Miracle-dz/UniFAR-FTUs huggingface dataset:
On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

Automobiles for our roadways are increasingly using advanced driver assistance systems. The adoption of such new technologies requires us to develop novel perception systems not only for accurately understanding the situational context of these vehicles, but also to infer the driver’s awareness in differentiating between safe and critical situations. This manuscript focuses on the specific problem of inferring driver awareness in the context of attention analysis and hazardous incident activity. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally employed computer vision systems provides exciting opportunities to explore the limitations of these sensor modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing- and deep learning-based tools. We show that our methods outperform previously reported studies to classify driver attention and detecting hazardous/non-hazardous situations for short time scales of two seconds. We use EEG and vision data for high resolution temporal classification (two seconds) while additionally also employing PPG and GSR over longer time periods. We evaluate our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) while the other was collected by us (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.
Connecting the electroweak sphaleron with gravitational waves

We present the relation between the sphaleron energy and the gravitational wave signals from a first order electroweak phase transition. The crucial ingredient is the scaling law between the sphaleron energy at the temperature of the phase transition and that at zero temperature. We estimate the baryon number preservation criterion, and observe that for a sufficiently strong phase transition, it is possible to probe the electroweak sphaleron using measurements of future space-based gravitational wave detectors.
"Does 4-4-2 exist?" - An Analytics Approach to Understand and Classify Football Team Formations in Single Match Situations

The chances to win a football match can be significantly increased if the right tactic is chosen and the behavior of the opposite team is well anticipated. For this reason, every professional football club employs a team of game analysts. However, at present game performance analysis is done manually and therefore highly time-consuming. Consequently, automated tools to support the analysis process are required. In this context, one of the main tasks is to summarize team formations by patterns such as 4-4-2. In this paper, we introduce an analytics approach that automatically classifies and visualizes the team formation based on the players' position data. We focus on single match situations instead of complete halftimes or matches to provide a more detailed analysis. A detailed analysis of individual match situations depending on ball possession and match segment length is provided. For this purpose, a visual summary is utilized that summarizes the team formation in a match segment. An expert annotation study is conducted that demonstrates 1) the complexity of the task and 2) the usefulness of the visualization of single situations to understand team formations. The suggested classification approach outperforms existing methods for formation classification. In particular, our approach gives insights about the shortcomings of using patterns like 4-4-2 to describe team formations.
SiPM behaviour under continuous light

This paper reports on the behaviour of Silicon Photomultiplier (SiPM) detectors under continuous light. Usually, the bias circuit of a SiPM has a resistor connected in series to it, which protects the sensor from drawing too high current. This resistor introduces a voltage drop when a SiPM draws a steady current, when illuminated by constant light. This reduces the actual SiPM bias and then its sensitivity to light. As a matter of fact, this effect changes all relevant SiPM features, both electrical (i.e. breakdown voltage, gain, pulse amplitude, dark count rate and optical crosstalk) and optical (i.e. photon detection efficiency). To correctly operate such devices, it is then fundamental to calibrate them under various illumination levels. In this work, we focus on the large area (∼1 cm2) hexagonal SiPM S10943-2832(X) produced by Hamamatsu HPK for the camera of a gamma-ray telescope with 4 m-diameter mirror, called the SST-1M. We characterize this device under light rates raging from 3 MHz up to 5 GHz of photons per sensor at room temperature (T = 25̂C). From these studies, a model is developed in order to derive the parameters needed to correct for the voltage drop effect. This model can be applied for instance in the analysis of the data acquired by the camera to correct for the effect. The experimental results are also compared with a toy Monte Carlo simulation and finally, a solution is proposed to compensate for the voltage drop.
On the Influence of Twitter Trolls during the 2016 US Presidential Election

It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called "troll" accounts were able to manipulate public opinion is still in question. Here we aim to quantify the influence of troll accounts and the impact they had on Twitter by analyzing 152.5 million tweets from 9.9 million users, including 822 troll accounts. The data collected during the US election campaign, contain original troll tweets before they were deleted by Twitter. From these data, we constructed a very large interaction graph; a directed graph of 9.3 million nodes and 169.9 million edges. Recently, Twitter released datasets on the misinformation campaigns of 8,275 state-sponsored accounts linked to Russia, Iran and Venezuela as part of the investigation on the foreign interference in the 2016 US election. These data serve as ground-truth identifier of troll users in our dataset. Using graph analysis techniques we qualify the diffusion cascades of web and media context that have been shared by the troll accounts. We present strong evidence that authentic users were the source of the viral cascades. Although the trolls were participating in the viral cascades, they did not have a leading role in them and only four troll accounts were truly influential.
The initial evolution of millisecond magnetars: an analytical solution

