Miracle-dz/UniFAR-FTUs huggingface dataset:
Complete vectorial optical mode converter using multi-layer metasurface

A vectorial optical mode converter that can transform an orthogonal set of multiple input vector beams into another orthogonal set of vector beams is attractive for a wide range of applications in optics and photonics. While multi-plane light conversion (MPLC) and metasurface technologies have been explored to individually address multiple spatial mode conversion and polarization mode manipulation, there is no universal methodology to simultaneously convert a set of multiple vectorial modes, having non-uniform spatial distributions in both their complex amplitude and polarization, to another set of multiple vectorial modes. In this paper, we introduce a novel device based on the MPLC concept, incorporating multi-layer locally birefringent metasurfaces, and present a general design formalism to realize complete vectorial mode conversion for arbitrary cases. The efficacy of the proposed method is confirmed through the demonstration of two distinctive devices: a mode-division-multiplexed dual-polarization coherent receiver and a spatial-mode-multiplexed vectorial holography system. Due to its versatility, the demonstrated protocol can be applied to design a myriad of multi-input-multi-output devices, offering a powerful tool to realize universal optical mode converters for diverse applications.
State-Specific Coupled-Cluster Methods for Excited States

We reexamine $\Delta$CCSD, a state-specific coupled-cluster (CC) with single and double excitations (CCSD) approach that targets excited states through the utilization of non-Aufbau determinants. This methodology is particularly efficient when dealing with doubly excited states, a domain where the standard equation-of-motion CCSD (EOM-CCSD) formalism falls short. Our goal here is to evaluate the effectiveness of $\Delta$CCSD when applied to other types of excited states, comparing its consistency and accuracy with EOM-CCSD. To this end, we report a benchmark on excitation energies computed with the $\Delta$CCSD and EOM-CCSD methods, for a set of molecular excited-state energies that encompasses not only doubly excited states but also doublet-doublet transitions and (singlet and triplet) singly-excited states of closed-shell systems. In the latter case, we rely on a minimalist version of multireference CC known as the two-determinant CCSD method to compute the excited states. Our dataset, consisting of 276 excited states stemming from the \textsc{quest} database [V\'eril \textit{et al.}, \textit{WIREs Comput. Mol. Sci.} \textbf{2021}, 11, e1517], provides a significant base to draw general conclusions concerning the accuracy of $\Delta$CCSD. Except for the doubly-excited states, we found that $\Delta$CCSD underperforms EOM-CCSD. For doublet-doublet transitions, the difference between the mean absolute errors (MAEs) of the two methodologies (of \SI{0.10}{\eV} and \SI{0.07}{\eV}) is less pronounced than that obtained for singly-excited states of closed-shell systems (MAEs of \SI{0.15}{\eV} and \SI{0.08}{\eV}). This discrepancy is largely attributed to a greater number of excited states in the latter set exhibiting multiconfigurational characters, which are more challenging for $\Delta$CCSD.
I am a Strange Dataset: Metalinguistic Tests for Language Models

Statements involving metalinguistic self-reference ("This paper has six sections.") are prevalent in many domains. Can large language models (LLMs) handle such language? In this paper, we present"I am a Strange Dataset", a new dataset for addressing this question. There are two subtasks: generation and verification. In generation, models continue statements like"The penultimate word in this sentence is"(where a correct continuation is"is"). In verification, models judge the truth of statements like"The penultimate word in this sentence is sentence. "(false). We also provide minimally different metalinguistic non-self-reference examples to complement the main dataset by probing for whether models can handle metalinguistic language at all. The dataset is hand-crafted by experts and validated by non-expert annotators. We test a variety of open-source LLMs (7B to 70B parameters) as well as closed-source LLMs through APIs. All models perform close to chance across both subtasks and even on the non-self-referential metalinguistic control data, though we find some steady improvement with model scale. GPT 4 is the only model to consistently do significantly better than chance, and it is still only in the 60% range, while our untrained human annotators score well in the 89-93% range. The dataset and evaluation toolkit are available at https://github.com/TristanThrush/i-am-a-strange-dataset.
An Information Theoretic Approach to Interaction-Grounded Learning

