Mediated by Lightweight Ontologies

In ontology-mediated query answering, access to incomplete data sources is mediated by a conceptual layer constituted by an ontology. To correctly compute answers to queries, it is necessary to perform complex reasoning over the constraints expressed by the ontology. In the literature, there exists a multitude of techniques incorporating the ontological knowledge into queries. However, few of these approaches were designed for comprehensibility of the query answers. In this article, we try to bridge these two qualities by adapting a proof framework originally applied to axiom entailment for conjunctive query answering. We investigate the data and combined complexity of determining the existence of a proof below a given quality threshold, which can be measured in different ways. By distinguishing various parameters such as the shape of a query, we obtain an overview of the complexity of this problem for the lightweight ontology languages DL-Lite_R and EL, and also have a brief look at temporal query answering.
Electrochemical Parameter Identification for Lithium-ion Batteries on Separated Time-scales

Lithium-ion batteries (LIBs) play an essential role in the energy sector and have been widely deployed in recent years. Generally, LIBs are managed in model-driven manners, leading to the need for parameter identiﬁcation. However, as an electrochemical system, the battery contains various parameters while the measurements are mainly the current and voltage, inducing an inherent ill-conditioned identiﬁcation problem. A ﬂexible and lightweight parameter identiﬁcation framework, including the test, model, and algorithm, is proposed in this work. Electrochemical parameters are grouped by time-variant features and identiﬁed on separated time-scales. Param- eters with slow dynamics are identiﬁed in a hybrid data-driven and model-driven approach based on the data of a quasi-static test covering the partial SOC range. Parameters with fast dynamics are identiﬁed using a speciﬁcally designed sensitivity-oriented stepwise optimization algorithm based on the data of a dynamic test consisting of a series of constant current (CC) pulses. The mixed impacts of diﬀerent parameters can be decoupled and the time costs of the test and computation can be reduced. Speciﬁcally, it takes a few hours to identify slow dynamic parameters and a few minutes to identify fast dynamic parameters. Numerical experiments on a typical LiNCM battery at diﬀerent life stages are conducted. The results show that the identiﬁcation accuracy of crucial parameters can reach over 95%, and the battery model error is reduced below 2 × 10 −3 in the voltage. Thus, the sensitivity analysis results are consistent with physical interpretations.
$C^*$-algebra Net: A New Approach Generalizing Neural Network Parameters to $C^*$-algebra

We propose a new framework that generalizes the parameters of neural network models to $C^*$-algebra-valued ones. $C^*$-algebra is a generalization of the space of complex numbers. A typical example is the space of continuous functions on a compact space. This generalization enables us to combine multiple models continuously and use tools for functions such as regression and integration. Consequently, we can learn features of data efficiently and adapt the models to problems continuously. We apply our framework to practical problems such as density estimation and few-shot learning and show that our framework enables us to learn features of data even with a limited number of samples. Our new framework highlights the potential possibility of applying the theory of $C^*$-algebra to general neural network models.
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis

Each year, expert-level performance is attained in increasingly-complex multiagent domains, where notable examples include Go, Poker, and StarCraft II. This rapid progression is accompanied by a commensurate need to better understand how such agents attain this performance, to enable their safe deployment, identify limitations, and reveal potential means of improving them. In this paper we take a step back from performance-focused multiagent learning, and instead turn our attention towards agent behavior analysis. We introduce a model-agnostic method for discovery of behavior clusters in multiagent domains, using variational inference to learn a hierarchy of behaviors at the joint and local agent levels. Our framework makes no assumption about agents' underlying learning algorithms, does not require access to their latent states or policies, and is trained using only offline observational data. We illustrate the effectiveness of our method for enabling the coupled understanding of behaviors at the joint and local agent level, detection of behavior changepoints throughout training, discovery of core behavioral concepts, demonstrate the approach's scalability to a high-dimensional multiagent MuJoCo control domain, and also illustrate that the approach can disentangle previously-trained policies in OpenAI's hide-and-seek domain.
Modelling Graphene-based Transparent Electrodes for Si Solar Cells by Artificial Neural Networks

