Miracle-dz/UniFAR-FTUs huggingface dataset:
Positive Consensus for Heterogeneous Multi-agent Systems

: In this paper, we consider a positive consensus problem for some high-order linear multi-agent systems. Compared with existing consensus results, the most distinct feature of the formulated problem is that the state variables of all heterogeneous agents are conﬁned to the positive orthant. To solve this problem, we ﬁrst present some auxiliary multi-agent systems as positive local reference generators and then incorporate the reference generator into some applicable decentralized tracking controller for each agent. The proposed two diﬀerent kinds of distributed algorithms are proven to solve the positive consensus problem fulﬁlling certain prescribed pattern for this multi-agent system under switching topology. A simulation example is ﬁnally given to illustrate the eﬀectiveness of our design.
Auroral Morphological Changes to the Formation of Auroral Spiral During the Late Substorm Recovery Phase: Polar UVI and Ground All‐Sky Camera Observations

The ultraviolet imager (UVI) of the Polar spacecraft and an all‐sky camera at Longyearbyen contemporaneously detected an auroral vortex structure (so‐called “auroral spiral”) on 10 January 1997. From space, the auroral spiral was observed as a “small spot” (one of an azimuthally aligned chain of similar spots) in the poleward region of the main auroral oval from 18 to 24 hr magnetic local time. These auroral spots were formed while the substorm‐associated auroral bulge was subsiding and several poleward‐elongated auroral streak‐like structures appeared during the late substorm recovery phase. During the spiral interval, the geomagnetically north‐south and east‐west components of the geomagnetic field, which were observed at several ground magnetic stations around Svalbard island, showed significant negative and positive bays caused by the field‐aligned currents related with the aurora spiral appearance. The negative bays were reflected in the variations of local geomagnetic activity index (SML) which was provided from the SuperMAG magnetometer network at high latitudes. To pursue the spiral source region in the magnetotail, we trace each UVI image along field lines to the magnetic equatorial plane of the nightside magnetosphere using an empirical magnetic field model. Interestingly, the magnetotail region corresponding to the auroral spiral covered a broad region from Xgsm ∼ −40 to −70 RE at Ygsm ∼ 8 to 12 RE. The appearance of this auroral spiral suggests that extensive areas of the magnetotail (but local regions in the ionosphere) remain active even when the substorm almost ceases, and geomagnetic conditions are almost stable.
From Service Quality to E-Service Quality: Measurement, Dimensions and Model

With the global increase in online services, there is a paradigm shift from service quality to e-service quality. In order to sustain this strategic change, there is need to measure and evaluate the quality of e-services. Consequently, the paper seeks to determine the relevant e-service quality dimensions for e-channels. The aim is to generate a concise set of dimensions that managers can use to measure e-service quality. The paper proposed an e-service quality model comprising seven e-service quality dimensions (website appearance, ease of use, reliability, security, personalisation, fulfilment and responsiveness) and overall e-service quality. The study employed a cross- sectional research design and quantitative research approach. The data were collected via a questionnaire from 400 e-channel users in Lagos State, Nigeria. However, 318 copies of the questionnaire were found useful. The data were analysed using mean, frequency, percentages, correlation and multiple regression analysis. The results revealed that the relevant e-service quality dimensions influencing overall e-service quality are reliability, security, fulfilment, ease of use and responsiveness. These e-service quality dimensions are expected to provide information for managers to evaluate and improve their e-channel service delivery.
Milky Way Thin and Thick Disk Kinematics with Gaia EDR3 and RAVE DR5

