Miracle-dz/UniFAR-FTUs huggingface dataset:
Inertial, barotropic, and baroclinic instabilities of the Bickley jet in two-layer rotating shallow water model

We undertake a detailed study of inertial instability of the barotropic Bickley jet and its nonlinear saturation in the 2 layer rotating shallow water (RSW) model on the f-plane and compare it with the classical barotropic and baroclinic instabilities. We start with analytical and numerical investigation of the linear stability problem under hypothesis of strict homogeneity in the along-flow direction (“symmetric instability”). The unstable modes are identified and their parameters are determined. The dependence of the instability on Rossby and Burger numbers of the jet is investigated. The nonlinear development of the instability is then studied with the help of high-resolution well-balanced finite-volume numerical code recently developed for multi-layer RSW, which is initialized with the most unstable mode found from the linear stability analysis. It is shown that symmetric inertial instability is saturated by reorganization of the mean flow, without full homogenization of the anticyclonic region, where.
Evaluation of Complex Mixture Toxicity in the Milwaukee Estuary (WI, USA) Using Whole‐Mixture and Component‐Based Evaluation Methods

Anthropogenic activities introduce complex mixtures into aquatic environments, necessitating mixture toxicity evaluation during risk assessment. There are many alternative approaches that can be used to complement traditional techniques for mixture assessment. Our study aimed to demonstrate how these approaches could be employed for mixture evaluation in a target watershed. Evaluations were carried out over 2 years (2017–2018) across 8–11 study sites in the Milwaukee Estuary (WI, USA). Whole mixtures were evaluated on a site‐specific basis by deploying caged fathead minnows (Pimephales promelas) alongside composite samplers for 96 h and characterizing chemical composition, in vitro bioactivity of collected water samples, and in vivo effects in whole organisms. Chemicals were grouped based on structure/mode of action, bioactivity, and pharmacological activity. Priority chemicals and mixtures were identified based on their relative contributions to estimated mixture pressure (based on cumulative toxic units) and via predictive assessments (random forest regression). Whole mixture assessments identified target sites for further evaluation including two sites targeted for industrial/urban chemical mixture effects assessment; three target sites for pharmaceutical mixture effects assessment; three target sites for further mixture characterization; and three low‐priority sites. Analyses identified 14 mixtures and 16 chemicals that significantly contributed to cumulative effects, representing high or medium priority targets for further ecotoxicological evaluation, monitoring, or regulatory assessment. Overall, our study represents an important complement to single‐chemical prioritizations, providing a comprehensive evaluation of the cumulative effects of mixtures detected in a target watershed. Furthermore, it demonstrates how different tools and techniques can be used to identify diverse facets of mixture risk and highlights strategies that can be considered in future complex mixture assessments. Environ Toxicol Chem 2023;42:1229–1256. © 2023 SETAC
Flushing water regularly inadequate to protect water quality in gravity-fed water supply systems with storage tanks during long-term lockdown

Flushing of water is recommended to prevent the growth of opportunistic pathogens, corrosion, and deterioration of the water supply network during short-term lockdowns. However, the efficacy of flushing fixtures in water supply networks with intermittent gravity-fed supply during long-term lockdowns is unknown. A laboratory-scale premise water supply network with a secondary storage tank connected to a gravity-fed drinking water distribution network was operated for 52 weeks to compare the efficacy of flushing water once a day (1D) vs. once in three days (3D) to preserve water quality. The tap water sampled from buildings (building) and overhead tanks (OHTs) prior to the pandemic served as a benchmark for comparison. The water quality trended as 1D = 3D < building < OHTs, with both the 1D and 3D yielding poor quality water. The water in 3D had higher levels of 16S rRNA gene copies, sul1, and intI1 relative to 1D; this difference was more pronounced (2-3 orders of magnitude) during summer. The levels of sul1, intI1, and DNA marker of Legionella pneumophila in biofilms sampled at the end of the operation of the laboratory-scale premise water supply network trended as elbows > visibly corroded pipes-sections > taps. The levels of heavy metals in some tap water samples exceeded the WHO recommendations and BIS standards for drinking water: 32% of 1D samples (30/93), and 31% of 3D samples (29/93) exceeded the standards for Pb, (10 ppb); while 48% (45/93) of 1D samples and 35% (33/93) of 3D samples exceeded the standards for Fe in drinking water (300 ppb).
Encoding Node Diffusion Competence and Role Significance for Network Dismantling

