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We perform a systematic study of inelastic nuclear rainbow scattering for the \oc system to the 2$^+$ (4.44 MeV) state of $^{12}$C at incident energies of 100--608 MeV with the coupled-channels method. The recently generalized nearside-farside decomposition for inelastic scattering was applied in combination with the multichannel deflection function analysis to elucidate the origin of the nuclear rainbow phenomenon and the suppression of the primary and supernumerary Airy minima in the inelastic scattering cross section. The systematic evolution of the Airy minima for the excited 2$^+$ (4.44 MeV) state of $^{12}$C was unambiguously determined. Our work suggests that there is no clear shift in the positions of the first Airy minima and a small shift at low energies for the second and third Airy minima between the inelastic and elastic scattering cross sections. Using the $K$-subamplitudes splitting technique combined with the generalized nearside-farside decomposition and deflection function, the distinct refractive pattern commonly suppressed in the inelastic heavy-ion scattering can be interpreted and provides new insights into the relationship between elastic and inelastic nuclear rainbow scattering.

Unsupervised domain adaptation (UDA) has become increasingly prevalent in scene text recognition (STR), especially where training and testing data reside in different domains. The efficacy of existing UDA approaches tends to degrade when there is a large gap between the source and target domains. To deal with this problem, gradually shifting or progressively learning to shift from domain to domain is the key issue. In this paper, we introduce the Stratified Domain Adaptation (StrDA) approach, which examines the gradual escalation of the domain gap for the learning process. The objective is to partition the training data into subsets so that the progressively self-trained model can adapt to gradual changes. We stratify the training data by evaluating the proximity of each data sample to both the source and target domains. We propose a novel method for employing domain discriminators to estimate the out-of-distribution and domain discriminative levels of data samples. Extensive experiments on benchmark scene-text datasets show that our approach significantly improves the performance of baseline (source-trained) STR models., Comment: 15 pages, 12 figures, 5 tables, include supplementary materials

We explore a robust version of the barycenter problem among $n$ centered Gaussian probability measures, termed Semi-Unbalanced Optimal Transport (SUOT)-based Barycenter, wherein the barycenter remains fixed while the others are relaxed using Kullback-Leibler divergence. We develop optimization algorithms on Bures-Wasserstein manifold, named the Exact Geodesic Gradient Descent and Hybrid Gradient Descent algorithms. While the Exact Geodesic Gradient Descent method is based on computing the exact closed form of the first-order derivative of the objective function of the barycenter along a geodesic on the Bures manifold, the Hybrid Gradient Descent method utilizes optimizer components when solving the SUOT problem to replace outlier measures before applying the Riemannian Gradient Descent. We establish the theoretical convergence guarantees for both methods and demonstrate that the Exact Geodesic Gradient Descent algorithm attains a dimension-free convergence rate. Finally, we conduct experiments to compare the normal Wasserstein Barycenter with ours and perform an ablation study., Comment: Ngoc-Hai Nguyen and Dung Le contributed equally to this work. 44 pages, 5 figures

Few-shot Continual Relations Extraction (FCRE) is an emerging and dynamic area of study where models can sequentially integrate knowledge from new relations with limited labeled data while circumventing catastrophic forgetting and preserving prior knowledge from pre-trained backbones. In this work, we introduce a novel method that leverages often-discarded language model heads. By employing these components via a mutual information maximization strategy, our approach helps maintain prior knowledge from the pre-trained backbone and strategically aligns the primary classification head, thereby enhancing model performance. Furthermore, we explore the potential of Large Language Models (LLMs), renowned for their wealth of knowledge, in addressing FCRE challenges. Our comprehensive experimental results underscore the efficacy of the proposed method and offer valuable insights for future work., Comment: Accepted to EMNLP 2024

Quantum computing has emerged as a powerful tool for solving complex problems intractable for classical computers, particularly in popular fields such as cryptography, optimization, and neurocomputing. In this paper, we present a new quantum-based approach named the Hierarchical Quantum Control Gates (HQCG) method for efficient understanding of Functional Magnetic Resonance Imaging (fMRI) data. This approach includes two novel modules: the Local Quantum Control Gate (LQCG) and the Global Quantum Control Gate (GQCG), which are designed to extract local and global features of fMRI signals, respectively. Our method operates end-to-end on a quantum machine, leveraging quantum mechanics to learn patterns within extremely high-dimensional fMRI signals, such as 30,000 samples which is a challenge for classical computers. Empirical results demonstrate that our approach significantly outperforms classical methods. Additionally, we found that the proposed quantum model is more stable and less prone to overfitting than the classical methods., Comment: Accepted to IEEE Workshop on Signal Processing Systems (SiPS 2024)