Reinforcement learning (RL) problems where the learner attempts to infer an unobserved reward from some feedback variables have been studied in several recent papers. The setting of Interaction-Grounded Learning (IGL) is an example of such feedback-based RL tasks where the learner optimizes the return by inferring latent binary rewards from the interaction with the environment. In the IGL setting, a relevant assumption used in the RL literature is that the feedback variable $Y$ is conditionally independent of the context-action $(X,A)$ given the latent reward $R$. In this work, we propose Variational Information-based IGL (VI-IGL) as an information-theoretic method to enforce the conditional independence assumption in the IGL-based RL problem. The VI-IGL framework learns a reward decoder using an information-based objective based on the conditional mutual information (MI) between $(X,A)$ and $Y$. To estimate and optimize the information-based terms for the continuous random variables in the RL problem, VI-IGL leverages the variational representation of mutual information to obtain a min-max optimization problem. Also, we extend the VI-IGL framework to general $f$-Information measures leading to the generalized $f$-VI-IGL framework for the IGL-based RL problems. We present numerical results on several reinforcement learning settings indicating an improved performance compared to the existing IGL-based RL algorithm.
AppealMod: Inducing Friction to Reduce Moderator Workload of Handling User Appeals

As content moderation becomes a central aspect of all social media platforms and online communities, interest has grown in how to make moderation decisions contestable. On social media platforms where individual communities moderate their own activities, the responsibility to address user appeals falls on volunteers from within the community. While there is a growing body of work devoted to understanding and supporting the volunteer moderators' workload, little is known about their practice of handling user appeals. Through a collaborative and iterative design process with Reddit moderators, we found that moderators spend considerable effort in investigating user ban appeals and desired to directly engage with users and retain their agency over each decision. To fulfill their needs, we designed and built AppealMod, a system that induces friction in the appeals process by asking users to provide additional information before their appeals are reviewed by human moderators. In addition to giving moderators more information, we expected the friction in the appeal process would lead to a selection effect among users, with many insincere and toxic appeals being abandoned before getting any attention from human moderators. To evaluate our system, we conducted a randomized field experiment in a Reddit community of over 29 million users that lasted for four months. As a result of the selection effect, moderators viewed only 30% of initial appeals and less than 10% of the toxically worded appeals; yet they granted roughly the same number of appeals when compared with the control group. Overall, our system is effective at reducing moderator workload and minimizing their exposure to toxic content while honoring their preference for direct engagement and agency in appeals.
Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study

Purpose: Body composition measurements from routine abdominal CT can yield personalized risk assessments for asymptomatic and diseased patients. In particular, attenuation and volume measures of muscle and fat are associated with important clinical outcomes, such as cardiovascular events, fractures, and death. This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool. Methods: We assessed the tools across 900 CT series from the publicly available SAROS dataset, focusing on muscle, subcutaneous fat, and visceral fat. The Dice score was employed to assess accuracy in subcutaneous fat and muscle segmentation. Due to the lack of ground truth segmentations for visceral fat, Cohen’s Kappa was utilized to assess segmentation agreement between the tools. Results: Our Internal tool achieved a 3% higher Dice (83.8 vs. 80.8) for subcutaneous fat and a 5% improvement (87.6 vs. 83.2) for muscle segmentation respectively. A Wilcoxon signed-rank test revealed that our results were statistically different with p < 0.01. For visceral fat, the Cohen’s kappa score of 0.856 indicated near-perfect agreement between the two tools. Our internal tool also showed very strong correlations for muscle volume (R2=0.99), muscle attenuation (R2=0.93), and subcutaneous fat volume (R2=0.99) with a moderate correlation for subcutaneous fat attenuation (R2=0.45). Conclusion: Our findings indicated that our Internal tool outperformed TotalSegmentator in measuring subcutaneous fat and muscle. The high Cohen’s Kappa score for visceral fat suggests a reliable level of agreement between the two tools. These results demonstrate the potential of our tool in advancing the accuracy of body composition analysis.
From law of the iterated logarithm to Zolotarev distance for supercritical branching processes in random environment

Consider $(Z_n)_{n\geq0}$ a supercritical branching process in an independent and identically distributed environment. Based on some recent development in martingale limit theory, we established law of the iterated logarithm, strong law of large numbers, invariance principle and optimal convergence rate in the central limit theorem under Zolotarev and Wasserstein distances of order $p\in(0,2)$ for the process $(\log Z_n)_{n\geq0}$.
Prediction of trace gas emissions and their climatic impacts: some geographical considerations

