Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Driver Interactions via Conditional Behavior Prediction

Interactive driving scenarios, such as lane changes, merges and unprotected turns, are some of the most challenging situations for autonomous driving. Planning in interactive scenarios requires accurately modeling the reactions of other agents to different future actions of the ego agent. We develop end-to-end models for conditional behavior prediction (CBP) that take as an input a query future trajectory for an ego-agent, and predict distributions over future trajectories for other agents conditioned on the query. Leveraging such a model, we develop a general-purpose agent interactivity score derived from probabilistic first principles. The interactivity score allows us to find interesting interactive scenarios for training and evaluating behavior prediction models. We further demonstrate that the proposed score is effective for agent prioritization under computational budget constraints.
First-principles calculations for hydrogenation of acceptor defects in Li-doped SnO2

The lowering of the formation energy of tin vacancy (VSn) in SnO2 was suggested by hydrogenation or doping lithium to substitute tin sites (LiSn). Based on first-principles calculations, we report the atomic structures of hydrogen-related VSn and LiSn complexes with the number of passivating hydrogen atoms in the range from one to four. We show that the complexes are stable in structures and lower than the isolated defects in the formation energies. The degree of stability of hydrogen and lithium in hydrogen-related LiSn complexes is close and is enhanced with only one passivating hydrogen atom, but is reduced in the presence of more passivating hydrogen atoms. We also show that there are some stretch-mode vibrational frequencies of O–H bonds in the complexes that are lower than that of isolated interstitial hydrogen. The lowering of the frequencies is possibly related to strong secondary bonds between hydrogen and non-passivated oxygen atoms around acceptor defects, which could support a signature for the experimental identification of these complexes.
A decoupling and linearizing discretization for poroelasticity with nonlinear permeability

We analyze a semi-explicit time discretization scheme of first order for poroelasticity with nonlinear permeability provided that the elasticity model and the flow equation are only weakly coupled. The approach leads to a decoupling of the equations and, at the same time, linearizes the nonlinearity without the need of further inner iteration steps. Hence, the computational speed-up is twofold without a loss in the convergence rate. We prove optimal first-order error estimates by considering a related delay system and investigate the method numerically for different examples with various types of nonlinear displacement-permeability relations.
Practical Quantum Error Correction with the XZZX Code and Kerr-Cat Qubits

The development of robust architectures capable of large-scale fault-tolerant quantum computation should consider both their quantum error-correcting codes and the underlying physical qubits upon which they are built, in tandem. Following this design principle, we demonstrate remarkable error-correction performance by concatenating the XZZX surface code with Kerr-cat qubits. We contrast several variants of fault-tolerant systems undergoing diﬀerent circuit-noise models that reﬂect the physics of Kerr-cat qubits. Our simulations show that our system is scalable below a threshold gate inﬁdelity of p CX ∼ 6.5% within a physically reasonable parameter regime, where p CX is the inﬁdelity of the noisiest gate of our system, the controlled- NOT gate. This threshold can be reached in a superconducting-circuit architecture with a Kerr nonlinearity of 10MHz, an approximately 6.25-photon cat qubit, single-photon lifetime of (cid:2) 64 μ s, and a thermal photon population (cid:3) 8%. Such parameters are routinely achieved in superconducting circuits. DOI:
Boosting Masked Face Recognition with Multi-Task ArcFace

In this article, we tackle the recognition of faces wearing surgical masks. Surgical masks have become a necessary piece of daily apparel because of the COVID-19-related worldwide health problem. Modern face recognition models are in trouble because they were not made to function with masked faces. Furthermore, in order to stop the infection from spreading, apps capable of detecting if the individuals are wearing masks are also required. To address these issues, we present an end-to-end approach for training face recognition models based on the ArcFace architecture, including various changes to the backbone and loss computation. We also use data augmentation to generate a masked version of the original dataset and mix them on the fly while training. Without incurring any additional computational costs, we modify the chosen network to output also the likelihood of wearing a mask. Thus, the face recognition loss and the mask-usage loss are merged to create a new function known as Multi-Task ArcFace (MTArcFace). The conducted experiments demonstrate that our method outperforms the baseline model results when faces with masks are considered, while achieving similar metrics on the original dataset. In addition, it obtains a 99.78% of mean accuracy in mask-usage classification.
A Framework using Contrastive Learning for Classification with Noisy Labels

