Miracle-dz/UniFAR-FTUs huggingface dataset:
Siamese Neural Networks for EEG-based Brain-computer Interfaces

Motivated by the inconceivable capability of human brain in simultaneously processing multi-modal signals and its real-time feedback to the outer world events, there has been a surge of interest in establishing a communication bridge between the human brain and a computer, which are referred to as Brain-computer Interfaces (BCI). To this aim, monitoring the electrical activity of brain through Electroencephalogram (EEG) has emerged as the prime choice for BCI systems. To discover the underlying and specific features of brain signals for different mental tasks, a considerable number of research works are developed based on statistical and data-driven techniques. However, a major bottleneck in development of practical and commercial BCI systems is their limited performance when the number of mental tasks for classification is increased. In this work, we propose a new EEG processing and feature extraction paradigm based on Siamese neural networks, which can be conveniently merged and scaled up for multi-class problems. The idea of Siamese networks is to train a double-input neural network based on a contrastive loss-function, which provides the capability of verifying if two input EEG trials are from the same class or not. In this work, a Siamese architecture, which is developed based on Convolutional Neural Networks (CNN) and provides a binary output on the similarity of two inputs, is combined with One vs. Rest (OVR) and One vs. One (OVO) techniques to scale up for multi-class problems. The efficacy of this architecture is evaluated on a 4-class Motor Imagery (MI) dataset from BCI Competition IV2a and the results suggest a promising performance compared to its counterparts.
Detecting Anomalous Time Series by GAMLSS-Akaike-Weights-Scoring

An extensible statistical framework for detecting anomalous time series including those with heavy-tailed distributions and non-stationarity in higher-order moments is introduced based on penalized likelihood distributional regression. Specifically, generalized additive models for location, scale, and shape are used to infer sample path representations defined by a parametric distribution with parameters comprised of basis functions. Akaike weights are then applied to each model and time series, yielding a probability measure that can be effectively used to classify and rank anomalous time series. A mathematical exposition is also given to justify the proposed Akaike weight scoring under a suitable model embedding as a way to asymptotically identify anomalous time series. Studies evaluating the methodology on both multiple simulations and real-world datasets also confirm that high accuracy can be obtained detecting many different and complex types of shape anomalies. Both code implementing GAWS for running on a local machine and the datasets referenced in this paper are available online.
Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis

Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop Variable-lag Granger causality and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allow causes to influence effects with arbitrary time delays. In addition, we propose methods for inferring both Variable-lag Granger causality and Transfer Entropy relations. In our approaches, we utilize an optimal warping path of Dynamic Time Warping to infer variable-lag causal relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. The software of this work is available in the R-CRAN package: VLTimeCausality.
CryptoSPN: Privacy-preserving Sum-Product Network Inference

AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e.g., the European GDPR. Using cryptographic techniques, it is possible to perform inference tasks remotely on sensitive client data in a privacy-preserving way: the server learns nothing about the input data and the model predictions, while the client learns nothing about the ML model (which is often considered intellectual property and might contain traces of sensitive data). While such privacy-preserving solutions are relatively efficient, they are mostly targeted at neural networks, can degrade the predictive accuracy, and usually reveal the network's topology. Furthermore, existing solutions are not readily accessible to ML experts, as prototype implementations are not well-integrated into ML frameworks and require extensive cryptographic knowledge. In this paper, we present CryptoSPN, a framework for privacy-preserving inference of sum-product networks (SPNs). SPNs are a tractable probabilistic graphical model that allows a range of exact inference queries in linear time. Specifically, we show how to efficiently perform SPN inference via secure multi-party computation (SMPC) without accuracy degradation while hiding sensitive client and training information with provable security guarantees. Next to foundations, CryptoSPN encompasses tools to easily transform existing SPNs into privacy-preserving executables. Our empirical results demonstrate that CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing

This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics. Our learning architecture jointly defines data in an Eulerian world space, using a static background grid, and a Lagrangian material space, using moving particles. By introducing this Eulerian-Lagrangian representation, we are able to naturally evolve and accumulate particle features using flow velocities generated from a generalized, high-dimensional force field. We demonstrate the efficacy of this system by solving various point cloud classification and segmentation problems with state-of-the-art performance. The entire geometric reservoir and data flow mimics the pipeline of the classic PIC/FLIP scheme in modeling natural flow, bridging the disciplines of geometric machine learning and physical simulation.
Learning Deep Analysis Dictionaries - Part II: Convolutional Dictionaries

