Miracle-dz/UniFAR-FTUs huggingface dataset:
Transductive Zero-Shot Learning for 3D Point Cloud Classification

Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However despite the increasing ubiquity of 3D sensors, the corresponding 3D point cloud classification problem has not been meaningfully explored and introduces new challenges. This paper extends, for the first time, transductive ZeroShot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification. To this end, a novel triplet loss is developed that takes advantage of unlabeled test data. While designed for the task of 3D point cloud classification, the method is also shown to be applicable to the more common use-case of 2D image classification. An extensive set of experiments is carried out, establishing state-of-the-art for ZSL and GZSL in the 3D point cloud domain, as well as demonstrating the applicability of the approach to the image domain.1
A New Benchmark for Evaluation of Cross-Domain Few-Shot Learning

Recent progress on few-shot learning has largely re-lied on annotated data for meta-learning, sampled from the same domain as the novel classes. However, in many applications, collecting data for meta-learning is infeasible or impossible. This leads to the cross-domain few-shot learn-ing problem, where a large domain shift exists between base and novel classes. Although some preliminary investigation of the few-shot methods under domain shift exists, a standard benchmark for cross-domain few-shot learning is not yet established. In this paper, we propose the cross-domain few-shot learning (CD-FSL) benchmark, consist-ing of images from diverse domains with varying similarity to ImageNet, ranging from crop disease images, satellite images, and medical images. Extensive experiments on the proposed benchmark are performed to compare an array of state-of-art meta-learning and transfer learning approaches, including various forms of single model fine-tuning and ensemble learning. The results demonstrate that current meta-learning methods underperform in relation to simple fine-tuning by 12.8% average accuracy. Accuracy of all methods tend to correlate with dataset similarity toImageNet. In addition, the relative performance gain with increasing number of shots is greater with transfer methods compared to meta-learning. Finally, we demonstrate that transferring from multiple pretrained models achieves best performance, with accuracy improvements of 14.9% and 1.9% versus the best of meta-learning and single model fine-tuning approaches, respectively. In summary, the proposed benchmark serves as a challenging platform to guide future research on cross-domain few-shot learning due to its spectrum of diversity and coverage
An improved Haar wavelet quasilinearization technique for a class of generalized Burger's equation

Solving Burgers' equation always poses challenge to researchers as for small values of viscosity the analytical solution breaks down. Here we propose to compute numerical solution for a class of generalised Burgers' equation described as $$ \frac{\partial w}{\partial t}+ w^{\mu}\frac{\partial w}{\partial x_{*}}=\nu w^{\delta}\frac{\partial^2 w}{\partial x^{2}_{*}},\hspace{0.2in}a \leq x_{*}\leq b,~~t\geq 0,$$ based on the Haar wavelet (HW) coupled with quasilinearization approach. In the process of numerical solution, finite forward difference is applied to discretize the time derivative, Haar wavelet to spatial derivative and non-linear term is linearized by quasilinearization technique. To discuss the accuracy and efficiency of the method $L_{\infty}$ and $L_{2}$-error norm are computed and they are compared with some existing results. We have proved the convergence of the proposed method. Computer simulations show that the present method gives accurate and better result even for small number of grid points for small values of viscosity.
Regularizing Deep Multi-Task Networks using Orthogonal Gradients

Deep neural networks are a promising approach towards multi-task learning because of their capability to leverage knowledge across domains and learn general purpose representations. Nevertheless, they can fail to live up to these promises as tasks often compete for a model's limited resources, potentially leading to lower overall performance. In this work we tackle the issue of interfering tasks through a comprehensive analysis of their training, derived from looking at the interaction between gradients within their shared parameters. Our empirical results show that well-performing models have low variance in the angles between task gradients and that popular regularization methods implicitly reduce this measure. Based on this observation, we propose a novel gradient regularization term that minimizes task interference by enforcing near orthogonal gradients. Updating the shared parameters using this property encourages task specific decoders to optimize different parts of the feature extractor, thus reducing competition. We evaluate our method with classification and regression tasks on the multiDigitMNIST, NYUv2 and SUN RGB-D datasets where we obtain competitive results.
Deep learning-based survival prediction for multiple cancer types using histopathology images

Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slides from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In multivariable Cox regression analysis of the combined cohort including all 10 cancers, the DLS was significantly associated with disease specific survival (hazard ratio of 1.58, 95% CI 1.28–1.70, p<0.0001) after adjusting for cancer type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS remained a significant predictor of survival in 5 of 10 cancer types. Compared to a baseline model including stage, age, and sex, the c-index of the model demonstrated an absolute 3.7% improvement (95% CI 1.0–6.5) in the combined cohort. Additionally, our models stratified patients within individual cancer stages, particularly stage II (p = 0.025) and stage III (p<0.001). By developing and evaluating prognostic models across multiple cancer types, this work represents one of the most comprehensive studies exploring the direct prediction of clinical outcomes using deep learning and histopathology images. Our analysis demonstrates the potential for this approach to provide significant prognostic information in multiple cancer types, and even within specific pathologic stages. However, given the relatively small number of cases and observed clinical events for a deep learning task of this type, we observed wide confidence intervals for model performance, thus highlighting that future work will benefit from larger datasets assembled for the purposes for survival modeling.
A detailed non-LTE analysis of LB-1: Revised parameters and surface abundances

Context. It has recently been proposed that LB-1 is a binary system at 4 kpc consisting of a B-type star of 8 M⊙ and a massive stellar black hole (BH) of 70 M⊙. This finding challenges our current theories of massive star evolution and formation of BHs at solar metallicity. Aims. Our objective is to derive the effective temperature, surface gravity, and chemical composition of the B-type component in order to determine its nature and evolutionary status and, indirectly, to constrain the mass of the BH. Methods. We use the non-LTE stellar atmosphere code FASTWIND to analyze new and archival high-resolution data. Results. We determine (Teff, log g) values of (14 000 ± 500 K, 3.50 ± 0.15 dex) that, combined with the Gaia parallax, imply a spectroscopic mass, from log g, of 3.2+2.1−1.9 M⊙ and an evolutionary mass, assuming single star evolution, of 5.2+0.3−0.6 M⊙. We determine an upper limit of 8 km s−1 for the projected rotational velocity and derive the surface abundances; we find the star to have a silicon abundance below solar, and to be significantly enhanced in nitrogen and iron and depleted in carbon and magnesium. Complementary evidence derived from a photometric extinction analysis and Gaia yields similar results for Teff and log g and a consistent distance around 2 kpc. Conclusions. We propose that the B-type star is a slightly evolved main sequence star of 3–5 M⊙ with surface abundances reminiscent of diffusion in late B/A chemically peculiar stars with low rotational velocities. There is also evidence for CN-processed material in its atmosphere. These conclusions rely critically on the distance inferred from the Gaia parallax. The goodness of fit of the Gaia astrometry also favors a high-inclination orbit. If the orbit is edge-on and the B-type star has a mass of 3–5 M⊙, the mass of the dark companion would be 4–5 M⊙, which would be easier to explain with our current stellar evolutionary models.
Thermal-viscous instability in tilted accretion disks: A possible application to IW Andromeda-type dwarf novae

IW And stars are a subgroup of dwarf novae characterized by repetition of the intermediate brightness state with oscillatory variations terminated by brightening. This group of dwarf novae is also known to exhibit a wide variety even within one system in long-term light curves, including the usual dwarf-nova outbursts, Z Cam-type standstills, and so on, besides the typical IW And-type variations mentioned above. Following recent observations suggesting that some IW And stars seem to have tilted disks, we have investigated how the thermal-viscous instability works in tilted accretion disks in dwarf novae and whether it could reproduce the essential features of the light curves in IW And stars. By adopting various simplifying assumptions for tilted disks, we have performed time-dependent one-dimensional numerical simulations of a viscous disk by taking into account various mass supply patterns to the disk; that is, the gas stream from the secondary star flows not only to the outer edge of the disk but also to the inner portions of the disk. We find that tilted disks can achieve a new kind of accretion cycle, in which the inner disk almost always stays in the hot state while the outer disk repeats outbursts, thereby reproducing alternating mid-brightness intervals with dips and brightening, which are quite reminiscent of the most characteristic observational light variations of IW And stars. Further, we have found that our simulations produce diverse light variations, depending on different mass supply patterns even without time variations in mass transfer rates. This could explain the wide variety in long-term light curves of IW And stars.
Fourth-order moment of the light field in atmosphere