This paper examines two major areas of uncertainty in the prediction of the impact of trace gas emissions on climate. The first is socioeconomic factors which determine the rate of such processes as resource use, industrial production or land conversion. The second is the feedback between the earth's land surface and climate. Since the land surface is the source of trace gas emissions, both natural and anthropogenic changes of vegetation will affect the nature and quantity of emissions. This paper demon strates large-scale land surface changes which have taken place naturally or from human activities, either intentionally or inadvertently, and describes the dwindling availability of natural resources, using water as an example. Vegetation is also examined as both a response to and a determining factor in climate. Hence, the intricate feedback between vegetation and climate complicates any attempt to predict climatic change. Better quantitative assessment of all relationships and processes is required to achieve realistic forecasts of global change.
Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for carotid artery haemodynamics

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid-structure interaction simulations with affordable one- and zero-dimensional reduced order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.
DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation

Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction data, neglecting to model the diverse matching relationships between users and items behind their interactions, leading to limited performance and weak interpretability. To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data. Specifically, we first implement the disentangling concept by unifying an attention-aware dual disentanglement and disentangled variational autoencoder to infer the disentangled latent representations of users and items. Further, to encourage the correspondence and independence of disentangled representations of users and items, we design a neighborhood-enhanced representation constraint with a customized contrastive mechanism to improve the representation quality. Extensive experiments on three real-world benchmarks show that our proposed model significantly outperforms several recent state-of-the-art baselines. Further empirical experimental results also illustrate the interpretability of the disentangled representations learned by DualVAE.
An event-based implementation of saliency-based visual attention for rapid scene analysis

Selective attention is an essential mechanism to filter sensory input and to select only its most important components, allowing the capacity-limited cognitive structures of the brain to process them in detail. The saliency map model, originally developed to understand the process of selective attention in the primate visual system, has also been extensively used in computer vision. Due to the wide-spread use of frame-based video, this is how dynamic input from non-stationary scenes is commonly implemented in saliency maps. However, the temporal structure of this input modality is very different from that of the primate visual system. Retinal input to the brain is massively parallel, local rather than frame-based, asynchronous rather than synchronous, and transmitted in the form of discrete events, neuronal action potentials (spikes). These features are captured by event-based cameras. We show that a computational saliency model can be obtained organically from such vision sensors, at minimal computational cost. We assess the performance of the model by comparing its predictions with the distribution of overt attention (fixations) of human observers, and we make available an event-based dataset that can be used as ground truth for future studies.
HaltingVT: Adaptive Token Halting Transformer for Efficient Video Recognition

Action recognition in videos poses a challenge due to its high computational cost, especially for Joint Space-Time video transformers (Joint VT). Despite their effectiveness, the excessive number of tokens in such architectures significantly limits their efficiency. In this paper, we propose HaltingVT, an efficient video transformer adaptively removing redundant video patch tokens, which is primarily composed of a Joint VT and a Glimpser module. Specifically, HaltingVT applies data-adaptive token reduction at each layer, resulting in a significant reduction in the overall computational cost. Besides, the Glimpser module quickly removes redundant tokens in shallow transformer layers, which may even be misleading for video recognition tasks based on our observations. To further encourage HaltingVT to focus on the key motion-related information in videos, we design an effective Motion Loss during training. HaltingVT acquires video analysis capabilities and token halting compression strategies simultaneously in a unified training process, without requiring additional training procedures or sub-networks. On the Mini-Kinetics dataset, we achieved 75.0% top-1 ACC with 24.2 GFLOPs, as well as 67.2% top-1 ACC with an extremely low 9.9 GFLOPs. The code is available at https://github.com/dun-research/HaltingVT.
Gnuastro: Measuring Radial Profiles from Images

Radial profiles play a crucial role in the analysis and interpretation of astronomical data, facilitating the extraction of spatial information. However, highly customizable (for different scenarios) measurements over each elliptical annulus can be challenging. In response, we present astscript-radial-profile, which is part of Gnuastro from version 0.15 and has an extensive documentation. A convenient feature of this program is its capability to make the measurements with different operators (mean, median, sigma-clipping, and many more) over ellipses, very quickly and directly on the command-line with minimal dependencies. This research note is reproducible with Maneage, on the Git commit f3ec942.
First Observational Evidence for an Interconnected Evolution between Time Lag and QPO Frequency among AGNs