We revisit the Fujii-Wetterich model [Phys.Rev.D 26, 2580 (1982) and Nucl.Phys.B 302, 645 (1988)] which allows the Higgs doublet to couple with a "cosmon" scalar $\chi$ of the background spacetime as $\chi^2\,\Phi^2$. Upon the SSB of the $SU(2)$ gauge, the VEV of the Higgs doublet is proportional to the field $\chi$. Fujii and Wetterich employed this linkage to make particle masses dependent on $\chi$. We shall present an $\textit alternative$ mechanism: at a given point $x^*$, the prevailing Higgs VEV will be used to $\textit construct$ a quantum of action $\hbar_*$ and a speed of light $c_*$ in association with $\chi(x^*)$. Specifically, each open set vicinity of a given point $x^*$ on the manifold is equipped with a replica of the Glashow-Weinberg-Salam action operating with its own effective values of $\hbar_*$ and $c_*$, whereas particle masses induced via Higgs SSB remain independent of $\chi(x^*)$. Our mechanism unambiguously generates the dependencies $\hbar_*\propto\chi^{-1/2}(x^*)$ and $c_*\propto\chi^{1/2}(x^*)$, causing these "fundamental constants" to vary along with the dynamical field $\chi$ across the manifold. For late-time cosmology, a varying $c$ along the trajectory of light waves from distant supernovae towards Earth renders the classic Lema\^itre redshift formula $1+z=a^{-1}$ inapplicable. Using the dependency $c_*\propto\chi^{1/2}(x^*)$, we derive the new (variable-$c$) Lema\^itre redshift relation and apply it to analyze the Pantheon Catalog of SneIa $\textit without$ invoking the dark energy hypothesis. Key consequences are: (1) Accounting for the Pantheon Catalog with a fit exceeding the quality of the $\Lambda$CDM model; (2) Explaining the late-time cosmic acceleration based on variable $c$, eliminating the need for dark energy; (3) Revitalizing Blanchard-Douspis-Rowan-Robinson-Sarkar's CMB power spectrum analysis that bypassed dark energy [A&A 412, 35 (2003)]., Comment: 14 pages, 3 figures

We present a unified framework for computing CVA sensitivities, hedging the CVA, and assessing CVA risk, using probabilistic machine learning meant as refined regression tools on simulated data, validatable by low-cost companion Monte Carlo procedures. Various notions of sensitivities are introduced and benchmarked numerically. We identify the sensitivities representing the best practical trade-offs in downstream tasks including CVA hedging and risk assessment., Comment: This is the long, preprint version of the eponymous paper forthcoming in Risk Magazine

A study of the nucleon mean-field potential in nuclear matter (NM) is done within an extended Hartree-Fock (HF) formalism, using the CDM3Y6 density dependent version of the M3Y interaction which is associated with the nuclear incompressibility $K\simeq 252$ MeV. The momentum dependence of nucleon optical potential (OP) in NM at the saturation density $\rho_0$ is shown to be due mainly to its exchange term up to $k\approx 2$ fm$^{-1}$, so that the Pauli nonlocality is expected to be the main origin of the nucleon effective mass at low momenta. Because nucleons in neutron-rich NM at $\rho\approx \rho_0$ are either weakly bound or unbound by the in-medium nucleon-nucleon interaction, the determination of the effective mass of nucleon scattered on targets with neutron excess at low energies should be of interest for the mean-field studies of neutron star matter. For this purpose, the folding model is used to calculate the nonlocal nucleon OP for the optical model analysis of elastic nucleon scattering on $^{40,48}$Ca, $^{90}$Zr, and $^{208}$Pb targets at energies $E<50$ MeV, to probe the model reliability and validate the WKB local approximation to obtain the local folded nucleon OP. The nucleon effective mass $m^*$ is then carefully deduced from the momentum dependence of the local folded nucleon OP which is resulted from the Pauli nonlocality of the exchange term. The neutron-proton effective mass splitting determined at $\rho\approx\rho_0$ from the central strength of the real folded nucleon OP for $^{48}$Ca, $^{90}$Zr, and $^{208}$Pb targets has been found to depend linearly on the neutron-proton asymmetry parameter as $m^*_{n-p}\approx (0.167\pm 0.018)\delta$, in a good agreement with the recent empirical constraints., Comment: Accepted for publication in Phys. Rev. C. arXiv admin note: text overlap with arXiv:1912.03061

We developed a state-space age-structured catch-at-length (ACL) assessment model for redfish in NAFO Divisions 3LN. The model was developed to address limitations in the surplus production model that was previously used to assess this stock. The ACL model included temporal variations in recruitment, growth, and mortality rates, which were limitations identified for the surplus production model. Our ACL model revealed some important discrepancies in survey and fishery length compositions. Our model also required large population dynamics process errors to achieve good fits to survey indices and catch estimates, which also demonstrated that additional understanding of these data and other model assumptions is required. As such, we do not propose the ACL model to provide management advice for 3LN redfish, but we do provide research recommendations that should provide a better basis to model the 3LN redfish stock dynamics. Recommendations include implementing sampling programs to determine redfish species/ecotypes in commercial and research survey catches and improving biological sampling for maturity and age., Comment: 27 pages including references, tables, and figures. In addition 12 pages figures in an Appendix

