Miracle-dz/UniFAR-FTUs huggingface dataset:
Attosecond phenomena in the relativistic λ3 regime

Modeling of relativistically strong laser pulses interacting with overdense plasma targets indicates that coherent motion of electrons leads to the generation of strong attosecond electromagnetic pulses and dense attosecond electron bunches [N. Naumova et al., Phys. Rev. Lett. 92,063902 (2004); ibid. 93, 195003 (2004)]. Particle-in-cell simulations predict that optimal conditions for these attosecond phenomena are achieved in the λ 3 regime, when few-cycle laser pulses are focused to a wavelength-limited spot, producing maximal intensity and maximal gradients with a given energy. The natural synchronism of these attosecond phenomena should enable relativistic attosecond optoelectronics. First experiments toward verification of attosecond pulse production confirm a basic element of attosecond pulse generation-relativistic deflection.
Backward illusory line motion: Visual motion perception can be influenced by retrospective stimulation

When a visual cue appears beside a horizontal line segment before the line appears, the illusory motion is perceived as a line extending from the side closest to the side farthest from the cue. This is known as illusory line motion (ILM). In Experiment 1, we presented the cue after the line onset and found that the line seemed to extend toward the side of the cue (backward ILM). In Experiment 2, we confirmed the robustness and replicability of the backward ILM. In Experiments 3 to 5, we investigated the role of endogenous and exogenous attention in the generation of backward ILM and found effects of attention, but not large enough to explain the backward ILM in Experiments 1 and 2. The current findings suggest that the direction of ILM depended on the temporal relation of whether the cue precedes or follows the stimulus appearance, and that attentional shift played a role in the perception of backward ILM.
Posttraumatic stress disorder and depression after the 2018 Strasbourg Christmas Market terrorist attack: a comparison of exposed and non-exposed police personnel

ABSTRACT Background: Police personnel are among the first responders exposed to terrorist attacks, raising in number in the late decades. Due to their profession, they are also exposed to repetitive violence, increasing their vulnerability to PTSD and depression. Objective: Our study aims at comparing the prevalence of PTSD and depression, and the risk factors associated with these conditions among directly and indirectly exposed versus non-exposed police personnel during the Strasbourg Christmas Market terrorist attack. Method: Three months after the attack, participants completed a survey assessing their sociodemographic characteristics, occupational data, degree of exposure, sleep debt around the event, event centrality (CES), and three mental health conditions: PTSD (PCL-5), depression (PHQ-9), and suicide risk (yes/no questions). Results: A total of 475 police personnel responded to the questionnaire: 263 were exposed to the attack (182 of them directly) and 212 were non-exposed. Among directly exposed participants, the prevalences of partial and complete PTSD were 12.6 and 6.6%, and the prevalence of moderate-to-severe depression was 11.5%. Multivariate analysis revealed that direct exposure was associated with a higher risk of PTSD (OR = 2.98 [1.10–8.12], p = .03). Direct exposure was not associated with a higher risk of depression (OR = 0.40 [0.10–1.10], p = .08). A significant sleep debt after the event was not associated with a higher risk of later PTSD (OR = 2.18 [0.81–5.91], p = .13) but was associated with depression (OR = 7.92 [2.40–26.5], p < .001). A higher event centrality was associated with both PTSD and depression (p < .001). Conclusions: Police personnel directly exposed to the Strasbourg Christmas Market terrorist attack were at higher risk of PTSD but not depression. Efforts to prevent and treat PTSD should focus on directly exposed police personnel. However, general mental health should be monitored for every personnel member. HIGHLIGHTS Direct exposure to the terrorist attack and gender (female) were significantly associated with a higher risk of PTSD among police personnel. Sleep debt after the attack was significantly associated with depression but not with PTSD. No occupational factor was associated with either PTSD or depression. Both PTSD and depression were significantly associated with a higher suicide risk. A higher event centrality was significantly associated with direct exposure and a higher risk of both PTSD and depression.
Caregiver fear of cancer recurrence: A systematic review and meta‐analysis of quantitative studies