Quasiperiodic oscillations (QPOs) have been widely observed in black hole X-ray binaries (BHBs), which often exhibit significant X-ray variations. Extensive research has explored the long-term evolution of the properties of QPOs in BHBs. In contrast, such evolution in active galactic nuclei (AGNs) has remained largely unexplored due to limited observational data. By using the 10 new XMM-Newton observations for the narrow-line Seyfert 1 galaxy RE J1034 + 396 from publicly available data, we analyze the characteristics of its X-ray QPOs and examine their long-term evolution. The hard-band (1–4 keV) QPOs are found in all 10 observations and the frequency of these QPOs evolves ranging at (2.47–2.83) × 10−4 Hz. Furthermore, QPO signals in the soft (0.3–1 keV) and hard bands exhibit strong coherence, although, at times, the variations in the soft band lead those in the hard band (the hard-lag mode), while at other times, it is the reverse (the soft-lag mode). The observations presented here serendipitously captured two ongoing lag reversals between these two modes within about two weeks, which are first seen in RE J1034 + 396 and also among all AGNs. A transition in QPO frequency also takes place within a two-week timeframe, two weeks prior to its corresponding lag reversal, indicating a possible coherence between the transitions of QPO frequency and lag mode with delay. The diagram of time lag versus QPO frequency clearly evidences this interconnected evolution with hysteresis, which is, for the first time, observed among AGNs.
Direct numerical simulations of a cylinder cutting a vortex

The interaction between a vortex and an impacting body which is oriented normally to it is complex due to the interaction of inviscid and viscous three-dimensional mechanisms. To model this process, we conducted direct numerical simulations of a thin cylinder intersecting a columnar vortex oriented normally to it. We identified the various stages of the interaction, including the separation of the boundary layer and the generation of secondary vorticity that interacts with the primary vortex. By varying the impact parameter and the Reynolds number, we were able to distinguish the two regimes of interaction mentioned in the literature: the weak and strong vortex regimes. We found that low impact parameters, representing strong vortices, led to ejection and interaction of secondary vorticity from the cylinder's boundary layer, while high impact parameters, representing weak vortices, led to approximately inviscid interaction of the cylinder with the primary vortex through deformations. We did not find a significant effect of the Reynolds number in the overall phenomenology, even if larger Reynolds numbers lead to the formation of increasingly smaller and more intense vortex structures in the parameter range studied. Finally, we analyzed the hydrodynamic force curves on the cylinder, showing that intense forces could be locally generated for some parameter regimes, but that the average force on the cylinder did not deviate substantially from baseline cases where no vortex was present. Our results shed light on the underlying mechanisms of vortex-body interactions and their dependence on various parameters.
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey

Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the agents in a fully decentralized manner. Due to the lack of information about other agents, it is challenging to derive algorithms that can converge to the optimal joint policy in a fully decentralized setting. Thus, this research area has not been thoroughly studied. In this paper, we seek to systematically review the fully decentralized methods in two settings: maximizing a shared reward of all agents and maximizing the sum of individual rewards of all agents, and discuss open questions and future research directions.
Catalyst-free MBE growth of PbSnTe nanowires with tunable aspect ratio

Topological crystalline insulators (TCIs) are interesting for their topological surface states, which hold great promise for scattering-free transport channels and fault-tolerant quantum computing. A promising TCI is SnTe. However, Sn-vacancies form in SnTe, causing a high hole density, hindering topological transport from the surface being measured. This issue could be relieved by using nanowires with a high surface-to-volume ratio. Furthermore, SnTe can be alloyed with Pb reducing the Sn-vacancies while maintaining its topological phase. Here we present the catalyst-free growth of monocrystalline PbSnTe in molecular beam epitaxy (MBE). By the addition of a pre-deposition stage before the growth, we have control over the nucleation phase and thereby increase the nanowire yield. This facilitates tuning the nanowire aspect ratio by a factor of four by varying the growth parameters. These results allow us to grow specific morphologies for future transport experiments to probe the topological surface states in a Pb1-xSnxTe-based platform.