Miracle-dz/UniFAR-FTUs huggingface dataset:
Spike-Timing-Dependent Inference of Synaptic Weights

A potential solution to the weight transport problem, which questions the biological plausibility of the backpropagation of error algorithm, is proposed. We derive our method based upon an (approximate) analysis of the dynamics of leaky integrate-and-ﬁre neurons. We thereafter validate our method and show that the use of spike timing alone out-competes existing biologically plausible methods for synaptic weight inference in spiking neural network models. Furthermore, our proposed method is also more ﬂexible, being applicable to any spiking neuron model, is conservative in how many parameters are required for implementation and can be deployed in an online-fashion with minimal computational overhead. These features, together with its biological plausibility, make it an attractive candidate technique for weight inference at single synapses.
RNN-based Online Learning: An Efficient First-Order Optimization Algorithm with a Convergence Guarantee

We investigate online nonlinear regression with continually running recurrent neural network networks (RNNs), i.e., RNN-based online learning. For RNN-based online learning, we introduce an efficient first-order training algorithm that theoretically guarantees to converge to the optimum network parameters. Our algorithm is truly online such that it does not make any assumption on the learning environment to guarantee convergence. Through numerical simulations, we verify our theoretical results and illustrate significant performance improvements achieved by our algorithm with respect to the state-of-the-art RNN training methods.
When are Bayesian model probabilities overconfident

Bayesian model comparison is often based on the posterior distribution over the set of compared models. This distribution is often observed to concentrate on a single model even when other measures of model fit or forecasting ability indicate no strong preference. Furthermore, a moderate change in the data sample can easily shift the posterior model probabilities to concentrate on another model. We document overconfidence in two high-profile applications in economics and neuroscience. To shed more light on the sources of overconfidence we derive the sampling variance of the Bayes factor in univariate and multivariate linear regression. The results show that overconfidence is likely to happen when i) the compared models give very different approximations of the data-generating process, ii) the models are very flexible with large degrees of freedom that are not shared between the models, and iii) the models underestimate the true variability in the data.
Learning entropy production via neural networks

This Letter presents a neural estimator for entropy production (NEEP), that estimates entropy production (EP) from trajectories of relevant variables without detailed information on the system dynamics. For steady state, we rigorously prove that the estimator, which can be built up from different choices of deep neural networks, provides stochastic EP by optimizing the objective function proposed here. We verify the NEEP with the stochastic processes of the bead spring and discrete flashing ratchet models and also demonstrate that our method is applicable to high-dimensional data and can provide coarse-grained EP for Markov systems with unobservable states.
Transformation-based Adversarial Video Prediction on Large-Scale Data

Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world video. In this work, we focus on the task of video prediction, where given a sequence of frames extracted from a video, the goal is to generate a plausible future sequence. We first improve the state of the art by performing a systematic empirical study of discriminator decompositions and proposing an architecture that yields faster convergence and higher performance than previous approaches. We then analyze recurrent units in the generator, and propose a novel recurrent unit which transforms its past hidden state according to predicted motion-like features, and refines it to to handle dis-occlusions, scene changes and other complex behavior. We show that this recurrent unit consistently outperforms previous designs. Our final model leads to a leap in the state-of-the-art performance, obtaining a test set Frechet Video Distance of 25.7, down from 69.2, on the large-scale Kinetics-600 dataset.
A high-resolution spectroscopic study of two new Na- and Al-rich field giants–likely globular cluster escapees in the Galactic halo

The stars SDSS J064655.6+411620.5 and SDSS J193712.01+502455.5 are relatively bright stars that were initially observed as part of the SDSS/MARVELS pre-survey. They were selected, on the basis of their weak CH $G$-bands, along with a total of 60 others, in the range of halo globular cluster metallicities for high-resolution spectroscopic follow-up as a part of the HESP-GOMPA survey (Hanle Echelle SPectrograph -- Galactic survey Of Metal Poor stArs). The stars exhibit typical nucleosynthesis signatures expected from the so-called second-generation stars of globular clusters. The light-element anti-correlation of Mg-Al is detected, along with elevated abundances of Na. Carbon is found to be depleted, which is compatible with expectation. Lithium is also detected in SDSS J064655.6+411620.5 and SDSS J193712.01+502455.5; the measured abundances are similar to those of normal halo giant stars. These bright escapees provide a unique opportunity to study the nucleosynthesis events of globular clusters in great detail, and shed light on their chemical-enrichment histories.
Multiperson Continuous Tracking and Identification From mm-Wave Micro-Doppler Signatures