Millisecond magnetars are often invoked as the central engine of some gamma-ray bursts (GRBs), specifically the ones showing a plateau phase. We argue that an apparent plateau phase may not be realized if the magnetic field of the nascent magnetar is in a transient rapid decay stage. Some GRBs that lack a clear plateau phase may also be hosting millisecond magnetars. We present an approximate analytical solution of the coupled set of equations describing the evolution of the angular velocity and the inclination angle between rotation and magnetic axis of a neutron star in the presence of a co-rotating plasma. We also show how the solution can be generalized to the case of evolving magnetic fields. We determine the evolution of the spin period, inclination angle, magnetic dipole moment and braking index of six putative magnetars associated with GRB 091018, GRB 070318, GRB 080430, GRB 090618, GRB 110715A, GRB 140206A through fitting, via Bayesian analysis, the X-ray afterglow light curves by using our recent model [\c{S}a\c{s}maz Mu\c{s} et al. 2019]. We find that within the first day following the formation of the millisecond magnetar, the inclination angle aligns rapidly, the magnetic dipole field may decay by a few times and the braking index varies by an order of magnitude.
A new take on the low-mass brown dwarf companions on wide orbits in Upper-Scorpius

Context. The Upper-Scorpius association (5–11 Myr) contains a unique population of low-mass (M ≤ 30MJup) brown dwarfs either free-floating, forming wide pairs, or on wide orbits around solar-type and massive stars. The detailed relative characterization of their physical properties (mass, radius, temperature, composition, and ongoing accretion) offers the opportunity to potentially explore their origin and mechanisms of formation. Aims. In this study, we aim to characterize the chemical and physical properties of three young, late-M brown dwarfs claimed to be companions of the Upper-Scorpius stars USco 161031.9-16191305, HIP 77900, and HIP 78530 using medium-resolution spectroscopy at UV (0.30−0.56μm; Rλ ~ 3300), optical (0.55−1.02μm; Rλ ~ 5400), and NIR (1.02−2.48μm; Rλ ~ 4300) wavelengths. The spectra of six free-floating analogs from the same association are analyzed for comparison and to explore the potential physical differences between these substellar objects found in different configurations. We also aim to examine and analyze hydrogen emission lines at UV and optical wavelengths to investigate the presence of ongoing accretion processes. Methods. The X-shooter spectrograph at VLT was used to obtain the spectra of the nine young brown dwarfs over the 0.3−2. 5μm range simultaneously. Performing a forward modeling of the observed spectra with the ForMoSA code, we infer the Teff, log (g), and radius of our objects. The code compares here the BT-SETTL15 models to the observed spectra using the Nested Sampling Bayesian inference method. Mass is determined using evolutionary models, and a new analysis of the physical association is presented based on Gaia-DR2 astrometry. Results. The Teff and log (g) determined for our companions are compatible with those found for free-floating analogs of the Upper-Scorpius association and with evolutionary model predictions at the age of the association. However the final accuracy on the Teff estimates is strongly limited by nonreproducibility of the BT-SETTL15 models in the range of Teff corresponding to the M8–M9 spectral types. We identified Hα, Hβ, Hγ, and Ca II H and K emission lines in the spectrum of several objects. We attribute these lines to chromospheric activity except for the free-floating object USco 1608-2315 for which they are indicative of active accretion (M˙ ≤ 10−10.76 M⊙ yr−1). We confirm the four-fold over-luminosity of USco 161031.9-16191305 B down to 0.3 μm, which could be explained in part by the activity of this object and if the companion is an unresolved multiple system.
Grammatical Error Correction in Low-Resource Scenarios

Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech. We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets. AKCES-GEC is published under CC BY-NC-SA 4.0 license at http://hdl.handle. net/11234/1-3057, and the source code of the GEC model is available at https://github.com/ufal/low-resource-gec-wnut2019.
Atoms in distant resonators can jointly absorb a single photon

The coherent nonlinear process where a single photon simultaneously excites two or more two-level systems (qubits) in a single-mode resonator has recently been theoretically predicted. Here we explore the case where the two qubits are placed in different resonators in an array of two or three weakly coupled resonators. Investigating different setups and excitation schemes, we show that this process can still occur with a probability approaching one under specific conditions. The obtained results provide interesting insights into subtle causality issues underlying the simultaneous excitation processes of qubits placed in distant resonators.
A catalogue of stellar diameters and fluxes for mid-infrared interferometry★

