Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind and wave forcing of longshore currents across a barred beach.

Abstract : Previous investigations of longshore currents have included simplifying assumptions and restriction (such as a planar beach, a steady and depth uniform flow, spatially-variant bed shear stress and turbulent momentum exchange, and the exclusion of surface wind stress. These assumptions are quantitatively investigated by calculating the relative importance of each term in the longshore momentum balance with an emphasis on the relative importance of wind forcing across the barred nearshore. Wind and wave forcing of longshore currents across a barred beach are examined using both a numerical model and field measurements. A local momentum balance was measured at various locations across the surf zone during the SUPERDUCK experiment held at the USACE CERC field Research Facility, Duck, N.C. in October 1986. A moveable sled was instrumented with pressure, current, and wind sensors to measure the various terms in the longshore momentum equation. Stability-dependent atmospheric drag coefficients for the surf zone are determined from wind stress measurements acquired just beyond the surf zone and wind speed measurements acquired from an anemometer atop the 9 m sled mast. Breaking waves were visually identified and electronically marked on the data tapes.
Well-Connected Communities in Real-World and Synthetic Networks

Integral to the problem of detecting communities through graph clustering is the expectation that they are"well connected". In this respect, we examine five different community detection approaches optimizing different criteria: the Leiden algorithm optimizing the Constant Potts Model, the Leiden algorithm optimizing modularity, Iterative K-Core Clustering (IKC), Infomap, and Markov Clustering (MCL). Surprisingly, all these methods produce, to varying extents, communities that fail even a mild requirement for well connectedness. To remediate clusters that are not well connected, we have developed the"Connectivity Modifier"(CM), which, at the cost of coverage, iteratively removes small edge cuts and re-clusters until all communities produced are well connected. Results from real-world and synthetic networks illustrate a tradeoff users make between well connected clusters and coverage, and raise questions about the"clusterability"of networks and models of community structure.
Size Lowerbounds for Deep Operator Networks

Deep Operator Networks are an increasingly popular paradigm for solving regression in infinite dimensions and hence solve families of PDEs in one shot. In this work, we aim to establish a first-of-its-kind data-dependent lowerbound on the size of DeepONets required for them to be able to reduce empirical error on noisy data. In particular, we show that for low training errors to be obtained on $n$ data points it is necessary that the common output dimension of the branch and the trunk net be scaling as $\Omega \left ( \sqrt[\leftroot{-1}\uproot{-1}4]{n} \right )$. This inspires our experiments with DeepONets solving the advection-diffusion-reaction PDE, where we demonstrate the possibility that at a fixed model size, to leverage increase in this common output dimension and get monotonic lowering of training error, the size of the training data might necessarily need to scale at least quadratically with it.
Gradient-Based Markov Chain Monte Carlo for MIMO Detection

Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical. Recently, stochastic sampling-based Bayesian inference techniques, such as Markov chain Monte Carlo (MCMC), have been combined with the gradient descent (GD) method to provide a promising framework for MIMO detection. In this work, we propose to efficiently approach optimal detection by exploring the discrete search space via MCMC random walk accelerated by Nesterov's gradient method. Nesterov's GD guides MCMC to make efficient searches without the computationally expensive matrix inversion and line search. Our proposed method operates using multiple GDs per random walk, achieving sufficient descent towards important regions of the search space before adding random perturbations, guaranteeing high sampling efficiency. To provide augmented exploration, extra samples are derived through the trajectory of Nesterov's GD by simple operations, effectively supplementing the sample list for statistical inference and boosting the overall MIMO detection performance. Furthermore, we design an early stopping tactic to terminate unnecessary further searches, remarkably reducing the complexity. Simulation results and complexity analysis reveal that the proposed method achieves exceptional performance in both uncoded and coded MIMO systems, adapts to realistic channel models, and scales well to large MIMO dimensions.
ICPC: Instance-Conditioned Prompting with Contrastive Learning for Semantic Segmentation

