Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of Viscoelastic Fluids

We present a numerical scheme for viscoelastic flow based on a second-order central differencing method recently introduced in the context of incompressible newtonian flow; the incompressibility constraint is treated with the projection method. The result is a simple and efficient scheme that is readily adaptable to a wide class of differential constitutive equations and flow geometries. We implement the new method on Couette? Taylor flow for a fluid governed by the Oldroyd-B constitutive equations. We simulate transient flow in a domain that includes at least eight wavelengths during many hundreds of natural periods. For weak elasticity, a stationary instability leading to Taylor vortices is observed. For a regime of parameters where both inertia and elasticity are important, the instability is oscillatory. In both cases the early stage growth rates are compared to linear stability calculations, showing good agreement. The oscillatory instability is fourfold degenerate and gives rise to two bifurcating branches: an axially traveling wave and a standing wave; only one of these solutions is stable. In the early stages of the instability, there is generally a combination of traveling and standing waves, depending on the initial conditions. As nonlinearities become important, the flow spontaneously breaks into coexisting regions of upward- and downward-going waves. Such flow can persist for long times, until the globally stable traveling wave takes over and a limit cycle is reached.
An exact algorithm for generating homogenous two-segment cutting patterns

An exact algorithm is proposed for generating homogenous two-segment patterns for the constrained two-dimensional guillotine-cutting problems of rectangular items. It is a bottom-up approach combined with branch-and-bound techniques. The stock plate is divided into two segments. Each segment consists of strips of the same length and direction. Only homogenous strips are considered, each of which contains items of the same type. The strip directions of the two segments may be either the same or perpendicular to each other. The algorithm uses a tree-search procedure. It starts from an initial lower bound, implicitly generates all possible segments through the assembly of strips, and constructs possible patterns through the combination of two segments. Tighter bounds are established to discard non-promising segments. The computational results indicate that the algorithm is efficient both in computation time and in material utilization, and is able to deal with relatively large-scale problems.
Rank-one ECS manifolds of dilational type

We study ECS manifolds, that is, pseudo-Riemannian manifolds with parallel Weyl tensor which are neither conformally flat nor locally symmetric. Every ECS manifold has rank 1 or 2, the rank being the dimension of a distinguished null parallel distribution discovered by Olszak, and a rank-one ECS manifold may be called translational or dilational, depending on whether the holonomy group of a natural flat connection in the Olszak distribution is finite or infinite. Some such manifolds are in a natural sense generic, which refers to the algebraic structure of the Weyl tensor. Known examples of compact ECS manifolds, in every dimension greater than 4, are all of rank 1 and translational, some of them generic, none of them locally homogeneous. As we show, generic compact rank-one ECS manifolds must be translational or locally homogeneous, provided that they arise as isometric quotients of a specific class of explicitly constructed"model"manifolds. This result is relevant since the clause starting with"provided that"may be dropped: according to a theorem which we prove in a forthcoming paper, the models just mentioned include the isometry types of the pseudo-Riemannian universal coverings of all generic compact rank-one ECS manifolds.
Semantic-aware Contrastive Learning for Electroencephalography-to-Text Generation with Curriculum Learning

Electroencephalography-to-Text generation (EEG-to-Text), which aims to directly generate natural text from EEG signals has drawn increasing attention in recent years due to the enormous potential for Brain-computer interfaces (BCIs). However, the remarkable discrepancy between the subject-dependent EEG representation and the semantic-dependent text representation poses a great challenge to this task. To mitigate this challenge, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C-SCL), which effectively re-calibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thus reducing the discrepancy. Specifically, our C-SCL pulls semantically similar EEG representations together while pushing apart dissimilar ones. Besides, in order to introduce more meaningful contrastive pairs, we carefully employ curriculum learning to not only craft meaningful contrastive pairs but also make the learning progressively. We conduct extensive experiments on the ZuCo benchmark and our method combined with diverse models and architectures shows stable improvements across three types of metrics while achieving the new state-of-the-art. Further investigation proves not only its superiority in both the single-subject and low-resource settings but also its robust generalizability in the zero-shot setting.
Unifying Synergies between Self-supervised Learning and Dynamic Computation