We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies, such as pseudo-labeling, sample selection with Gaussian Mixture models, and weighted supervised contrastive learning have, been combined into a fine-tuning phase following the pre-training. In this paper, we provide an extensive empirical study showing that a preliminary contrastive learning step brings a significant gain in performance when using different loss functions: non robust, robust, and early-learning regularized. Our experiments performed on standard benchmarks and real-world datasets demonstrate that: (i) the contrastive pre-training increases the robustness of any loss function to noisy labels and (ii) the additional fine-tuning phase can further improve accuracy, but at the cost of additional complexity.
Interpolation of Microscale Stress and Strain Fields Based on Mechanical Models

In this short contribution we introduce a new procedure to recover the stress and strain fields for particle systems by mechanical models. Numerical tests for simple loading conditions have shown an excellent match between the estimated values and the reference values. The estimated stress field is also consistent with the so called Quasicontinuum stress field, which suggests its potential application for scale bridging techniques. The estimated stress fields for complicated loading conditions such as defect and indentation are also demonstrated
M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection

The widespread dissemination of Deepfakes demands effective approaches that can detect perceptually convincing forged images. In this paper, we aim to capture the subtle manipulation artifacts at different scales using transformer models. In particular, we introduce a Multi-modal Multi-scale TRansformer (M2TR), which operates on patches of different sizes to detect local inconsistencies in images at different spatial levels. M2TR further learns to detect forgery artifacts in the frequency domain to complement RGB information through a carefully designed cross modality fusion block. In addition, to stimulate Deepfake detection research, we introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods. We conduct extensive experiments to verify the effectiveness of the proposed method, which outperforms state-of-the-art Deepfake detection methods by clear margins.
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization

We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use of transformer networks helps preserving the spatial information of the embedded patches, which is later processed by a Gaussian mixture density network to localize the anomalous areas. In addition, we also publish BTAD, a real-world industrial anomaly dataset. Our results are compared with other state-of-the-art algorithms using publicly available datasets like MNIST and MVTec.
Reducing the complexity of chemical networks via interpretable autoencoders

In many astrophysical applications, the cost of solving a chemical network represented by a system of ordinary differential equations (ODEs) grows significantly with the size of the network, and can often represent a significant computational bottleneck, particularly in coupled chemo-dynamical models. Although standard numerical techniques and complex solutions tailored to thermochemistry can somewhat reduce the cost, more recently, machine learning algorithms have begun to attack this challenge via data-driven dimensional reduction techniques. In this work, we present a new class of methods that take advantage of machine learning techniques to reduce complex data sets (autoencoders), the optimization of multi-parameter systems (standard backpropagation), and the robustness of well-established ODE solvers to to explicitly incorporate time-dependence. This new method allows us to find a compressed and simplified version of a large chemical network in a semi-automated fashion that can be solved with a standard ODE solver, while also enabling interpretability of the compressed, latent network. As a proof of concept, we tested the method on an astrophysically-relevant chemical network with 29 species and 224 reactions, obtaining a reduced but representative network with only 5 species and 12 reactions, and a x65 speed-up.
First dust measurements with the Solar Orbiter Radio and plasma wave instrument

Context. Impacts of dust grains on spacecraft are known to produce typical impulsive signals in the voltage waveform recorded at the terminals of electric antennas. Such signals (as may be expected) are routinely detected by the Time Domain Sampler (TDS) system of the Radio and Plasma Waves (RPW) instrument on board Solar Orbiter. Aims. We investigate the capabilities of RPW in terms of interplanetary dust studies and present the first analysis of dust impacts recorded by this instrument. Our purpose is to characterize the dust population observed in terms of size, flux, and velocity. Methods. We briefly discuss previously developed models of voltage pulse generation after a dust impact onto a spacecraft and present the relevant technical parameters for Solar Orbiter RPW as a dust detector. Then we present the statistical analysis of the dust impacts recorded by RPW/TDS from April 20, 2020 to February 27, 2021 between 0.5 AU and 1 AU. Results. The study of the dust impact rate along Solar Orbiter’s orbit shows that the dust population studied presents a radial velocity component directed outward from the Sun. Its order of magnitude can be roughly estimated as vr,dust ' 50 km s−1, which is consistent with the flux of impactors being dominated by β-meteoroids. We estimate the cumulative flux of these grains at 1 AU to be roughly Fβ ' 8 × 10−5 m−2 s−1 for particles of a radius r & 100 nm. The power law index δ of the cumulative mass flux of the impactors is evaluated by two differents methods, namely: direct observations of voltage pulses and indirect effect on the impact rate dependency on the impact speed. Both methods give the following result: δ ' 0.3−0.4. Conclusions. Solar Orbiter RPW proves to be a suitable instrument for interplanetary dust studies, and the dust detection algorithm implemented in the TDS subsystem an efficient tool for fluxes estimation. These first results are promising for the continuation of the mission, in particular, for the in situ study of the inner Solar System dust cloud outside of the ecliptic plane, which Solar Orbiter will be the first spacecraft to explore.
Constraints on axionic fuzzy 
dark matter from light bending 
and Shapiro time delay