In this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space. This leads to a complicated setting, which requires one to deal with the randomness and complexity of the resulting (composite) backscattered signal. The proposed system combines several processing steps: at first, the signal is filtered to remove artifacts, reflections, and random noise that do not originate from humans. Hence, a density-based classification algorithm is executed to separate the Doppler signatures of different users. The final blocks are trajectory tracking and user identification, respectively, based on Kalman filters and deep neural networks. Our results demonstrate that the integration of the last-mentioned processing stages is critical toward achieving robustness and accuracy in multiuser settings. Our technique is tested both on a single-target public data set, for which it outperforms state-of-the-art methods, and on our own measurements, obtained with a 77 GHz radar on multiple subjects simultaneously moving in two different indoor environments. The system works in an online fashion, permitting the continuous identification of multiple subjects with accuracies up to 98%, e.g., with four subjects sharing the same physical space, and with a small accuracy reduction when tested with unseen data from a challenging real-life scenario that was not part of the model learning phase.
Machine Learning based Anomaly Detection for 5G Networks

Protecting the networks of tomorrow is set to be a challenging domain due to increasing cyber security threats and widening attack surfaces created by the Internet of Things (IoT), increased network heterogeneity, increased use of virtualisation technologies and distributed architectures. This paper proposes SDS (Software Defined Security) as a means to provide an automated, flexible and scalable network defence system. SDS will harness current advances in machine learning to design a CNN (Convolutional Neural Network) using NAS (Neural Architecture Search) to detect anomalous network traffic. SDS can be applied to an intrusion detection system to create a more proactive and end-to-end defence for a 5G network. To test this assumption, normal and anomalous network flows from a simulated environment have been collected and analyzed with a CNN. The results from this method are promising as the model has identified benign traffic with a 100% accuracy rate and anomalous traffic with a 96.4% detection rate. This demonstrates the effectiveness of network flow analysis for a variety of common malicious attacks and also provides a viable option for detection of encrypted malicious network traffic.
Cascaded Human-Object Interaction Recognition

Rapid progress has been witnessed for human-object interaction (HOI) recognition, but most existing models are confined to single-stage reasoning pipelines. Considering the intrinsic complexity of the task, we introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. Each of the two networks is also connected to its predecessor at the previous stage, enabling cross-stage information propagation. The interaction recognition network has two crucial parts: a relation ranking module for high-quality HOI proposal selection and a triple-stream classifier for relation prediction. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding. Further beyond relation detection on a bounding-box level, we make our framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling. Our approach reached the $1^{st}$ place in the ICCV2019 Person in Context Challenge, on both relation detection and segmentation tasks. It also shows promising results on V-COCO.
Most stars (and planets?) are born in intense radiation fields

Protostars and young stars are strongly spatially ‘clustered’ or ‘correlated’ within their natal giant molecular clouds. We demonstrate that such clustering leads to the conclusion that the incident bolometric radiative flux upon a random young star/disc is enhanced (relative to volume-averaged fluxes) by a factor that increases with the total stellar mass of the complex. Because the Galactic cloud mass function is top-heavy, the typical star in our Galaxy experienced a much stronger radiative environment than those forming in well-observed nearby (but relatively small) clouds, exceeding fluxes in the Orion Nebular Cluster by factors of ≳30. Heating of the circumstellar disc around a median young star is dominated by this external radiation beyond $\sim \! 50$ au. And if discs are not well shielded by ambient dust, external ultraviolet irradiation can dominate over the host star down to sub-au scales. Another consequence of stellar clustering is an extremely broad Galaxy-wide distribution of incident flux (spanning >10 decades), with half the Galactic star formation in a substantial ‘tail’ towards even more intense background radiation. We also show that the strength of external irradiation is amplified superlinearly in high-density environments such as the Galactic Centre, starbursts, or high-redshift galaxies.
Dense Dilated Convolutions’ Merging Network for Land Cover Classification

Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In this article, we propose a novel architecture called the dense dilated convolutions’ merging network (DDCM-Net) to address this task. The proposed DDCM-Net consists of dense dilated image convolutions merged with varying dilation rates. This effectively utilizes rich combinations of dilated convolutions that enlarge the network’s receptive fields with fewer parameters and features compared with the state-of-the-art approaches in the remote sensing domain. Importantly, DDCM-Net obtains fused local- and global-context information, in effect incorporating surrounding discriminative capability for multiscale and complex-shaped objects with similar color and textures in very high-resolution aerial imagery. We demonstrate the effectiveness, robustness, and flexibility of the proposed DDCM-Net on the publicly available ISPRS Potsdam and Vaihingen data sets, as well as the DeepGlobe land cover data set. Our single model, trained on three-band Potsdam and Vaihingen data sets, achieves better accuracy in terms of both mean intersection over union (mIoU) and F1-score compared with other published models trained with more than three-band data. We further validate our model on the DeepGlobe data set, achieving state-of-the-art result 56.2% mIoU with much fewer parameters and at a lower computational cost compared with related recent work.
Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images