Percolation theory shows that removing a small fraction of critical nodes can lead to the disintegration of a large network into many disconnected tiny subnetworks. The network dismantling task focuses on how to efficiently select the least such critical nodes. Most existing approaches focus on measuring nodes’ importance from either functional or topological viewpoint. Different from theirs, we argue that nodes’ importance can be measured from both of the two complementary aspects: The functional importance can be based on the nodes’ competence in relaying network information; While the topological importance can be measured from nodes’ regional structural patterns. In this paper, we propose an unsupervised learning framework for network dismantling, called DCRS, which encodes and fuses both node diffusion competence and role significance. Specifically, we propose a graph diffusion neural network which emulates information diffusion for competence encoding; We divide nodes with similar egonet structural patterns into a few roles, and construct a role graph on which to encode node role significance. The DCRS converts and fuses the two encodings to output a final ranking score for selecting critical nodes. Experiments on both real-world networks and synthetic networks demonstrate that our scheme significantly outperforms the state-of-the-art competitors for its mostly requiring much fewer nodes to dismantle a network.
BERT-based Authorship Attribution on the Romanian Dataset called ROST

Being around for decades, the problem of Authorship Attribution is still very much in focus currently. Some of the more recent instruments used are the pre-trained language models, the most prevalent being BERT. Here we used such a model to detect the authorship of texts written in the Romanian language. The dataset used is highly unbalanced, i.e., significant differences in the number of texts per author, the sources from which the texts were collected, the time period in which the authors lived and wrote these texts, the medium intended to be read (i.e., paper or online), and the type of writing (i.e., stories, short stories, fairy tales, novels, literary articles, and sketches). The results are better than expected, sometimes exceeding 87\% macro-accuracy.
Private, Anonymous, Collateralizable Commitments vs. MEV

In this work, we introduce the private, anonymous, collateralizable commitments (PACCs) framework. PACCs allow any smart contract wallet holder to collateralize a claim, request, or commitment in general, in a private and anonymous manner. PACCs can prove arbitrarily much or little about the wallet generating the commitment, and/or the transaction which is being committed. We demonstrate that PACCs can be applied to effectively eliminate maximal-extractable value (MEV) in DeFi where it currently occurs, shifting MEV instead to censorship. After describing our protocol with detail, we provide an implementation using the Ethereum blockchain, and whose benchmarks prove how PACCs are completely feasible.
A performance evaluation of WDM-Nyquist systems generated by optical flat comb source and based on POLMUX-QPSK, POLMUX-DQPSK, POLMUX-16QAM and POLMUX-64QAM

In this paper, we implement a WDM-Nyquist transmission system with 12.5 GHz channel spacing generated through an Optical Flat Comb Source (OFCS). Each channel could carry one of four different modulation format as Polarization Multiplexing-Quadrature Phase Shift Keying (POLMUX-QPSK), Polarization Multiplexing-Differential Quadrature Phase Shift Keying (POLMUX-DQPSK), Polarization Multiplexing-Quadrature Amplitude Modulation based on 16 (POLMUX-16QAM) and 64 (POLMUX-64QAM) with Return-to-Zero (RZ) pulse carving and 33% duty cycle. Numerical simulations of 1 Tbit/s superchannel have been carried out, in order to evaluate the performances of the different modulation format using appropriate metrics. We discuss their back-to-back receiver sensitivity and the required Optical-to-Noise Signal Ratio (OSNR) for 3.8•10-3 and 10-9 Bit Error Rate (BER). We find that POLMUXQPSK has the best receiver sensitivity and the lower OSNR penalty compared to the other modulation formats. With POLMUX-QPSK sensitivity as reference, we can observe a power penalty of 13.4 dB (for a BER equal to 3.8•10-3) for POLMUX-64QAM. In addition, we can observe an OSNR penalty of 21 dB in OSNR for POLMUX-64QAM compared to POLMUX-QPSK modulation format for a BER equal to 3.8•10-3. However, it is important to mention that POLMUX- 64QAM presents higher Spectral Efficiency (SE). We study also the robustness of these four optical modulation formats for transmission of 1 Tbit/s in dispersion compensated WDM-Nyquist systems using two kinds of transmission fibers: SMF (Single Mode Fiber) and NZDSF (Non-Zero Dispersion Shifted Fiber). We find that POLMUX-QPSK is the suitable modulation format in dispersion compensated WDM-Nyquist systems using NZDSF fiber. We simulate the nonlinear effect tolerance by considering self-phase modulation (SPM), cross-phase modulation (XPM) and four-wave mixing (FWM) for the different modulation format. We observe that POLMUX-QPSK has the best robustness against the cited nonlinear fiber effects in NZDSF fiber.
Bounded Littlewood identities for cylindric Schur functions