In this paper, we introduce a Deep Convolutional Analysis Dictionary Model (DeepCAM) by learning convolutional dictionaries instead of unstructured dictionaries as in the case of deep analysis dictionary model introduced in the companion paper. Convolutional dictionaries are more suitable for processing high-dimensional signals like for example images and have only a small number of free parameters. By exploiting the properties of a convolutional dictionary, we present an efficient convolutional analysis dictionary learning approach. A L-layer DeepCAM consists of L layers of convolutional analysis dictionary and element-wise soft-thresholding pairs and a single layer of convolutional synthesis dictionary. Similar to DeepAM, each convolutional analysis dictionary is composed of a convolutional Information Preserving Analysis Dictionary (IPAD) and a convolutional Clustering Analysis Dictionary (CAD). The IPAD and the CAD are learned using variations of the proposed learning algorithm. We demonstrate that DeepCAM is an effective multilayer convolutional model and, on single image super-resolution, achieves performance comparable with other methods while also showing good generalization capabilities.
Stochastic reaction networks in dynamic compartment populations

Significance Many biochemical processes in living systems take place in compartmentalized environments, where individual compartments can interact with each other and undergo dynamic remodeling. Studying such processes through mathematical models poses formidable challenges because the underlying dynamics involve a large number of states, which evolve stochastically with time. Here we propose a mathematical framework to study stochastic biochemical networks in compartmentalized environments. We develop a generic population model, which tracks individual compartments and their molecular composition. We then show how the time evolution of this system can be studied effectively through a small number of differential equations, which track the statistics of the population. Our approach is versatile and renders an important class of biological systems computationally accessible. Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems, but a general and sufficiently effective approach remains lacking. In this work, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes, including subcellular compartmentalization and tissue homeostasis.
Compositional construction of control barrier functions for interconnected control systems

In this paper, we provide a compositional framework for synthesizing hybrid controllers for interconnected discrete-time control systems enforcing specifications expressed by co-Büchi automata. In particular, we first decompose the given specification to simpler reachability tasks based on automata representing the complements of original co-Büchi automata. Then, we provide a systematic approach to solve those simpler reachability tasks by computing cor-responding control barrier functions. We show that such control barrier functions can be constructed compositionally by assuming some small-gain type conditions and composing so-called local control barrier functions computed for subsystems. We provide two systematic techniques to search for local control barrier functions for subsystems based on the sum-of-squares optimization program and counter-example guided inductive synthesis approach. Finally, we illustrate the effectiveness of our results through two large-scale case studies.
Variation of fundamental constants and white dwarfs

Abstract Theories that attempt to unify the four fundamental interactions and alternative theories of gravity predict time and/or spatial variation of the fundamental constants of nature. Different versions of these theories predict different behaviours for these variations. As a consequence, experimental and observational bounds are an important tool to check the validity of such proposals. In this paper, we review constraints on the possible variation of the fundamental constants from astronomical observations and geophysical experiments designed to test the constancy of the fundamental constants of nature over different timescales. We also focus on the limits that can be obtained from white dwarfs, which can constrain the variation of the constants with the gravitational potential.
Fast 3D Indoor Scene Synthesis with Discrete and Exact Layout Pattern Extraction

We present a fast framework for indoor scene synthesis, given a room geometry and a list of objects with learnt priors. Unlike existing data-driven solutions, which often extract priors by co-occurrence analysis and statistical model fitting, our method measures the strengths of spatial relations by tests for complete spatial randomness (CSR), and extracts complex priors based on samples with the ability to accurately represent discrete layout patterns. With the extracted priors, our method achieves both acceleration and plausibility by partitioning input objects into disjoint groups, followed by layout optimization based on the Hausdorff metric. Extensive experiments show that our framework is capable of measuring more reasonable relations among objects and simultaneously generating varied arrangements in seconds.
Undamped transverse electric mode in undoped two-dimensional tilted Dirac cone materials

