Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models

Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an innovative method that employs to classify and reconstruct images from the ImageNet dataset using electroencephalography (EEG) data from subjects that had viewed the images themselves (i.e."brain decoding"). We analyzed EEG recordings from 6 participants, each exposed to 50 images spanning 40 unique semantic categories. These EEG readings were converted into spectrograms, which were then used to train a convolutional neural network (CNN), integrated with a knowledge distillation procedure based on a pre-trained Contrastive Language-Image Pre-Training (CLIP)-based image classification teacher network. This strategy allowed our model to attain a top-5 accuracy of 80%, significantly outperforming a standard CNN and various RNN-based benchmarks. Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images which had elicited EEG activity. Therefore, our architecture not only decodes images from neural activity but also offers a credible image reconstruction from EEG only, paving the way for e.g. swift, individualized feedback experiments. Our research represents a significant step forward in connecting neural signals with visual cognition.
Trend Following with Momentum Versus Moving Average: A Tale of Differences

Despite the ever-growing interest in trend following and a series of publications in academic journals, there is still a great shortage of theoretical results on the properties of trend following rules. Our paper fills this gap by comparing and contrasting the two most popular trend following rules, the Momentum (MOM) and Moving Average (MA) rules, from a theoretical perspective. Our approach is based on the return-based formulation of trading rules and modelling the price trends by an autoregressive return process. We provide theoretical results on the similarity between various trend following rules and the forecast accuracy of trading rules. Our results show that the similarity between the MOM and MA rules is rather high and increases with increasing trend strength. However, as compared to the MOM rule, the MA rules have a more robust forecast accuracy of the future direction of price trends. As a result, under uncertain market dynamics the MA rules tend to gain an advantage over the MOM rule. Overall, the results reported in this paper help traders to understand more deeply the properties of trend following rules as well as the differences and similarities between them.
Compressed Real Numbers for AI: a case-study using a RISC-V CPU

As recently demonstrated, Deep Neural Networks (DNN), usually trained using single precision IEEE 754 floating point numbers (binary32), can also work using lower precision. Therefore, 16-bit and 8-bit compressed format have attracted considerable attention. In this paper, we focused on two families of formats that have already achieved interesting results in compressing binary32 numbers in machine learning applications, without sensible degradation of the accuracy: bfloat and posit. Even if 16-bit and 8-bit bfloat/posit are routinely used for reducing the storage of the weights/biases of trained DNNs, the inference still often happens on the 32-bit FPU of the CPU (especially if GPUs are not available). In this paper we propose a way to decompress a tensor of bfloat/posits just before computations, i.e., after the compressed operands have been loaded within the vector registers of a vector capable CPU, in order to save bandwidth usage and increase cache efficiency. Finally, we show the architectural parameters and considerations under which this solution is advantageous with respect to the uncompressed one.
Ocean fertilisation: Science and regulation

The threat of climate change may be the greatest social and environmental challenge of our time. Yet if the increase in warming is to be stabilised, then a reduction in atmospheric carbon dioxide (CO2) is needed. Sink technologies such as ocean fertilisation claim to do this by stimulating phytoplankton to grow into massive blooms, thereby drawing down large amounts of CO2 from the atmosphere into the deep ocean. But the science is unproven and concerns have been raised, not only about its feasibility, but also the environmental and legal implications. This thesis examines the process of ocean fertilisation and the capacity and effectiveness of current international and domestic legal regimes to regulate it. The science and feasibility of ocean fertilisation, primarily as a carbon mitigation measure, but also for carbon trading and seafood production, were considered. Three case studies were used as models to test how each legal instrument could be applied to the selected criteria to measure either capacity or effectiveness. Criteria were drawn from two critical areas of concern — the protection of the environment and enforcement. The research established that current Australian domestic law would most likely have adequate checks and balances to regulate ocean fertilisation activities within Australia’s exclusive economic zone and territorial waters, with the exception of some external territories where compliance and enforcement may be problematic. The international law was found to be less effective; the main concern was the use of flags of convenience to bypass regulation on the high seas. Areas of conflict were found, particularly between ocean fertilisation for scientific research and commercial purposes. While there are no existing international legal instruments for ocean fertilisation generally, there is a framework for the assessment of ocean fertilisation for scientific research, leaving the future of commercial ocean fertilisation operations still undetermined. A model for the development of a new legal instrument to regulate ocean fertilisation activities, incorporating both research and commercial applications, was suggested.
Applying Deep Learning to Calibrate Stochastic Volatility Models