Fear of cancer recurrence (FCR) is reported by both cancer survivors and caregivers however less is known about caregiver FCR. This study aimed to (a) conduct a meta‐analysis to compare survivor and caregiver FCR levels; (b) examine the relationship between caregiver FCR and depression, and anxiety; (c) evaluate psychometric properties of caregiver FCR measures.
Pure subgroups of completely decomposable groups and a group class problem

In the work of Herden and Strüngmann (In Models, modules and Abelian groups (2008) 169–186 de Gruyter), an embedding problem for torsion-free Abelian groups was considered. It was shown for a large class of such groups, including the class of all bounded extensions of completely decomposable groups, that any member of the class can be purely embedded into some completely decomposable group. Moreover, an algorithm was given that determines explicitly the pure embedding and the completely decomposable overgroup. We continue the approach from the work of Herden and Strüngmann (In Models, modules and Abelian groups (2008) 169–186 de Gruyter) improving the algorithm and extending the main theorem to a broader class of torsion-free Abelian groups including some Hawaiian groups from the article of Mader and Strüngmann (J. Algebra 229 (2000) 205– 233) and thus complementing the main result from the article of Strüngmann (Proc. Amer. Math. Soc. 137 (2009) 3657–3668). A byproduct and starting point for this generalization will be a discussion of the following group class problem: Which groups G have the property that for any cardinal κ any subgroup U of the direct sum G is the kernel of some endomorphism of G?
Collective Förster energy transfer modified by planar plasmonic mirror (Presentation Recording)

This is an invited presentation devoted to the Förster energy transfer in plasmonic systems. Förster energy transfer processes are now actively studied in various fields that bridge physics, biology and medicine. One can try to control the efficiency of the transfer by embedding the donors and acceptors into the structured electromagnetic environment. Available experimental studies yields contradictory reports on suppressed [1], enhanced [2] or unaffected [3] transfer. We present a rigorous Green function theory of the collective Förster energy transfer between the arrays of donor and acceptor molecules lying on the planar metallic mirror that has been previously available only for spherical nanoparticles [4]. We reveal strong modification of the effective transfer rate by the mirror. The rate can be either suppressed or enhanced depending on the relative positions between acceptor and donor arrays. This is a collective effect, completely absent for a single donor-acceptor pair put above the mirror. Our results may explain the slowdown of the transfer rate recently observed in experiment for dye molecules put on top of plasmonic mirrors and layered hyperbolic metamaterials [1]. [1] T. Tumkur, J. Kitur, C. Bonner, A. Poddubny, E. Narimanov and M. Noginov , Faraday Discuss. , 2014 , DOI: 10.1039/C4FD00184B [2] C. Blum, N. Zijlstra, A. Lagendijk, M. Wubs, A. P. Mosk, V. Subramaniam, and W. L. Vos, Phys. Rev. Lett. 109, 203601 (2012). [3] P. Andrew and W. L. Barnes, Science 290, 785 (2000). [4] V.N. Pustovit, A.M. Urbas, and T.V. Shahbazyan, Phys. Rev. B 88, 245427(2013)
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption

Training large-scale CNNs that during inference can be run under Homomorphic Encryption (HE) is challenging due to the need to use only polynomial operations. This limits HE-based solutions adoption. We address this challenge and pioneer in providing a novel training method for large polynomial CNNs such as ResNet-152 and ConvNeXt models, and achieve promising accuracy on encrypted samples on large-scale dataset such as ImageNet. Additionally, we provide optimization insights regarding activation functions and skip-connection latency impacts, enhancing HE-based evaluation efficiency. Finally, to demonstrate the robustness of our method, we provide a polynomial adaptation of the CLIP model for secure zero-shot prediction, unlocking unprecedented capabilities at the intersection of HE and transfer learning.
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing

Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typically, GNNs are constructed by incorporating alternating layers which learn transformations of graph node features, along with graph pooling layers that use graph pooling operators (e.g., Max-pooling) to effectively reduce the number of nodes while preserving the semantic information of the graph. Recently, to enhance GNNs in graph learning tasks, Manifold-Mixup, a data augmentation technique that produces synthetic graph data by linearly mixing a pair of graph data and their labels, has been widely adopted. However, the performance of Manifold-Mixup can be highly affected by graph pooling operators, and there have not been many studies that are dedicated to uncovering such affection. To bridge this gap, we take an early step to explore how graph pooling operators affect the performance of Mixup-based graph learning. To that end, we conduct a comprehensive empirical study by applying Manifold-Mixup to a formal characterization of graph pooling based on 11 graph pooling operations (9 hybrid pooling operators, 2 non-hybrid pooling operators). The experimental results on both natural language datasets (Gossipcop, Politifact) and programming language datasets (JAVA250, Python800) demonstrate that hybrid pooling operators are more effective for Manifold-Mixup than the standard Max-pooling and the state-of-the-art graph multiset transformer (GMT) pooling, in terms of producing more accurate and robust models.
Second Harmonic Imaging of Nasal, Auricular, and Costal Cartilage

There is little knowledge about the histological organization of facial and costal cartilages in terms of matrix structure and cell morphology. Second harmonic generation (SHG) imaging is a nonlinear imaging technique that capitalizes on signal generation from highly ordered macromolecules such as collagen fibers. The purpose of this study was to use SHG microscopy to image collagen extracellular matrix (ECM) structure, chondrocyte size, and density of these cartilages.
Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach

Representation learning for time series has been an important research area for decades. Since the emergence of the foundation models, this topic has attracted a lot of attention in contrastive self-supervised learning, to solve a wide range of downstream tasks. However, there have been several challenges for contrastive time series processing. First, there is no work considering noise, which is one of the critical factors affecting the efficacy of time series tasks. Second, there is a lack of efficient yet lightweight encoder architectures that can learn informative representations robust to various downstream tasks. To fill in these gaps, we initiate a novel sampling strategy that promotes consistent representation learning with the presence of noise in natural time series. In addition, we propose an encoder architecture that utilizes dilated convolution within the Inception block to create a scalable and robust network architecture with a wide receptive field. Experiments demonstrate that our method consistently outperforms state-of-the-art methods in forecasting, classification, and abnormality detection tasks, e.g. ranks first over two-thirds of the classification UCR datasets, with only $40\%$ of the parameters compared to the second-best approach. Our source code for CoInception framework is accessible at https://github.com/anhduy0911/CoInception.
Island of Reissner-Nordstr$\mathbf{\ddot{o}}$m anti-de Sitter black holes in the large $d$ limit

We study the information paradox of Reissner-Nordstr${\ddot{o}}$m anti-de Sitter (RN-AdS$_{d+1}$) black holes in the large dimension limit by using the island formula. The entanglement entropy of Hawking radiation is calculated both for the non-extremal and the extremal cases, in which the boundary of the radiation region is close to the outer horizon. For the non-extremal case, the entanglement entropy of Hawking radiation obeys the Page curve, i.e. the entanglement entropy of Hawking radiation increases with time and reaches saturation about twice Bekenstein-Hawking entropy at the Page time. For the extremal case, the entanglement entropy of Hawking radiation becomes ill-defined in the absence of the island due to the appearance of the singularity at the origin of the radial coordinate, while when the island exists, the entanglement entropy is found to be equal to the Bekenstein-Hawking entropy. In addition, for the case where the boundary of the radiation region is close to the horizon, there are some obvious constraints required by the existence of island solution for both non-extremal and extremal cases, which can be utilized to put constraints on the size of the black hole. These results reveal new features of the semi-classical large $d$ black holes from the island perspective.
One-machine n-part-type optimal setup scheduling: analytical characterization of switching surfaces