Computationally expensive training strategies make self-supervised learning (SSL) impractical for resource constrained industrial settings. Techniques like knowledge distillation (KD), dynamic computation (DC), and pruning are often used to obtain a lightweightmodel, which usually involves multiple epochs of fine-tuning (or distilling steps) of a large pre-trained model, making it more computationally challenging. In this work we present a novel perspective on the interplay between SSL and DC paradigms. In particular, we show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting without any additional fine-tuning or pruning steps. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off and therefore yields a generic and multi-purpose architecture for application specific industrial settings. Extensive experiments on several image classification benchmarks including CIFAR-10/100, STL-10 and ImageNet-100, demonstrate that the proposed training strategy provides a dense and corresponding gated sub-network that achieves on-par performance compared with the vanilla self-supervised setting, but at a significant reduction in computation in terms of FLOPs, under a range of target budgets (td ).
Outage Performance of Fluid Antenna System (FAS)-aided Terahertz Communication Networks

Millimeter-wave networks have already been successfully rolled out in many countries and now the research direction heads toward new technologies and standards to enable Tbps rates for future sixth-generation (6G) wireless communication systems. This work studies a point-to-point terahertz (THz) communication network exploiting the concept of a fluid antenna system (FAS) over correlated alpha-mu fading channels, nicely fitting the THz communication. Furthermore, the considered system is expanded to the selection-combining-FAS (SC-FAS) and maximum-gain-combining-FAS (MGC-FAS) diversity variates at the receiver side. The proposed FAS and its diversity configuration techniques are aimed to combat the high path loss, blockages, and molecular absorption effect related to the THz band. Our contribution includes comprehensive outage probability (OP) performance analysis for the THz band given the non-diversity and diversity FAS receivers. Moreover, the derived outage probability formulas are verified via Monte Carlo simulations. Numerical results have confirmed the superior performance of the MGC-FAS scheme in terms of OP. Finally, this work justifies that a higher number of antenna ports dramatically improves the system performance, even in the presence of correlation.
SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis

5G is the 5th generation cellular network protocol. It is the state-of-the-art global wireless standard that enables an advanced kind of network designed to connect virtually everyone and everything with increased speed and reduced latency. Therefore, its development, analysis, and security are critical. However, all approaches to the 5G protocol development and security analysis, e.g., property extraction, protocol summarization, and semantic analysis of the protocol specifications and implementations are completely manual. To reduce such manual effort, in this paper, we curate SPEC5G the first-ever public 5G dataset for NLP research. The dataset contains 3,547,586 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on NLP tasks, we use this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task. Our results show the value of our 5G-centric dataset in 5G protocol analysis automation. We believe that SPEC5G will enable a new research direction into automatic analyses for the 5G cellular network protocol and numerous related downstream tasks. Our data and code are publicly available.
Quantum Monte Carlo algorithm for solving Black-Scholes PDEs for high-dimensional option pricing in finance and its proof of overcoming the curse of dimensionality

In this paper we provide a quantum Monte Carlo algorithm to solve high-dimensional Black-Scholes PDEs with correlation for high-dimensional option pricing. The payoff function of the option is of general form and is only required to be continuous and piece-wise affine (CPWA), which covers most of the relevant payoff functions used in finance. We provide a rigorous error analysis and complexity analysis of our algorithm. In particular, we prove that the computational complexity of our algorithm is bounded polynomially in the space dimension $d$ of the PDE and the reciprocal of the prescribed accuracy $\varepsilon$ and so demonstrate that our quantum Monte Carlo algorithm does not suffer from the curse of dimensionality.
HexPlane: A Fast Representation for Dynamic Scenes

Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior approaches build on NeRF and rely on implicit representations. This is slow since it requires many MLP evaluations, constraining real-world applications. We show that dynamic 3D scenes can be explicitly represented by six planes of learned features, leading to an elegant solution we call HexPlane. A HexPlane computes features for points in spacetime by fusing vectors extracted from each plane, which is highly efficient. Pairing a HexPlane with a tiny MLP to regress output colors and training via volume rendering gives impressive results for novel view synthesis on dynamic scenes, matching the image quality of prior work but reducing training time by more than 100×. Extensive ablations confirm our HexPlane design and show that it is robust to different feature fusion mechanisms, coordinate systems, and decoding mechanisms. HexPlane is a simple and effective solution for representing 4D volumes, and we hope they can broadly contribute to modeling spacetime for dynamic 3D scenes.11Project page: https://caoang327.github. io/HexPlane.
Self-Averaging of Digital MemComputing Machines

Digital memcomputing machines (DMMs) are a new class of computing machines that employ nonquantum dynamical systems with memory to solve combinatorial optimization problems. Here, we show that the time to solution (TTS) of DMMs follows an inverse Gaussian distribution, with the TTS self-averaging with increasing problem size, irrespective of the problem they solve. We provide both an analytical understanding of this phenomenon and numerical evidence by solving instances of the 3-SAT (satisfiability) problem. The self-averaging property of DMMs with problem size implies that they are increasingly insensitive to the detailed features of the instances they solve. This is in sharp contrast to traditional algorithms applied to the same problems, illustrating another advantage of this physics-based approach to computation.
Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction

We study object interaction anticipation in egocentric videos. This task requires an understanding of the spatio-temporal context formed by past actions on objects, coined action context. We propose TransFusion, a multimodal transformer-based architecture. It exploits the representational power of language by summarizing the action context. TransFusion leverages pre-trained image captioning and vision-language models to extract the action context from past video frames. This action context together with the next video frame is processed by the multimodal fusion module to forecast the next object interaction. Our model enables more efficient end-to-end learning. The large pre-trained language models add common sense and a generalisation capability. Experiments on Ego4D and EPIC-KITCHENS-100 show the effectiveness of our multimodal fusion model. They also highlight the benefits of using language-based context summaries in a task where vision seems to suffice. Our method outperforms state-of-the-art approaches by 40.4% in relative terms in overall mAP on the Ego4D test set. We validate the effectiveness of TransFusion via experiments on EPIC-KITCHENS-100. Video and code are available at https://eth-ait.github. io/transfusion-proj/.
The infinity-Laplacian in smooth convex domains and in a square

<abstract><p>We extend some theorems for the infinity-ground state and for the infinity-potential, known for convex polygons, to other domains in the plane, by applying Alexandroff's method to the curved boundary. A recent <italic>explicit</italic> solution disproves a conjecture. </p></abstract>
A Simple Recipe for Competitive Low-compute Self supervised Vision Models

Self-supervised methods in vision have been mostly focused on large architectures as they seem to suffer from a significant performance drop for smaller architectures. In this paper, we propose a simple self-supervised distillation technique that can train high performance low-compute neural networks. Our main insight is that existing joint-embedding based SSL methods can be repurposed for knowledge distillation from a large self-supervised teacher to a small student model. Thus, we call our method Replace one Branch (RoB) as it simply replaces one branch of the joint-embedding training with a large teacher model. RoB is widely applicable to a number of architectures such as small ResNets, MobileNets and ViT, and pretrained models such as DINO, SwAV or iBOT. When pretraining on the ImageNet dataset, RoB yields models that compete with supervised knowledge distillation. When applied to MSN, RoB produces students with strong semi-supervised capabilities. Finally, our best ViT-Tiny models improve over prior SSL state-of-the-art on ImageNet by $2.3\%$ and are on par or better than a supervised distilled DeiT on five downstream transfer tasks (iNaturalist, CIFAR, Clevr/Count, Clevr/Dist and Places). We hope RoB enables practical self-supervision at smaller scale.
Boundedness of the dyadic maximal function on graded Lie groups

Let $1<p\leq \infty$ and let $n\geq 2. $ It was proved independently by C. Calder\'on, R. Coifman and G. Weiss that the dyadic maximal function \begin{equation*} \mathcal{M}^{d\sigma}_Df(x)=\sup_{j\in\mathbb{Z}}\left|\smallint\limits_{\mathbb{S}^{n-1}}f(x-2^jy)d\sigma(y)\right| \end{equation*} is a bounded operator on $L^p(\mathbb{R}^n)$ where $d\sigma(y)$ is the surface measure on $\mathbb{S}^{n-1}. $ In this paper we prove an analogue of this result on arbitrary graded Lie groups. More precisely, to any finite Borel measure $d\sigma$ with compact support on a graded Lie group $G,$ we associate the corresponding dyadic maximal function $\mathcal{M}_D^{d\sigma}$ using the homogeneous structure of the group. Then, we prove a criterion in terms of the order (at zero and at infinity) of the group Fourier transform $\widehat{d\sigma}$ of $d\sigma$ with respect to a fixed Rockland operator $\mathcal{R}$ on $G$ that assures the boundedness of $\mathcal{M}_D^{d\sigma}$ on $L^p(G)$ for all $1<p\leq \infty. $
Sensemaking About Contraceptive Methods Across Online Platforms

Selecting a birth control method is a complex healthcare decision. While birth control methods provide important benefits, they can also cause unpredictable side effects and be stigmatized, leading many people to seek additional information online, where they can find reviews, advice, hypotheses, and experiences of other birth control users. However, the relationships between their healthcare concerns, sensemaking activities, and online settings are not well understood. We gather texts about birth control shared on Twitter, Reddit, and WebMD -- platforms with different affordances, moderation, and audiences -- to study where and how birth control is discussed online. Using a combination of topic modeling and hand annotation, we identify and characterize the dominant sensemaking practices across these platforms, and we create lexicons to draw comparisons across birth control methods and side effects. We use these to measure variations from survey reports of side effect experiences and method usage. Our findings characterize how online platforms are used to make sense of difficult healthcare choices and highlight unmet needs of birth control users.
Anisotropic magnetism and electronic structure of trigonal EuAl$_2$Ge$_2$ single crystals

