Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell jamming and unjamming in development: physical aspects

Collective cell migration is essential for a wide range of biological processes such as: morphogenesis, wound healing, and cancer spreading. However, it is well known that migrating epithelial collectives frequently undergo jamming, stay trapped some period of time, and then start migration again. Consequently, only a part of epithelial cells actively contributes to the tissue development. In contrast to epithelial cells, migrating mesenchymal collectives successfully avoid the jamming. It has been confirmed that the epithelial unjamming cannot be treated as the epithelial-to-mesenchymal transition. Some other mechanism is responsible for the epithelial jamming/unjamming. Despite extensive research devoted to study the cell jamming/unjamming, we still do not understand the origin of this phenomenon. The origin is connected to physical factors such as: the cell compressive residual stress accumulation and surface characteristics of migrating (unjamming) and resting (jamming) epithelial clusters which depend primarily on the strength of cell-cell adhesion contacts and cell contractility. The main goal of this theoretical consideration is to clarify these cause-consequence relations.
Symmetry and stability of orientationally ordered collective motions of self-propelled, semiflexible filaments

Ordered, collective motions commonly arise spontaneously in systems of many interacting, active units, ranging from cellular tissues and bacterial colonies to self-propelled colloids and animal flocks. Active phases are especially rich when the active units are sufficiently anisotropic to produce liquid crystalline order and thus active nematic phenomena, with important biophysical examples provided by cytoskeletal filaments including microtubules and actin. Gliding assay experiments have provided a testbed to study the collective motions of these cytoskeletal filaments and unlocked diverse collective active phases, including states with long-range orientational order. However, it is not well-understood how such long-range order emerges from the interplay of passive and active aligning mechanisms. We use Brownian dynamics simulations to study the collective motions of semiflexible filaments that self-propel in quasi-two dimensions, in order to gain insights into the aligning mechanisms at work in these gliding assay systems. We find that, without aligning torques in the microscopic model, long-range orientational order can only be achieved when the filaments are able to overlap. The symmetry (nematic or polar) of the long-range order that first emerges is shown to depend on the energy cost of filament overlap and on filament flexibility. However, our model also predicts that a long-range-ordered active nematic state is merely transient, whereas long-range polar order is the only active dynamical steady state in systems with finite filament rigidity.
Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning

Since performing exercises (including, e.g.,practice tests) forms a crucial component oflearning, and creating such exercises requiresnon-trivial effort from the teacher. There is agreat value in automatic exercise generationin digital tools in education. In this paper, weparticularly focus on automatic creation of gap-filling exercises for language learning, specifi-cally grammar exercises. Since providing anyannotation in this domain requires human ex-pert effort, we aim to avoid it entirely and ex-plore the task of converting existing texts intonew gap-filling exercises, purely based on anexample exercise, without explicit instructionor detailed annotation of the intended gram-mar topics. We contribute (i) a novel neuralnetwork architecture specifically designed foraforementioned gap-filling exercise generationtask, and (ii) a real-world benchmark datasetfor French grammar. We show that our modelfor this French grammar gap-filling exercisegeneration outperforms a competitive baselineclassifier by 8% in F1 percentage points, achiev-ing an average F1 score of 82%. Our model im-plementation and the dataset are made publiclyavailable to foster future research, thus offeringa standardized evaluation and baseline solutionof the proposed partially annotated data predic-tion task in grammar exercise creation.
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction

Search engine plays a crucial role in satisfying users' diverse information needs. Recently, Pretrained Language Models (PLMs) based text ranking models have achieved huge success in web search. However, many state-of-the-art text ranking approaches only focus on core relevance while ignoring other dimensions that contribute to user satisfaction, e.g., document quality, recency, authority, etc. In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner. In particular, we leverage the capacities of PLMs on both textual and numerical inputs, and apply a multi-field input that modularizes each dimension of user satisfaction as an input field. Overall, SAT-Ranker is an effective, extensible, and data-centric framework that has huge potential for industrial applications. On rigorous offline and online experiments, SAT-Ranker obtains remarkable gains on various evaluation sets targeting different dimensions of user satisfaction. It is now fully deployed online to improve the usability of our search engine.
Locally Affine Invariant Descriptors for Shape Matching and Retrieval