Ultralight axion like particles (ALPs) of mass ma ∈ (10−21eV − 10−22eV) with axion decay constant fa ∼ 1017GeV can be candidates for fuzzy dark matter (FDM). If celestial bodies like Earth and Sun are immersed in a low mass axionic FDM potential and if the ALPs have coupling with nucleons then the coherent oscillation of the axionic field results a long range axion hair outside of the celestial bodies. The range of the axion mediated Yukawa type fifth force is determined by the distance between the Earth and the Sun which fixes the upper bound of the mass of axion as ma . 10−18eV. The long range axionic Yukawa potential between the Earth and Sun changes the gravitational potential between them and contribute to the light bending and the Shapiro time delay. From the observational uncertainties of those experiments, we put an upper bound on the axion decay constant as fa . 9.85 × 106GeV, which is the stronger bound obtained from Shapiro time delay. This implies if ALPs are FDM, then they do not couple to nucleons. ∗ Email Address: tanmay@prl.res.in 1 ar X iv :2 10 4. 09 77 2v 1 [ he pph ] 2 0 A pr 2 02 1
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era

Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challenging to explain the effect of each information stream on the SER systems. Further, more clarification is required for analysing the impact of ASR's word error rate (WER) on linguistic emotion recognition per se and in the context of fusion with acoustic information exploitation in the age of deep ASR systems. In order to tackle the above issues, we create transcripts from the original speech by applying three modern ASR systems, including an end-to-end model trained with recurrent neural network-transducer loss, a model with connectionist temporal classification loss, and a wav2vec framework for self-supervised learning. Afterwards, we use pre-trained textual models to extract text representations from the ASR outputs and the gold standard. For extraction and learning of acoustic speech features, we utilise openSMILE, openXBoW, DeepSpectrum, and auDeep. Finally, we conduct decision-level fusion on both information streams -- acoustics and linguistics. Using the best development configuration, we achieve state-of-the-art unweighted average recall values of $73.6\,\%$ and $73.8\,\%$ on the speaker-independent development and test partitions of IEMOCAP, respectively.
Prevention of Lipid Inhibition in Anaerobic Processes by Introducing a Two-Phase System

The inhibitory effect of lipids and prevention of this inhibition in a two-phase anaerobic process were examined using laboratory-scale reactors and batch experiments. Lipids were satisfactorily degraded in a two-phase anaerobic filter while in a single-phase system, inhibition resulted in poor lipid degradation. Unsaturated long-chain fatty acids (LFAs) had a greater inhibitory effect than saturated LFAs. Methane production as well as beta-oxidation (degradation of saturated LFAs) were inhibited by unsaturated LFAs. The saturation of unsaturated LFAs was not inhibited, and palmitate (C16:0) was accumulated in the degradation of oleate (C18:l) or linoleate (C18:2). Greater inhibition was observed at low pH values. Continuous operation of a suspended-growth acidogenic reactor showed that hydraulic retention times (HRTs) of no less than 8 hours were necessary to mitigate the inhibition in a two-phase process. The fact that saturation of oleate occurred at HRTs no less than 8 hours suggests that the saturation of unsaturated LFAs in an acidogenic reactor is essential in the prevention of lipid inhibition in two-phase anaerobic processes.
Measurement and Characterization of Heat and Mass Diffusion in PEMFC Porous Media