The authors consider optimal setup scheduling of a single reliable machine. Production flow of n different part types and the setup process are described by differential equations. Setup change rates are control variables. Necessary conditions on optimal setup changes are characterized analytically, and optimal setup change times are derived for a given setup change sequence. The linearization of optimal setup switching surfaces is derived, indicating the existence of attractors observed in numerical optimal solutions. The approach developed in this paper establishes a strong basis for studying multimachine production systems and for constructing tractable near-optimal numerical solution techniques.
Proteinoid Microspheres as Protoneural Networks

Proteinoids, also known as thermal proteins, possess a fascinating ability to generate microspheres that exhibit electrical spikes resembling the action potentials of neurons. These spiking microspheres, referred to as protoneurons, hold the potential to assemble into proto-nanobrains. In our study, we investigate the feasibility of utilizing a promising electrochemical technique called differential pulse voltammetry (DPV) to interface with proteinoid nanobrains. We evaluate DPV’s suitability by examining critical parameters such as selectivity, sensitivity, and linearity of the electrochemical responses. The research systematically explores the influence of various operational factors, including pulse width, pulse amplitude, scan rate, and scan time. Encouragingly, our findings indicate that DPV exhibits significant potential as an efficient electrochemical interface for proteinoid nanobrains. This technology opens up new avenues for developing artificial neural networks with broad applications across diverse fields of research.
Differential Game Analysis for Cooperation Models in Automotive Supply Chain under Low-Carbon Emission Reduction Policies

In the context of reducing carbon emissions in the automotive supply chain, collaboration between vehicle manufacturers and retailers has proven to be an effective measure for enhancing carbon emission reduction within the enterprise. This study aims to evaluate the effectiveness of such collaboration by constructing a differential game model that incorporates carbon trading and consumer preferences for low-carbon products. The model examines the decision-making process of an automotive supply chain comprising a vehicle manufacturer and multiple retailers. By utilizing the Hamilton-Jacobi-Bellman equation, we analyze the equilibrium strategies of the participants under both a decentralized model and a Stackelberg leader-follower game model. In the decentralized model, the vehicle manufacturer optimizes its carbon emission reduction efforts, while each retailer independently determines its low-carbon promotion efforts and vehicle retail price. In the Stackelberg leader-follower game model, the vehicle manufacturer cooperates with the retailers by offering them a subsidy. Consequently, the manufacturer plays as the leader, making decisions on carbon emission reduction efforts and the subsidy rate, while the retailers, as followers, compute their promotion efforts and retail prices accordingly. Through theoretical analysis and numerical experiments considering the manufacturer's and retailers' efforts, the low-carbon reputation of vehicles, and the overall system profits under both models, we conclude that compared to the decentralized model, where each party pursues individual profits, the collaboration in the Stackelberg game yields greater benefits for both parties. Furthermore, this collaborative approach promotes the long-term development of the automotive supply chain.
Non-associative Frobenius algebras of type $^1E_6$ with trivial Tits algebras

Very recently, Maurice Chayet and Skip Garibaldi have introduced a class of commutative non-associative algebras, for each simple linear algebraic group over an arbitrary field (with some minor restriction on the characteristic). In a previous paper, we gave an explicit description of these algebras for groups of type $G_2$ and $F_4$ in terms of the octonion algebras and the Albert algebras, respectively. In this paper, we attempt a similar approach for type $E_6$.
Mandarin Electrolaryngeal Speech Voice Conversion using Cross-domain Features