Transparent electrodes based on conductive transparent oxides (TCO) are increasingly invading the photovoltaic (PV) field because of their unique ability to reconcile high transparency with good electrical conductivity. The TCO market is dominated by the Indium oxide doped with Tin (ITO) with a resistivity of 30-80 Ω/sq and a transmittance of 90 % in the visible range. Yet, its cost is rising due to the high indium content, is one of the reason that encouraging research on alternative materials essential for the development of PV technologies. It is in this theme that graphene, a material with exceptional properties, is tested as a design material for transparent electrodes for Si solar cells. In this paper, we optimized optically and electronically the graphene-based transparent electrodes (G-TE) by proposing a model of simulation based on artificial intelligence and specifically artificial neural networks (ANN) which is the ANN-model. Therefore, to achieve an appropriate characterisation of a behaviour of G-TE for the Si solar cells, the ANN-model has been performed to simulate and optimise different parameters of the G-TE, by controlling graphene layer number, tuning graphene work function, and deduce the suitable transmittance and resistivity in order to have a complete adjustment for these parameters. Our study mentioned that a G-TE with three layers of graphene and a work function of 4.75 eV leads for a sheet resistance of 50 Ω/sq and transmittance of 91.4 %; these results suggest that G-TE is a promising candidate in the TCO field.
Automatic Summarization of Russian Texts: Comparison of Extractive and Abstractive Methods

This paper investigates the problem of creating summaries of Russian-language texts based on extractive (TextRank and LexRank) and abstractive (mBART, ruGPT3Small, ruGPT3Large, ruT5-base and ruT5-large) methods. For our experiments, we used the Russian-language corpus of news articles Gazeta and the Russianlanguage parts of the MLSUM and XL-Sum corpora. We computed ROUGE-N, ROUGE-L, BLEU, METEOR and BERTScore metrics to evaluate the quality of summarization. According to the experimental results, the methods are ranked (from best to worst) as follows: ruT5-large, mBART, ruT5-base, LexRank, ruGPT3Large, TextRank, ruGPT3Small. The study also highlights the salient features of summaries obtained by various methods. In particular, mBART summaries are less abstractive than ruGPT3Large and ruT5-large, and ruGPT3Large summaries are often incomplete and contain errors.
Observation of cnoidal wave localization in nonlinear topolectric circuits

We observe a localized cnoidal (LCn) state in an electric circuit network. Its formation derives from the interplay of non-linearity and the topology inherent to a Su-Schrieffer-Heeger (SSH) chain of inductors. Varicap diodes act as voltage-dependent capacitors, and create a non-linear on-site potential. For a sinusoidal voltage excitation around midgap frequency, we show that the voltage response in the non-linear SSH circuit follows the Korteweg-de Vries equation. The topological SSH boundary state which relates to a midgap impedance peak in the linearized limit is distorted into the LCn state in the non-linear regime, where the cnoidal eccentricity decreases from edge to bulk.
Finite Expression Method for Solving High-Dimensional Partial Differential Equations

Designing efficient and accurate numerical solvers for high-dimensional partial differential equations (PDEs) remains a challenging and important topic in computational science and engineering, mainly due to the"curse of dimensionality"in designing numerical schemes that scale in dimension. This paper introduces a new methodology that seeks an approximate PDE solution in the space of functions with finitely many analytic expressions and, hence, this methodology is named the finite expression method (FEX). It is proved in approximation theory that FEX can avoid the curse of dimensionality. As a proof of concept, a deep reinforcement learning method is proposed to implement FEX for various high-dimensional PDEs in different dimensions, achieving high and even machine accuracy with a memory complexity polynomial in dimension and an amenable time complexity. An approximate solution with finite analytic expressions also provides interpretable insights into the ground truth PDE solution, which can further help to advance the understanding of physical systems and design postprocessing techniques for a refined solution.
A slice-based algorithm for automatic and feature-preserving hole-filling in a CAD mesh model