Modern supervised semantic segmentation methods are usually finetuned based on the supervised or self-supervised models pre-trained on ImageNet. Recent work shows that transferring the knowledge from CLIP to semantic segmentation via prompt learning can achieve promising performance. The performance boost comes from the feature enhancement with multimodal alignment, i.e., the dot product between vision and text embeddings. However, how to improve the multimodal alignment for better transfer performance in dense tasks remains underexplored. In this work, we focus on improving the quality of vision-text alignment from two aspects of prompting design and loss function, and present an instance-conditioned prompting with contrastive learning (ICPC) framework. First, compared with the static prompt designs, we reveal that dynamic prompting conditioned on image content can more efficiently utilize the text encoder for complex dense tasks. Second, we propose an align-guided contrastive loss to refine the alignment of vision and text embeddings. We further propose lightweight multi-scale alignment for better performance. Extensive experiments on three large-scale datasets (ADE20K, COCO-Stuff10k, and ADE20K-Full) demonstrate that ICPC brings consistent improvements across diverse backbones. Taking ResNet-50 as an example, ICPC outperforms the state-of-the-art counterpart by 1.71%, 1.05%, and 1.41% mIoU on the three datasets, respectively.
Passing from plat closure to standard closure of braids in $\mathbb{R}^3$, in handlebodies and in thickened surfaces

Given a knot or link in the form of plat closure of a braid, we describe an algorithm to obtain a braid representing the same knot or link with the standard closure, and vice-versa. We analyze the three cases of knots and links: in \(\mathbb{R}^3\), in handlebodies and in thickened surfaces. We show that the algorithm is quadratic in the number of crossings and loop generators of the braid when passing from plat to standard closure, while it is linear when passing from standard to plat closure.
Immersed-Boundary Fluid-Structure Interaction of Membranes and Shells

This paper presents a general and robust method for the fluid-structure interaction of membranes and shells undergoing large displacement and large added-mass effects by coupling an immersed-boundary method with a shell finite-element model. The immersed boundary method can accurately simulate the fluid velocity and pressure induced by dynamic bodies undergoing large displacements using a computationally efficient pressure projection finite volume solver. The structural solver can be applied to bending and membrane-related problems, making our partitioned solver very general. We use a strongly-coupled algorithm that avoids the expensive computation of the inverse Jacobian within the root-finding iterations by constructing it from input-output pairs of the coupling variables from the previous time steps. Using two examples with large deformations and added mass contributions, we demonstrate that the resulting quasi-Newton scheme is stable, accurate, and computationally efficient.
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling

A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy negative sampling. However, negative sampling increases the proportion of positive interactions in the training data, and therefore models trained with negative sampling tend to overestimate the probabilities of positive interactions – a phenomenon we call overconfidence. While the absolute values of the predicted scores/probabilities are not important for the ranking of retrieved recommendations, overconfident models may fail to estimate nuanced differences in the top-ranked items, resulting in degraded performance. In this paper, we show that overconfidence explains why the popular SASRec model underperforms when compared to BERT4Rec. This is contrary to the BERT4Rec authors’ explanation that the difference in performance is due to the bi-directional attention mechanism. To mitigate overconfidence, we propose a novel Generalised Binary Cross-Entropy Loss function (gBCE) and theoretically prove that it can mitigate overconfidence. We further propose the gSASRec model, an improvement over SASRec that deploys an increased number of negatives and the gBCE loss. We show through detailed experiments on three datasets that gSASRec does not exhibit the overconfidence problem. As a result, gSASRec can outperform BERT4Rec (e.g. +9.47% NDCG on the MovieLens-1M dataset), while requiring less training time (e.g. -73% training time on MovieLens-1M). Moreover, in contrast to BERT4Rec, gSASRec is suitable for large datasets that contain more than 1 million items.
Heterogeneous Multi-Agent Reinforcement Learning via Mirror Descent Policy Optimization

This paper presents an extension of the Mirror Descent method to overcome challenges in cooperative Multi-Agent Reinforcement Learning (MARL) settings, where agents have varying abilities and individual policies. The proposed Heterogeneous-Agent Mirror Descent Policy Optimization (HAMDPO) algorithm utilizes the multi-agent advantage decomposition lemma to enable efficient policy updates for each agent while ensuring overall performance improvements. By iteratively updating agent policies through an approximate solution of the trust-region problem, HAMDPO guarantees stability and improves performance. Moreover, the HAMDPO algorithm is capable of handling both continuous and discrete action spaces for heterogeneous agents in various MARL problems. We evaluate HAMDPO on Multi-Agent MuJoCo and StarCraftII tasks, demonstrating its superiority over state-of-the-art algorithms such as HATRPO and HAPPO. These results suggest that HAMDPO is a promising approach for solving cooperative MARL problems and could potentially be extended to address other challenging problems in the field of MARL.
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval

In this work, by re-examining the"matching"nature of Anomaly Detection (AD), we propose a new AD framework that simultaneously enjoys new records of AD accuracy and dramatically high running speed. In this framework, the anomaly detection problem is solved via a cascade patch retrieval procedure that retrieves the nearest neighbors for each test image patch in a coarse-to-fine fashion. Given a test sample, the top-K most similar training images are first selected based on a robust histogram matching process. Secondly, the nearest neighbor of each test patch is retrieved over the similar geometrical locations on those"global nearest neighbors", by using a carefully trained local metric. Finally, the anomaly score of each test image patch is calculated based on the distance to its"local nearest neighbor"and the"non-background"probability. The proposed method is termed"Cascade Patch Retrieval"(CPR) in this work. Different from the conventional patch-matching-based AD algorithms, CPR selects proper"targets"(reference images and locations) before"shooting"(patch-matching). On the well-acknowledged MVTec AD, BTAD and MVTec-3D AD datasets, the proposed algorithm consistently outperforms all the comparing SOTA methods by remarkable margins, measured by various AD metrics. Furthermore, CPR is extremely efficient. It runs at the speed of 113 FPS with the standard setting while its simplified version only requires less than 1 ms to process an image at the cost of a trivial accuracy drop. The code of CPR is available at https://github.com/flyinghu123/CPR.
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks

Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy. Recently, synthetic images generated by text-to-image diffusion models, have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. To address this, we start by uncovering that diffusion models' cross-attention layers inherently provide annotation-free attention masks aligned with corresponding text inputs on generated images. We then investigate the problems of three prevalent unsupervised learning techniques ( i.e., contrastive learning, masked modeling, and vision-language pretraining) and introduce customized solutions by fully exploiting the aforementioned free attention masks. Our approach is validated through extensive experiments that show consistent improvements in baseline models across various downstream tasks, including image classification, detection, segmentation, and image-text retrieval. By utilizing our method, it is possible to close the performance gap between unsupervised pretraining on synthetic data and real-world scenarios.
Improved dimension dependence in the Bernstein von Mises Theorem via a new Laplace approximation bound

We prove a new simple and explicit bound on the total variation distance between a measure $\pi\propto e^{-nf}$ on $\mathbb R^d$ and its Laplace approximation. The bound is proportional to $d/\sqrt n$, which has recently been shown to be the tight rate in terms of dimension dependence. Our bound holds under weak regularity conditions on $f$ and at least linear growth of $f$ at infinity. We then apply this bound to prove the first ever Bernstein-von Mises (BvM) theorems on the asymptotic normality of posterior distributions in the regime $n\gg d^2$. This improves on the tightest previously known condition, $n\gg d^3$. We establish the BvM for the following data-generating models: 1) exponential families, 2) arbitrary probability mass functions on $d+1$ states, and 3) logistic regression with Gaussian design. Our statements of the BvM are nonasymptotic, taking the form of explicit high-probability bounds. We also show in a general setting that the prior can have a stronger regularizing effect than previously known while still vanishing in the large sample limit.
Positional Encodings for Light Curve Transformers: Playing with Positions and Attention

We conducted empirical experiments to assess the transferability of a light curve transformer to datasets with different cadences and magnitude distributions using various positional encodings (PEs). We proposed a new approach to incorporate the temporal information directly to the output of the last attention layer. Our results indicated that using trainable PEs lead to significant improvements in the transformer performances and training times. Our proposed PE on attention can be trained faster than the traditional non-trainable PE transformer while achieving competitive results when transfered to other datasets.
Packing $T$-connectors in graphs needs more connectivity

Strengthening the classical concept of Steiner trees, West and Wu [J. Combin. Theory Ser. B 102 (2012), 186--205] introduced the notion of a $T$-connector in a graph $G$ with a set $T$ of terminals. They conjectured that if the set $T$ is $3k$-edge-connected in $G$, then $G$ contains $k$ edge-disjoint $T$-connectors. We disprove this conjecture by constructing infinitely many counterexamples for $k=1$ and for each even $k$.
Simply tell me how - On Trustworthiness and Technology Acceptance of Attribute-Based Credentials

Attribute-based Credential Systems (ACS) have been long proposed as privacy-preserving means of attribute-based authentication, yet neither been considered particularly usable nor found wide-spread adoption, to date. To establish what variables drive the adoption of \ACS as a usable security and privacy technology, we investigated how intrinsic and presentation properties impact their perceived trustworthiness and behavioral intent to adopt them. We conducted two confirmatory, fractional-factorial, between-subject, random-controlled trials with a total UK-representative sample of $N = 812$ participants. Each participant inspected one of 24 variants of Anonymous Credential System Web site, which encoded a combination of three intrinsic factors (\textsf{provider}, \textsf{usage}, \textsf{benefits}) and three presentation factors (\textsf{simplicity}, presence of \textsf{people}, level of available \textsf{support}). Participants stated their privacy and faith-in-technology subjective norms before the trial. After having completed the Web site inspection, they reported on the perceived trustworthiness, the technology adoption readiness, and their behavioral intention to follow through. We established a robust covariance-based structural equation model of the perceived trustworthiness and technology acceptance, showing that communicating facilitating conditions as well as demonstrating results drive the overall acceptance and behavioral intent. Of the manipulated causal variables, communicating with simplicity and on the everyday usage had the greatest and most consistently positive impact on the overall technology acceptance. After earlier correlational empirical research on ACS technology acceptance, ours is the first research showing cause-effect relations in a structural latent factor model with substantial sample size.
Mind your Language (Model): Fact-Checking LLMs and their Role in NLP Research and Practice