Patients who have had their entire larynx removed, including the vocal folds, owing to throat cancer may experience difficulties in speaking. In such cases, electrolarynx devices are often prescribed to produce speech, which is commonly referred to as electrolaryngeal speech (EL speech). However, the quality and intelligibility of EL speech are poor. To address this problem, EL voice conversion (ELVC) is a method used to improve the intelligibility and quality of EL speech. In this paper, we propose a novel ELVC system that incorporates cross-domain features, specifically spectral features and self-supervised learning (SSL) embeddings. The experimental results show that applying cross-domain features can notably improve the conversion performance for the ELVC task compared with utilizing only traditional spectral features.
Deep Model Compression Also Helps Models Capture Ambiguity

Natural language understanding (NLU) tasks face a non-trivial amount of ambiguous samples where veracity of their labels is debatable among annotators. NLU models should thus account for such ambiguity, but they approximate the human opinion distributions quite poorly and tend to produce over-confident predictions. To address this problem, we must consider how to exactly capture the degree of relationship between each sample and its candidate classes. In this work, we propose a novel method with deep model compression and show how such relationship can be accounted for. We see that more reasonably represented relationships can be discovered in the lower layers and that validation accuracies are converging at these layers, which naturally leads to layer pruning. We also see that distilling the relationship knowledge from a lower layer helps models produce better distribution. Experimental results demonstrate that our method makes substantial improvement on quantifying ambiguity without gold distribution labels. As positive side-effects, our method is found to reduce the model size significantly and improve latency, both attractive aspects of NLU products.
FasterViT: Fast Vision Transformers with Hierarchical Attention

We design a new family of hybrid CNN-ViT neural networks, named FasterViT, with a focus on high image throughput for computer vision (CV) applications. FasterViT combines the benefits of fast local representation learning in CNNs and global modeling properties in ViT. Our newly introduced Hierarchical Attention (HAT) approach decomposes global self-attention with quadratic complexity into a multi-level attention with reduced computational costs. We benefit from efficient window-based self-attention. Each window has access to dedicated carrier tokens that participate in local and global representation learning. At a high level, global self-attentions enable the efficient cross-window communication at lower costs. FasterViT achieves a SOTA Pareto-front in terms of accuracy \vs image throughput. We have extensively validated its effectiveness on various CV tasks including classification, object detection and segmentation. We also show that HAT can be used as a plug-and-play module for existing networks and enhance them. We further demonstrate significantly faster and more accurate performance than competitive counterparts for images with high resolution. Code is available at https://github.com/NVlabs/FasterViT.
Bayesian test of the mass of the graviton with planetary ephemerides

In this work, we investigated Bayesian methodologies for constraining in the Solar System a Yukawa suppression of the Newtonian potential -- which we interpret as the effect of a non-null graviton mass -- by considering its impact on planetary orbits. Complementary to the previous results obtained with INPOP planetary ephemerides, we consider here a Markov Chain Monte Carlo approach associated with a Gaussian Process Regression for improving the resolution of the constraints driven by planetary ephemerides on the graviton mass in the Solar System. At the end of the procedure, a posterior for the mass of the graviton is presented, providing an upper bound at $1.01 \times 10^{-24} \; eV c^{-2}$ (resp. $\lambda_g \geq 122.48 \times 10^{13} \; km$) with a $99.7\%$ confidence level. The threshold value represents an improvement of 1 order of magnitude relative to the previous estimations. This updated determination of the upper bound is mainly due to the Bayesian methodology, although the use of new planetary ephemerides (INPOP21a used here versus INPOP19a used previously) already induces a gain of a factor 3 with respect to the previous limit. The INPOP21a ephemerides is characterized by the addition of new Juno and Mars orbiter data, but also by a better Solar System modeling, with notably a more realistic model of the Kuiper belt. Finally, by testing the sensitivity of our results to the choice of the $\textit{a priori}$ distribution of the graviton mass, it turns out that the selection of a prior more favorable to zero-mass graviton (that is, here, General Relativity) seems to be more supported by the observations than non-zero mass graviton, leading to a possible conclusion that planetary ephemerides are more likely to favor General Relativity.