ABSTRACTThis paper describes an automatic feature-preserving mesh hole filling algorithm to repair a mesh model, obtained by tessellating a 3D CAD model, with arbitrary holes or gaps. Missing triangles in a mesh model result in such holes or gaps. The proposed algorithm fills the hole while maintaining curvature continuity across the boundaries of the hole. In addition, the algorithm is able to identify missing features such as edges or vertices and reconstruct these. A composite surface patch bound by the boundary of the hole is first constructed and then tessellated to be in conformity with the original mesh to obtain the repaired mesh model. The input mesh remains unchanged in regions away from the holes so any features in the input mesh are preserved. The reconstruction also works in the presence of holes with islands. Algorithm can also fill holes in real world mesh model. Focus is for small holes that result in one missing corner per hole. Results from an implementation tested on various mesh models.
Graphical Join: A New Physical Join Algorithm for RDBMSs

Join operations (especially n-way, many-to-many joins) are known to be time- and resource-consuming. At large scales, with respect to table and join-result sizes, current state of the art approaches (in-cluding both binary-join plans which use Nested-loop/Hash/Sort-merge Join algorithms or, alternatively, worst-case optimal join algorithms (WOJAs)), may even fail to produce any answer given reasonable resource and time constraints. In this work, we introduce a new approach for n-way equi-join processing, the Graphical Join (GJ). The key idea is two-fold: First, to map the physical join computation problem to PGMs and introduce tweaked inference algorithms which can compute a Run-Length Encoding (RLE) based join-result summary, entailing all statistics necessary to materialize the join result. Second, and most importantly, to show that a join algorithm, like GJ, which produces the above join-result summary and then desummarizes it, can introduce large performance bene fi ts in time and space. Comprehensive experimentation is undertaken with join queries from the JOB, TPCDS, and lastFM datasets, comparing GJ against PostgresQL and MonetDB and a state of the art WOJA implemented within the Umbra system. The results for in-memory join computation show performance improvements up to 64 × , 388 × , and 6 × faster than PostgreSQL, MonetDB and Um- bra, respectively. For on-disk join computation, GJ is faster than PostgreSQL, MonetDB and Umbra by up to 820 × , 717 × and 165 × , re- spectively. Furthermore, GJ space needs are up to 21,488 × , 38,333 × , and 78,750 × smaller than PostgresQL, MonetDB, and Umbra, re- spectively.
Enabling Undergrounding of Long-Distance Transmission Lines with Low Frequency AC Technology

With increasing prevalence of severe natural hazards and the ignition of wildfires by power lines, many power system planners are considering converting overhead lines to underground cables to mitigate risks related to these events. Systems with a large proportion of underground cables can bring challenges due to the capacitance and losses of the cables, factors which may limit the potential for hardening in critical areas. Low frequency AC (LFAC) transmission solves these problems, as lowering the frequency decreases both the effects of capacitance and the losses. This paper presents a tractable frequency-dependent model for underground cables and incorporates it into the open source optimal power flow tool VariableFrequencyOPF.jl. The model and implementation are used to demonstrate the benefits of LFAC in a case study involving two multi-terminal cable upgrades with LFAC, including a comparison with HVDC. The results demonstrate the value of LFAC systems for both power flow control and reduction of losses.
Using EBGAN for Anomaly Intrusion Detection

As an active network security protection scheme, intrusion detection system (IDS) undertakes the important responsibility of detecting network attacks in the form of malicious network traffic. Intrusion detection technology is an important part of IDS. At present, many scholars have carried out extensive research on intrusion detection technology. However, developing an efficient intrusion detection method for massive network traffic data is still difficult. Since Generative Adversarial Networks (GANs) have powerful modeling capabilities for complex high-dimensional data, they provide new ideas for addressing this problem. In this paper, we put forward an EBGAN-based intrusion detection method, IDS-EBGAN, that classifies network records as normal traffic or malicious traffic. The generator in IDS-EBGAN is responsible for converting the original malicious network traffic in the training set into adversarial malicious examples. This is because we want to use adversarial learning to improve the ability of discriminator to detect malicious traffic. At the same time, the discriminator adopts Autoencoder model. During testing, IDS-EBGAN uses reconstruction error of discriminator to classify traffic records.
On black bounces, wormholes and trapped ghosts