We present a detailed analysis of kinematics of the Milky Way disk in the solar neighborhood using the Gaia DR3 catalog. To determine the local kinematics of the stellar disks of the Milky Way we use a complete sample of 278,228 red giant branch (RGB) stars distributed in a cylinder, centered at the Sun with a 1 kpc radius and half-height of 0.5 kpc. We determine separately the kinematical properties of RGB stars for each Galactic hemisphere in search of possible asymmetries. The kinematical properties of the RGB stars reveal the existence of two kinematically distinct components: the thin disk with mean velocities V R , V ϕ , V Z of about −1, −239, 0 km s−1, correspondingly, and velocity dispersions σ R , σ ϕ , σ Z of 31, 20, and 11 km s−1, and the Thick disk with mean velocity components of about +1, −225, 0 km s−1, and velocity dispersions of 49, 35, and 22 km s−1. We find that up to 500 pc in height above/below the Galactic plane, Thick disk stars comprise about half the stars of the disk. Such a high amount of RGB stars with Thick disk kinematics points at the secular evolution scenario origin for the Thick disk of the Milky Way.
Prompt Consistency for Zero-Shot Task Generalization

One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting. To achieve this, NLP tasks are framed as natural language prompts, generating a response indicating the predicted output. Nonetheless, the performance in such settings often lags far behind its supervised counterpart, suggesting a large space for potential improvement. In this paper, we explore methods to utilize unlabeled data to improve zero-shot performance. Specifically, we take advantage of the fact that multiple prompts can be used to specify a single task, and propose to regularize prompt consistency, encouraging consistent predictions over this diverse set of prompts. Our method makes it possible to fine-tune the model either with extra unlabeled training data, or directly on test input at inference time in an unsupervised manner. In experiments, our approach outperforms the state-of-the-art zero-shot learner, T0 (Sanh et al., 2022), on 9 out of 11 datasets across 4 NLP tasks by up to 10.6 absolute points in terms of accuracy. The gains are often attained with a small number of unlabeled examples.
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning

Deep reinforcement learning (RL) is a data-driven method capable of discovering complex control strategies for high-dimensional systems, making it promising for flow control applications. In particular, the present work is motivated by the goal of reducing energy dissipation in turbulent flows, and the example considered is the spatiotemporally chaotic dynamics of the Kuramoto–Sivashinsky equation (KSE). A major challenge associated with RL is that substantial training data must be generated by repeatedly interacting with the target system, making it costly when the system is computationally or experimentally expensive. We mitigate this challenge in a data-driven manner by combining dimensionality reduction via an autoencoder with a neural ODE framework to obtain a low-dimensional dynamical model from just a limited data set. We substitute this data-driven reduced-order model (ROM) in place of the true system during RL training to efficiently estimate the optimal policy, which can then be deployed on the true system. For the KSE actuated with localized forcing (‘jets’) at four locations, we demonstrate that we are able to learn a ROM that accurately captures the actuated dynamics as well as the underlying natural dynamics just from snapshots of the KSE experiencing random actuations. Using this ROM and a control objective of minimizing dissipation and power cost, we extract a control policy from it using deep RL. We show that the ROM-based control strategy translates well to the true KSE and highlight that the RL agent discovers and stabilizes an underlying forced equilibrium solution of the KSE system. We show that this forced equilibrium captured in the ROM and discovered through RL is related to an existing known equilibrium solution of the natural KSE.
Measuring DNS over TCP in the era of increasing DNS response sizes

The Domain Name System (DNS) is one of the most crucial parts of the Internet. Although the original standard defined the usage of DNS over UDP (DoUDP) as well as DNS over TCP (DoTCP), UDP has become the predominant protocol used in the DNS. With the introduction of new Resource Records (RRs), the sizes of DNS responses have increased considerably. Since this can lead to truncation or IP fragmentation, the fallback to DoTCP as required by the standard ensures successful DNS responses by overcoming the size limitations of DoUDP. However, the effects of the usage of DoTCP by stub resolvers are not extensively studied to this date. We close this gap by presenting a view at DoTCP from the Edge, issuing 12.1M DNS requests from 2,500 probes toward Public as well as Probe DNS recursive resolvers. In our measurement study, we observe that DoTCP is generally slower than DoUDP, where the relative increase in Response Time is less than 37% for most resolvers. While optimizations to DoTCP can be leveraged to further reduce the response times, we show that support on Public resolvers is still missing, hence leaving room for optimizations in the future. Moreover, we also find that Public resolvers generally have comparable reliability for DoTCP and DoUDP. However, Probe resolvers show a significantly different behavior: DoTCP queries targeting Probe resolvers fail in 3 out of 4 cases, and, therefore, do not comply with the standard. This problem will only aggravate in the future: As DNS response sizes will continue to grow, the need for DoTCP will solidify.
Design of in-situ reactive wall systems - a combined hydraulical-geochemical-economical simulation study