In this paper, we present the service robot MARLIN and its integration with the K4R platform, a cloud system for complex AI applications in retail. At its core, this platform contains so-called semantic digital twins, a semantically annotated representation of the retail store. MARLIN continuously exchanges data with the K4R platform, improving the robot's capabilities in perception, autonomous navigation, and task planning. We exploit these capabilities in a retail intralogistics scenario, specifically by assisting store employees in stocking shelves. We demonstrate that MARLIN is able to update the digital representation of the retail store by detecting and classifying obstacles, autonomously planning and executing replenishment missions, adapting to unforeseen changes in the environment, and interacting with store employees. Experiments are conducted in simulation, in a laboratory environment, and in a real store. We also describe and evaluate a novel algorithm for autonomous navigation of articulated tractor-trailer systems. The algorithm outperforms the manufacturer's proprietary navigation approach and improves MARLIN's navigation capabilities in confined spaces.

The Brans Class I solution in Brans-Dicke gravity is a staple in the study of gravitational theories beyond General Relativity. Discovered in 1961, it describes the exterior vacuum of a spherical Brans-Dicke star and is characterized by two adjustable parameters. Surprisingly, the relationship between these parameters and the properties of the star has not been rigorously established. In this Proceeding, we bridge this gap by deriving $\textit{the}$ complete exterior solution of Brans Class I, expressed in terms of the total energy and total pressure of the spherisymmetric gravity source. The solution allows for the $\textit{exact}$ derivation of $\textit{all}$ post-Newtonian parameters in Brans-Dicke gravity for far field regions of a spherical source. Particularly for the $\gamma$ parameter, instead of the conventional result $\gamma_{\,\text{PPN}}=\frac{\omega+1}{\omega+2}$, we obtain the analytical expression $\gamma_{\,\text{exact}}=\frac{\omega+1+(\omega+2)\,\Theta}{\omega+2+(\omega+1)\,\Theta}$ where $\Theta$ is the ratio of the total pressure $P_{\parallel}^{*}+2P_{\perp}^{*}$ and total energy $E^{*}$ contained within the mass source. Our $\textit{non-perturbative}$ $\gamma$ formula is valid for all field strengths and types of matter comprising the mass source. Consequently, observational constraints on $\gamma$ thus set $\textit{joint}$ bounds on $\omega$ and $\varTheta$, with the latter representing a global characteristic of the mass source. More broadly, our formula highlights the importance of pressure (when $\varTheta\neq0$) in spherical Brans-Dicke stars, and potentially in stars within other modified theories of gravitation., Comment: Proceeding of the 12th Bolyai-Gauss-Lobachevsky Conference (BGL-2024): Non-Euclidean Geometry in Modern Physics and Mathematics, Budapest 2024. arXiv admin note: substantial text overlap with arXiv:2404.00094

Instruction finetuning (IFT) is critical for aligning Large Language Models (LLMs) to follow instructions. While many effective IFT datasets have been introduced recently, they predominantly focus on high-resource languages like English. To better align LLMs across a broad spectrum of languages and tasks, we propose a fully synthetic, novel taxonomy (Evol) guided Multilingual, Multi-turn instruction finetuning dataset, called M2Lingual. It is constructed by first selecting a diverse set of seed examples and then utilizing the proposed Evol taxonomy to convert these seeds into complex and challenging multi-turn instructions. We demonstrate the effectiveness of M2Lingual by training LLMs of varying sizes and showcasing the enhanced performance across a diverse set of languages. We contribute the 2 step Evol taxonomy with the guided generation code: https://github.com/ServiceNow/M2Lingual, as well as the first fully synthetic, general and task-oriented, multi-turn, multilingual dataset built with Evol - M2Lingual: https://huggingface.co/datasets/ServiceNow-AI/ M2Lingual - containing 182K total IFT pairs, covering 70 languages and 17+ NLP tasks., Comment: 39 pages

Enhancing the electronic and diffusion properties of lithium-ion batteries is crucial for improving the performance of the fast-growing energy storage devices. Recently, fast-charging capability of commercial-like lithium-ion anodes with the least modification of the current manufactoring technology is of great interest. Here we use first principles methods with density functional theory and the climbing image-nudged elastic band method to evaluate the impact of an external electric field on the stability, electronic and diffusion properties of penta-graphene nanoribbons upon lithium adsorption. We show that by adsorbing a lithium atom, these semiconductor nanoribbons become metal with a formation energy of - 0.22 (eV). The lithium-ion mobility of this material is comparable to that of a common carbon graphite layer. Under a relatively small vertical electric field, the structural stability of these lithium-ion systems is even more stable, and their diffusion coefficient is enhanced significantly of ~719 times higher than that of the material in the absence of an applied electric field and ~521 times higher than in the case of commercial graphitic carbon layers. Our results highlight the role of an external electric field as a novel switch to improve the efficiency of lithium-ion batteries with penta-graphene nanoribbon electrodes and open a new horizon for the use of more environmentally friendly pentagonal materials as anode materials in lithium-ion battery industry., Comment: 21 pages, 5 figures

