Miracle-dz/UniFAR-FTUs huggingface dataset:
MusE GAs FLOw and Wind (MEGAFLOW) X. The cool gas and covering fraction of Mg  ii in galaxy groups

We present a study of the cool gas (≈104 K) traced by Mg ii absorptions around groups of galaxies in the MEGAFLOW survey. Using a combination of two algorithms we blindly identify 32 groups of more than 5 galaxies at 0.3 < z < 1.5 with $10.7 < \log _{10}(M/\rm {\rm M}_{\odot }) < 13.7$. Among them 26 can be used to study potential counterpart Mg ii absorptions. We report that 21 out of the total 120 Mg ii absorption systems present in MEGAFLOW are associated with groups. We observe that the Mg ii rest-frame equivalent width ($W_{\rm r}^{2796}$) drops at an impact parameter of ≈150 projected kpc from the closest galaxy and ≈ one virial radius from the identified group center indicating that Mg ii halos scale with the mass of the groups. The impact parameter where the covering fraction exceeds 50 % is $\log _{10}(b/\rm kpc) = 2.17 \pm 0.47$ (2σ) and (b/Rvir) = 1.67 ± 0.98, which is ≈3 times larger than for field galaxies ($\log _{10}(b/\rm kpc)=1.67\pm 0.15$). Finally, we estimate the cool gas column density profile in groups (from the $W_{\rm r}^{2796}$) and show that its shape follows closely the typical dark matter column density profile for halos at similar redshift and masses.
Group Sequential Design Under Non-proportional Hazards

Non-proportional hazards (NPH) are often observed in clinical trials with time-to-event endpoints. A common example is a long-term clinical trial with a delayed treatment effect in immunotherapy for cancer. When designing clinical trials with time-to-event endpoints, it is crucial to consider NPH scenarios to gain a complete understanding of design operating characteristics. In this paper, we focus on group sequential design for three NPH methods: the average hazard ratio, the weighted logrank test, and the MaxCombo combination test. For each of these approaches, we provide analytic forms of design characteristics that facilitate sample size calculation and bound derivation for group sequential designs. Examples are provided to illustrate the proposed methods. To facilitate statisticians in designing and comparing group sequential designs under NPH, we have implemented the group sequential design methodology in the gsDesign2 R package at https://cran.r-project.org/web/packages/gsDesign2/.
VLTSeg: Simple Transfer of CLIP-Based Vision-Language Representations for Domain Generalized Semantic Segmentation

Domain generalization (DG) remains a significant challenge for perception based on deep neural networks (DNN), where domain shifts occur due to lighting, weather, or geolocation changes. In this work, we propose VLTSeg to enhance domain generalization in semantic segmentation, where the network is solely trained on the source domain and evaluated on unseen target domains. Our method leverages the inherent semantic robustness of vision-language models. First, by substituting traditional vision-only backbones with pre-trained encoders from CLIP and EVA-CLIP as transfer learning setting we find that in the field of DG, vision-language pre-training significantly outperforms supervised and self-supervised vision pre-training. We thus propose a new vision-language approach for domain generalized segmentation, which improves the domain generalization SOTA by 7.6% mIoU when training on the synthetic GTA5 dataset. We further show the superior generalization capabilities of vision-language segmentation models by reaching 76.48% mIoU on the popular Cityscapes-to-ACDC benchmark, outperforming the previous SOTA approach by 6.9% mIoU on the test set at the time of writing. Additionally, our approach shows strong in-domain generalization capabilities indicated by 86.1% mIoU on the Cityscapes test set, resulting in a shared first place with the previous SOTA on the current leaderboard at the time of submission.
GAPS: Geometry-Aware, Physics-Based, Self-Supervised Neural Garment Draping

Recent neural, physics-based modeling of garment deformations allows faster and visually aesthetic results as opposed to the existing methods. Material-specific parameters are used by the formulation to control the garment inextensibility. This delivers unrealistic results with physically implausible stretching. Oftentimes, the draped garment is pushed inside the body which is either corrected by an expensive post-processing, thus adding to further inconsistent stretching; or by deploying a separate training regime for each body type, restricting its scalability. Additionally, the flawed skinning process deployed by existing methods produces incorrect results on loose garments. In this paper, we introduce a geometrical constraint to the existing formulation that is collision-aware and imposes garment inextensibility wherever possible. Thus, we obtain realistic results where draped clothes stretch only while covering bigger body regions. Furthermore, we propose a geometry-aware garment skinning method by defining a body-garment closeness measure which works for all garment types, especially the loose ones.
Formations organization in robotic swarm using the thermal motion equivalent method

