Miracle-dz/UniFAR-FTUs huggingface dataset:
Recoupling Mechanism for exotic mesons and baryons

The infinite chain of transitions of one pair of mesons (channel I) into another pair of mesons (channel II) can produce bound states and resonances in both channels even if no interactions inside channels exist. These resonances which can occur also in meson-baryon channels are called channelcoupling (CC) resonances. A new mechanism of CC resonances is proposed where transitions occur due to a rearrangement of confining strings inside each channel – the recoupling mechanism. The amplitude of this recoupling mechanism is expressed via overlap integrals of the wave functions of participating mesons (baryons). The explicit calculation with the known wave functions yields the peak at E = 4.12 GeV for the transitions J/ψ + φ ↔ D∗ s + D̄∗ s , which can be associated with χc1(4140), the peak at 4.5 GeV for the transitions J/ψ+p ↔ Σc+D ∗, which can be associated with Pc(4457), and a narrow peak at 3.98 GeV with the width 10 MeV for the transitions D − s +D∗ 0 ↔ J/ψ +K∗−, which can be associated with th recently discovered Zcs(3985).
Observing relativistic features in large-scale structure surveys – II. Doppler magnification in an ensemble of relativistic simulations

The standard cosmological model is inherently relativistic, and yet a wide range of cosmological observations can be predicted accurately from essentially Newtonian theory. This is not the case on ‘ultralarge’ distance scales, around the cosmic horizon size, however, where relativistic effects can no longer be neglected. In this paper, we present a novel suite of 53 fully relativistic simulations generated using the gevolution code, each covering the full sky out to z ≈ 0.85, and approximately 1930 deg2 out to z ≈ 3.55. These include a relativistic treatment of massive neutrinos, as well as the gravitational potential that can be used to exactly calculate observables on the past light cone. The simulations are divided into two sets, the first being a set of 39 simulations of the same fiducial cosmology (based on the Euclid Flagship 2 cosmology) with different realizations of the initial conditions, and the second that fixes the initial conditions, but varies each of seven cosmological parameters in turn. Taken together, these simulations allow us to perform statistical studies and calculate derivatives of any relativistic observable with respect to cosmological parameters. As an example application, we compute the cross-correlation between the Doppler magnification term in the convergence, κv, and the CDM + baryon density contrast, δcb, which arises only in a (special) relativistic treatment. We are able to accurately recover this term as predicted by relativistic perturbation theory, and study its sample variance and derivatives with respect to cosmological parameters.
A reinforcement learning control approach for underwater manipulation under position and torque constraints

In marine operations underwater manipulators play a primordial role. However, due to uncertainties in the dynamic model and disturbances caused by the environment, low-level control methods require great capabilities to adapt to change. Furthermore, under position and torque constraints the requirements for the control system are greatly increased. Reinforcement learning is a data driven control technique that can learn complex control policies without the need of a model. The learning capabilities of these type of agents allow for great adaptability to changes in the operative conditions. In this article we present a novel reinforcement learning low-level controller for the position control of an underwater manipulator under torque and position constraints. The reinforcement learning agent is based on an actor-critic architecture using sensor readings as state information. Simulation results using the Reach Alpha 5 underwater manipulator show the advantages of the proposed control strategy.
ColdGAN: Resolving Cold Start User Recommendation by using Generative Adversarial Networks

Mitigating the new user cold-start problem has been critical in the recommendation system for online service providers to influence user experience in decision making which can ultimately affect the intention of users to use a particular service. Previous studies leveraged various side information from users and items; however, it may be impractical due to privacy concerns. In this paper, we present ColdGAN, an end-to-end GAN based model with no use of side information to resolve this problem. The main idea of the proposed model is to train a network that learns the rating distributions of experienced users given their cold-start distributions. We further design a time-based function to restore the preferences of users to cold-start states. With extensive experiments on two real-world datasets, the results show that our proposed method achieves significantly improved performance compared with the state-of-the-art recommenders.
The first AI simulation of a black hole

Abstract We report the results from our ongoing pilot investigation of the use of deep learning techniques for forecasting the state of turbulent flows onto black holes. Deep neural networks seem to learn well black hole accretion physics and evolve the accretion flow orders of magnitude faster than traditional numerical solvers, while maintaining a reasonable accuracy for a long time.
A Tidally Induced Global Corrugation Pattern in an External Disk Galaxy Similar to the Milky Way