The identities which are in the literature often called ``bounded Littlewood identities"are determinantal formulas for the sum of Schur functions indexed by partitions with bounded height. They have interesting combinatorial consequences such as connections between standard Young tableaux of bounded height, lattice walks in a Weyl chamber, and noncrossing matchings. In this paper we prove affine analogs of the bounded Littlewood identities. These are determinantal formulas for sums of cylindric Schur functions. We also study combinatorial aspects of these identities. As a consequence we obtain an unexpected connection between cylindric standard Young tableaux and \( r \)-noncrossing and \( s \)-nonnesting matchings.
Type Theory as a Language Workbench

Language Workbenches offer language designers an expressive environment in which to create their DSLs. Similarly, research into mechanised meta-theory has shown how dependently typed languages provide expressive environments to formalise and study DSLs and their meta-theoretical properties. But can we claim that dependently typed languages qualify as language workbenches? We argue yes! We have developed an exemplar DSL called Velo that showcases not only dependently typed techniques to realise and manipulate IRs, but that dependently typed languages make fine language workbenches. Velo is a simple verified language with well-typed holes and comes with a complete compiler pipeline: parser, elaborator, REPL, evaluator, and compiler passes. Specifically, we describe our design choices for well-typed IRs design that includes support for well-typed holes, how CSE is achieved in a well-typed setting, and how the mechanised type-soundness proof for Velo is the source of the evaluator.
Surface and hypersurface meshing techniques for space-time finite element methods

A general method is introduced for constructing two-dimensional (2D) surface meshes embedded in three-dimensional (3D) space time, and 3D hypersurface meshes embedded in four-dimensional (4D) space time. In particular, we begin by dividing the space-time domain into time slabs. Each time slab is equipped with an initial plane (hyperplane), in conjunction with an unstructured simplicial surface (hypersurface) mesh that covers the initial plane. We then obtain the vertices of the terminating plane (hyperplane) of the time slab from the vertices of the initial plane using a space-time trajectory-tracking approach. Next, these vertices are used to create an unstructured simplicial mesh on the terminating plane (hyperplane). Thereafter, the initial and terminating boundary vertices are stitched together to form simplicial meshes on the intermediate surfaces or sides of the time slab. After describing this new mesh-generation method in rigorous detail, we provide the results of multiple numerical experiments which demonstrate its validity and flexibility.
Efficient Node Selection in Private Personalized Decentralized Learning

Personalized decentralized learning is a promising paradigm for distributed learning, enabling each node to train a local model on its own data and collaborate with other nodes to improve without sharing any data. However, this approach poses significant privacy risks, as nodes may inadvertently disclose sensitive information about their data or preferences through their collaboration choices. In this paper, we propose Private Personalized Decentralized Learning (PPDL), a novel approach that combines secure aggregation and correlated adversarial multi-armed bandit optimization to protect node privacy while facilitating efficient node selection. By leveraging dependencies between different arms, represented by potential collaborators, we demonstrate that PPDL can effectively identify suitable collaborators solely based on aggregated models. Additionally, we show that PPDL surpasses previous non-private methods in model performance on standard benchmarks under label and covariate shift scenarios.
Fermion scattering on topological solitons in the $\mathbb{CP}^{N-1}$ model