The magnetic and electronic properties of the layered Zintl-phase compound EuAl$_2$Ge$_2$ crystallizing in the trigonal CaAl$_2$Si$_2$-type structure are reported. Our neutron-diffraction measurements show that EuAl$_2$Ge$_2$ undergoes A-type antiferromagnetic (AFM) ordering below $T_{\rm N} = 27.5(5)$~K, with the Eu moments (Eu$^{2+}$, $S = 7/2$) aligned ferromagnetically in the $ab$ plane. The $H = 0$ magnetic structure consists of trigonal AFM domains associated with $ab$-plane magnetic anisotropy and a field-induced reorientation of the Eu spins in the domains is evident at $T = 2$~K below the critical field $H_{c1} = 2.5(1)$ kOe. Electrical resistivity and ARPES measurements show that EuAl$_2$Ge$_2$ is metallic both above and below $T_{\rm N}$. In the AFM phase, we directly observe folded bands in ARPES due to the doubling of the magnetic unit cell along the $c$ axis with an enhancement of quasiparticle weight due to the complex change in the coupling between the magnetic moments and itinerant electrons on cooling below $T_{\rm N}$. The observed electronic structure is well reproduced by first-principle calculations, which also predict the presence of nontrivial electronic states near the Fermi level in the AFM phase with $Z_2$ topological numbers 1;(000).
Improvements and algorithmical considerations on a recent three‐dimensional model describing stress‐induced solid phase transformations

During mechanical loading–unloading cycles shape‐memory alloys (SMA) are able to undergo large deformations without showing residual strains (pseudoelasticity) or recovering them through thermal cycles (shape memory effect). Motivated by stress‐induced solid phase transformations, these unique behaviours induce the SMA exploitation in innovative and commercially valuable applications, stimulating, consequently, the interest in the development of constitutive models. Also if many models are now available in the literature, effective three‐dimensional proposals are still few and limited in several aspects.
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion

Real-world data generation often involves complex inter-dependencies among instances, violating the IID-data hypothesis of standard learning paradigms and posing a challenge for uncovering the geometric structures for learning desired instance representations. To this end, we introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states that progressively incorporate other instances' information by their interactions. The diffusion process is constrained by descent criteria w.r. t. ~a principled energy function that characterizes the global consistency of instance representations over latent structures. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs, which gives rise to a new class of neural encoders, dubbed as DIFFormer (diffusion-based Transformers), with two instantiations: a simple version with linear complexity for prohibitive instance numbers, and an advanced version for learning complex structures. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks, such as node classification on large graphs, semi-supervised image/text classification, and spatial-temporal dynamics prediction.
Compact Optimization Learning for AC Optimal Power Flow

This article reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability issues due to the high dimensionality of the output space. This article first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA). It then proposes Compact Learning, a new method that learns in a subspace of the principal components and translates the vectors into the original output space. This compression reduces the number of trainable parameters substantially, improving scalability and effectiveness. Compact Learning is evaluated on a variety of test cases from the PGLib and a realistic French transmission system having renewable energy changes with up to 30,000 buses. The article also shows that the output of Compact Learning can be used to warm-start an exact AC solver to restore feasibility, while bringing significant speed-ups.
Who wants to cooperate-and why? Attitude and perception of crowd workers in online labor markets

Existing literature and studies predominantly focus on how crowdsource workers individually complete tasks and projects. Our study examines crowdsource workers’ willingness to work collaboratively . We report results from a survey of 122 workers on a leading online labor platform (Upwork) to examine crowd workers’ behavioral preferences for collaboration and explore several antecedents of cooperative behaviors. We then test if actual cooperative behavior matches with workers’ behavioral preferences through an incentivized social dilemma experiment. We find that respondents cooperate at a higher rate (85%) than reported in previous comparable studies (between 50-75%). This high rate of cooperation is likely explained by an ingroup bias. Using a sequential mediation model we demonstrate the importance of a sense of shared expectations and accountability for cooperation. We contribute to a better understanding of the potential for collaborative work in crowdsourcing by accessing if and what social factors and collective culture exist among crowd workers. We discuss the implications of our results for platform designers by highlighting the importance of platform features that promote shared expectations and improve accountability. Overall, contrary to existing literature and predictions, our results suggest that crowd workers display traits that are more consistent with belonging to a coherent group with a shared collective culture, rather than being anonymous actors in a transaction-based market.