Stochastic volatility models, where the volatility is a stochastic process, can capture most of the essential stylized facts of implied volatility surfaces and give more realistic dynamics of the volatility smile/skew. However, they come with the significant issue that they take too long to calibrate. Alternative calibration methods based on Deep Learning (DL) techniques have been recently used to build fast and accurate solutions to the calibration problem. Huge and Savine developed a Differential Machine Learning (DML) approach, where Machine Learning models are trained on samples of not only features and labels but also differentials of labels to features. The present work aims to apply the DML technique to price vanilla European options (i.e. the calibration instruments), more specifically, puts when the underlying asset follows a Heston model and then calibrate the model on the trained network. DML allows for fast training and accurate pricing. The trained neural network dramatically reduces Heston calibration's computation time. In this work, we also introduce different regularisation techniques, and we apply them notably in the case of the DML. We compare their performance in reducing overfitting and improving the generalisation error. The DML performance is also compared to the classical DL (without differentiation) one in the case of Feed-Forward Neural Networks. We show that the DML outperforms the DL. The complete code for our experiments is provided in the GitHub repository: https://github.com/asridi/DML-Calibration-Heston-Model
Stable In-hand Manipulation with Finger Specific Multi-agent Shadow Reward

Deep Reinforcement Learning has shown its capability to solve the high degrees of freedom in control and the complex interaction with the object in the multi-finger dexterous in-hand manipulation tasks. Current DRL approaches prefer sparse rewards to dense rewards for the ease of training but lack behavior constraints during the manipulation process, leading to aggressive and unstable policies that are insufficient for safety-critical in-hand manipulation tasks. Dense rewards can regulate the policy to learn stable manipulation behaviors with continuous reward constraints but are hard to empirically define and slow to converge optimally. This work proposes the Finger-specific Multi-agent Shadow Reward (FMSR) method to determine the stable manipulation constraints in the form of dense reward based on the state-action occupancy measure, a general utility of DRL that is approximated during the learning process. Information Sharing (IS) across neighboring agents enables consensus training to accelerate the convergence. The methods are evaluated in two in-hand manipulation tasks on the Shadow Hand. The results show FMSR+IS converges faster in training, achieving a higher task success rate and better manipulation stability than conventional dense reward. The comparison indicates FMSR+IS achieves a comparable success rate even with the behavior constraint but much better manipulation stability than the policy trained with a sparse reward.
Hodge-Aware Contrastive Learning

Simplicial complexes prove effective in modeling data with multiway dependencies, such as data defined along the edges of networks or within other higher-order structures. Their spectrum can be decomposed into three interpretable subspaces via the Hodge decomposition, resulting foundational in numerous applications. We leverage this decomposition to develop a contrastive self-supervised learning approach for processing simplicial data and generating embeddings that encapsulate specific spectral information.Specifically, we encode the pertinent data invariances through simplicial neural networks and devise augmentations that yield positive contrastive examples with suitable spectral properties for downstream tasks. Additionally, we reweight the significance of negative examples in the contrastive loss, considering the similarity of their Hodge components to the anchor. By encouraging a stronger separation among less similar instances, we obtain an embedding space that reflects the spectral properties of the data. The numerical results on two standard edge flow classification tasks show a superior performance even when compared to supervised learning techniques. Our findings underscore the importance of adopting a spectral perspective for contrastive learning with higher-order data.
A revised age greater than 50 Myr for the young cluster IC 4665

IC 4665 is one of only a dozen young open clusters with a ``lithium depletion boundary"(LDB) age. Using an astrometrically and spectroscopically filtered sample of cluster members, we show that both the positions of its low mass stars in Gaia absolute colour-magnitude diagrams and the lithium depletion seen among its K- and early M-stars are discordant with the reported LDB age of (32 +4/-5) Myr. Re-analysis of archival spectra suggests that the LDB of IC 4665 has not been detected and that the published LDB age should be interpreted as a lower limit. Empirical comparisons with similar datasets from other young clusters with better-established LDB ages indicate that IC 4665 is bracketed in age by the clusters IC 2602 and IC 2391 at (55 +/- 3) Myr.
Variational Quantum Linear Solver enhanced Quantum Support Vector Machine