We study the two-dimensional (2D) line-of-sight velocity (V los) field of the low-inclination, late-type galaxy VV304a. The resulting 2D kinematic map reveals a global, coherent, and extended perturbation that is likely associated with a recent interaction with the massive companion VV304b. We use multiband imaging and a suite of test-particle simulations to quantify the plausible strength of in-plane flows due to nonaxisymmetric perturbations and show that the observed velocity flows are much too large to be driven either by a spiral structure or by a bar. We use fully cosmological hydrodynamical simulations to characterize the contribution from in- and off-plane velocity flows to the V los field of recently interacting galaxy pairs like the VV304 system. We show that, for recently perturbed low-inclination galactic disks, the structure of the residual velocity field, after subtraction of an axisymmetric rotation model, can be dominated by vertical flows. Our results indicate that the V los perturbations in VV304a are consistent with a corrugation pattern. Its V los map suggests the presence of a structure similar to the Monoceros ring seen in the Milky Way. Our study highlights the possibility of addressing important questions regarding the nature and origin of vertical perturbations by measuring the line-of-sight velocities in low-inclination nearby galaxies.
Noise2Context: Context-assisted Learning 3D Thin-layer for Low Dose CT.

PURPOSE Computed tomography (CT) has played a vital role in medical diagnosis, assessment, and therapy planning, etc. In clinical practice, concerns about the increase of X-ray radiation exposure attract more and more attention. To lower the X-ray radiation, low-dose CT (LDCT) has been widely adopted in certain scenarios, while it will induce the degradation of CT image quality. In this paper, we proposed a deep learning-based method that can train denoising neural networks without any clean data. METHODS In this work, for 3D thin-slice LDCT scanning, we first drive an unsupervised loss function which was equivalent to a supervised loss function with paired noisy and clean samples when the noise in the different slices from a single scan was uncorrelated and zero-mean. Then, we trained the denoising neural network to map one noise LDCT image to its two adjacent LDCT images in a single 3D thin-layer LDCT scanning, simultaneously. In essence, with some latent assumptions, we proposed an unsupervised loss function to train the denoising neural network in an unsupervised manner, which integrated the similarity between adjacent CT slices in 3D thin-layer LDCT. RESULTS Further experiments on Mayo LDCT dataset and a realistic pig head were carried out. In the experiments using Mayo LDCT dataset, our unsupervised method can obtain performance comparable to that of the supervised baseline. With the realistic pig head, our method can achieve optimal performance at different noise levels as compared to all the other methods that demonstrated the superiority and robustness of the proposed Noise2Context. CONCLUSIONS In this work, we present a generalizable LDCT image denoising method without any clean data. As a result, our method not only gets rid of the complex artificial image priors but also amounts of paired high-quality training datasets.
Towards Mass Adoption of Contact Tracing Apps - Learning from Users' Preferences to Improve App Design

Contact tracing apps have become one of the main approaches to control and slow down the spread of COVID-19 and ease up lockdown measures. While these apps can be very effective in stopping the transmission chain and saving lives, their adoption remains under the expected critical mass. The public debate about contact tracing apps emphasizes general privacy reservations and is conducted at an expert level, but lacks the user perspective related to actual designs. To address this gap, we explore user preferences for contact tracing apps using market research techniques, and specifically conjoint analysis. Our main contributions are empirical insights into individual and group preferences, as well as insights for prescriptive design. While our results confirm the privacy-preserving design of most European contact tracing apps, they also provide a more nuanced understanding of acceptable features. Based on market simulation and variation analysis, we conclude that adding goal-congruent features will play an important role in fostering mass adoption.
KPZ-type fluctuation bounds for interacting diffusions in equilibrium

We study the fluctuations in equilibrium of a class of Brownian motions interacting through a potential. For a certain choice of exponential potential, the distribution of the system coincides with differences of free energies of the stationary semi-discrete or O'Connell-Yor polymer. We show that for Gaussian potentials, the fluctuations are of order $N^{\frac{1}{4}}$ when the time and system size coincide, whereas for a class of more general convex potentials $V$ the fluctuations are of order at most $N^{\frac{1}{3}}$. In the O'Connell-Yor case, we recover the known upper bounds for the fluctuation exponents using a dynamical approach, without reference to the polymer partition function interpretation.
Bi-Directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents

Deep reinforcement learning models are notoriously data hungry, yet real-world data is expensive and time consuming to obtain. The solution that many have turned to is to use simulation for training before deploying the robot in a real environment. Simulation offers the ability to train large numbers of robots in parallel, and offers an abundance of data. However, no simulation is perfect, and robots trained solely in simulation fail to generalize to the real-world, resulting in a “sim-vs-real gap”. How can we overcome the trade-off between the abundance of less accurate, artificial data from simulators and the scarcity of reliable, real-world data? In this letter, we propose Bi-directional Domain Adaptation (BDA), a novel approach to bridge the sim-vs-real gap in both directions– <italic>real2sim</italic> to bridge the visual domain gap, and <italic>sim2real</italic> to bridge the dynamics domain gap. We demonstrate the benefits of BDA on the task of PointGoal Navigation. BDA with only 5 k real-world (state, action, next-state) samples matches the performance of a policy fine-tuned with <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>600 k samples, resulting in a speed-up of <inline-formula><tex-math notation="LaTeX">$\sim 120\times$</tex-math></inline-formula>.
Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations

Intelligent personal assistant systems for information-seeking conversations are increasingly popular in real-world applications, especially for e-commerce companies. With the development of research in such conversation systems, the pseudo-relevance feedback (PRF) has demonstrated its effectiveness in incorporating relevance signals from external documents. However, the existing studies are either based on heuristic rules or require heavy manual labeling. In this work, we treat the PRF selection as a learning task and proposed a reinforced learning based method that can be trained in an end-to-end manner without any human annotations. More specifically, we proposed a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT based response ranker to rank the PRF-enhanced responses. The performance of the ranker serves as rewards to guide the selector to extract useful PRF terms, and thus boost the task performance. Extensive experiments on both standard benchmark and commercial datasets show the superiority of our reinforced PRF term selector compared with other potential soft or hard selection methods. Both qualitative case studies and quantitative analysis show that our model can not only select meaningful PRF terms to expand response candidates but also achieve the best results compared with all the baseline methods on a variety of evaluation metrics. We have also deployed our method on online production in an e-commerce company, which shows a significant improvement over the existing online ranking system.
Deep Magnification-Arbitrary Upsampling over 3D Point Clouds

This paper addresses the problem of generating dense point clouds from given sparse point clouds to model the underlying geometric structures of objects/scenes. To tackle this challenging issue, we propose a novel end-to-end learning based framework, namely MAPU-Net. Specifically, by taking advantage of the linear approximation theorem, we first formulate the problem explicitly, which boils down to determining the interpolation weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted interpolation weights and normal-guided displacements, by analyzing the local geometry of the input point cloud. MAPU-Net can be interpreted by the explicit formulation, and thus is more memory-efficient than existing ones. In sharp contrast to the existing methods that work only for a pre-defined and fixed upsampling factor, MAPU-Net, a single neural network with one-time training, can handle an arbitrary upsampling factor, which is highly desired in real-world applications. In addition, we propose a simple yet effective training strategy to drive such a flexible ability. Extensive experiments on both synthetic and real world data demonstrate the superiority of the proposed MAPU-Net over state-of-the-art methods both quantitatively and qualitatively. To the best of our knowledge, this is the first end-to-end learning based method that is capable of achieving magnification-arbitrary upsampling over 3D point clouds.
Conséquences de l’acclimatation à différents régimes thermiques sur les taux métaboliques standards des tacons du saumon de l’Atlantique(Salmo salar)

Fish that inhabits river are subjected to circadian fluctuations in water temperature. These fluctuations can influence growth, digestion and metabolic processes. Bioenergetic models are traditionally modeled using data recorded from fish kept at constant water temperatures. These models could underestimate the energy spent by fish subjected to fluctuating temperatures. Thus, with intermittent-flow respirometry, the objectives of this study were to: (1) quantify the differences between standard metabolic rates of fish acclimatized to a constant temperature (20.2 o C ± 0.5 o C) and two circadian fluctuating temperatures (19.8 o C ± 2.0 o C; 19.5 o C ± 3.0 o C) and (2) compare two methodology for calculate standard metabolic rates of fish subjected to circadian fluctuating temperatures: intermittent-flow respirometry with (a) constant temperature or (b) fluctuating temperatures. Fish held at circadian fluctuating temperatures have higher standard metabolic rates than fish acclimatized to a constant temperature. At 20.2 o C ± 0.5 o C, standard metabolic rates was 25% to 32% lower for fish held at constant temperature than for fish held under circadian fluctuations. However, results of the two different strategies used to calculate standard metabolic rates of fish subjected to circadian fluctuating temperatures did not differ.
Signature based on molecular subtypes of deoxyribonucleic acid methylation predicts overall survival in gastric cancer