Due to its decentralised, distributed and scalable nature, swarm robotics has great potential for applications ranging from agriculture to environmental monitoring and logistics. Various swarm control methods and algorithms are currently known, such as virtual leader, vector and potential field, and others. Such methods often show good results in specific conditions and tasks. The variety of tasks solved by the swarm requires the development of a universal control algorithm. In this paper, we propose an evolution of a thermal motion equivalent method (TMEM) inspired by the behavioural similarity of thermodynamic interactions between molecules. Previous research has shown the high efficiency of such a method for terrain monitoring tasks. This work addresses the problem of swarm formation of geometric structures, as required for logistics and formation movement tasks. It is shown that the formation of swarm geometric structures using the TMEM is possible with a special nonlinear interaction function of the agents. A piecewise linear interaction function is proposed that allows the formation of a stable group of agents. The results of the paper are validated by numerical modelling of the swarm dynamics. A linear quadrocopter model is considered as an agent. The fairness of the choice of the interaction function is shown.
Improved Stability and Controller Design Criteria for Two-dimensional Differential-Algebraic-Equation Systems via LMI Approach

This paper addresses issues concerning asymptotic stability testing and controller design for the two-dimensional Rosser model in Differential-Algebraic-Equations systems (DAEs). We present sufficient stability criteria based on the Lyapunov approach, utilizing a set of Linear-Matrix-Inequalities (LMIs) tailored for two-dimensional DAEs. Furthermore, we establish a set of sufficient conditions for determining the feasibility of both state- and output-feedback controllers. Our methods eliminate the need for decomposing the two-dimensional DAEs into separate algebraic and differential components.
Unsupervised Approach to Evaluate Sentence-Level Fluency: Do We Really Need Reference?

Fluency is a crucial goal of all Natural Language Generation (NLG) systems. Widely used automatic evaluation metrics fall short in capturing the fluency of machine-generated text. Assessing the fluency of NLG systems poses a challenge since these models are not limited to simply reusing words from the input but may also generate abstractions. Existing reference-based fluency evaluations, such as word overlap measures, often exhibit weak correlations with human judgments. This paper adapts an existing unsupervised technique for measuring text fluency without the need for any reference. Our approach leverages various word embeddings and trains language models using Recurrent Neural Network (RNN) architectures. We also experiment with other available multilingual Language Models (LMs). To assess the performance of the models, we conduct a comparative analysis across 10 Indic languages, correlating the obtained fluency scores with human judgments. Our code and human-annotated benchmark test-set for fluency is available at https://github.com/AnanyaCoder/TextFluencyForIndicLanaguges.
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents

Data heterogeneity presents significant challenges for federated learning (FL). Recently, dataset distillation techniques have been introduced, and performed at the client level, to attempt to mitigate some of these challenges. In this paper, we propose a highly efficient FL dataset distillation framework on the server side, significantly reducing both the computational and communication demands on local devices while enhancing the clients' privacy. Unlike previous strategies that perform dataset distillation on local devices and upload synthetic data to the server, our technique enables the server to leverage prior knowledge from pre-trained deep generative models to synthesize essential data representations from a heterogeneous model architecture. This process allows local devices to train smaller surrogate models while enabling the training of a larger global model on the server, effectively minimizing resource utilization. We substantiate our claim with a theoretical analysis, demonstrating the asymptotic resemblance of the process to the hypothetical ideal of completely centralized training on a heterogeneous dataset. Empirical evidence from our comprehensive experiments indicates our method's superiority, delivering an accuracy enhancement of up to 40% over non-dataset-distillation techniques in highly heterogeneous FL contexts, and surpassing existing dataset-distillation methods by 18%. In addition to the high accuracy, our framework converges faster than the baselines because rather than the server trains on several sets of heterogeneous data distributions, it trains on a multi-modal distribution. Our code is available at https://github.com/FedDG23/FedDG-main.git
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE

When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2 dimensional view of the data. We describe ENS-t-SNE: an algorithm for Embedding Neighborhoods Simultaneously that generalizes the t-Stochastic Neighborhood Embedding approach. By using different viewpoints in ENS-t-SNE's 3D embedding, one can visualize different types of clusters within the same high-dimensional dataset. This enables the viewer to see and keep track of the different types of clusters, which is harder to do when providing multiple 2D embeddings, where corresponding points cannot be easily identified. We illustrate the utility of ENS-t-SNE with real-world applications and provide an extensive quantitative evaluation with datasets of different types and sizes.
An in-silico study of travel time estimation using Eikonal equation with finite element method in ultrasound imaging

