Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretable Convolutional Neural Networks for Subject-Independent Motor Imagery Classification

Deep learning frameworks have become increasingly popular in brain-computer interface (BCI) study thanks to their outstanding performance. However, in terms of the classification model alone, they are treated as black boxes as they do not provide any information on what led them to reach a particular decision. In other words, we cannot convince whether the neuro-physiological factor or simply noise is the factor of high performance. Because of this disadvantage, it is difficult to ensure adequate reliability compared to their high performance. In this study, we propose an explainable deep learning model aimed at classifying EEG signal which is obtained from the motor-imagery (MI) task. Layer-wise relevance propagation (LRP) was adopted on the model to interpret the reason that the model derived certain classification output. We visualized the heatmap which indicates the output of the LRP in the form of topography to certify neuro-physiological factors. Furthermore, we classified EEG in the subject-independent manner to learn robust and generalized EEG features by avoiding subject dependency. The methodology also provides the advantage of avoiding the expense of building training data for each subject. With our proposed model, we obtained generalized heatmap patterns for all subjects. As a result, we can conclude that our proposed model provides neuro-physiologically reliable interpretation.
Properties and applications of a predicted population of runaway He-sdO/B stars ejected from single degenerate He-donor SNe

This study builds on previous works, producing the most extensive prediction of the properties of such a hypothetical population to date, taking into account both Chandrasekhar and non-Chandrasekhar mass events. These results are then used to define criteria for membership of this population and characterise putative subpopulations This study contains 6x10^6 individual ejection trajectories out of the Galactic plane calculated with the stellar kinematics framework SHyRT, which are analysed with regard to their bulk observational properties. These are then put into context with the only previously identified population member US\,708 and applied to a number of other possible candidate objects. We find that two additional previously observed objects possess properties to warrant a designation as candidate objects. Characterisation of these object with respect to the predicted population finds all of them to be extreme in at least one astrometric observable. We find that current observations support a Galactic SN rate on the order of ~3x10^-7/yr to ~2x10^-6/yr, three orders of magnitude below the inferred Galactic SN Ia rate and two orders of magnitude below the formation rate of predicted He-donor progenitors. The number of currently observed population members suggests that the He-donor scenariois not a dominant contributor to the number of observed SNe Ia. However, we find that, even at the low event rate suggested, the majority of possibly detectable population members is still undetected. The extreme nature of current population members suggests that a still larger number of objects has simply evaded detection up to this point, hinting at a higher contribution than is currently supported by observation. - abridged -
Frequency‐selective surface composed of aperture‐coupled high‐impedance surfaces

A compact new FSS structure which provides a narrow band‐pass response is reported. The structure is composed of two high‐impedance surfaces of tightly coupled dipole‐strip conductor arrays placed back‐to‐back with a common ground plane. Each element on one surface is coupled to its neighbour on the opposite surface, via a slot aperture etched in the ground plane. A design procedure is described for optimisation of the dipole and coupling‐slots dimensions in order to excite and mode match the appropriate transmission frequency. The computed results demonstrate the coupling mechanism and transmission performance. Free‐space measurements of the FSS confirm the narrowband transmission characteristics which are potentially useful for antenna and radome‐type applications. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1022–1025, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21589
Evidence of Nodal Superconductivity in Monolayer 1H‐TaS2 with Hidden Order Fluctuations

Unconventional superconductors represent one of the fundamental directions in modern quantum materials research. In particular, nodal superconductors are known to appear naturally in strongly correlated systems, including cuprate superconductors and heavy‐fermion systems. Van der Waals materials hosting superconducting states are well known, yet nodal monolayer van der Waals superconductors have remained elusive. Here, using low‐temperature scanning tunneling microscopy (STM) and spectroscopy (STS) experiments, it is shown that pristine monolayer 1H‐TaS2 realizes a nodal superconducting state. Non‐magnetic disorder drives the nodal superconducting state to a conventional gapped s‐wave state. Furthermore, many‐body excitations emerge close to the gap edge, signalling a potential unconventional pairing mechanism. The results demonstrate the emergence of nodal superconductivity in a van der Waals monolayer, providing a building block for van der Waals heterostructures exploiting unconventional superconducting states.
Acceleration techniques for optimization over trained neural network ensembles