We present the Mid-infrared stellar Diameters and Fluxes compilation Catalogue (MDFC) dedicated to long-baseline interferometry at mid-infrared wavelengths (3–13 $\mu$m). It gathers data for half a million stars, i.e. nearly all the stars of the Hipparcos-Tycho catalogue whose spectral type is reported in the SIMBAD data base. We cross-match 26 data bases to provide basic information, binarity elements, angular diameter, magnitude and flux in the near and mid-infrared, as well as flags that allow us to identify the potential calibrators. The catalogue covers the entire sky with 465 857 stars, mainly dwarfs and giants from B to M spectral types closer than 18 kpc. The smallest reported values reach 0.16 $\mu$Jy in L and 0.1 $\mu$Jy in N for the flux, and 2 microarcsec for the angular diameter. We build four lists of calibrator candidates for the L and Nbands suitable with the Very Large Telescope Interferometer (VLTI) sub- and main arrays using the MATISSE instrument. We identify 1621 candidates for L and 44 candidates for N with the Auxiliary Telescopes (ATs), 375 candidates for both bands with the ATs, and 259 candidates for both bands with the Unit Telescopes (UTs). Predominantly cool giants, these sources are small and bright enough to belong to the primary lists of calibrator candidates. In the near future, we plan to measure their angular diameter with 1 per cent accuracy.
Track to Reconstruct and Reconstruct to Track

Object tracking and 3D reconstruction are often performed together, with tracking used as input for reconstruction. However, the obtained reconstructions also provide useful information for improving tracking. We propose a novel method that closes this loop, first tracking to reconstruct, and then reconstructing to track. Our approach, MOTSFusion (Multi-Object Tracking, Segmentation and dynamic object Fusion), exploits the 3D motion extracted from dynamic object reconstructions to track objects through long periods of complete occlusion and to recover missing detections. Our approach first builds up short tracklets using 2D optical flow, and then fuses these into dynamic 3D object reconstructions. The precise 3D object motion of these reconstructions is used to merge tracklets through occlusion into long-term tracks, and to locate objects when detections are missing. On KITTI, our reconstruction-based tracking reduces the number of ID switches of the initial tracklets by more than 50%, and outperforms all previous approaches for both bounding box and segmentation tracking. Code available: https://github.com/tobiasfshr/MOTSFusion.
A theoretical analysis of the error correction capability of LDPC and MDPC codes under parallel bit-flipping decoding

Iterative decoders used for decoding low-density parity-check (LDPC) and moderate-density parity-check (MDPC) codes are not characterized by a deterministic decoding radius and their error rate performance is usually assessed through intensive Monte Carlo simulations. However, several applications like code-based cryptography need guaranteed low values of the error rate, which are infeasible to assess through simulations, thus requiring the development of theoretical models for the error rate of these codes under iterative decoding. Some models of this type already exist, but become computationally intractable for parameters of practical interest. Other approaches attempt at approximating the code ensemble behaviour through assumptions, which however are hardly verified by a specific code and can barely be tested for very low error rate values. In this paper we propose a theoretical analysis of the error correction capability of LDPC and MDPC codes under a single-iteration parallel bit-flipping decoder that does not require any assumption. This allows us to derive a theoretical bound on the error rate of such a decoding algorithm, which hence results in a guaranteed error correction capability for any single code. We show an example of application of the new bound to the context of code-based cryptography, where guaranteed error rates are needed to achieve some strong security notions.
NGEMM: Optimizing GEMM for Deep Learning via Compiler-based Techniques

Quantization has emerged to be an effective way to significantly boost the performance of deep neural networks (DNNs) by utilizing low-bit computations. Despite having lower numerical precision, quantized DNNs are able to reduce both memory bandwidth and computation cycles with little losses of accuracy. Integer GEMM (General Matrix Multiplication) is critical to running quantized DNN models efficiently, as GEMM operations often dominate the computations in these models. Various approaches have been developed by leveraging techniques such as vectorization and memory layout to improve the performance of integer GEMM. However, these existing approaches are not fast enough in certain scenarios. We developed NGEMM, a compiler-based GEMM implementation for accelerating lower-precision training and inference. NGEMM has better use of the vector units by avoiding unnecessary vector computation that is introduced during tree reduction. We compared NGEMM's performance with the state-of-art BLAS libraries such as MKL. Our experimental results showed that NGEMM outperformed MKL non-pack and pack version by an average of 1.86x and 1.16x, respectively. We have applied NGEMM to a number of production services in Microsoft.
Towards Domain Invariant Heart Sound Abnormality Detection Using Learnable Filterbanks