Prediction of survivability in a patient for tumor progression is useful to estimate the effectiveness of a treatment protocol. In our work, we present a model to take into account the heterogeneous nature of a tumor to predict survival. The tumor heterogeneity is measured in terms of its mass by combining information regarding the radiodensity obtained in images with the gross tumor volume (GTV). We propose a novel feature called Tumor Mass within a GTV (TMG), that improves the prediction of survivability, compared to existing models which use GTV. Weekly variation in TMG of a patient is computed from the image data and also estimated from a cell survivability model. The parameters obtained from the cell survivability model are indicatives of changes in TMG over the treatment period. We use these parameters along with other patient metadata to perform survival analysis and regression. Cox's Proportional Hazard survival regression was performed using these data. Significant improvement in the average concordance index from 0.47 to 0.64 was observed when TMG is used in the model instead of GTV. The experiments show that there is a difference in the treatment response in responsive and non-responsive patients and that the proposed method can be used to predict patient survivability.
Risk Analysis of Divide-and-Conquer ERM

—Theoretical analysis of the divide-and-conquer based distributed learning with least square loss in the reproducing kernel Hilbert space (RKHS) have recently been explored within the framework of learning theory. However, the studies on learning theory for general loss functions and hypothesis spaces remain limited. To ﬁll the gap, we study the risk performance of distributed empirical risk minimization (ERM) for general loss functions and hypothesis spaces. The main contributions are two-fold. First, we derive two risk bounds of optimal rates under certain basic assumptions on the hypothesis space, as well as the smoothness, Lipschitz continuity, strong convexity of the loss function. Second, we further develop two more general risk bounds for distributed ERM without the restriction of strong convexity.
Optical Thermometry with Quantum Emitters in Hexagonal Boron Nitride.

Nanoscale optical thermometry is a promising non-contact route for measuring local temperature with both high sensitivity and spatial resolution. In this work, we present a deterministic optical thermometry technique based on quantum emitters in nanoscale hexagonal boron-nitride. We show that these nanothermometers exhibit better performance than that of homologous, all-optical nanothermometers both in sensitivity and range of working temperature. We demonstrate their effectiveness as nanothermometers by monitoring the local temperature at specific locations in a variety of custom-built micro-circuits. This work opens new avenues for nanoscale temperature measurements and heat flow studies in miniaturized, integrated devices.
Corner states and topological transitions in two-dimensional higher-order topological sonic crystals with inversion symmetry

Macroscopic two-dimensional (2D) sonic crystals with inversion symmetry are studied to reveal higher-order topological physics in classical wave systems. By tuning a single geometry parameter, the band topology of the bulk and the edges can be controlled simultaneously. The bulk band gap forms an acoustic analog of topological crystalline insulators with edge states which are gapped due to symmetry reduction on the edges. In the presence of mirror symmetry, the band topology of the edge states can be characterized by the Zak phase, illustrating the band topology in a hierarchy of dimensions, which is the key feature of higher-order topological phenomena. Moreover, the edge bands undergo topological transitions without closing the bulk band gap, revealing rich topological physics in both the bulk and edges. We characterize the higher-order topological bands using symmetry indicators in the Brillouin zone and link the symmetry indicators with the corner charge---a corner topological index. This approach allows us to identify higher-order topological boundary states through symmetry properties of the Bloch bands. We then generalize the higher-order topological phenomena protected by the inversion symmetry to arch-shaped sonic crystals with nonsymmorphic symmetries where similar but slightly different topological phenomena are revealed. In these systems, the rich, multidimensional topological transitions can be exploited for topological transfer among 0D (corner), 1D (edge), and 2D (bulk) acoustic modes by tuning the geometry of acoustic metamaterials.
Bures Geometry on C*-algebraic State Spaces

The inner geometry induced by the Bures distance function on the state space of a unital C*-algebra, or on parts of it, is considered in detail. As a key result the local dilation function is calculated at a state when the state argument is bound to vary along certain parameterized curves passing through this state. The parameterized curves considered are those possessing differentiable local implementations as vector states relative to some unital *-representation in the vicinity of the state in question. The space of all tangent forms at a given state correspondent to this category of curves is specified as normed linear space with a quadratic norm which is depending from the state in a characteristic manner. In analyzing the structure of the local dilation function special emphasis is laid on describing such parts of the state space, in restriction to which the line element would be of Finslerian type, and which were geodesically convex subsets. All results obtained are illustrated by examples, and the implications obtained are compared to those known from the investigations of Uhlmann et al in the finite dimensional case.
On the opening angle of magnetized jets from neutron-star mergers: the case of GRB170817A