Transverse electric (TE) modes can not propagate through the conducting solids. This is because the continuum of particle-hole excitations of conductors contaminates with the TE mode and dampes it out. But in solids hosting tilted Dirac cone (TDC) that admit a description in terms of a modified Minkowski spacetime, the new spacetime structure remedies this issue and therefore a tilted Dirac cone material (TDM) supports the propagation of an undamped TE mode which is sustained by density fluctuations. The resulting TE mode propagates at fermionic velocities which strongly confines the mode to the surface of the two-dimensional (2D) TDM.
Time Difference of Arrival Estimation from Frequency-Sliding Generalized Cross-Correlations Using Convolutional Neural Networks

The interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years. Time delay estimation (TDE) in adverse scenarios is a challenging problem, where classical approaches based on generalized cross-correlations (GCCs) have been widely used for decades. Recently, the frequency-sliding GCC (FS-GCC) was proposed as a novel technique for TDE based on a sub-band analysis of the cross-power spectrum phase, providing a structured two-dimensional representation of the time delay information contained across different frequency bands. Inspired by deep-learning-based image denoising solutions, we propose in this paper the use of convolutional neural networks (CNNs) to learn the time-delay patterns contained in FS-GCCs extracted in adverse acoustic conditions. Our experiments confirm that the proposed approach provides excellent TDE performance while being able to generalize to different room and sensor setups.
Disorder detection with costly observations

Abstract We study the Wiener disorder detection problem where each observation is associated with a positive cost. In this setting, a strategy is a pair consisting of a sequence of observation times and a stopping time corresponding to the declaration of disorder. We characterize the minimal cost of the disorder problem with costly observations as the unique fixed point of a certain jump operator, and we determine the optimal strategy.
Surface roughness in finite element meshes

We present a practical approach for constructing meshes of general rough surfaces with given autocorrelation functions based on the unstructured meshes of nominally smooth surfaces. The approach builds on a well-known method to construct correlated random numbers from white noise using a decomposition of the autocorrelation matrix. We discuss important details arising in practical applications to the physicalmodeling of surface roughness and provide a software implementation to enable use of the approach with a broad range of numerical methods in various fields of science and engineering.
Linearly Constrained Gaussian Processes with Boundary Conditions

One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear (partial and ordinary) differential equations together with their boundary conditions. We construct multi-output Gaussian process priors with realizations dense in the solution set of such systems, in particular any solution (and only such solutions) can be represented to arbitrary precision by Gaussian process regression. The construction is fully algorithmic via Grobner bases and it does not employ any approximation. It builds these priors combining two parametrizations via a pullback: the first parametrizes the solutions for the system of differential equations and the second parametrizes all functions adhering to the boundary conditions.
Alar leapfrog". A technique for repositioning the total alar cartilage at primary cleft lip repair.

: A technique is described to reconstruct the normal degree of projection of the alar dome of the unilateral cleft lip nose by leapfrogging the spreadeagled alar cartilage up onto the back of the sturdy septum and upper lateral cartilage arch. The medial crus enclosed in a hemiforked flap slides along the membranous septal incision; the lateral crus slides along the intercartilaginous incision; they join each other as the stem of a Y, where they are secured in their new relationship to the septum and upper lateral cartilage. As the limbs approach each other, a standing cone creates the dome projection externally and a fornix within. The dipped rim rises and the charming nasal dimple reappears. Almost 200 primary cases of unilateral cleft lip nose have been treated by this technique over the past 12 years by the author. Alar dome and alar rim relapse have been infrequent. Less than 5 per cent of patients (or parents) have requested revision up to puberty. Final assessment of the effect of this radical surgery may well require a further 10 years' follow-up.
The infrared view of dust and molecules around V4334 Sgr (Sakurai’s object): a 20-yr retrospective