This work proposes novel locally affine invariant de- scriptors for shape representation. The descriptors are theoreti- cally simple and solid, derived from the matrix theories. They can be used for matching and retrieval of shapes under affine transfor- mation, articulated motion or nonrigid deformation. Comparisons of the work with the state-of-the-art shape descriptors are per- formedbasedonsyntheticandsomewell-knowndatabases.Theex- perimentsvalidatethattheproposeddescriptorsachievehigherre- trieval accuracy and have faster running speed than most of other approaches.
A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-Resolution

Reference-based super-resolution (RefSR) has gained considerable success in the field of super-resolution with the addition of high-resolution reference images to reconstruct low-resolution (LR) inputs with more high-frequency details, thereby overcoming some limitations of single image super-resolution (SISR). Previous research in the field of RefSR has mostly focused on two crucial aspects. The first is accurate correspondence matching between the LR and the reference (Ref) image. The second is the effective transfer and aggregation of similar texture information from the Ref images. Nonetheless, an important detail of perceptual loss and adversarial loss has been underestimated, which has a certain adverse effect on texture transfer and reconstruction. In this study, we propose a feature reuse framework that guides the step-by-step texture reconstruction process through different stages, reducing the negative impacts of perceptual and adversarial loss. The feature reuse framework can be used for any RefSR model, and several RefSR approaches have improved their performance after being retrained using our framework. Additionally, we introduce a single image feature embedding module and a texture-adaptive aggregation module. The single image feature embedding module assists in reconstructing the features of the LR inputs itself and effectively lowers the possibility of including irrelevant textures. The texture-adaptive aggregation module dynamically perceives and aggregates texture information between the LR inputs and the Ref images using dynamic filters. This enhances the utilization of the reference texture while reducing reference misuse. The source code is available at https://github.com/Yi-Yang355/FRFSR.
An Effective Meaningful Way to Evaluate Survival Models

One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by the model and the true event time, over all subjects. Unfortunately, this is challenging because, in practice, the test set includes (right) censored individuals, meaning we do not know when a censored individual actually experienced the event. In this paper, we explore various metrics to estimate MAE for survival datasets that include (many) censored individuals. Moreover, we introduce a novel and effective approach for generating realistic semi-synthetic survival datasets to facilitate the evaluation of metrics. Our findings, based on the analysis of the semi-synthetic datasets, reveal that our proposed metric (MAE using pseudo-observations) is able to rank models accurately based on their performance, and often closely matches the true MAE -- in particular, is better than several alternative methods.
Interactions between the Seasonal Cycle and El Niño-Southern Oscillation in an Intermediate Coupled Ocean-Atmosphere Model

Abstract The nonlinear interactions between the seasonal cycle and El Nino-Southern Oscillation (ENSO) in the coupled ocean-atmosphere system are examined using a newly developed intermediate coupled ocean-atmosphere model. The model permits coupling between total sea surface temperature (SST) and total surface winds and thus is able to produce its own seasonal cycle. This coupling approach allows for the examination of full dynamic interactions between the seasonal cycle and interannual oscillations. Numerical simulations with realistic surface heat fluxes indicate that this model is capable of capturing the essential variability of the coupled ocean-atmosphere system on seasonal-to-interannual timescale in the tropical Pacific. Model sensitivity experiments were carried out by independently varying the external forcing strength and coupling strength. These experiments reveal a very different behavior of the coupled system with and without the seasonal cycle. In the presence of the seasonal cycle, the co.
CLIPGraphs: Multimodal Graph Networks to Infer Object-Room Affinities

This paper introduces a novel method for determining the best room to place an object in, for embodied scene rearrangement. While state-of-the-art approaches rely on large language models (LLMs) or reinforcement learned (RL) policies for this task, our approach, CLIPGraphs, efficiently combines commonsense domain knowledge, data-driven methods, and recent advances in multimodal learning. Specifically, it (a) encodes a knowledge graph of prior human preferences about the room location of different objects in home environments, (b) incorporates vision-language features to support multimodal queries based on images or text, and (c) uses a graph network to learn object-room affinities based on embeddings of the prior knowledge and the vision-language features. We demonstrate that our approach provides better estimates of the most appropriate location of objects from a benchmark set of object categories in comparison with state-of-the-art baselines.11Supplementary material and code: https://clipgraphs.github. io
Conservation Laws and Symmetries. II