The observations of GW170817/GRB170817A have confirmed that the coalescence of a neutron-star binary is the progenitor of a short gamma-ray burst. In the standard picture of a short gamma-ray burst, a collimated highly relativistic outflow is launched after merger and it successfully breaks out from the surrounding ejected matter. Using initial conditions inspired from numerical-relativity binary neutron-star merger simulations, we have performed general-relativistic hydrodynamic (HD) and magnetohydrodynamic (MHD) simulations in which the jet is launched and propagates self-consistently. The complete set of simulations suggests that: (i) MHD jets have an intrinsic energy and velocity polar structure with a ``hollow core'' subtending an angle $\theta_{\rm core}\approx4^{\circ}-5^{\circ}$ and an opening angle of $\theta_{\rm jet}\gtrsim10^{\circ}$; (ii) MHD jets eject significant amounts of matter and two orders of magnitude more than HD jets; (iii) the energy stratification in MHD jets naturally yields the power-law energy scaling $E(>\Gamma\beta)\propto(\Gamma\beta)^{-4.5}$; (iv) MHD jets provide fits to the afterglow data from GRB170817A that are comparatively better than those of the HD jets and without free parameters; (v) finally, both of the best-fit HD/MHD models suggest an observation angle $\theta_{\rm obs} \simeq 21^{\circ}$ for GRB170817A.
Distinguishing between flaring and nonflaring active regions

Context. Large-scale solar eruptions significantly affect space weather and damage space-based human infrastructures. It is necessary to predict large-scale solar eruptions; it will enable us to protect the vulnerable infrastructures of our modern society. Aims. We investigate the difference between flaring and nonflaring active regions. We also investigate whether it is possible to forecast a solar flare. Methods. We used photospheric vector magnetogram data from the Solar Dynamic Observatory’s Helioseismic Magnetic Imager to study the time evolution of photospheric magnetic parameters on the solar surface. We built a database of flaring and nonflaring active regions observed on the solar surface from 2010 to 2017. We trained a machine-learning algorithm with the time evolution of these active region parameters. Finally, we estimated the performance obtained from the machine-learning algorithm. Results. The strength of some magnetic parameters such as the total unsigned magnetic flux, the total unsigned magnetic helicity, the total unsigned vertical current, and the total photospheric magnetic energy density in flaring active regions are much higher than those of the non-flaring regions. These magnetic parameters in a flaring active region evolve fast and are complex. We are able to obtain a good forecasting capability with a relatively high value of true skill statistic. We also find that time evolution of the total unsigned magnetic helicity and the total unsigned magnetic flux provides a very high ability of distinguishing flaring and nonflaring active regions. Conclusions. We can distinguish a flaring active region from a nonflaring region with good accuracy. We confirm that there is no single common parameter that can distinguish all flaring active regions from the nonflaring regions. However, the time evolution of the top two magnetic parameters, the total unsigned magnetic flux and the total unsigned magnetic helicity, have a very high distinguishing capability.
Multi-modal Self-Supervision from Generalized Data Transformations

The recent success of self-supervised learning can be largely attributed to content-preserving transformations, which can be used to easily induce invariances. While transformations generate positive sample pairs in contrastive loss training, most recent work focuses on developing new objective formulations, and pays relatively little attention to the transformations themselves. In this paper, we introduce the framework of Generalized Data Transformations to (1) reduce several recent self-supervised learning objectives to a single formulation for ease of comparison, analysis, and extension, (2) allow a choice between being invariant or distinctive to data transformations, obtaining different supervisory signals, and (3) derive the conditions that combinations of transformations must obey in order to lead to well-posed learning objectives. This framework allows both invariance and distinctiveness to be injected into representations simultaneously, and lets us systematically explore novel contrastive objectives. We apply it to study multi-modal self-supervision for audio-visual representation learning from unlabelled videos, improving the state-of-the-art by a large margin, and even surpassing supervised pretraining. We demonstrate results on a variety of downstream video and audio classification and retrieval tasks, on datasets such as HMDB-51, UCF-101, DCASE2014, ESC-50 and VGG-Sound. In particular, we achieve new state-of-the-art accuracies of 72.8% on HMDB-51 and 95.2% on UCF-101.