A single polymer electrolyte membrane fuel cell (PEMFC) is comprised of several submillimetre thick layers of varying porosity sandwiched together. The thickness of each layer, which typically ranges from 10 to 200μm, is kept small in order to minimize the transport resistance of heat, mass, electrons, and protons, that limit reaction rate. However, the thickness of these materials presents a significant challenge to engineers characterizing the transport properties through them, which is of considerable importance to the development and optimization of fuel cells. The objective of this research is to address the challenges associated with measuring the heat conduction and gas diffusion transport properties of thin porous media used in PEMFCs. An improvement in the accuracy of the guarded heat flow technique for measuring thermal conductivity and the modified Loschmidt Cell technique for measuring gas diffusivity are presented for porous media with a sub-millimetre thickness. The marketable improvement in accuracy is achieved by analysing parameters in each apparatus that are sensitive to measurement error and have the largest contribution to measurement uncertainty, and then developing ways to minimize the error. The experimental apparatuses are used to investigate the transport properties of the gas diffusion layer (GDL) and the microporous layer (MPL), while the methods would also be useful in the study of the catalyst layer (CL). Gas diffusion through porous media is critical for the high current density operation of a PEMFC, where the electrochemical reaction becomes rate-limited by the diffusive flux of reactants reaching reaction sites. However, geometric models that predict diffusivity of the GDL have been identified as inaccurate in current literature. Experimental results give a better estimate of diffusivity, but published works to date have been limited by high measurement uncertainty. In this thesis, the effective diffusivity of various GDLs are measured using a modified Loschmidt cell and the relative differences between GDLs are explained using scanning electron microscopy and the method of standard porosimetry. The experimental results from this study and others in current literature are used to develop a generalized correlation for predicting diffusivity as a function of porosity in the through-plane direction of a GDL. The thermal conductivity and contact resistance of porous media are important for
Artificial out-of-plane Ising antiferromagnet on the kagome lattice with very small farther-neighbor couplings

Despite their simple formulation, short range classical antiferromagnetic Ising models on frustrated lattices give rise to exotic phases of matter, in particular due to their macroscopic ground state degeneracy. Recent experiments on artificial spin systems comprising arrays of chirally coupled nanomagnets provide a significant strengthening of the nearest neighbour couplings compared to systems with dipolar-coupled nanomagnets. This opens the way to design artificial spin systems emulating Ising models with nearest neighbour couplings. In this paper, we compare the results of an extensive investigation with tensor network and Monte Carlo simulations of the nearest- and further-neighbour ($J_1-J_2-J_{3||}$) kagome Ising antiferromagnet with the experimental spin-spin correlations of a kagome lattice of chirally coupled nanomagnets. Even though the ratios between the further neighbour couplings and the nearest neighbour coupling estimated from micromagnetic simulations are much smaller than for dipolar-coupled nanomagnets, we show that they still play an essential role in the selection of the correlations.
B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval

Pre-training and fine-tuning have achieved remarkable success in many downstream natural language processing (NLP) tasks. Recently, pre-training methods tailored for information retrieval (IR) have also been explored, and the latest success is the PROP method which has reached new SOTA on a variety of ad-hoc retrieval benchmarks. The basic idea of PROP is to construct therepresentative words prediction (ROP) task for pre-training inspired by the query likelihood model. Despite its exciting performance, the effectiveness of PROP might be bounded by the classical unigram language model adopted in the ROP task construction process. To tackle this problem, we propose a bootstrapped pre-training method (namely B-PROP) based on BERT for ad-hoc retrieval. The key idea is to use the powerful contextual language model BERT to replace the classical unigram language model for the ROP task construction, and re-train BERT itself towards the tailored objective for IR. Specifically, we introduce a novel contrastive method, inspired by the divergence-from-randomness idea, to leverage BERT's self-attention mechanism to sample representative words from the document. By further fine-tuning on downstream ad-hoc retrieval tasks, our method achieves significant improvements over PROP and other baselines, and further pushes forward the SOTA on a variety of ad-hoc retrieval tasks.
Dynamical phase diagram of a one-dimensional Bose-Josephson junction

We study the dynamics of one-dimensional bosons trapped in a box potential, in the presence of a barrier creating a tunable weak-link, thus realizing a one dimensional Bose Josephson junction. By varying the initial population imbalance and the barrier height we evidence different dynamical regimes. In particular we show that at large barriers a two mode model captures accurately the dynamics, while for low barriers the dynamics involves dispersive shock waves and solitons. We study a quench protocol that can be readily implemented in experiments and show that self-trapping resonances can occur. This phenomenon can be understood qualitatively within the two-mode model.
Chiral anomaly trapped in Weyl metals: Nonequilibrium valley polarization at zero magnetic field

In Weyl semimetals, the application of parallel electric and magnetic fields leads to valley polarization---an occupation disbalance of valleys of opposite chirality---a direct consequence of the chiral anomaly. In this work, we present numerical tools to explore such nonequilibrium effects in spatially confined three-dimensional systems with a variable disorder potential, giving exact solutions to leading order in the disorder potential and the applied electric field. Application to a Weyl-metal slab shows that valley polarization also occurs without an external magnetic field as an effect of chiral anomaly ``trapping'': Spatial confinement produces chiral bulk states, which enable the valley polarization in a similar way as the chiral states induced by a magnetic field. Despite its finite-size origin, the valley polarization can persist up to macroscopic length scales if the disorder potential is sufficiently long ranged, so that direct inter-valley scattering is suppressed and the relaxation then goes via the Fermi-arc surface states.