The reciprocal relationship between conservation laws and symmetries is established for those theories wherein the equations of motion are derivable from a variational principle. It is shown, for a general variational problem with arbitrary number of independent and dependent variables, that to every divergenceless vector there corresponds another which differs from it, in general, by terms that vanish when the Euler-Lagrange equations are satisfied and which has the structure obtained by applying Noether's theorem to some symmetry transformation. Thus existence of a continuity equation implies some invariance property of the variational problem (converse of Noether's theorem). The Lagrangian is invariant, in general, up to a divergence. Derivatives of dependent variables of any arbitrary finite order are allowed to appear in the Lagrangian; it is assumed, however, that it does not contain independent variables explicitly. A systematic procedure is formulated to deduce the invariance property associated with a given conservation law and is illustrated by some examples.
Vortex line entanglement in active Beltrami flows

Over the last decade, substantial progress has been made in understanding the topology of quasi-two-dimensional (2-D) non-equilibrium fluid flows driven by ATP-powered microtubules and microorganisms. By contrast, the topology of three-dimensional (3-D) active fluid flows still poses interesting open questions. Here, we study the topology of a spherically confined active flow using 3-D direct numerical simulations of generalized Navier–Stokes (GNS) equations at the scale of typical microfluidic experiments. Consistent with earlier results for unbounded periodic domains, our simulations confirm the formation of Beltrami-like bulk flows with spontaneously broken chiral symmetry in this model. Furthermore, by leveraging fast methods to compute linking numbers, we explicitly connect this chiral symmetry breaking to the entanglement statistics of vortex lines. We observe that the mean of linking number distribution converges to the global helicity, consistent with the asymptotic result by Arnold [In Vladimir I. Arnold – Collected Works (ed. A.B. Givental, B.A. Khesin, A.N. Varchenko, V.A. Vassiliev & O.Y. Viro), pp. 357–375. Springer]. Additionally, we characterize the rate of convergence of this measure with respect to the number and length of observed vortex lines, and examine higher moments of the distribution. We find that the full distribution is well described by a k-Gamma distribution, in agreement with an entropic argument. Beyond active suspensions, the tools for the topological characterization of 3-D vector fields developed here are applicable to any solenoidal field whose curl is tangent to or cancels at the boundaries of a simply connected domain.
The mathematics problem: the construction of a market-led education discourse in the Republic of Ireland

Educational change in the neoliberal state is permeated by the effects of forces from outside the field of education itself. The process of governmentality welcomes, indeed demands, the participation of those non-state actors valorised by neoliberalism as well as government agencies dedicated to the advancement of such groups. Inevitably, the concerns of such organisations become central to how the state sees education. This article traces the assembly of national and international agents from industry, business and special interest groups around the concept of ‘knowledge economy’. It treats this assemblage as an apparatus (dispositif), examining how the construction of an economic problem is brought to bear on the demand for educational change, and how this construction of the problem is used to shape public opinion in order to prepare the public for a radical change of direction. Confining itself to the reform of mathematics education introduced in the Republic of Ireland in 2010, this article traces the emergence of a mathematics discourse focused on market-led education. It interrogates the construction of ‘the mathematics problem’ or ‘crisis in maths’ and argues that the discourse of the present construction is economic in nature, centring as it does on human capital production and market-led reform.
Driving Context into Text-to-Text Privatization

Metric Differential Privacy enables text-to-text privatization by adding calibrated noise to the vector of a word derived from an embedding space and projecting this noisy vector back to a discrete vocabulary using a nearest neighbor search. Since words are substituted without context, this mechanism is expected to fall short at finding substitutes for words with ambiguous meanings, such as ‘bank’. To account for these ambiguous words, we leverage a sense embedding and incorporate a sense disambiguation step prior to noise injection. We encompass our modification to the privatization mechanism with an estimation of privacy and utility. For word sense disambiguation on the Words in Context dataset, we demonstrate a substantial increase in classification accuracy by 6.05%.
Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization

Bayesian optimization (BO) provides a powerful framework for optimizing black-box, expensive-to-evaluate functions. It is therefore an attractive tool for engineering design problems, typically involving multiple objectives. Thanks to the rapid advances in fabrication and measurement methods as well as parallel computing infrastructure, querying many design problems can be heavily parallelized. This class of problems challenges BO with an unprecedented setup where it has to deal with very large batches, shifting its focus from sample efficiency to iteration efficiency. We present a novel Bayesian optimization framework specifically tailored to address these limitations. Our key contribution is a highly scalable, sample-based acquisition function that performs a non-dominated sorting of not only the objectives but also their associated uncertainty. We show that our acquisition function in combination with different Bayesian neural network surrogates is effective in data-intensive environments with a minimal number of iterations. We demonstrate the superiority of our method by comparing it with state-of-the-art multi-objective optimizations. We perform our evaluation on two real-world problems -- airfoil design and 3D printing -- showcasing the applicability and efficiency of our approach. Our code is available at: https://github.com/an-on-ym-ous/lbn_mobo
PassGPT: Password Modeling and (Guided) Generation with Large Language Models