The paper presents a coupled hydraulical-geochemical-economical simulation model for the design of in-situ reactive wall systems. More specific, the model is used for cost-optimization and sensitivity analysis of a funnel-and-gate system with an in-situ sorption reactor. The groundwater flow and advective transport are simulated under steady-state conditions using a finite-difference numerical model. This model is coupled to an analytical solution describing the sorption kinetics of hydrophobic organic compounds within the reactor (gate). The third part of the model system is an economical model which calculates (a) the investment costs for the funnel-and-gate construction and (b) the operation cost based on the number of reactor refills, which depends on the breakthrough time for a given contaminant and the anticipated total operation time. For practical applications a simplified approximation of the cost-function is derived and tested.
Large mode-2 internal solitary waves in three-layer flows

Abstract In this paper, we investigate mode-2 solitary waves in a three-layer stratified flow model. Localised travelling wave solutions to both the fully nonlinear problem (Euler equations), and the three-layer Miyata–Choi–Camassa equations are found numerically and compared. Mode-2 solitary waves with speeds slower than the linear mode-1 long-wave speed are typically generalised solitary waves with infinite tails consisting of a resonant mode-1 periodic wave train. Herein, we evidence the existence of mode-2 embedded solitary waves, that is, we show that for specific values of the parameters, the amplitude of the oscillations in the tail are zero. For sufficiently thick middle layers, we also find branches of mode-2 solitary waves with speeds that extend beyond the mode-1 linear waves and are no longer embedded. In addition, we show how large amplitude embedded solitary waves are intimately linked to the conjugate states of the problem.
Relatively acceptable notation

Shapiro's notations for natural numbers, and the associated desideratum of acceptability - the property of a notation that all recursive functions are computable in it - is well-known in philosophy of computing. Computable structure theory, however, although capable of fully reconstructing Shapiro's approach, seems to be off philosophers' radar. Based on the case study of natural numbers with standard order, we make initial steps to reconcile these two perspectives. First, we lay the elementary conceptual groundwork for the reconstruction of Shapiro's approach in terms of computable structures and show, on a few examples, how results pertinent to the former can inform our understanding of the latter. Secondly, we prove a new result, inspired by Shapiro's notion of acceptability, but also relevant for computable structure theory. The result explores the relationship between the classical notion of degree spectrum of a computable function on the structure in question - specifically, having all c.e. degrees as a spectrum - and our ability to compute the (image of the) successor from the (image of the) function in any computable copy of the structure. The latter property may be otherwise seen as relativized acceptability of every notation for the structure.
The origin of ultramassive White Dwarfs: Hints from Gaia EDR3

Gaia Data Release 2 revealed a population of ultramassive white dwarfs on the Q branch that are moving anomalously fast for a local disc population with their young photometric ages. As the velocity dispersion of stars in the local disc increases with age, a proposed explanation of these white dwarfs is that they experience a cooling delay that causes current cooling models to infer photometric ages much younger than their true ages. To explore this explanation, we investigate the kinematics of ultramassive white dwarfs within 200 pc of the Sun using the improved Gaia Early Data Release 3 observations. We analyse the transverse motions of 0.95–1.25 M⊙ white dwarfs, subdivided by mass and age, and determine the distributions of the three-dimensional components of the transverse velocities. The results are compared to expectations based on observed kinematics of local main sequence stars. We find a population of photometrically young (∼0.5–1.5 Gyr) ultramassive (∼1.15–1.25 M⊙) white dwarfs for which the transverse velocity component in the direction of Galactic rotation is more dispersed than for local disc stars of any age; thus, it is too dispersed to be explained by any cooling delay in white dwarfs originating from the local disc. Furthermore, the dispersion ratio of the velocity components in the Galactic plane for this population is also inconsistent with a local disc origin. We discuss some possible explanations of this kinematically anomalous population, such as a halo origin or production through dynamical effects of stellar triple systems.
The Johnson-Lindenstrauss Lemma for Clustering and Subspace Approximation: From Coresets to Dimension Reduction