We study optimization problems where the objective function is modeled through feedfor-ward neural networks with rectiﬁed linear unit (ReLU) activation. Recent literature has explored the use of a single neural network to model either uncertain or complex elements within an ob-jective function. However, it is well known that ensembles of neural networks produce more stable predictions and have better generalizability than models with single neural networks, which suggests the application of ensembles of neural networks in a decision-making pipeline. We study how to incorporate a neural network ensemble as the objective function of an optimization model and explore computational approaches for the ensuing problem. We present a mixed-integer linear program based on existing popular big-𝑀 formulations for optimizing over a single neural network. We develop two acceleration techniques for our model, the ﬁrst one is a preprocessing procedure to tighten bounds for critical neurons in the neural network while the second one is a set of valid inequalities based on Benders decomposition. Experimental evaluations of our solution methods are conducted on one global optimization problem and two real-world data sets; the results suggest that our optimization algorithm outperforms the adaption of an state-of-the-art approach in terms of computational time and optimality gaps.
Branching Time Active Inference with Bayesian Filtering

Abstract Branching time active inference is a framework proposing to look at planning as a form of Bayesian model expansion. Its root can be found in active inference, a neuroscientific framework widely used for brain modeling, as well as in Monte Carlo tree search, a method broadly applied in the reinforcement learning literature. Up to now, the inference of the latent variables was carried out by taking advantage of the flexibility offered by variational message passing, an iterative process that can be understood as sending messages along the edges of a factor graph. In this letter, we harness the efficiency of an alternative method for inference, Bayesian filtering, which does not require the iteration of the update equations until convergence of the variational free energy. Instead, this scheme alternates between two phases: integration of evidence and prediction of future states. Both phases can be performed efficiently, and this provides a forty times speedup over the state of the art.
Randomized Response Mechanisms for Differential Privacy Data Analysis: Bounds and Applications

Randomized response, as a basic building-block for differentially private mechanism, has given rise to great interest and found various potential applications in science communities. In this work, we are concerned with three-elements randomized response (RR3) along with relevant applications to the analysis of weighted bipartite graph upon differentially private guarantee. We develop a principled framework for estimating statistics produced by RR3-based mechanisms, and then prove the corresponding estimations to be unbiased. At the same time, we study in detail several fundamental and significant members in RR3 family, and derive the closed-form solutions to unbiased estimations. Next, we show potential applications of several RR3-based mechanisms into the estimation of average degree and average weighted value on weighted bipartite graph when requiring local differential privacy guarantee. In the meantime, we determine the lower bounds for choice of relevant parameters by minimizing variance of statistics in order to design optimal RR3-based local differential private mechanisms, with which we optimize previous protocols in the literature and put forward a version that achieves the tight bound. Last but most importantly, we observe that in the analysis of relational data such as weighted bipartite graph, a portion of privacy budget in local differential private mechanism is sometimes “consumed” by mechanism itself accidentally, resulting to a more stronger privacy guarantee than we would get by simply sequential compositions.
On The Reliability Of Machine Learning Applications In Manufacturing Environments

The increasing deployment of advanced digital technologies such as Internet of Things (IoT) devices and Cyber-Physical Systems (CPS) in industrial environments is enabling the productive use of machine learning (ML) algorithms in the manufacturing domain. As ML applications transcend from research to productive use in real-world industrial environments, the question of reliability arises. Since the majority of ML models are trained and evaluated on static datasets, continuous online monitoring of their performance is required to build reliable systems. Furthermore, concept and sensor drift can lead to degrading accuracy of the algorithm over time, thus compromising safety, acceptance and economics if undetected and not properly addressed. In this work, we exemplarily highlight the severity of the issue on a publicly available industrial dataset which was recorded over the course of 36 months and explain possible sources of drift. We assess the robustness of ML algorithms commonly used in manufacturing and show, that the accuracy strongly declines with increasing drift for all tested algorithms. We further investigate how uncertainty estimation may be leveraged for online performance estimation as well as drift detection as a first step towards continually learning applications. The results indicate, that ensemble algorithms like random forests show the least decay of confidence calibration under drift.
Quantifying Multimodality in World Models

Model-based Deep Reinforcement Learning (RL) assumes the availability of a model of an environment's underlying transition dynamics. This model can be used to predict future effects of an agent's possible actions. When no such model is available, it is possible to learn an approximation of the real environment, e.g. by using generative neural networks, sometimes also called World Models. As most real-world environments are stochastic in nature and the transition dynamics are oftentimes multimodal, it is important to use a modelling technique that is able to reflect this multimodal uncertainty. In order to safely deploy such learning systems in the real world, especially in an industrial context, it is paramount to consider these uncertainties. In this work, we analyze existing and propose new metrics for the detection and quantification of multimodal uncertainty in RL based World Models. The correct modelling&detection of uncertain future states lays the foundation for handling critical situations in a safe way, which is a prerequisite for deploying RL systems in real-world settings.
AI Ethics Principles in Practice: Perspectives of Designers and Developers