Parameterized Quantum Circuits (PQCs) have been acknowledged as a leading strategy to utilize near-term quantum advantages in multiple problems, including machine learning and combinatorial optimization. When applied to specific tasks, the parameters in the quantum circuits are trained to minimize the target function. Although there have been comprehensive studies to improve the performance of the PQCs on practical tasks, the errors caused by the quantum noise downgrade the performance when running on real quantum computers. In particular, when the quantum state is transformed through multiple quantum circuit layers, the effect of the quantum noise happens cumulatively and becomes closer to the maximally mixed state or complete noise. This paper studies the relationship between the quantum noise and the diffusion model. Then, we propose a novel diffusion-inspired learning approach to mitigate the quantum noise in the PQCs and reduce the error for specific tasks. Through our experiments, we illustrate the efficiency of the learning strategy and achieve state-of-the-art performance on classification tasks in the quantum noise scenarios.

Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited. This paper, therefore, revisits the quantum visual encoding strategies, the initial step in quantum machine learning. Investigating the root cause, we uncover that the existing quantum encoding design fails to ensure information preservation of the visual features after the encoding process, thus complicating the learning process of the quantum machine learning models. In particular, the problem, termed "Quantum Information Gap" (QIG), leads to a gap of information between classical and corresponding quantum features. We provide theoretical proof and practical demonstrations of that found and underscore the significance of QIG, as it directly impacts the performance of quantum machine learning algorithms. To tackle this challenge, we introduce a simple but efficient new loss function named Quantum Information Preserving (QIP) to minimize this gap, resulting in enhanced performance of quantum machine learning algorithms. Extensive experiments validate the effectiveness of our approach, showcasing superior performance compared to current methodologies and consistently achieving state-of-the-art results in quantum modeling., Comment: Accepted to Quantum Machine Intelligence

Unsupervised vision clustering, a cornerstone in computer vision, has been studied for decades, yielding significant outcomes across numerous vision tasks. However, these algorithms involve substantial computational demands when confronted with vast amounts of unlabeled data. Conversely, quantum computing holds promise in expediting unsupervised algorithms when handling large-scale databases. In this study, we introduce QClusformer, a pioneering Transformer-based framework leveraging quantum machines to tackle unsupervised vision clustering challenges. Specifically, we design the Transformer architecture, including the self-attention module and transformer blocks, from a quantum perspective to enable execution on quantum hardware. In addition, we present QClusformer, a variant based on the Transformer architecture, tailored for unsupervised vision clustering tasks. By integrating these elements into an end-to-end framework, QClusformer consistently outperforms previous methods running on classical computers. Empirical evaluations across diverse benchmarks, including MS-Celeb-1M and DeepFashion, underscore the superior performance of QClusformer compared to state-of-the-art methods.

This Letter aims to advance unexplored properties of a new class of Closed Timelike Curves recently discovered in scalar-tensor gravity, reported in Universe 9, 467 (2023) and Eur.$\,$Phys.$\,$J.$\,$C 83, 626 (2023). Therein, it was shown that when the Weak Energy Condition is violated, the topology of spacetime in scalar-tensor gravity is altered, enabling the formation of two-way traversable wormholes. Furthermore, each of these wormholes acts a gateway between two $\textit{time-mirrored}$ worlds, where the two asymptotically flat sheets in the Kruskal-Szekeres diagram are glued antipodally along $\textit{three}$ directions -- time $t$ and the polar and azimuth angles $(\theta,\,\varphi)$ of the 2-sphere -- to form a wormhole throat. This contrasts with the standard embedding diagram which typically glues the sheets only along the $\theta$ and $\varphi$ directions. Crucially, due to the `gluing' along the $t$ direction, the wormhole becomes a portal connecting the two spacetime sheets with $\textit{opposite}$ physical time flows, enabling the emergence of closed timelike loops which straddle the throat. We shall point out that this portal $\textit{mathematically}$ permits the possibility of backward propagation of information $\textit{against}$ time. This feature is ubiquitous for wormholes in scalar-tensor theories. In addition, we formulate the Feynman sum for transition amplitudes of microscopic particles in the proximity of a wormhole throat in which we account for timelike paths that experience time reversal., Comment: To appear in Phys. Lett. B; 6 pages, 5 figures; slight revisions for clarification, with references added

A novel class of vacuum metrics expressible in analytical form was recently found for pure $\mathcal R^2$ gravity, based on a groundwork put forth by Buchdahl in 1962. These Buchdahl-inspired solutions offer a practical framework for testing ${\cal R}^2$ gravity through empirical observations. Within a subclass of asymptotically flat Buchdahl-inspired vacuum spacetimes, we identified a parameter $\epsilon$ measuring the deviation from the classic Schwarzschild metric, which corresponds to $\epsilon=0$. In this paper, we employ observational data from the S2 star's orbit around Sgr A* in the Milky Way galactic center and perform Monte Carlo Markov Chain simulations to probe the effects of the new metrics on the orbit of the S2 star. Our analysis presented herein reports a range at 95\% confidence level on the deviation parameter as $\epsilon\in(-0.6690,\ 0.4452)$. While no decisive evidence either in favor or in disfavor of the asymptotically flat Buchdahl-inspired spacetimes has been achieved, the obtained bound is compatible with the tighter results using other data of different nature as recently reported in Eur.\,Phys.\,J.\,C $\bf 84$, 330 (2024). As a meaningful test probing into a strong-field regime, our present study calls for further observations with prolonged period and improved accuracy in order to tighten the bound for $\epsilon$ using the S2 star orbit., Comment: 11 pages, 5 figures, 1 table; v2: published in JCAP