We study the effect of Johnson-Lindenstrauss transforms in various projective clustering problems, generalizing recent results which only applied to center-based clustering [MMR19]. We ask the general question: for a Euclidean optimization problem and an accuracy parameter $\epsilon \in (0, 1)$, what is the smallest target dimension $t \in \mathbb{N}$ such that a Johnson-Lindenstrauss transform $\Pi \colon \mathbb{R}^d \to \mathbb{R}^t$ preserves the cost of the optimal solution up to a $(1+\epsilon)$-factor. We give a new technique which uses coreset constructions to analyze the effect of the Johnson-Lindenstrauss transform. Our technique, in addition applying to center-based clustering, improves on (or is the first to address) other Euclidean optimization problems, including: $\bullet$ For $(k,z)$-subspace approximation: we show that $t = \tilde{O}(zk^2 / \epsilon^3)$ suffices, whereas the prior best bound, of $O(k/\epsilon^2)$, only applied to the case $z = 2$ [CEMMP15]. $\bullet$ For $(k,z)$-flat approximation: we show $t = \tilde{O}(zk^2/\epsilon^3)$ suffices, completely removing the dependence on $n$ from the prior bound $\tilde{O}(zk^2 \log n/\epsilon^3)$ of [KR15]. $\bullet$ For $(k,z)$-line approximation: we show $t = O((k \log \log n + z + \log(1/\epsilon)) / \epsilon^3)$ suffices, and ours is the first to give any dimension reduction result.
Statistical inference for the two‐sample problem under likelihood ratio ordering, with application to the ROC curve estimation

The receiver operating characteristic (ROC) curve is a powerful statistical tool and has been widely applied in medical research. In the ROC curve estimation, a commonly used assumption is that larger the biomarker value, greater severity the disease. In this article, we mathematically interpret “greater severity of the disease” as “larger probability of being diseased. ” This in turn is equivalent to assume the likelihood ratio ordering of the biomarker between the diseased and healthy individuals. With this assumption, we first propose a Bernstein polynomial method to model the distributions of both samples; we then estimate the distributions by the maximum empirical likelihood principle. The ROC curve estimate and the associated summary statistics are obtained subsequently. Theoretically, we establish the asymptotic consistency of our estimators. Via extensive numerical studies, we compare the performance of our method with competitive methods. The application of our method is illustrated by a real‐data example.
ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach

Structured Sentiment Analysis (SSA) deals with extracting opinion tuples in a text, where each tuple (h, e, t, p) consists of h, the holder, who expresses a sentiment polarity p towards a target t through a sentiment expression e. While prior works explore graph-based or sequence labeling-based approaches for the task, we in this paper present a novel unified generative method to solve SSA, a SemEval2022 shared task. We leverage a BART-based encoder-decoder architecture and suitably modify it to generate, given a sentence, a sequence of opinion tuples. Each generated tuple consists of seven integers respectively representing the indices corresponding to the start and end positions of the holder, target, and expression spans, followed by the sentiment polarity class associated between the target and the sentiment expression. We perform rigorous experiments for both Monolingual and Cross-lingual subtasks, and achieve competitive Sentiment F1 scores on the leaderboard in both settings.
Clues Before Answers: Generation-Enhanced Multiple-Choice QA