The scattering of Dirac fermions in the background fields of topological solitons of the $(2+1)$-dimensional $\mathbb{CP}^{N-1}$ model is studied using analytical and numerical methods. It is shown that the exact solutions for fermionic wave functions can be expressed in terms of the confluent Heun functions. The question of the existence of bound states for the fermion-soliton system is then investigated. General formulae describing fermion scattering are obtained, and a symmetry property for the partial phase shifts is derived. The amplitudes and cross-sections of the fermion-soliton scattering are obtained in an analytical form within the framework of the Born approximation, and the symmetry properties and asymptotic forms of the Born amplitudes are investigated. The dependences of the first few partial phase shifts on the fermion momentum are obtained by numerical methods, and some of their properties are investigated and discussed.
Dual‐Gate GaAs‐Nanowire FET for Room Temperature Charge‐Qubit Operation: A NEGF Approach

The performance of dual‐gate GaAs‐nanowire field‐effect‐transistor (FET) is investigated as a charge‐qubit device operating at room temperature. In compatibility with the state‐of‐the‐art classical bit technology, it is shown that the single gate of a nanowire FET can be replaced by two localized gates to achieve such charge‐qubit operation. On application of relevant biases to the localized gates, two voltage tunable quantum dots are created within the nanowire channel with electrostatically controlled single‐state‐occupancy and interdot coupling leading to charge‐qubit operation at room temperature. The associated electron transport is theoretically modeled on the basis of nonequilibrium Green's function formalism. The “initialization” and “manipulation” for qubit operation are performed by applying suitable gate voltages, whereas the “measurement” is executed by applying a small drain bias to obtain a pulse current of ≈pA order. A ≈25 MHz frequency of coherent oscillation is observed for the qubit and a characteristic decay time of ≈ 70 ns is achieved. The results suggest that such dual gate nanowire FET is a promising architecture for charge‐qubit operation at room temperature.
Extended state observer-based sliding mode control for PWM-based DC–DC buck power converter systems with mismatched disturbances

This study investigates an extended state observer (ESO)-based sliding mode control (SMC) approach for pulse-width modulation-based DC-DC buck converter systems subject to mismatched disturbances. By designing a novel sliding-mode manifold incorporated with a disturbance estimation technique, an ESO-based SMC method is designed to deal with mismatched disturbances. A rigorous stability analysis is also presented. As compared with the nominal SMC method, the proposed method obtains a better disturbance rejection ability even the disturbances do not satisfy the so-called matching condition. Simulations and experimental comparison results are implemented to verify the effectiveness of the proposed control method.
Distilling Internet-Scale Vision-Language Models into Embodied Agents

Instruction-following agents must ground language into their observation and action spaces. Learning to ground language is challenging, typically requiring domain-specific engineering or large quantities of human interaction data. To address this challenge, we propose using pretrained vision-language models (VLMs) to supervise embodied agents. We combine ideas from model distillation and hindsight experience replay (HER), using a VLM to retroactively generate language describing the agent's behavior. Simple prompting allows us to control the supervision signal, teaching an agent to interact with novel objects based on their names (e.g., planes) or their features (e.g., colors) in a 3D rendered environment. Fewshot prompting lets us teach abstract category membership, including pre-existing categories (food vs toys) and ad-hoc ones (arbitrary preferences over objects). Our work outlines a new and effective way to use internet-scale VLMs, repurposing the generic language grounding acquired by such models to teach task-relevant groundings to embodied agents.
Fully transparent nonvolatile memory employing amorphous oxides as charge trap and transistor’s channel layer