Large language models (LLMs) successfully model natural language from vast amounts of text without the need for explicit supervision. In this paper, we investigate the efficacy of LLMs in modeling passwords. We present PassGPT, a LLM trained on password leaks for password generation. PassGPT outperforms existing methods based on generative adversarial networks (GAN) by guessing twice as many previously unseen passwords. Furthermore, we introduce the concept of guided password generation, where we leverage PassGPT sampling procedure to generate passwords matching arbitrary constraints, a feat lacking in current GAN-based strategies. Lastly, we conduct an in-depth analysis of the entropy and probability distribution that PassGPT defines over passwords and discuss their use in enhancing existing password strength estimators.
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection

Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable. However, conventional uncertainty measures such as Shannon entropy do not provide an effective way to infer the real uncertainty associated with the model's predictions. In this paper, we introduce a novel data-driven measure of uncertainty relative to an observer for misclassification detection. By learning patterns in the distribution of soft-predictions, our uncertainty measure can identify misclassified samples based on the predicted class probabilities. Interestingly, according to the proposed measure, soft-predictions corresponding to misclassified instances can carry a large amount of uncertainty, even though they may have low Shannon entropy. We demonstrate empirical improvements over multiple image classification tasks, outperforming state-of-the-art misclassification detection methods.
Multi-Objective Population Based Training

Population Based Training (PBT) is an efficient hyperparameter optimization algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter optimization problems involve two or more conflicting objectives. In this work, we therefore introduce a multi-objective version of PBT, MO-PBT. Our experiments on diverse multi-objective hyperparameter optimization problems (Precision/Recall, Accuracy/Fairness, Accuracy/Adversarial Robustness) show that MO-PBT outperforms random search, single-objective PBT, and the state-of-the-art multi-objective hyperparameter optimization algorithm MO-ASHA.
Contribution of brownian rotation and particle assembly polarisation to the particle response in magnetic particle spectrometry

Abstract The spectrometry of super-paramagnetic iron-oxide nanoparticles is a central tool for characterising particles that are used in Magnetic Particle Imaging. In Magnetic Particle Imaging, nanoparticles are excited by a magnetic field and the particle response is measured. Until now, the influence of the trajectory sequence on the dynamic particle relaxation has not been scoped. With a multi-dimensional Magnetic Particle Spectrometer, analysing the behaviour of different trajectories on the particles becomes possible. In this paper, the contribution of Brownian rotation and assembly polarisation on the particle signal is being analysed.
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations

Extracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC). To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations in a supervised manner. SACL applies contrast-aware adversarial training to generate worst-case samples and uses joint class-spread contrastive learning to extract structured representations. It can effectively utilize label-level feature consistency and retain fine-grained intra-class features. To avoid the negative impact of adversarial perturbations on context-dependent data, we design a contextual adversarial training (CAT) strategy to learn more diverse features from context and enhance the model’s context robustness. Under the framework with CAT, we develop a sequence-based SACL-LSTM to learn label-consistent and context-robust features for ERC. Experiments on three datasets show that SACL-LSTM achieves state-of-the-art performance on ERC. Extended experiments prove the effectiveness of SACL and CAT.
Two-View Geometry Scoring Without Correspondences

Camera pose estimation for two-view geometry traditionally relies on RANSAC. Normally, a multitude of image correspondences leads to a pool of proposed hypotheses, which are then scored to find a winning model. The inlier count is generally regarded as a reliable indicator of “consensus”. We examine this scoring heuristic, and find that it favors disappointing models under certain circumstances. As a remedy, we propose the Fundamental Scoring Network (FSNet), which infers a score for a pair of overlap-ping images and any proposed fundamental matrix. It does not rely on sparse correspondences, but rather embodies a two-view geometry model through an epipolar attention mechanism that predicts the pose error of the two images. FSNet can be incorporated into traditional RANSAC loops. We evaluate FSNet onfundamental and essential matrix estimation on indoor and outdoor datasets, and establish that FSNet can successfully identify good poses for pairs of images with few or unreliable correspondences. Besides, we show that naively combining FSNet with MAGSAC++ scoring approach achieves state of the art results.