A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it trains a generative encoder-decoder model which is both powerful and universal. However, a side effect of twisting a generation target to fit the classification nature of MCQA is the under-utilization of the decoder and the knowledge that can be decoded. To exploit the generation capability and underlying knowledge of a pre-trained encoder-decoder model, in this paper, we propose a generation-enhanced MCQA model named GenMC. It generates a clue from the question and then leverages the clue to enhance a reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.
A Survey of Decentralized Online Learning

—Decentralized online learning (DOL) has been increasingly researched in the last decade, mostly motivated by its wide applications in sensor networks, commercial buildings, robotics (e.g., decentralized target tracking and formation con-trol), smart grids, deep learning, and so forth. In this problem, there are a network of agents who may be cooperative (i.e., decentralized online optimization) or noncooperative (i.e., online game) through local information exchanges, and the local cost function of each agent is often time-varying in dynamic and even adversarial environments. At each time, a decision must be made by each agent based on historical information at hand without knowing future information on cost functions. Although this problem has been extensively studied in the last decade, a comprehensive survey is lacking. Therefore, this paper provides a thorough overview of DOL from the perspective of problem settings, communication, computation, and performances. In addition, some potential future directions are also discussed in details.
ExSum: From Local Explanations to Model Understanding

Interpretability methods are developed to understand the working mechanisms of black-box models, which is crucial to their responsible deployment. Fulfilling this goal requires both that the explanations generated by these methods are correct and that people can easily and reliably understand them. While the former has been addressed in prior work, the latter is often overlooked, resulting in informal model understanding derived from a handful of local explanations. In this paper, we introduce explanation summary (ExSum), a mathematical framework for quantifying model understanding, and propose metrics for its quality assessment. On two domains, ExSum highlights various limitations in the current practice, helps develop accurate model understanding, and reveals easily overlooked properties of the model. We also connect understandability to other properties of explanations such as human alignment, robustness, and counterfactual similarity and plausibility.
Channel Measurement, Characterization and Modeling for Terahertz Indoor Communications Above 200 GHz

Terahertz (THz) communications are envisioned as a promising technology for sixth-generation (6G) and beyond systems, owing to its unprecedented multi-gigahertz (GHz) bandwidth. In this paper, channel measurement campaigns in indoor scenarios at 201-209~GHz are reported. Four different communication scenarios including 90 transmitter-receiver pairs are measured in two channel measurement campaigns of a meeting room and an office room, respectively. The two measurement campaigns contains four scenarios, namely, a meeting room, cubicle area, hallway and non-line-of-sight (NLoS) case. The propagation of multi-path components (MPCs) in the four scenarios is characterized by the power-delay-angular profiles. Based on them, the temporal and spatial consistency for varying receiver locations in the complex hallway and NLoS scenarios are verified. To characterize, the large-scale best-direction and omni-directional path losses in indoor scenarios are separately analyzed and modeled by the close-in (CI) model. Furthermore, the small-scale channel parameters, e.g., the number of clusters, delay spread, angular spread, and cluster time-of-arrival are analyzed and modeled by proper distributions. As a general framework, a ray-tracing-statistical hybrid model is proposed for wireless propagation at 201-209~GHz, although, admittedly, the measurement results and analysis reveal that the channel characteristics in various indoor scenarios exhibit noticeable differences that need tailored parameter settings.
Optimal Rates for Distributed Learning with Random Features

In recent studies, the generalization properties for distributed learning and random features assumed the existence of the target concept over the hypothesis space. However, this strict condition is not applicable to the more common non-attainable case. In this paper, using refined proof techniques, we first extend the optimal rates for distributed learning with random features to the non-attainable case. Then, we reduce the number of required random features via data-dependent generating strategy, and improve the allowed number of partitions with additional unlabeled data. Theoretical analysis shows these techniques remarkably reduce computational cost while preserving the optimal generalization accuracy under standard assumptions. Finally, we conduct several experiments on both simulated and real-world datasets, and the empirical results validate our theoretical findings.