As consensus across the various published AI ethics principles is approached, a gap remains between high-level principles and practical techniques that can be readily adopted to design and develop responsible AI systems. We examine the practices and experiences of researchers and engineers from Australia’s national scientific research agency (CSIRO), who are involved in designing and developing AI systems for many application areas. Semi-structured interviews were used to examine how the practices of the participants relate to and align with a set of high-level AI ethics principles proposed by the Australian Government. The principles comprise: (1) privacy protection and security, (2) reliability and safety, (3) transparency and explainability, (4) fairness, (5) contestability, (6) accountability, (7) human-centred values, (8) human, social and environmental well-being. Discussions on the gained insights from the interviews include various tensions and trade-offs between the principles, and provide suggestions for implementing each high-level principle. We also present suggestions aiming to enhance associated support mechanisms.
Building on Huang et al. GlossBERT for Word Sense Disambiguation

We propose to take on the problem ofWord Sense Disambiguation (WSD). In language, words of the same form can take different meanings depending on context. While humans easily infer the meaning or gloss of such words by their context, machines stumble on this task.As such, we intend to replicated and expand upon the results of Huang et al.GlossBERT, a model which they design to disambiguate these words (Huang et al.,2019). Specifically, we propose the following augmentations: data-set tweaking(alpha hyper-parameter), ensemble methods, and replacement of BERT with BART andALBERT. The following GitHub repository contains all code used in this report, which extends on the code made available by Huang et al.
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training

Adversarial training is a popular method to robustify models against adversarial attacks. However, it exhibits much more severe overfitting than training on clean inputs. In this work, we investigate this phenomenon from the perspective of training instances, i.e., training input-target pairs. Based on a quantitative metric measuring instances' difficulty, we analyze the model's behavior on training instances of different difficulty levels. This lets us show that the decay in generalization performance of adversarial training is a result of the model's attempt to fit hard adversarial instances. We theoretically verify our observations for both linear and general nonlinear models, proving that models trained on hard instances have worse generalization performance than ones trained on easy instances. Furthermore, we prove that the difference in the generalization gap between models trained by instances of different difficulty levels increases with the size of the adversarial budget. Finally, we conduct case studies on methods mitigating adversarial overfitting in several scenarios. Our analysis shows that methods successfully mitigating adversarial overfitting all avoid fitting hard adversarial instances, while ones fitting hard adversarial instances do not achieve true robustness.
Digital audio's final frontier

Digital technology continues its march from media like CDs and DVDs toward your audio speakers. Today, amplifiers based on digital principles are already having a profound effect on equipment efficiency and size. They are also beginning to set the standard for sound quality. What distinguishes Class D amplifiers from all others is that their power transistors are always operated either fully on or fully off. This is the only and complete definition of Class D. In their most obvious form, these systems will be able to accept a stream of bits from a CD or MP3 player and convert it into an analog signal that can drive a set of speakers.
Breaking the conformal freedom of spacetime with supernova neutrino imaging

It is known that a geometric measurement of the light cones of supernovae determines the conformal class of the visible part of the spacetime. The conformal factor is physically meaningful but cannot be determined geometrically by anything with zero mass, such as the photon. We show that measuring the neutrino cones in addition to light cones completely removes this gauge freedom. We describe the physical model in great detail, including why ultrarelativistic neutrinos are the only option.
The Fast Radio Burst-emitting Magnetar SGR 1935+2154—Proper Motion and Variability from Long-term Hubble Space Telescope Monitoring

We present deep Hubble Space Telescope (HST) near-infrared (NIR) observations of the magnetar SGR 1935+2154 from 2021 June, approximately 6 yr after the first HST observations, a year after the discovery of fast-radio-burst-like emission from the source, and in a period of exceptional high-frequency activity. Although not directly taken during a bursting period the counterpart is a factor of ∼1.5–2.5 brighter than seen at previous epochs with F140W(AB) = 24.65 ± 0.02 mag. We do not detect significant variations of the NIR counterpart within the course of any one orbit (i.e., on minutes to hour timescales), and contemporaneous X-ray observations show SGR 1935+2154 to be at the quiescent level. With a time baseline of 6 yr from the first identification of the counterpart we place stringent limits on the proper motion (PM) of the source, with a measured PM of μ = 3.1 ± 1.5 mas yr−1. The direction of PM indicates an origin of SGR 1935+2154 very close to the geometric center of SNR G57.2+08, further strengthening their association. At an adopted distance of 6.6 ± 0.7 kpc, the corresponding tangential space velocity is ν T = 97 ± 48 km s−1 (corrected for differential Galactic rotation and peculiar solar motion), although its formal statistical determination may be compromised owing to few epochs of observation. The current velocity estimate places it at the low end of the kick distribution for pulsars, and makes it among the lowest known magnetar kicks. When collating the few-magnetar kick constraints available, we find full consistency between the magnetar kick distribution and the much larger pulsar kick sample.
ES-CRF: Embedded Superpixel CRF for Semantic Segmentation