Estimating the travel time of ultrasound in an inhomogeneous medium is crucial for high-quality imaging, as with an accurate estimate of the distribution of speed of sound, phase aberration, which is normally viewed as one of the reasons for image degradation, can be corrected by multiple methods. In order to estimate the ultrasound travel time in an inhomogeneous medium, the approach using solutions to the eikonal equation is attractive, since it can be easily integrated into a conventional delay-and-sum beamformer. In our manuscript, we first propose employing the finite element method to solve the eikonal equation in ultrasound imaging, which is easy to extend to a high-order numerical scheme and potentially runs faster on GPU. Through a series of in-silicon experiments, our results are comparable to those of fast marching methods.
SPECRUN: The Danger of Speculative Runahead Execution in Processors

Runahead execution is a continuously evolving microarchitectural technique for processor performance. This paper introduces the first transient execution attack on the runahead execution, called SPECRUN, which exploits the unresolved branch prediction during runahead execution. We show that SPECRUN eliminates the limitation on the number of transient instructions posed by the reorder buffer size, enhancing the exploitability and harmfulness of the attack. We concretely demonstrate a proof-of-concept attack that causes leaking secrets from a victim process, validate the merit of SPECRUN, and design a secure runahead execution scheme. This paper highlights the need to consider the security of potential optimization techniques before implementing them in a processor.
From Spin States to Socially Integrated Ising Models: Proposed Applications of Graph States, Stabilizer States, Toric States to Opinion Dynamics

Recent research has developed the Ising model from physics, especially statistical mechanics, and it plays an important role in quantum computing, especially quantum annealing and quantum Monte Carlo methods. The model has also been used in opinion dynamics as a powerful tool for simulating social interactions and opinion formation processes. Individual opinions and preferences correspond to spin states, and social pressure and communication dynamics are modeled through interactions between spins. Quantum computing makes it possible to efficiently simulate these interactions and analyze more complex social networks.Recent research has incorporated concepts from quantum information theory such as Graph State, Stabilizer State, and Surface Code (or Toric Code) into models of opinion dynamics. The incorporation of these concepts allows for a more detailed analysis of the process of opinion formation and the dynamics of social networks. The concepts lie at the intersection of graph theory and quantum theory, and the use of Graph State in opinion dynamics can represent the interdependence of opinions and networks of influence among individuals. It helps to represent the local stability of opinions and the mechanisms for correcting misunderstandings within a social network. It allows us to understand how individual opinions are subject to social pressures and cultural influences and how they change over time.Incorporating these quantum theory concepts into opinion dynamics allows for a deeper understanding of social interactions and opinion formation processes. Moreover, these concepts can provide new insights not only in the social sciences, but also in fields as diverse as political science, economics, marketing, and urban planning.
The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits

This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method. We devise a fast folding algorithm parallelized on a GPU to amplify low signal-to-noise ratio transit signals, allowing a search at high precision and speed. A CNN trained on two million synthetic light curves reports a score indicating the likelihood of a planetary signal at each period. While the GPFC method has broad applicability across period ranges, this research specifically focuses on detecting ultra-short-period planets with orbital periods less than one day. GPFC improves on speed by three orders of magnitude over the predominant Box-fitting Least Squares (BLS) method. Our simulation results show GPFC achieves 97% training accuracy, higher true positive rate at the same false positive rate of detection, and higher precision at the same recall rate when compared to BLS. GPFC recovers 100% of known ultra-short-period planets in Kepler light curves from a blind search. These results highlight the promise of GPFC as an alternative approach to the traditional BLS algorithm for finding new transiting exoplanets in data taken with Kepler and other space transit missions such as K2, TESS and future PLATO and Earth 2.0.
Fast and Robust Sparsity-Aware Block Diagonal Representation

The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients that are concentrated in blocks. However, recovering a block diagonal affinity matrix is challenging in real-world applications, in which the data may be subject to outliers and heavy-tailed noise that obscure the hidden cluster structure. To address this issue, we first analyze the effect of different fundamental outlier types in graph-based cluster analysis. A key idea that simplifies the analysis is to introduce a vector that represents a block diagonal matrix as a piece-wise linear function of the similarity coefficients that form the affinity matrix. We reformulate the problem as a robust piece-wise linear fitting problem and propose a Fast and Robust Sparsity-Aware Block Diagonal Representation (FRS-BDR) method, which jointly estimates cluster memberships and the number of blocks. Comprehensive experiments on a variety of real-world applications demonstrate the effectiveness of FRS-BDR in terms of clustering accuracy, robustness against corrupted features, computation time and cluster enumeration performance.
SoK: The Gap Between Data Rights Ideals and Reality