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method., Comment: 7 pages, 3 figures

The development of Large Language Models (LLMs) has predominantly focused on high-resource languages, leaving extremely low-resource languages like Irish with limited representation. This work presents UCCIX, a pioneering effort on the development of an open-source Irish-based LLM. We propose a novel framework for continued pre-training of LLMs specifically adapted for extremely low-resource languages, requiring only a fraction of the textual data typically needed for training LLMs according to scaling laws. Our model, based on Llama 2-13B, outperforms much larger models on Irish language tasks with up to 12% performance improvement, showcasing the effectiveness and efficiency of our approach. We also contribute comprehensive Irish benchmarking datasets, including IrishQA, a question-answering dataset, and Irish version of MT-bench. These datasets enable rigorous evaluation and facilitate future research in Irish LLM systems. Our work aims to preserve and promote the Irish language, knowledge, and culture of Ireland in the digital era while providing a framework for adapting LLMs to other indigenous languages.

Extensive elastic scattering data measured at energies around 10 to 20 MeV/nucleon for some identical systems, like 12C+12C and 16O+16O, exhibit the nuclear rainbow pattern of broad Airy oscillations of the cross section at medium and large angles. Due to the identity of the scattered projectile and recoiled target, the rainbow pattern at angles around and beyond $\theta_{\rm c.m.}\approx 90^\circ$ is strongly deteriorated by the boson exchange. The nuclear rainbow features in the identical-particles elastic scattering discussed so far are based on the nearside-farside (NF) decomposition of the scattering amplitude given by an optical model calculation neglecting the projectile-target exchange symmetry. Moreover, the NF decomposition method was developed in the 70s by Fuller for nonidentical systems only, and the details of how the exchange symmetry of an identical system affects the evolution of nuclear rainbow remain unexplored. Therefore, the Fuller method is generalized in this work for the elastic scattering of two identical (spin-zero) nuclei, with the projectile-target exchange symmetry taken explicitly into account. The results obtained for elastic 12C+12C and 16O+16O scattering at low energies show the exchange symmetry results in a symmetric interchange of the nearside and farside patterns at angles passing $\theta_{\rm c.m.}=90^\circ$, which requires a more subtle interpretation of nuclear rainbow. We also found that a similar NF interchange occurs in a nonidentical nucleus-nucleus system with the core-core symmetry, where the elastic cross section at backward angles is due mainly to the elastic transfer of cluster or nucleon between two identical cores. This interesting effect is illustrated in the elastic 16O+12C scattering at low energies where the elastic $\alpha$ transfer between two 12C cores has been proven to enhance the elastic cross section at backward angles., Comment: 10 pages, 10 figures

Action recognition has become one of the popular research topics in computer vision. There are various methods based on Convolutional Networks and self-attention mechanisms as Transformers to solve both spatial and temporal dimensions problems of action recognition tasks that achieve competitive performances. However, these methods lack a guarantee of the correctness of the action subject that the models give attention to, i.e., how to ensure an action recognition model focuses on the proper action subject to make a reasonable action prediction. In this paper, we propose a multi-view attention consistency method that computes the similarity between two attentions from two different views of the action videos using Directed Gromov-Wasserstein Discrepancy. Furthermore, our approach applies the idea of Neural Radiance Field to implicitly render the features from novel views when training on single-view datasets. Therefore, the contributions in this work are three-fold. Firstly, we introduce the multi-view attention consistency to solve the problem of reasonable prediction in action recognition. Secondly, we define a new metric for multi-view consistent attention using Directed Gromov-Wasserstein Discrepancy. Thirdly, we built an action recognition model based on Video Transformers and Neural Radiance Fields. Compared to the recent action recognition methods, the proposed approach achieves state-of-the-art results on three large-scale datasets, i.e., Jester, Something-Something V2, and Kinetics-400.