Objective: Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases. While machine learning based systems can aid in automatically screening patients, the robustness of these systems is affected by numerous factors including the stethoscope/sensor, environment, and data collection protocol. This article studies the adverse effect of domain variability on heart sound abnormality detection and develops strategies to address this problem. Methods: We propose a novel Convolutional Neural Network (CNN) layer, consisting of time-convolutional (tConv) units, that emulate Finite Impulse Response (FIR) filters. The filter coefficients can be updated via backpropagation and be stacked in the front-end of the network as a learnable filterbank. Results: On publicly available multi-domain datasets, the proposed method surpasses the top-scoring systems found in the literature for heart sound abnormality detection (a binary classification task). We utilized sensitivity, specificity, F-1 score and Macc (average of sensitivity and specificity) as performance metrics. Our systems achieved relative improvements of up to 11.84% in terms of MAcc, compared to state-of-the-art methods. Conclusion: The results demonstrate the effectiveness of the proposed learnable filterbank CNN architecture in achieving robustness towards sensor/domain variability in PCG signals. Significance: The proposed methods pave the way for deploying automated cardiac screening systems in diversified and underserved communities.
Evaluation of Deep Learning-based prediction models in Microgrids

It is crucial today that economies harness renewable energies and integrate them into the existing grid. Conventionally, energy has been generated based on forecasts of peak and low demands. Renewable energy can neither be produced on demand nor stored efficiently.Thus, the aim of this paper is to evaluate Deep Learning-based forecasts of energy consumption to align energy consumption with renewable energy production. Using a dataset from a use-case related to landfill leachate management, multiple prediction models were used to forecast energy demand. The results were validated based on the same dataset from the recycling industry.Shallow models showed the lowest Mean Absolute Percentage Error (MAPE), significantly outperforming a persistence baseline for both, long-term (30 days), mid-term (7 days) and short-term (1 day) forecasts. A potential decrease of up to 23% in peak energy demand was found that could lead to a reduction of 3,091 kg in CO2-emissions per year.Our approach requires low finanacial investments for energy-management hardware, making it suitable for usage in Small and Medium sized Enterprises (SMEs).
Research on insect pest image detection and recognition based on bio-inspired methods

Insect pest recognition is necessary for crop protection in many areas of the world. In this paper we propose an automatic classifier based on the fusion between saliency methods and convolutional neural networks. Saliency methods are famous image processing algorithms that highlight the most relevant pixels of an image. In this paper, we use three different saliency methods as image preprocessing and creat three different images for every saliency method. Hence, we create 3x3=9 new images for every original image to train different convolutional neural networks. We evaluate the performance of every preprocessing/network couple and we also evaluate the performance of their ensemble. We test our approach of both a small dataset and on the large IP102 dataset. Our ensemble reaches the state of the art accuracy in both datasets, approaching the performance of human experts on the smaller one. Besides, we share our MATLAB code at: this https URL.
Monotonic Nonparametric Dose Response Model

Toxicologists are often concerned with determining the dosage to which an individual can be exposed with an acceptable risk of adverse effect. These types of studies have been conducted widely in the past, and many novel approaches have been developed. Parametric techniques utilizing ANOVA and nonlinear regression models are well represented in the literature. The biggest drawback of parametric approaches is the need to specify the correct model. Recently, there has been an interest in nonparametric approaches to tolerable dosage estimation. In this work, we focus on the monotonically decreasing dose response model where the response is a percent to control. This poses two constraints to the nonparametric approach. The doseresponse function must be one at control (dose = 0), and the function must always be positive. Here we propose a Bayesian solution to this problem using a novel class of nonparametric models. A basis function developed in this research is the Alamri Monotonic spline (AM-spline). Our approach is illustrated using both simulated data and an experimental dataset from pesticide related research at the US Environmental Protection Agency.
An improved analysis and unified perspective on deterministic and randomized low rank matrix approximations

We introduce a Generalized LU-Factorization (\textbf{GLU}) for low-rank matrix approximation. We relate this to past approaches and extensively analyze its approximation properties. The established deterministic guarantees are combined with sketching ensembles satisfying Johnson-Lindenstrauss properties to present complete bounds. Particularly good performance is shown for the sub-sampled randomized Hadamard transform (SRHT) ensemble. Moreover, the factorization is shown to unify and generalize many past algorithms. It also helps to explain the effect of sketching on the growth factor during Gaussian Elimination.