As information economies burgeon, they unlock innovation and economic wealth while posing novel threats to civil liberties and altering power dynamics between individuals, companies, and governments. Legislatures have reacted with privacy laws designed to empower individuals over their data. These laws typically create rights for"data subjects"(individuals) to make requests of data collectors (companies and governments). The European Union General Data Protection Regulation (GDPR) exemplifies this, granting extensive data rights to data subjects, a model embraced globally. However, the question remains: do these rights-based privacy laws effectively empower individuals over their data? This paper scrutinizes these approaches by reviewing 201 interdisciplinary empirical studies, news articles, and blog posts. We pinpoint 15 key questions concerning the efficacy of rights allocations. The literature often presents conflicting results regarding the effectiveness of rights-based frameworks, but it generally emphasizes their limitations. We offer recommendations to policymakers and Computer Science (CS) groups committed to these frameworks, and suggest alternative privacy regulation approaches.
Enhanced persistent orientation of asymmetric-top molecules induced by cross-polarized terahertz pulses

We investigate the persistent orientation of asymmetric-top molecules induced by time-delayed THz pulses that are either collinearly or cross polarized. Our theoretical and numerical results demonstrate that the orthogonal configuration outperforms the collinear one, and a significant degree of persistent orientation - approximately 10% at 5 K and nearly 3% at room temperature - may be achieved through parameter optimization. The dependence of the persistent orientation factor on temperature and field parameters is studied in detail. The proposed application of two orthogonally polarized THz pulses is both practical and efficient. Its applicability under standard laboratory conditions lays a solid foundation for future experimental realization of THz-induced persistent molecular orientation.
Universal thermodynamic bounds on the Fano factor of discriminatory networks with unidirectional transitions

We derive a universal lower bound on the Fano factors of general biochemical discriminatory networks involving irreversible catalysis steps, based on the thermodynamic uncertainty relation, and compare it to a numerically exact Pareto optimal front. This bound is completely general, involving only the reversible entropy production per product formed and the error fraction of the system. We then show that by judiciously choosing which transitions to include in the reversible entropy production, one can derive a family of bounds that can be fine-tuned to include physical observables at hand. Lastly, we test our bound by considering three discriminatory schemes: a multi-stage Michaelis-Menten network, a Michaelis-Menten network with correlations between subsequent products, and a multi-stage kinetic proofreading network, where for the latter application the bound is altered to include the hydrolytic cost of the proofreading steps. We find that our bound is remarkably tight.
CEScore: Simple and Efficient Confidence Estimation Model for Evaluating Split and Rephrase

The split and rephrase (SR) task aims to divide a long, complex sentence into a set of shorter, simpler sentences that convey the same meaning. This challenging problem in NLP has gained increased attention recently because of its benefits as a pre-processing step in other NLP tasks. Evaluating quality of SR is challenging, as there no automatic metric fit to evaluate this task. In this work, we introduce CEScore, as novel statistical model to automatically evaluate SR task. By mimicking the way humans evaluate SR, CEScore provides 4 metrics (Sscore, Gscore, Mscore, and CEscore) to assess simplicity, grammaticality, meaning preservation, and overall quality, respectively. In experiments with 26 models, CEScore correlates strongly with human evaluations, achieving 0.98 in Spearman correlations at model-level. This underscores the potential of CEScore as a simple and effective metric for assessing the overall quality of SR models.
Optimal rate of convergence to nondegenerate asymptotic profiles for fast diffusion in domains

This paper is concerned with the Cauchy-Dirichlet problem for fast diffusion equations posed in bounded domains, where every energy solution vanishes in finite time and a suitably rescaled solution converges to an asymptotic profile. Bonforte and Figalli (CPAM, 2021) first proved an exponential convergence to nondegenerate positive asymptotic profiles for nonnegative rescaled solutions in a weighted $L^2$ norm for smooth bounded domains by developing a nonlinear entropy method. However, the optimality of the rate remains open to question. In the present paper, their result is fully extended to possibly sign-changing asymptotic profiles as well as general bounded domains by improving an energy method along with a quantitative gradient inequality developed by the first author (ARMA, 2023). Moreover, a (quantitative) exponential stability result for least-energy asymptotic profiles follows as a corollary, and it is further employed to prove the optimality of the exponential rate.