Modern semantic segmentation methods devote much attention to adjusting feature representations to improve the segmentation performance in various ways, such as metric learning, architecture design, etc . However, almost all those methods neglect the particularity of boundary pixels. These pixels are prone to obtain confusing features from both sides due to the continuous expansion of receptive ﬁelds in CNN networks. In this way, they will mislead the model optimization direction and make the class weights of such categories that tend to share many adjacent pixels lack discrimination, which will damage the overall performance. In this work, we dive deep into this problem and propose a novel method named E mbedded S uperpixel CRF ( ES-CRF ) to address it. ES-CRF involves two main aspects. On the one hand, ES-CRF innovatively fuses the CRF mechanism into the CNN network as an organic whole for more effective end-to-end optimization. It utilizes CRF to guide the message passing between pixels in high-level features to purify the feature representation of boundary pixels, with the help of inner pixels belong to the same object. On the other hand, superpixel is integrated into ES-CRF to exploit the local object prior for more reliable message passing. Finally, our proposed method yields new records on two challenging benchmarks, i.e . , Cityscapes and ADE20K. Moreover, we make detailed theoretical analysis to verify the superiority of ES-CRF.
Bath deformations, islands, and holographic complexity

Considering a doubly holographic model, we study the evolution of holographic subregion complexity corresponding to deformations of bath state by a relevant scalar operator, which corresponds to a renormalization group flow from the AdS-Schwarzschild to the Kasner universe in the bulk. The subregion complexity shows a discontinuous jump at Page time at a fixed perturbation, where the discontinuity depends solely on the system's parameters. We show that the amount of discontinuity decreases with the perturbation as well as with the scaling dimension of the relevant scalar operator.
EgoBody: Human Body Shape, Motion and Social Interactions from Head-Mounted Devices

Understanding social interactions from ﬁrst-person views is crucial for many applications, ranging from assistive robotics to AR/VR. A ﬁrst step for reasoning about interactions is to understand human pose and shape. However, research in this area is currently hindered by the lack of data. Existing datasets are limited in terms of either size, annotations, ground-truth capture modalities or the diversity of interactions. We address this shortcoming by proposing EgoBody, a novel large-scale dataset for social interactions in complex 3D scenes. We employ Microsoft HoloLens2 headsets to record rich egocentric data streams (including RGB, depth, eye gaze, head and hand tracking). To obtain accurate 3D ground-truth, we calibrate the headset with a multi-Kinect rig and ﬁt expressive SMPL-X body meshes to multi-view RGB-D frames, reconstructing 3D human poses and shapes relative to the scene. We collect 68 sequences, spanning diverse sociological interaction categories, and propose the ﬁrst benchmark for 3D full-body pose and shape estimation from egocentric views. Our dataset and code will be available for
Specifications and implementation of the RT MHD control system for the EC launcher of FTU

To perform real time plasma control experiments using EC heating waves by using the new fast launcher installed on FTU a dedicated data acquisition and elabora- tion system has been designed recently. A prototypical version of the acquisition/control system has been recently developed and will be tested on FTU machine in its next ex- perimental campaign. The open-source framework MARTe (Multi-threaded Application Real-Time executor) on Linux/RTAI real-time operating system has been chosen as soft- ware platform to realize the control system. Standard open-architecture industrial PCs, based either on VME bus and CompactPCI bus equipped with standard input/output cards are the chosen hardware platform.
Spatial Computing in Modular Spiking Neural Networks with a Robotic Embodiment

One of the challenges in modern neuroscience is creating a brain-on-a-chip. Such a semiartificial device based on neural networks grown in vitro should interact with the environment when embodied in a robot. A crucial point in this endeavor is developing a neural network architecture capable of associative learning. This work proposes a mathematical model of a midscale modular spiking neural network (SNN) to study learning mechanisms within the brain-on-a-chip context. We show that besides spike-timing-dependent plasticity (STDP), synaptic and neuronal competitions are critical factors for successful learning. Moreover, the shortest pathway rule can implement the synaptic competition responsible for processing conditional stimuli coming from the environment. This solution is ready for testing in neuronal cultures. The neuronal competition can be implemented by lateral inhibition actuating over the SNN modulus responsible for unconditional responses. Empirical testing of this approach is challenging and requires the development of a technique for growing cultures with a given ratio of excitatory and inhibitory neurons. We test the modular SNN embedded in a mobile robot and show that it can establish the association between touch (unconditional) and ultrasonic (conditional) sensors. Then, the robot can avoid obstacles without hitting them, relying on ultrasonic sensors only.