Miracle-dz/UniFAR-FTUs huggingface dataset:
A strategy for very large eddy simulation of complex turbulent flows

This paper describes a strategy for the very large eddy simulation (VLES) of complex turbulent flows, such as flows in the aircraft engine combustors or flows around the rotorcrafts. The large scales of turbulence are directly calculated; the effects of the rest unresolved scales are approximated by the eddy viscosity models evolved from the state-ofthe-art models used in the Reynolds-averaged Navier-Stokes (RANS). The dependent variables and governing equations are based on the concept of temporal filtering. The contents of both resolved and unresolved scales are regulated by the width of the filter. In this approach, the dependent variables and governing equations will naturally evolve from RANS to VLES and further towards LES (large eddy simulation) when the width of the temporal filter is decreased from the turbulent integral time scale to its fraction and all the way towards the Taylor micro time scale. We refer this strategy as the partially resolved numerical simulation (PRNS). The main feature of PRNS is that its constitutive equations for the unresolved turbulence have no relationship with the mesh size, hence formally grid independent; its subscale models have the nature of a non-linear dynamic system capable of effectively controlling the subscale stresses to perform VLES with a relatively coarse grid. In addition, its implementation to a turbulent CFD code is straightforward.
LBM Simulation of a Droplet Dripping Down a Hole

Abstract A droplet dripping through a hole, which is located on a horizontal plate, was simulated in the current work. To overcome the numerical instabilities and accommodate the high density and viscosity ratios, Lee’s LBM model was used to simulate the two-phase flow. Also wettability effect was taken into account by imposing the contact angle between solid-liquid and gas phases using Cahn’s wetting theory. The effects of gravitational acceleration, surface tension, equilibrium contact angle, viscosity ratio, density ratio, and also the geometrical parameters, such as the height and width of the hole, have been investigated separately. In addition to numerical results, high numerical stability and ease of using surface tension and contact angle for controlling fascinated the authors to model this problem. High numerical stability in capturing the remaining fine particles on the surface and slender strip of a droplet passing through a small hole is a significant contribution. The results show that gravitational force and adhesion force caused by surface tension, are opposing forces influencing droplet behaviour. Enhancing the contact angle to more than 90 degrees makes the surface hydrophobic and the droplet will drip under gravitational force. Also increasing the surface tension causes increasing adhesion force, which leads to droplet decay. In this case some parts of the droplet will remain on the surface.
The effects of turbulent drag reducing additives on hydrocyclone operation : an evaluation of the flow behaviour and particle mechanics

The effects of adding drag reducing additives to a pulp processing hydrocyclone were experimentally investigated. This work was found to contribute towards improving particle separation efficiencies and reducing energy consumption. To effectively evaluate the performance improvements, the flow field was initially measured using laser Doppler velocimetry. In the presence of a particulate phase, the motions of variously sized particles were measured using a three dimensional, dual camera set-up. Quantification of the drag reducing potential of the various polymer solutions and fibre suspensions studied was experimentally determined using an integral analysis for a fixed control volume. The addition of drag reducing polymer additives was found to fundamentally change the hydrocyclone flow field from what is classically observed with water alone. For the conditions studied in this work, the effectiveness of a hydrocyclone towards removing contaminants would likely be reduced, as a particles separation zone was limited. The addition of polymer additives to a hydrocyclone was found to increase the size of particles susceptible to overflow removal. It was found that particles of density 1280 kg/m3 and diameter 500 – 600 μm displayed inwards motion in a 0.03% APAM solution, where purely outwards motion was measured for identically sized particles suspended in water. The flow field, however, indicated that overflow removal is limited to only a small region near the vortex finder. Polymer additives were found to be effective in reducing energy consumption in a hydrocyclone. Maximum drag reduction was found to occur at a reject ratio of 50% for polymer solutions, independent of inlet velocity. The energy savings potential for polymer additives in a pulp processing hydrocyclone, however, was found to be limited to the inlet velocity. Most in process hydrocyclones operate well above the minimum inlet velocities measured for rejects ratios of 25% and 50%, suggesting that additional energy savings would likely occur. The phenomenological degradation of the polymer agents investigated in this work suggests that the practical use of these additives would be difficult. This was found to be most significant with
Mutual Interference Mitigation in PMCW Automotive Radar