BACKGROUND Gastric cancer (GC) ranks as the third leading cause of cancer-related death worldwide. Epigenetic alterations contribute to tumor heterogeneity in early stages. AIM To identify the specific deoxyribonucleic acid (DNA) methylation sites that influence the prognosis of GC patients and explore the prognostic value of a model based on subtypes of DNA methylation. METHODS Patients were randomly classified into training and test sets. Prognostic DNA methylation sites were identified by integrating DNA methylation profiles and clinical data from The Cancer Genome Atlas GC cohort. In the training set, unsupervised consensus clustering was performed to identify distinct subgroups based on methylation status. A risk score model was built based on Kaplan-Meier, least absolute shrinkage and selector operation, and multivariate Cox regression analyses. A test set was used to validate this model. RESULTS Three subgroups based on DNA methylation profiles in the training set were identified using 1061 methylation sites that were significantly associated with survival. These methylation subtypes reflected differences in T, N, and M category, age, stage, and prognosis. Forty-one methylation sites were screened as specific hyper- or hypomethylation sites for each specific subgroup. Enrichment analysis revealed that they were mainly involved in pathways related to carcinogenesis, tumor growth, and progression. Finally, two methylation sites were chosen to generate a prognostic model. The high-risk group showed a markedly poor prognosis compared to the low-risk group in both the training [hazard ratio (HR) = 2.24, 95% confidence interval (CI): 1.28-3.92, P < 0.001] and test (HR = 2.12, 95%CI: 1.19-3.78, P = 0.002) datasets. CONCLUSION DNA methylation-based classification reflects the epigenetic heterogeneity of GC and may contribute to predicting prognosis and offer novel insights for individualized treatment of patients with GC.
Trajectory and Passive Beamforming Design for IRS-aided Multi-Robot NOMA Indoor Networks

A novel intelligent reflecting surface (IRS)-aided multi-robot network is proposed, where multiple mobile wheeled robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of all robots by jointly optimizing trajectories and NOMA decoding orders of robots, reflecting coefficients of the IRS, and the power allocation of the AP, subject to the quality of service (QoS) of each robot. To tackle this problem, a dueling double deep Q-network (D3QN) based algorithm is invoked for jointly determining the phase shift matrix and robots’ trajectories. Specifically, the trajectories for robots contain a set of local optimal positions, which reveals that robots make the optimal decision at each step. Numerical results demonstrated that the proposed D3QN algorithm outperforms the conventional algorithm, while the performance of IRS-NOMA network is better than the orthogonal multiple access (OMA) network.
MTCRNN: A multi-scale RNN for directed audio texture synthesis

Audio textures are a subset of environmental sounds, often defined as having stable statistical characteristics within an adequately large window of time but may be unstructured locally. They include common everyday sounds such as from rain, wind, and engines. Given that these complex sounds contain patterns on multiple timescales, they are a challenge to model with traditional methods. We introduce a novel modelling approach for textures, combining recurrent neural networks trained at different levels of abstraction with a conditioning strategy that allows for user-directed synthesis. We demonstrate the model's performance on a variety of datasets, examine its performance on various metrics, and discuss some potential applications.
The dynamics of learning with feedback alignment

Direct Feedback Alignment (DFA) is emerging as an efficient and biologically plausible alternative to the ubiquitous backpropagation algorithm for training deep neural networks. Despite relying on random feedback weights for the backward pass, DFA successfully trains state-of-the-art models such as Transformers. On the other hand, it notoriously fails to train convolutional networks. An understanding of the inner workings of DFA to explain these diverging results remains elusive. Here, we propose a theory for the success of DFA. We first show that learning in shallow networks proceeds in two steps: an alignment phase, where the model adapts its weights to align the approximate gradient with the true gradient of the loss function, is followed by a memorisation phase, where the model focuses on fitting the data. This two-step process has a degeneracy breaking effect: out of all the low-loss solutions in the landscape, a network trained with DFA naturally converges to the solution which maximises gradient alignment. We also identify a key quantity underlying alignment in deep linear networks: the conditioning of the alignment matrices. The latter enables a detailed understanding of the impact of data structure on alignment, and suggests a simple explanation for the well-known failure of DFA to train convolutional neural networks. Numerical experiments on MNIST and CIFAR10 clearly demonstrate degeneracy breaking in deep non-linear networks and show that the align-then-memorize process occurs sequentially from the bottom layers of the network to the top.
Quantifying Explainers of Graph Neural Networks in Computational Pathology

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities’ notion, thus complicating comprehension by pathologists. In this work, we address this by adopting biological entity-based graph processing and graph explainers enabling explanations accessible to pathologists. In this context, a major challenge becomes to discern meaningful explainers, particularly in a standardized and quantifiable fashion. To this end, we propose herein a set of novel quantitative metrics based on statistics of class separability using pathologically measurable concepts to characterize graph explainers. We employ the proposed metrics to evaluate three types of graph explainers, namely the layer-wise relevance propagation, gradient-based saliency, and graph pruning approaches, to explain Cell-Graph representations for Breast Cancer Subtyping. The proposed metrics are also applicable in other domains by using domain-specific intuitive concepts. We validate the qualitative and quantitative findings on the BRACS dataset, a large cohort of breast cancer RoIs, by expert pathologists. The code, data, and models can be accessed here 1.