Quantum Support Vector Machines (QSVM) play a vital role in using quantum resources for supervised machine learning tasks, such as classification. However, current methods are strongly limited in terms of scalability on Noisy Intermediate Scale Quantum (NISQ) devices. In this work, we propose a novel approach called the Variational Quantum Linear Solver (VQLS) enhanced QSVM. This is built upon our idea of utilizing the variational quantum linear solver to solve system of linear equations of a least squares-SVM on a NISQ device. The implementation of our approach is evaluated by an extensive series of numerical experiments with the Iris dataset, which consists of three distinct iris plant species. Based on this, we explore the practicality and effectiveness of our algorithm by constructing a classifier capable of classification in a feature space ranging from one to seven dimensions. Furthermore, by strategically exploiting both classical and quantum computing for various subroutines of our algorithm, we effectively mitigate practical challenges associated with the implementation. These include significant improvement in the trainability of the variational ansatz and notable reductions in run-time for cost calculations. Based on the numerical experiments, our approach exhibits the capability of identifying a separating hyperplane in an 8-dimensional feature space. Moreover, it consistently demonstrated strong performance across various instances with the same dataset.
Aerial Manipulator Force Control Using Control Barrier Functions

—This article studies the problem of applying normal forces on a surface, using an underactuated aerial vehicle equipped with a dexterous robotic arm. A force-motion high-level controller is designed based on a Lyapunov function encompassing alignment and exerted force errors. This controller is coupled with a Control Barrier Function constraint under an optimization scheme using Quadratic Programming. This aims to enforce a prescribed relationship between the approaching motion for the end-effector and its alignment with the surface, thus ensuring safe operation. An adaptive low-level controller is devised for the aerial vehicle, capable of tracking velocity commands generated by the high-level controller. Simulations are presented to demonstrate the force exertion stability and safety of the controller in cases of large disturbances.
Hybrid ASR for Resource-Constrained Robots: HMM - Deep Learning Fusion

This paper presents a novel hybrid Automatic Speech Recognition (ASR) system designed specifically for resource-constrained robots. The proposed approach combines Hidden Markov Models (HMMs) with deep learning models and leverages socket programming to distribute processing tasks effectively. In this architecture, the HMM-based processing takes place within the robot, while a separate PC handles the deep learning model. This synergy between HMMs and deep learning enhances speech recognition accuracy significantly. We conducted experiments across various robotic platforms, demonstrating real-time and precise speech recognition capabilities. Notably, the system exhibits adaptability to changing acoustic conditions and compatibility with low-power hardware, making it highly effective in environments with limited computational resources. This hybrid ASR paradigm opens up promising possibilities for seamless human-robot interaction. In conclusion, our research introduces a pioneering dimension to ASR techniques tailored for robotics. By employing socket programming to distribute processing tasks across distinct devices and strategically combining HMMs with deep learning models, our hybrid ASR system showcases its potential to enable robots to comprehend and respond to spoken language adeptly, even in environments with restricted computational resources. This paradigm sets a innovative course for enhancing human-robot interaction across a wide range of real-world scenarios.
Tackling the dimensions in imaging genetics with CLUB-PLS

A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes. That entails executing one genome-wide association study (GWAS) for each pre-defined imaging measure. Although this approach has been tremendously successful, one shortcoming is that phenotypes must be pre-defined. Consequently, effects that are not confined to pre-selected regions of interest or that reflect larger brain-wide patterns can easily be missed. In this work we introduce a Partial Least Squares (PLS)-based framework, which we term Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in both domains as well as with large sample sizes. One key factor of the framework is to use cluster bootstrap to provide robust statistics for single input features in both domains. We applied CLUB-PLS to investigating the genetic basis of surface area and cortical thickness in a sample of 33,000 subjects from the UK Biobank. We found 107 genome-wide significant locus-phenotype pairs that are linked to 386 different genes. We found that a vast majority of these loci could be technically validated at a high rate: using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85 locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold.
Enhanced Non-linear Response by Manipulating the Dirac Point in the (111) LaTiO$_3$/SrTiO$_3$ Interface

Tunable spin-orbit interaction (SOI) is an important feature for future spin-based devices. In the presence of a magnetic field, SOI induces an asymmetry in the energy bands, which can produce non-linear transport effects ($V\sim I^2$). Here, we focus on such effects to study the role of SOI in the (111) LaTiO$_3$/SrTiO$_3$ interface. This system is a convenient platform for understanding the role of SOI since it exhibits a single-band Hall-response through the entire gate-voltage range studied. We report a pronounced rise in the non-linear resistance at a critical in-plane field $H_{cr}$. This rise disappears with a small out-of-plane field. We explain these results by considering the location of the Dirac point formed at the crossing of the spin-split energy bands. An in-plane magnetic field pushes this point outside of the Fermi surface, and consequently changes the symmetry of the Fermi contours and intensifies the non-linear transport. An out-of-plane magnetic field opens a gap at the Dirac point, thereby significantly diminishing the non-linear effects. We propose that magnetoresistance effects previously reported in interfaces with SOI could be comprehended within our suggested scenario.
: A new treatment