This paper addresses the challenge of mutual interference (MI) in phase-modulated continuous wave (PMCW) millimeter-wave (mmWave) automotive radar systems. The increasing demand for advanced driver assistance systems (ADAS) has led to a proliferation of vehicles equipped with mmWave radar systems that operate in the same frequency band, resulting in MI that can degrade radar performance creating safety hazards. We consider scenarios involving two similar PMCW radar systems and propose an effective technique for a cooperative design of transmit waveforms such that the MI between them is minimized. The proposed approach is numerically evaluated via simulations of a mmWave automotive radar system. The results demonstrate that the proposed technique notably reduces MI and enhances radar detection performance while imposing very little computational cost and a negligible impact on existing infrastructure in practical automotive radar systems.
Adversarial Training Should Be Cast as a Non-Zero-Sum Game

One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are trained against adversarially chosen perturbations of data. Despite the promise of this approach, algorithms based on this paradigm have not engendered sufficient levels of robustness and suffer from pathological behavior like robust overfitting. To understand this shortcoming, we first show that the commonly used surrogate-based relaxation used in adversarial training algorithms voids all guarantees on the robustness of trained classifiers. The identification of this pitfall informs a novel non-zero-sum bilevel formulation of adversarial training, wherein each player optimizes a different objective function. Our formulation yields a simple algorithmic framework that matches and in some cases outperforms state-of-the-art attacks, attains comparable levels of robustness to standard adversarial training algorithms, and does not suffer from robust overfitting.
Dual Node and Edge Fairness-Aware Graph Partition

Fair graph partition of social networks is a crucial step toward ensuring fair and non-discriminatory treatments in unsupervised user analysis. Current fair partition methods typically consider node balance, a notion pursuing a proportionally balanced number of nodes from all demographic groups, but ignore the bias induced by imbalanced edges in each cluster. To address this gap, we propose a notion edge balance to measure the proportion of edges connecting different demographic groups in clusters. We analyze the relations between node balance and edge balance, then with line graph transformations, we propose a co-embedding framework to learn dual node and edge fairness-aware representations for graph partition. We validate our framework through several social network datasets and observe balanced partition in terms of both nodes and edges along with good utility. Moreover, we demonstrate our fair partition can be used as pseudo labels to facilitate graph neural networks to behave fairly in node classification and link prediction tasks.
MEAN ERGODIC THEOREMS FOR ASYMPTOTIC ISOMETRY SEMIGROUPS IN BANACH SPACES (Nonlinear Analysis and Convex Analysis)

$v_{n}(x_{n})$ for any Banach limits $\mu$ and $\nu$ . Day [7] defined a notion of almost convergence for bounded real-valued functions defined on an amenable semigroup. On the other hand, von Neumann $[15]$ introduced a notion of almost periodicity for bounded real-valued functions defined on an abstract group and proved the existence of the mean values for those functions. Later, Bochner and von Neumann [3] proved the existence of the mean values for vector-valued almost periodic functions defined on an abstract group with values in a complete locally convex space. Motivated by the works of Lorentz and von Neumann, we [13] introduced notions of almost convergence in the sense of Lorentz and the mean values for vector-valued bounded functions defined on a left amenable semigroup with values in a locally convex space and also obtained characterizations of almost convergence for those functions in the case of coinmutative semigroups. By applying these notions and characterizations to transformation semigroups, we prove non-linear mean ergodic theorems for non-Lipschitzian asymptotically isometric semigroups on a compact convex subset of a general Banach space; see also [2], [19], [16], [17] and [14]. In this case, however, the mean value for such a semigroup is not always a common fixed point for it. $-$ In this paper, we first introduce a notion of asymptotic isometry semigroups of continuous self-mappings of a closed convex subset $C$ of a Banach space $E$ , motivated by Hyers and Ulam [10] and discuss the action of such a semigroup $S$ on the $\omega$-limit set $\omega(x)$ of cluster points of the orbit of $x\in C$ under $S$ by using Banach-Ulam’s theorem and the structure theorem for the kernel of semigroups (Clifford’s theorem). Next, we prove non-linear mean ergodic theorems for these 数理解析研究所講究録
SURT 2.0: Advances in Transducer-Based Multi-Talker Speech Recognition