Much of the recent discourse within the NLP research community has been centered around Large Language Models (LLMs), their functionality and potential -- yet not only do we not have a working definition of LLMs, but much of this discourse relies on claims and assumptions that are worth re-examining. This position paper contributes a definition of LLMs, explicates some of the assumptions made regarding their functionality, and outlines the existing evidence for and against them. We conclude with suggestions for research directions and their framing in future work.
Tidal inlet equilibrium area experiments, inlet laboratory investigations

Abstract : This study was designed to examine the relationship among channel area, tidal period, tidal prism, and maximum channel velocity. Movable-bed model experiments were run to define an equilibrium area for different tidal periods and sediments. The magnitude of the areas measured provided additional data for the relationship of the tidal prism versus minimum channel area in a size range slightly larger than previous laboratory data in the continuum to very large field inlets. These data may help define the tidal prism-minimum channel cross-sectional area relationship in the midrange channel size.
Heparin in the Management of Extensive Burns

Summary The severe burn injury patients (more than 40%) were studied in two randomized groups. Both groups were treated similarly except that one group received heparin both topically and systemically. Significant relief of pain, early healing in cases of superficial and deep dermal burns without hypertrophic scarring and contracture formation, early separation of eschar, excellent graft take in full thickness burns were few of the important advantages observed in heparin group patients. Significant decrease in the incidence of respiratory complications, septic shock and decreased mortality were also noticed in patients receiving heparin. The only disadvantage being that the doses required are quite heavy. No complication in the form of bleeding disorder, despite heavy dosages have been observed.
A literature review of Black and Latiné youth’s experience of inequitable feedback: How can middle school educators provide motivationally-supportive feedback?

Abstract Policies and practices in middle school continue to perpetuate inequities faced by students from historically marginalized groups. In the current article, we present a literature review that focuses on feedback as one avenue by which inequities are exacerbated, specifically for Black and Latiné middle school students. Furthermore, we describe Black and Latiné students’ inequitable feedback experiences and the concerning motivational consequences. Specifically, Black and Latiné students experience (a) less encouragement compared to their white counterparts, (b) a positive feedback bias, and (c) more negative feedback. Lastly, we go beyond describing the motivationally-unsupportive feedback and provide recommendations for educators to provide motivationally-supportive, equitable feedback to all students – especially Black and Latiné students. In particular, we discuss specific strategies for (1) reducing one’s biases (i.e. developing a critical racial and cultural consciousness; checking viewpoints) and (2) providing motivationally-supportive feedback (e.g. be specific; provide steps for improvement).
Implementing industry 4.0 for flexibility, quality, and productivity improvement: technology arrangements for different purposes

ABSTRACT Productivity, quality, and flexibility are key production targets pursued by companies that adopt Industry 4.0. However, it is unclear how Industry 4.0 technologies can help achieve these different and sometimes competing targets. This study investigates this relationship through a survey of 92 manufacturers. The study employs Exploratory Factor Analysis to define four main technology arrangements based on 18 Industry 4.0 technologies: Vertical Integration, Virtual Manufacturing, Advanced Manufacturing Processing Technologies, and Online Traceability. Then, independent samples tests were conducted to compare the implementation status of these arrangements when manufacturing flexibility, process quality, and productivity are (or are not) pursued as the main production targets. The results show that Vertical Integration is a general-purpose technology arrangement because it supports all targets. On the other hand, Virtual Manufacturing and Online Traceability are specific-purpose arrangements, adopted especially for flexibility and productivity targets, respectively. Advanced Manufacturing Processing Technologies, in turn, is an integrative-purpose technology arrangement since it is adopted when two competing targets are pursued: productivity and manufacturing flexibility. The study ends with a decision model to implement Industry 4.0 based on the production targets a company may pursue. It shows the interconnection and trade-offs between these production targets and the Industry 4.0 technologies adopted.