Understanding the severity of conditions shown in images in medical diagnosis is crucial, serving as a key guide for clinical assessment, treatment, as well as evaluating longitudinal progression. This paper proposes Con- PrO: a novel representation learning method for severity assessment in medical images using Contrastive learningintegrated Preference Optimization. Different from conventional contrastive learning methods that maximize the distance between classes, ConPrO injects into the latent vector the distance preference knowledge between various severity classes and the normal class. We systematically examine the key components of our framework to illuminate how contrastive prediction tasks acquire valuable representations. We show that our representation learning framework offers valuable severity ordering in the feature space while outperforming previous state-of-the-art methods on classification tasks. We achieve a 6% and 20% relative improvement compared to a supervised and a self-supervised baseline, respectively. In addition, we derived discussions on severity indicators and related applications of preference comparison in the medical domain., Comment: 8 pages

This paper focuses on self-supervised monocular depth estimation in dynamic scenes trained on monocular videos. Existing methods jointly estimate pixel-wise depth and motion, relying mainly on an image reconstruction loss. Dynamic regions1 remain a critical challenge for these methods due to the inherent ambiguity in depth and motion estimation, resulting in inaccurate depth estimation. This paper proposes a self-supervised training framework exploiting pseudo depth labels for dynamic regions from training data. The key contribution of our framework is to decouple depth estimation for static and dynamic regions of images in the training data. We start with an unsupervised depth estimation approach, which provides reliable depth estimates for static regions and motion cues for dynamic regions and allows us to extract moving object information at the instance level. In the next stage, we use an object network to estimate the depth of those moving objects assuming rigid motions. Then, we propose a new scale alignment module to address the scale ambiguity between estimated depths for static and dynamic regions. We can then use the depth labels generated to train an end-to-end depth estimation network and improve its performance. Extensive experiments on the Cityscapes and KITTI datasets show that our self-training strategy consistently outperforms existing self/unsupervised depth estimation methods., Comment: Accepted to CVPR2024

We derive the complete expression for the Brans Class I exterior spacetime explicitly in terms of the energy and pressures profiles of a stationary spherisymmetric gravity source. This novel and generic expression is achieved in a parsimonious manner, requiring only a subset of the Brans-Dicke field equation and the scalar equation. For distant orbiting test particles, this expression promptly provides a simple, closed and exact formula of the [textgreek]\textgreek{g} Eddington parameter, which reads {\gamma}_{exact}=(({\omega}+1+({\omega}+2){\Theta})/({\omega}+2+({\omega}+1){\Theta})), where {\Theta} is the ratio of the star's "total pressure" integral over its energy integral. This non-perturbative result reproduces the usual Post-Newtonian (({\omega}+1)/({\omega}+2)) expression in the case of a "Newtonian star", in which the pressure is negligible with respect to the energy density. Furthermore, it converges to the General Relativity value ({\gamma}_{GR}=1) as the star's equation of state approaches that of ultra-relativistic matter (in which case {\Theta} approaches 1), a behavior consistent with broader studies on scalar-tensor gravity. Our derivation underscores the essence of these results involving (1) the key relevant portion of the Brans-Dicke field equations, (2) the uniqueness of the Brans Class I vacuum solution for the non-phantom action, viz. {\omega}>-3/2, and (3) the involvement of only two free parameters in this solution, hence requiring two quantities (energy and pressure integrals) of the mass source to fully characterize the solution. From a practical standpoint, it elucidates how a given stellar interior structure model determines the star's exterior gravitational field and impacts the motions of light objects (such as planets and accretion disks) orbiting it., Comment: 1 figure. Correcting a typo in Eq (19) (an unjustified 4*pi factor has been removed)

Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact. Some languages are more well-connected than others, and target languages can benefit from transferring from closely related languages; for many languages, the set of closely related languages does not include English. In this work, we study the impact of source language for cross-lingual transfer, demonstrating the importance of selecting source languages that have high contact with the target language. We also construct a novel benchmark dataset for close contact Chinese-Japanese-Korean-Vietnamese (CJKV) languages to further encourage in-depth studies of language contact. To comprehensively capture contact between these languages, we propose to integrate Romanized transcription beyond textual scripts via Contrastive Learning objectives, leading to enhanced cross-lingual representations and effective zero-shot cross-lingual transfer., Comment: Accepted at LREC-COLING 2024

In modern decentralized applications, ensuring communication efficiency and privacy for the users are the key challenges. In order to train machine-learning models, the algorithm has to communicate to the data center and sample data for its gradient computation, thus exposing the data and increasing the communication cost. This gives rise to the need for a decentralized optimization algorithm that is communication-efficient and minimizes the number of gradient computations. To this end, we propose the primal-dual sliding with conditional gradient sliding framework, which is communication-efficient and achieves an $\varepsilon$-approximate solution with the optimal gradient complexity of $O(1/\sqrt{\varepsilon}+\sigma^2/{\varepsilon^2})$ and $O(\log(1/\varepsilon)+\sigma^2/\varepsilon)$ for the convex and strongly convex setting respectively and an LO (Linear Optimization) complexity of $O(1/\varepsilon^2)$ for both settings given a stochastic gradient oracle with variance $\sigma^2$. Compared with the prior work \cite{wai-fw-2017}, our framework relaxes the assumption of the optimal solution being a strict interior point of the feasible set and enjoys wider applicability for large-scale training using a stochastic gradient oracle. We also demonstrate the efficiency of our algorithms with various numerical experiments.