Simpson and Visser recently proposed a phenomenological way to avoid some kinds of space-time singularities by replacing a parameter whose zero value corresponds to a singularity (say, r ) with the manifestly nonzero expression r ( u ) = √ u 2 + b 2 , where u is a new coordinate, and b = const > 0. This trick, generically leading to a regular minimum of r beyond a black hole horizon (called a “black bounce”), may hopefully mimic some expected results of quantum gravity, and was previously applied to regularize the Schwarzschild, Reissner-Nordstr¨om, Kerr and some other metrics. In this paper it is applied to regularize the Fisher solution with a massless canonical scalar ﬁeld in general relativity (resulting in a traversable wormhole) and a family of static, spherically symmetric dilatonic black holes (resulting in regular black holes and wormholes). These new regular metrics represent exact solutions of general relativity with a sum of stress-energy tensors of a scalar ﬁeld with nonzero self-interaction potential and a magnetic ﬁeld in the framework of nonlinear electrodynamics with a Lagrangian function L ( F ), F = F µν F µν . A novel feature in the present study is that the scalar ﬁelds involved have “trapped ghost” properties, that is, are phantom in a strong-ﬁeld region and canonical outside it, with a smooth transition between the regions. It is also shown that any static, spherically symmetric metric can be obtained as an exact solution to the Einstein equations with the stress-energy tensor of the above ﬁeld combination.
Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping

—Efﬁcient object level representation for monocular semantic simultaneous localization and mapping (SLAM) still lacks a widely accepted solution. In this paper, we propose the use of an efﬁcient representation, based on structural points, for the geometry of objects to be used as landmarks in a monocular semantic SLAM system based on the pose-graph formulation. In particular, an inverse depth parametrization is proposed for the landmark nodes in the pose-graph to store object position, orientation and size/scale. The proposed formulation is general and it can be applied to different geometries; in this paper we focus on indoor environments where human- made artifacts commonly share a planar rectangular shape, e.g., windows, doors, cabinets, etc. The approach can be easily extended to urban scenarios where similar shapes exists as well. Experiments in simulation show good performance, particularly in object geometry reconstruction.
KOLOMVERSE: KRISO open large-scale image dataset for object detection in the maritime universe

Over the years, datasets have been developed for various object detection tasks. Object detection in the maritime domain is essential for the safety and navigation of ships. However, there is still a lack of publicly available large-scale datasets in the maritime domain. To overcome this challenge, we present KOLOMVERSE, an open large-scale image dataset for object detection in the maritime domain by KRISO (Korea Research Institute of Ships and Ocean Engineering). We collected 5,845 hours of video data captured from 21 territorial waters of South Korea. Through an elaborate data quality assessment process, we gathered around 2,151,470 4K resolution images from the video data. This dataset considers various environments: weather, time, illumination, occlusion, viewpoint, background, wind speed, and visibility. The KOLOMVERSE consists of ﬁve classes (ship, buoy, ﬁshnet buoy, lighthouse and wind farm) for maritime object detection. The dataset has images of 3840 × 2160 pixels and to our knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain. We performed object detection experiments and evaluated our dataset on several pre-trained state-of-the-art architectures to show the effectiveness and usefulness of our dataset. The dataset is available at: https://github.com/MaritimeDataset/KOLOMVERSE .
A Multivariate Point Process Model for Simultaneously Recorded Neural Spike Trains

The current state-of-the-art in neurophysiological data collection allows for simultaneous recording of tens to hundreds of neurons, for which point processes are an appropriate statistical modelling framework. However, existing point process models lack multivariate generalizations which are both ﬂexible and computationally tractable. This paper introduces a multivariate generalization of the Skellam process with resetting (SPR), a point process tailored to model individual neural spike trains. The multivariate SPR (MSPR) is biologically justiﬁed as it mimics the process of neural integration. Its ﬂexible dependence structure and a fast parameter estimation method make it well-suited for the analysis of simultaneously recorded spike trains from multiple neurons. The strengths and weaknesses of the MSPR are demonstrated through simulation and analysis of experimental data.