A fully transparent nonvolatile memory with the conventional sandwich gate insulator structure was demonstrated. Wide band gap amorphous GaInZnO (a-GIZO) thin films were employed as both the charge trap layer and the transistor channel layer. An excellent program window of 3.5 V with a stressing time of 100 ms was achieved through the well-known Fowler–Nordheim tunneling method. Due to the similar energy levels extracted from the experimental data, the asymmetrical program/erase characteristics are believed to be the result of the strong trapping of the injected negative charges in the shallow donor levels of the GIZO film.
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention

With the frequent happening of privacy leakage and the enactment of privacy laws across different countries, data owners are reluctant to directly share their raw data and labels with any other party. In reality, a lot of these raw data are stored in the graph database, especially for finance. For collaboratively building graph neural networks (GNNs), federated learning (FL) may not be an ideal choice for the vertically partitioned setting where privacy and efficiency are the main concerns. Moreover, almost all the existing federated GNNs are mainly designed for homogeneous graphs, which simplify various types of relations as the same type, thus largely limiting their performance. We bridge this gap by proposing a split learning-based GNN (SplitGNN), where this model is divided into two sub-models: the local GNN model includes all the private data related computation to generate local node embeddings, whereas the global model calculates global embeddings by aggregating all the participants’ local embeddings. Our SplitGNN allows the isolated heterogeneous neighborhood to be collaboratively utilized. To better capture representations, we propose a novel Heterogeneous Attention (HAT) algorithm and use both node-based and path-based attention mechanisms to learn various types of nodes and edges with multi-hop relation features. We demonstrate the effectiveness of our SplitGNN on node classification tasks for two standard public datasets. Experimental results validate that our proposed SplitGNN significantly outperforms the state-of-the-art (SOTA) methods
Fighting the sign problem in a chiral random matrix model with contour deformations

We studied integration contour deformations in the chiral random matrix theory of Stephanov [1] with the goal of alleviating the ﬁnite-density sign problem. We considered simple ansätze for the deformed integration contours, and optimized their parameters. We ﬁnd that optimization of a single parameter manages to considerably improve on the severity of the sign problem. We show numerical evidence that the improvement achieved is exponential in the degrees of freedom of the system, i.e., the size of the random matrix. We also compare the optimization method with contour deformations coming from the holomorphic ﬂow equations.
Visual Belief Elicitation Reduces the Incidence of False Discovery

Visualization supports exploratory data analysis (EDA), but EDA frequently presents spurious charts, which can mislead people into drawing unwarranted conclusions. We investigate interventions to prevent false discovery from visualized data. We evaluate whether eliciting analyst beliefs helps guard against the over-interpretation of noisy visualizations. In two experiments, we exposed participants to both spurious and ‘true’ scatterplots, and assessed their ability to infer data-generating models that underlie those samples. Participants who underwent prior belief elicitation made 21% more correct inferences along with 12% fewer false discoveries. This benefit was observed across a variety of sample characteristics, suggesting broad utility to the intervention. However, additional interventions to highlight counterevidence and sample uncertainty did not provide significant advantage. Our findings suggest that lightweight, belief-driven interactions can yield a reliable, if moderate, reduction in false discovery. This work also suggests future directions to improve visual inference and reduce bias.
Improving Adversarial Transferability with Scheduled Step Size and Dual Example

Deep neural networks are widely known to be vulnerable to adversarial examples, especially showing significantly poor performance on adversarial examples generated under the white-box setting. However, most white-box attack methods rely heavily on the target model and quickly get stuck in local optima, resulting in poor adversarial transferability. The momentum-based methods and their variants are proposed to escape the local optima for better transferability. In this work, we notice that the transferability of adversarial examples generated by the iterative fast gradient sign method (I-FGSM) exhibits a decreasing trend when increasing the number of iterations. Motivated by this finding, we argue that the information of adversarial perturbations near the benign sample, especially the direction, benefits more on the transferability. Thus, we propose a novel strategy, which uses the Scheduled step size and the Dual example (SD), to fully utilize the adversarial information near the benign sample. Our proposed strategy can be easily integrated with existing adversarial attack methods for better adversarial transferability. Empirical evaluations on the standard ImageNet dataset demonstrate that our proposed method can significantly enhance the transferability of existing adversarial attacks.