We provide a concrete example exhibiting marked deviation from the PPN approximation in a modified theory of gravity. Specifically, we derive the exact formula for the Robertson parameter $\gamma$ in Brans-Dicke gravity for compact mass sources, explicitly incorporating the pressure content of these sources. We achieve this by exploiting the $\textit integrability$ of the 00-component of the Brans-Dicke field equation. In place of the conventional PPN result $\gamma_{PPN}=\frac{\omega+1}{\omega+2}$, we obtain the analytical expression $\gamma_{\,exact}=\frac{\omega+1+(\omega+2)\varTheta}{\omega+2+(\omega+1)\varTheta}$ where $\varTheta$ is the ratio of the total pressure $P_\parallel^*+2P_\perp^*$ and total energy $E^*$ contained within the mass source. Our $\textit non\text{-}perturbative$ formula is valid for all field strengths and types of matter comprising the mass source. We draw four key conclusions: (1) The usual $\gamma_{PPN}$ formula is violated in the presence of pressure, viz. when $\varTheta\neq0$, revealing a limitation of the PPN approximation in Brans-Dicke gravity. (2) The PPN result mainly stems from the assumption of pressureless matter. Even in the weak-field star case, non-zero pressure leads to a violation of the PPN $\gamma$ formula. Conversely, the PPN result is a good approximation for low-pressure matter, i.e. when $\varTheta\approx0$, for all field strengths. (3) Observational constraints on $\gamma$ set $\textit joint$ bounds on $\omega$ and $\varTheta$, with the latter representing a global characteristic of a mass source. If the equation of state of matter in the mass source approaches the ultra-relativistic form, entailing $\varTheta\simeq1$, $\gamma_{\,exact}$ converges to 1 $\textit irrespective$ of $\omega$. (4) In a broader context, our findings indicate the latent significance of considering the interior structure of stars in observational astronomy., Comment: To appear in EPJC. Figure 2 improved with new details; Section 9 added; Section 10 significantly expanded and enhanced

We propose a distributional formulation of the spanning problem of a multi-asset payoff by vanilla basket options. This problem is shown to have a unique solution if and only if the payoff function is even and absolutely homogeneous, and we establish a Fourier-based formula to calculate the solution. Financial payoffs are typically piecewise linear, resulting in a solution that may be derived explicitly, yet may also be hard to numerically exploit. One-hidden-layer feedforward neural networks instead provide a natural and efficient numerical alternative for discrete spanning. We test this approach for a selection of archetypal payoffs and obtain better hedging results with vanilla basket options compared to industry-favored approaches based on single-asset vanilla hedges.

Facial Expression Recognition (FER) is a critical task within computer vision with diverse applications across various domains. Addressing the challenge of limited FER datasets, which hampers the generalization capability of expression recognition models, is imperative for enhancing performance. Our paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification, particularly showcased in the 6th Affective Behavior Analysis in-the-wild (ABAW) competition. By utilizing low-level feature information from the ipsilateral view (auxiliary view) before learning the high-level feature that emphasizes the shift in the human facial expression, our work seeks to provide a straightforward yet innovative way to improve the examined view (main view). We also suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model, focusing on pivotal local elements. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset, as observed in both training and validation contexts., Comment: 6 pages; added references for section 1; corrected typo for email author

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

A novel class of Buchdahl-inspired metrics with closed-form expressions was recently obtained based on Buchdahl's seminal work on searching for static, spherically symmetric metrics in ${\cal R}^{2}$ gravity in vacuo. Buchdahl-inspired spacetimes provide an interesting framework for testing predictions of ${\cal R}^{2}$ gravity models against observations. To test these Buchdahl-inspired spacetimes, we consider observational constraints imposed on the deviation parameter, which characterizes the deviation of the asymptotically flat Buchdahl-inspired metric from the Schwarzschild spacetime. We utilize several recent solar system experiments and observations of the S2 star in the Galactic center and the black hole shadow. By calculating the effects of Buchdahl-inspired spacetimes on astronomical observations both within and outside of the solar system, including the deflection angle of light by the Sun, gravitational time delay, perihelion advance, shadow, and geodetic precession, we determine observational constraints on the corresponding deviation parameters by comparing theoretical predictions with the most recent observations. Among these constraints, we find that the tightest one comes from the Cassini mission's measurement of gravitational time delay., Comment: 13 pages, no figure, to appear in EPJC