The Streaming Unmixing and Recognition Transducer (SURT) model was proposed recently as an end-to-end approach for continuous, streaming, multi-talker speech recognition (ASR). Despite impressive results on multi-turn meetings, SURT has notable limitations: (i) it suffers from leakage and omission related errors; (ii) it is computationally expensive, due to which it has not seen adoption in academia; and (iii) it has only been evaluated on synthetic mixtures. In this work, we propose several modifications to the original SURT which are carefully designed to fix the above limitations. In particular, we (i) change the unmixing module to a mask estimator that uses dual-path modeling, (ii) use a streaming zipformer encoder and a stateless decoder for the transducer, (iii) perform mixture simulation using force-aligned subsegments, (iv) pre-train the transducer on single-speaker data, (v) use auxiliary objectives in the form of masking loss and encoder CTC loss, and (vi) perform domain adaptation for far-field recognition. We show that our modifications allow SURT 2.0 to outperform its predecessor in terms of multi-talker ASR results, while being efficient enough to train with academic resources. We conduct our evaluations on 3 publicly available meeting benchmarks — LibriCSS, AMI, and ICSI, where our best model achieves WERs of 16.9%, 44.6% and 32.2%, respectively, on far-field unsegmented recordings.
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Summary Objectives : Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research. Methods : We conducted a comprehensive search of multiple databases, including PubMed, Web of Science, IEEE Xplore, and Google Scholar, to collect relevant publications from the past two years (2021-2022). The studies selected for review were based on their relevance to the topic and the publication quality. Results : A total of 78 articles were included in our analysis. We identified three main categories of GRL methods and summarized their methodological foundations and notable models. In terms of GRL applications, we focused on two main topics: drug and disease. We analyzed the study frameworks and achievements of the prominent research. Based on the current state-of-the-art, we discussed the challenges and future directions. Conclusions : GRL methods applied in the biomedical field demonstrated several key characteristics, including the utilization of attention mechanisms to prioritize relevant features, a growing emphasis on model interpretability, and the combination of various techniques to improve model performance. There are also challenges needed to be addressed, including mitigating model bias, accommodating the heterogeneity of large-scale knowledge graphs, and improving the availability of high-quality graph data. To fully leverage the potential of GRL, future efforts should prioritize these areas of research.
FHA-Kitchens: A Novel Dataset for Fine-Grained Hand Action Recognition in Kitchen Scenes

A typical task in the field of video understanding is hand action recognition, which has a wide range of applications. Existing works either mainly focus on full-body actions, or the defined action categories are relatively coarse-grained. In this paper, we propose FHA-Kitchens, a novel dataset of fine-grained hand actions in kitchen scenes. In particular, we focus on human hand interaction regions and perform deep excavation to further refine hand action information and interaction regions. Our FHA-Kitchens dataset consists of 2,377 video clips and 30,047 images collected from 8 different types of dishes, and all hand interaction regions in each image are labeled with high-quality fine-grained action classes and bounding boxes. We represent the action information in each hand interaction region as a triplet, resulting in a total of 878 action triplets. Based on the constructed dataset, we benchmark representative action recognition and detection models on the following three tracks: (1) supervised learning for hand interaction region and object detection, (2) supervised learning for fine-grained hand action recognition, and (3) intra- and inter-class domain generalization for hand interaction region detection. The experimental results offer compelling empirical evidence that highlights the challenges inherent in fine-grained hand action recognition, while also shedding light on potential avenues for future research, particularly in relation to pre-training strategy, model design, and domain generalization. The dataset will be released at https://github.com/tingZ123/FHA-Kitchens.
A Survey on User-Space Storage and Its Implementations