We present an analysis of the evolution of circumstellar dust and molecules in the environment of the very late thermal pulse object V4334 Sgr (Sakurai’s object) over an ∼20-yr period, drawing on ground-, airborne-, and space-based infrared photometry and spectroscopy. The dust emission, which started in 1997, resembles a blackbody that cooled from ∼1200 K in 1998 August to ∼180 K in 2016 July. The dust mass, assuming amorphous carbon, was ∼5 × 10−10 M⊙ in 1998 August, and we estimate that the total dust mass was ∼2 × 10−5 M⊙ by ∼2016. The appearance of a near-infrared excess in 2008 suggests that a new episode of (or renewed) mass-loss began then. We infer lower limits on the bolometric luminosity of the embedded star from that of the dust shell, which rose to ∼16 000 L⊙ before declining to ∼3000 L⊙. There is evidence for weak 6–7 μm absorption, which we attribute to hydrogenated amorphous carbon formed in material ejected by Sakurai’s object during a mass ejection phase that preceded the 1997 event. We detect small hydrocarbon and other molecules in the spectra, and trace the column densities in hydrogen cyanide (HCN) and acetylene (C2H2). We use the former to determine the 12C/13C ratio to be 6.4 ± 0.7, 14 times smaller than the Solar system value.
Observations of the low-luminosity Type Iax supernova 2019gsc: a fainter clone of SN 2008ha?

We present optical photometric and spectroscopic observations of the faint-and-fast evolving Type Iax supernova (SN) 2019gsc, extending from the time of g-band maximum until about 50 d post-maximum, when the object faded to an apparent r-band magnitude mr = 22.48 ± 0.11 mag. SN 2019gsc reached a peak luminosity of only Mg = −13.58 ± 0.15 mag, and is characterized with a post-maximum decline rate Δm15(g) = 1.08 ± 0.14 mag. These light curve parameters are comparable to those measured for SN 2008ha of Mg = −13.89 ± 0.14 mag at peak and Δm15(g) =1.80 ± 0.03 mag. The spectral features of SN 2019gsc also resemble those of SN 2008ha at similar phases. This includes both the extremely low ejecta velocity at maximum, ∼3000 km s−1, and at late-time (phase +54 d) strong forbidden iron and cobalt lines as well as both forbidden and permitted calcium features. Furthermore, akin to SN 2008ha, the bolometric light curve of SN 2019gsc is consistent with the production of ≈0.003 ± 0.001 M⊙ of 56Ni. The explosion parameters, Mej ≈ 0.13 M⊙ and Ek ≈ 12 × 1048 erg, are also similar to those inferred for SN 2008ha. We estimate a subsolar oxygen abundance for the host galaxy of SN 2019gsc (12 + log10(O/H) =8.10 ± 0.18 dex), consistent with the equally metal-poor environment of SN 2008ha. Altogether, our data set for SN 2019gsc indicates that this is a member of a small but growing group of extreme SN Iax that includes SN 2008ha and SN 2010ae.
Near-Lossless Post-Training Quantization of Deep Neural Networks via a Piecewise Linear Approximation

Quantization plays an important role for energy-efﬁcient deployment of deep neural networks (DNNs) on resource-limited devices. Post-training quantization is crucial since it does not require retraining or accessibility to the full training dataset. The conventional post-training uniform quantization scheme achieves satisfactory results by converting DNNs from full-precision to 8-bit integers, however, it suffers from signiﬁcant performance degradation when quantizing to lower precision such as 4 bits. In this paper, we propose a piecewise linear quantization method to enable accurate post-training quantization. Inspired from the fact that the weight tensors have bell-shaped distributions with long tails, our approach breaks the entire quantization range into two non-overlapping regions for each tensor, with each region being assigned an equal number of quantization levels. The optimal break-point that divides the entire range is found by minimizing the quantization error. Extensive results show that the proposed method achieves state-of-the-art performance on image classiﬁcation, semantic segmentation and object detection. It is possible to quantize weights to 4 bits without re-training while nearly maintaining the performance of the original full-precision model.
Probabilistic direction-dependent ionospheric calibration for LOFAR-HBA

Direction-dependent calibration and imaging is a vital part of producing radio images that are deep and have a high fidelity and highly dynamic range with a wide-field low-frequency array such as LOFAR. Currently, dedicated facet-based direction-dependent calibration algorithms rely on the assumption that the size of the isoplanatic patch is much larger than the separation between bright in-field calibrators. This assumption is often violated owing to the dynamic nature of the ionosphere, and as a result, direction-dependent errors affect image quality between calibrators. In this paper we propose a probabilistic physics-informed model for inferring ionospheric phase screens, providing a calibration for all sources in the field of view. We apply our method to a randomly selected observation from the LOFAR Two-Metre Sky Survey archive, and show that almost all direction-dependent effects between bright calibrators are corrected and that the root-mean-squared residuals around bright sources are reduced by 32% on average.