We present an exact static spherisymmetric solution for the Brans-Dicke action sourced by a self-gravitating massless Klein-Gordon helicity-0 field. In contrast to the Maxwell electromagnetic field, a Klein-Gordon field possesses an energy-momentum tensor with $\textit{non-vanishing trace}$. Upon a Weyl mapping into the Einstein frame, the transformed Brans-Dicke scalar field takes on the role of a "dilaton" coupled with the Klein-Gordon field. Despite this dilatonic coupling, the field equations of the resulting Einstein-Klein-Gordon-dilaton action are fully soluble when employing the harmonic radial coordinate. The exact solution derived herein can serve as a prototype for future Brans-Dicke gravity studies involving trace-carrying matter fields. Notably, in the limit of infinite $\omega$, the Brans-Dicke scalar field exhibits an anomalous behavior of ${\cal O}\,(1/\sqrt\omega)$ as opposed to ${\cal O}\,(1/\omega)$. As a consequence, the solution converges to a spacetime configuration of General Relativity sourced by the original Klein-Gordon field and a free scalar field, the latter of which is the ${\cal O}\,(1/\sqrt\omega)$ "remnant" of the Brans-Dicke scalar field. Furthermore, we provide a formal mathematical proof substantiating these two conclusions. Although the ${\cal O}\,(1/\sqrt\omega)$ anomaly has been previously discovered for Brans-Dicke vacuum and Brans-Dicke-Maxwell electrovacuum, our findings establish its prevalence in Brans-Dicke gravity $\textit{regardless}$ of the trace of the energy-momentum tensor of the source. Taken together, the ${\cal O}\,(1/\sqrt\omega)$ anomaly challenges the conventional belief in the ${\cal O}\,(1/\omega)$ signature commonly associated with Brans-Dicke gravity. In particular, it may have implications in improving the relativistic corrections to Newtonian gravity beyond the weak-field parametrized post-Newtonian formalism., Comment: Final published version

High-entropy alloys (HEAs) stand out between multi-component alloys due to their attractive microstructures and mechanical properties. In this investigation, molecular dynamics (MD) simulation and machine learning were used to ascertain the deformation mechanism of AlCoCuCrFeNi HEAs under the influence of temperature, strain rate, and grain sizes. First, the MD simulation shows that the yield stress decreases significantly as the strain and temperature increase. In other cases, changes in strain rate and grain size have less effect on mechanical properties than changes in strain and temperature. The alloys exhibited superplastic behavior under all test conditions. The deformity mechanism discloses that strain and temperature are the main sources of beginning strain, and the shear bands move along the uniaxial tensile axis inside the workpiece. Furthermore, the fast phase shift of inclusion under mild strain indicates the relative instability of the inclusion phase of HCP. Ultimately, the dislocation evolution mechanism shows that the dislocations are transported to free surfaces under increased strain when they nucleate around the grain boundary. Surprisingly, the ML prediction results also confirm the same characteristics as those confirmed from the MD simulation. Hence, the combination of MD and ML reinforces the confidence in the findings of mechanical characteristics of HEA. Consequently, this combination fills the gaps between MD and ML, which can significantly save time human power and cost to conduct real experiments for testing HEA deformation in practice., Comment: It is under revision to submit for a publication

We introduce the class of extended admissible groups, which include both fundamental groups of non-geometric 3-manifolds and Croke-Kleiner admissible groups. We show that the class of extended admissible groups is quasi-isometrically rigid.

In this paper, we employ Credit Default Swaps (CDS) to model the joint and conditional distress probabilities of banks in Europe and the U.S. using factor copulas. We propose multi-factor, structured factor, and factor-vine models where the banks in the sample are clustered according to their geographic location. We find that within each region, the co-dependence between banks is best described using both, systematic and idiosyncratic, financial contagion channels. However, if we consider the banking system as a whole, then the systematic contagion channel prevails, meaning that the distress probabilities are driven by a latent global factor and region-specific factors. In all cases, the co-dependence structure of bank CDS spreads is highly correlated in the tail. The out-of-sample forecasts of several measures of systematic risk allow us to identify the periods of distress in the banking sector over the recent years including the COVID-19 pandemic, the interest rate hikes in 2022, and the banking crisis in 2023.

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most $n$ supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence the need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT due to its incompatible rounding procedure, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of $\mathcal{\widetilde O}(n^2/\varepsilon^4)$. Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an $\varepsilon$-approximate solution to the POT problem in $\mathcal{\widetilde O}(n^{2.5}/\varepsilon)$, which is better in $\varepsilon$ than revised Sinkhorn. The second method, Dual Extrapolation, achieves the computation complexity of $\mathcal{\widetilde O}(n^2/\varepsilon)$, thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced., Comment: Accepted to AAAI 2024

In precision agriculture, the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield. The current machine vision model requires a large volume of data to achieve high performance. However, there are approximately 5.5 million different insect species in the world. None of the existing insect datasets can cover even a fraction of them due to varying geographic locations and acquisition costs. In this paper, we introduce a novel "Insect-1M" dataset, a game-changing resource poised to revolutionize insect-related foundation model training. Covering a vast spectrum of insect species, our dataset, including 1 million images with dense identification labels of taxonomy hierarchy and insect descriptions, offers a panoramic view of entomology, enabling foundation models to comprehend visual and semantic information about insects like never before. Then, to efficiently establish an Insect Foundation Model, we develop a micro-feature self-supervised learning method with a Patch-wise Relevant Attention mechanism capable of discerning the subtle differences among insect images. In addition, we introduce Description Consistency loss to improve micro-feature modeling via insect descriptions. Through our experiments, we illustrate the effectiveness of our proposed approach in insect modeling and achieve State-of-the-Art performance on standard benchmarks of insect-related tasks. Our Insect Foundation Model and Dataset promise to empower the next generation of insect-related vision models, bringing them closer to the ultimate goal of precision agriculture.