The storage stack in the traditional operating system is primarily optimized towards improving the CPU utilization and hiding the long I/O latency imposed by the slow I/O devices such as hard disk drivers (HDDs). However, the emerging storage media experience significant technique shifts in the past decade, which exhibit high bandwidth and low latency. These high-performance storage devices, unfortunately, suffer from the huge overheads imposed by the system software including the long storage stack and the frequent context switch between the user and kernel modes. Many researchers have investigated huge efforts in addressing this challenge by constructing a direct software path between a user process and the underlying storage devices. We revisit such novel designs in the prior work and present a survey in this paper. Specifically, we classify the former research into three categories according to their commonalities. We then present the designs of each category based on the timeline and analyze their uniqueness and contributions. This paper also reviews the applications that exploit the characteristics of theses designs. Given that the user-space storage is a growing research field, we believe this paper can be an inspiration for future researchers, who are interested in the user-space storage system designs.
Adversaries with Limited Information in the Friedkin-Johnsen Model

In recent years, online social networks have been the target of adversaries who seek to introduce discord into societies, to undermine democracies and to destabilize communities. Often the goal is not to favor a certain side of a conflict but to increase disagreement and polarization. To get a mathematical understanding of such attacks, researchers use opinion-formation models from sociology, such as the Friedkin--Johnsen model, and formally study how much discord the adversary can produce when altering the opinions for only a small set of users. In this line of work, it is commonly assumed that the adversary has full knowledge about the network topology and the opinions of all users. However, the latter assumption is often unrealistic in practice, where user opinions are not available or simply difficult to estimate accurately. To address this concern, we raise the following question: Can an attacker sow discord in a social network, even when only the network topology is known? We answer this question affirmatively. We present approximation algorithms for detecting a small set of users who are highly influential for the disagreement and polarization in the network. We show that when the adversary radicalizes these users and if the initial disagreement/polarization in the network is not very high, then our method gives a constant-factor approximation on the setting when the user opinions are known. To find the set of influential users, we provide a novel approximation algorithm for a variant of MaxCut in graphs with positive and negative edge weights. We experimentally evaluate our methods, which have access only to the network topology, and we find that they have similar performance as methods that have access to the network topology and all user opinions. We further present an NP-hardness proof, which was left as an open question by Chen and Racz [IEEE Transactions on Network Science and Engineering, 2021].
Fixed point distribution on Hilbert scheme of points

Let $\mathbf{k}$ be a closed field of characteristic zero. We prove that all monomial ideals sit in the curvilinear component of the Hilbert scheme of points of the affine space $\mathbb{A}_{\mathbf{k}}^n$, answering a long-standing question about the distribution of torus-fixed points among punctual components. This result confirms that the punctual Hilbert scheme is connected, a property previously established only for the full Hilbert scheme in 1966 by Hartshorne.
Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction

Document-level relation extraction (DocRE) attracts more research interest recently. While models achieve consistent performance gains in DocRE, their underlying decision rules are still understudied: Do they make the right predictions according to rationales? In this paper, we take the first step toward answering this question and then introduce a new perspective on comprehensively evaluating a model.Specifically, we first conduct annotations to provide the rationales considered by humans in DocRE. Then, we conduct investigations and discover the fact that: In contrast to humans, the representative state-of-the-art (SOTA) models in DocRE exhibit different reasoning processes. Through our proposed RE-specific attacks, we next demonstrate that the significant discrepancy in decision rules between models and humans severely damages the robustness of models. After that, we introduce mean average precision (MAP) to evaluate the understanding and reasoning capabilities of models. According to the extensive experimental results, we finally appeal to future work to consider evaluating the understanding ability of models because the improved ability renders models more trustworthy and robust to be deployed in real-world scenarios. We make our annotations and code publicly available.
A Universal Unbiased Method for Classification from Aggregate Observations

In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances, due to privacy concerns or unaffordable annotation costs. This motivates the study on classification from aggregate observations (CFAO), where the supervision is provided to groups of instances, instead of individual instances. CFAO is a generalized learning framework that contains various learning problems, such as multiple-instance learning and learning from label proportions. The goal of this paper is to present a novel universal method of CFAO, which holds an unbiased estimator of the classification risk for arbitrary losses -- previous research failed to achieve this goal. Practically, our method works by weighing the importance of each label for each instance in the group, which provides purified supervision for the classifier to learn. Theoretically, our proposed method not only guarantees the risk consistency due to the unbiased risk estimator but also can be compatible with arbitrary losses. Extensive experiments on various problems of CFAO demonstrate the superiority of our proposed method.
An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

Origin-destination~(OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow-related research, fostering interdisciplinary perspectives and modes of thinking.