The quasiclassical distribution function for photon density in the phase space is obtained from solution of the kinetic equation. This equation describes propagation of paraxial laser beams in the Earth atmosphere where ‘collision integral’ embodies the influence of turbulence. The anisotropy of photon distribution is shown and explained. For long propagation path, the explicit expression for fourth-order moment of light is obtained as a sum of linear and quadratic forms of the average distribution function. This moment describes a spatial correlation of four light waves, giving the information about the photon distribution in the cross-section of the beam. The fourth moment can be measured using two small detectors outside the central part of the beam. The linear form describes the shot noise (quantum fluctuations) of photons. The range where the shot noise exceeds the classical noise is found analytically. Derived photon fluctuations are the valuable source of information about statistical properties and local structure of the laser radiation that can be used for applications.
Adapting Behaviour for Learning Progress

Determining what experience to generate to best facilitate learning (i.e. exploration) is one of the distinguishing features and open challenges in reinforcement learning. The advent of distributed agents that interact with parallel instances of the environment has enabled larger scales and greater flexibility, but has not removed the need to tune exploration to the task, because the ideal data for the learning algorithm necessarily depends on its process of learning. We propose to dynamically adapt the data generation by using a non-stationary multi-armed bandit to optimize a proxy of the learning progress. The data distribution is controlled by modulating multiple parameters of the policy (such as stochasticity, consistency or optimism) without significant overhead. The adaptation speed of the bandit can be increased by exploiting the factored modulation structure. We demonstrate on a suite of Atari 2600 games how this unified approach produces results comparable to per-task tuning at a fraction of the cost.
The structure of dissipative dark matter halos

Dissipative dark matter self-interactions can affect halo evolution and change its structure. We perform a series of controlled N-body simulations to study impacts of the dissipative interactions on halo properties. The interplay between gravitational contraction and collisional dissipation can significantly speed up the onset of gravothermal collapse, resulting in a steep inner density profile. For reasonable choices of model parameters controlling the dissipation, the collapse timescale can be a factor of 10–100 shorter than that predicted in purely elastic self-interacting dark matter. The effect is maximized when energy loss per collision is comparable to characteristic kinetic energy of dark matter particles in the halo. Our simulations provide guidance for testing the dissipative nature of dark matter with astrophysical observations.
Dota 2 with Large Scale Deep Reinforcement Learning

On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.
Pion observables with the Minkowski Space Pion Model

The pion structure in Minkowski space is described in terms of an analytic model of the Bethe-Salpeter amplitude combined with Euclidean Lattice QCD results for the running quark mass. In the present work, a pion model previously proposed, which allows for a Nakanishi integral representation, is studied in order to verify the sensitivity of the pion electromagnetic form factor to small variations of the quark self-energy. In addition, we extend the previous work, providing the Nakanishi integral representation for the invariants associated with a decomposition of the pion Bethe-Salpeter amplitude.
Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods

In this article, we report on the efficiency and effectiveness of multiagent reinforcement learning methods (MARL) for the computation of flight delays to resolve congestion problems in the Air Traffic Management (ATM) domain. Specifically, we aim to resolve cases where demand of airspace use exceeds capacity (demand-capacity problems), via imposing ground delays to flights at the pre-tactical stage of operations (i.e. few days to few hours before operation). Casting this into the multiagent domain, agents, representing flights, need to decide on own delays w.r. t. own preferences, having no information about others' payoffs, preferences and constraints, while they plan to execute their trajectories jointly with others, adhering to operational constraints. Specifically, we formalize the problem as a multiagent Markov Decision Process (MA-MDP) and we show that it can be considered as a Markov game in which interacting agents need to reach an equilibrium: What makes the problem more interesting is the dynamic setting in which agents operate, which is also due to the unforeseen, emergent effects of their decisions in the whole system. We propose collaborative multiagent reinforcement learning methods to resolve demand-capacity imbalances: Extensive experimental study on real-world cases, shows the potential of the proposed approaches in resolving problems, while advanced visualizations provide detailed views towards understanding the quality of solutions provided.
Federated Meta-Learning with Fast Convergence and Efficient Communication

Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers.
Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons

We study artificial neural networks with nonlinear waves as a computing reservoir. We discuss universality and the conditions to learn a dataset in terms of output channels and nonlinearity. A feed-forward three-layered model, with an encoding input layer, a wave layer, and a decoding readout, behaves as a conventional neural network in approximating mathematical functions, real-world datasets, and universal Boolean gates. The rank of the transmission matrix has a fundamental role in assessing the learning abilities of the wave. For a given set of training points, a threshold nonlinearity for universal interpolation exists. When considering the nonlinear Schrödinger equation, the use of highly nonlinear regimes implies that solitons, rogue, and shock waves do have a leading role in training and computing. Our results may enable the realization of novel machine learning devices by using diverse physical systems, as nonlinear optics, hydrodynamics, polaritonics, and Bose-Einstein condensates. The application of these concepts to photonics opens the way to a large class of accelerators and new computational paradigms. In complex wave systems, as multimodal fibers, integrated optical circuits, random, topological devices, and metasurfaces, nonlinear waves can be employed to perform computation and solve complex combinatorial optimization.
FISR: Deep Joint Frame Interpolation and Super-Resolution with A Multi-scale Temporal Loss

Super-resolution (SR) has been widely used to convert low-resolution legacy videos to high-resolution (HR) ones, to suit the increasing resolution of displays (e.g. UHD TVs). However, it becomes easier for humans to notice motion artifacts (e.g. motion judder) in HR videos being rendered on larger-sized display devices. Thus, broadcasting standards support higher frame rates for UHD (Ultra High Definition) videos (4K@60 fps, 8K@120 fps), meaning that applying SR only is insufficient to produce genuine high quality videos. Hence, to up-convert legacy videos for realistic applications, not only SR but also video frame interpolation (VFI) is necessitated. In this paper, we first propose a joint VFI-SR framework for up-scaling the spatio-temporal resolution of videos from 2K 30 fps to 4K 60 fps. For this, we propose a novel training scheme with a multi-scale temporal loss that imposes temporal regularization on the input video sequence, which can be applied to any general video-related task. The proposed structure is analyzed in depth with extensive experiments.
Dermoid Cyst Rupture Presenting as Subarachnoid Hemorrhage: Report of a Rare Case

Dermoid cysts are usually asymptomatic tumors and consist <0.5% of all primary intracranial tumors. Rupture of the cyst can occur spontaneously, due to head trauma or during the intraoperative or postoperative period. Although patients have been reported to be asymptomatic after cyst rupture, its etiology is not fully understood. Here, we present a 53-year-old male patient who was admitted to the emergency department with loss of consciousness following sudden onset of headache. He had a stiff neck on physical examination. Although complaints and findings suggested spontaneous subarachnoid hemorrhage, cranial computed tomography and magnetic resonance imaging demonstrated rupture of a dermoid cyst.
Training Deep Learning models with small datasets

The growing use of Machine Learning has produced significant advances in many fields. For image-based tasks, however, the use of deep learning remains challenging in small datasets. In this article, we review, evaluate and compare the current state of the art techniques in training neural networks to elucidate which techniques work best for small datasets. We further propose a path forward for the improvement of model accuracy in medical imaging applications. We observed best results from one cycle training, discriminative learning rates with gradual freezing and parameter modification after transfer learning. We also established that when datasets are small, transfer learning plays an important role beyond parameter initialization by reusing previously learned features. Surprisingly we observed that there is little advantage in using pre-trained networks in images from another part of the body compared to Imagenet. On the contrary, if images from the same part of the body are available then transfer learning can produce a significant improvement in performance with as little as 50 images in the training data.
Fulfillment of unitarity for the non-Hermitian decaying probability density involving the full set of resonance states to the Schr\"odinger equation

By using an exact analytical non-Hermitian formalism involving the full set of resonance (quasinormal) states and complex energy eigenvalues for quantum tunneling decay, we show that unitarity holds at any instant of time for the nonstationary decaying probability density in the whole space, thus providing a fundamental description of the quantum mechanical decay process.