From the Department of Thoracic Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan. Disclosures: Author has nothing to disclose with regard to commercial support. Received for publication March 29, 2020; revisions received March 29, 2020; accepted for publication March 30, 2020; available ahead of print April 11, 2020. Address for reprints: Hiroshi Date, MD, Department of Thoracic Surgery, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan (E-mail: hdate@kuhp.kyoto-u.ac.jp). J Thorac Cardiovasc Surg 2021;161:e436-7 0022-5223/$36.00 Copyright 2020 by The American Association for Thoracic Surgery https://doi.org/10.1016/j.jtcvs. 2020.03. 127
Failure of the Baym-Kadanoff construction to match consistently quantum dynamics with thermodynamic critical behavior

We disclose a serious deficiency of the Baym-Kadanoff construction of thermodynamically consistent conserving approximations. There are two vertices in this scheme: dynamical and conserving. The divergence of each indicates a phase instability. We show that each leads to incomplete and qualitatively different behavior at different critical points. The diagrammatically controlled dynamical vertex from the Schwinger-Dyson equation does not obey the Ward identity and cannot be continued beyond its singularity. The standardly used dynamical vertex alone cannot, hence, conclusively decide about the stability of the high-temperature phase. On the other hand, the divergence in the conserving vertex, obeying the conservation laws, does not invoke critical behavior of the spectral function and the specific heat. Moreover, the critical behavior of the conserving vertex may become spurious in low-dimensional systems. Consequently, the description of the critical behavior of correlated electrons becomes consistent and reliable only if the fluctuations of the order parameter in the conserving vertex lead to a divergence coinciding with that of the dynamical one.
Intense high-order harmonic generation in giant fullerene molecule C$_{240}$

In this work the extreme nonlinear optical response of a giant fullerene molecule C$_{240}$ in strong laser field is studied. The investigation of high-order harmonic generation in such quantum nanostructure is presented modeling the C$_{240}$ molecule and its interaction with the laser field in the scope of the tight-binding mean-field approach. Electron-electron interaction is modeled by the parametrized Ohno potentail, which takes into account long-range Coulomb interaction. The essential role of many body Coulomb interaction in determining of harmonics intensities is demonstrated. We also consider vacancy-deffected molecule C$_{240}$. The presence of a single vacancy breaks the icosahedral symmetry leading to the emergence of intense even-order harmonics. We examine the dependence of moderate harmonics on laser frequency that shows the multiphoton resonant nature of high harmonics generation. The dependence of cutoff harmonics on both laser intensity and frequency are examined too.
Central limit theorem for the random variables associated with the IDS of the Anderson model on lattice

We consider the existence of the integrated density of states (IDS) of the Anderson model on the Hilbert space $\ell^2(\mathbb{Z}^d)$ as analogues to the law of large numbers (LLN). In this work, we prove the analogues central limit theorem (CLT) for the collection of random variables associated with the integrated density of states when the test functions are polynomials. Then, we extend the result for the class of test functions $C^1_P(\mathbb{R})$, the set of all differentiable (first-order) functions on the real line whose derivative is continuous and has at most polynomial growth.
EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization

Visual localization is the task of estimating a 6-DoF camera pose of a query image within a provided 3D reference map. Thanks to recent advances in various 3D sensors, 3D point clouds are becoming a more accurate and affordable option for building the reference map, but research to match the points of 3D point clouds with pixels in 2D images for visual localization remains challenging. Existing approaches that jointly learn 2D-3D feature matching suffer from low inliers due to representational differences between the two modalities, and the methods that bypass this problem into classification have an issue of poor refinement. In this work, we propose EP2P-Loc, a novel large-scale visual localization method that mitigates such appearance discrepancy and enables end-to-end training for pose estimation. To increase the number of inliers, we propose a simple algorithm to remove invisible 3D points in the image, and find all 2D-3D correspondences without keypoint detection. To reduce memory usage and search complexity, we take a coarse-to-fine approach where we extract patch-level features from 2D images, then perform 2D patch classification on each 3D point, and obtain the exact corresponding 2D pixel coordinates through positional encoding. Finally, for the first time in this task, we employ a differentiable PnP for end-to-end training. In the experiments on newly curated large-scale indoor and outdoor benchmarks based on 2D-3D-S and KITTI, we show that our method achieves the state-of-the-art performance compared to existing visual localization and image-to-point cloud registration methods.