Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of element-specific damping in ultrafast, helicity-independent, all-optical switching dynamics in amorphous (Gd,Tb)Co thin films

Ultrafast control of the magnetization in ps timescales by fs laser pulses offers an attractive avenue for applications such as fast magnetic devices for logic and memory. However, ultrafast helicity-independent all-optical switching (HI-AOS) of the magnetization has thus far only been observed in Gd-based, ferrimagnetic amorphous ($a$-) rare earth-transition metal ($a$-RE-TM) systems, and a comprehensive understanding of the reversal mechanism remains elusive. Here, we report HI-AOS in ferrimagnetic $a\text{\ensuremath{-}}{\mathrm{Gd}}_{22\ensuremath{-}x}{\mathrm{Tb}}_{x}{\mathrm{Co}}_{78}$ thin films, from $x=0$ to 18, and elucidate the role of Gd in HI-AOS in $a$-RE-TM alloys and multilayers. Increasing Tb content results in increasing perpendicular magnetic anisotropy and coercivity, without modifying magnetization density, and slower remagnetization rates and higher critical fluences for switching but still shows picosecond HI-AOS. Simulations of the atomistic spin dynamics based on the two-temperature model reproduce these results qualitatively and predict that the lower damping on the RE sublattice arising from the small spin-orbit coupling of Gd (with $L=0$) is instrumental for the faster dynamics and lower critical fluences of the Gd-rich alloys. Annealing $a\text{\ensuremath{-}}{\mathrm{Gd}}_{10}{\mathrm{Tb}}_{12}{\mathrm{Co}}_{78}$ leads to slower dynamics which we argue is due to an increase in damping. These simulations strongly indicate that accounting for element-specific damping is crucial in understanding HI-AOS phenomena. The results suggest that engineering the element-specific damping of materials can open up new classes of materials that exhibit low-energy, ultrafast HI-AOS.
Forced Oscillatory Motion of Trapped Counter-Propagating Solitons

Both the group velocity and phase velocity of two solitons can be synchronized by a Kerr-effect mediated interaction, causing what is known as soliton trapping. Trapping can occur when solitons travel through single-pass optical fibers or when circulating in optical resonators. Here, we demonstrate and theoretically explain a new manifestation of soliton trapping that occurs between counter-propagating solitons in microresonators. When counter-pumping a microresonator using slightly detuned pump frequencies and in the presence of backscattering, the group velocities of clockwise and counter-clockwise solitons undergo periodic modulation instead of being locked to a constant velocity. Upon emission from the microcavity, the solitons feature a relative oscillatory motion having an amplitude that can be larger than the soliton pulse width. This relative motion introduces a sideband fine structure into the optical spectrum of the counter-propagating solitons. Our results highlight the significance of coherent pumping in determining soliton dynamics within microresonators and add a new dimension to the physics of soliton trapping.
Revisiting the low temperature electron-phonon relaxation of noble metals

The low temperature electron-phonon (e-ph) relaxation near the surface of noble metals, Cu and Ag, is studied by using the density-functional theory approach. The appearance of the surface phonon mode can give rise to a strong enhancement of the Eliashberg function at low frequency $\omega$. Assuming the Eliashberg function proportional to the square of $\omega$ in the low frequency limit, the e-ph relaxation time obtained from the surface calculations is shorter than that from the bulk calculation. The calculated e-ph relaxation time for the former is in agreement with a recent experiment for thin films.
Instance Separation Emerges from Inpainting

Deep neural networks trained to inpaint partially occluded images show a deep understanding of image composition and have even been shown to remove objects from images convincingly. In this work, we investigate how this implicit knowledge of image composition can be leveraged for fully self-supervised instance separation. We propose a measure for the independence of two image regions given a fully self-supervised inpainting network and separate objects by maximizing this independence. We evaluate our method on two microscopy image datasets and show that it reaches similar segmentation performance to fully supervised methods.
The relative f-invariant and non-uniform random sofic approximations

Abstract The f-invariant is an isomorphism invariant of free-group measure-preserving actions introduced by Lewis Bowen, who first used it to show that two finite-entropy Bernoulli shifts over a finitely generated free group can be isomorphic only if their base measures have the same Shannon entropy. Bowen also showed that the f-invariant is a variant of sofic entropy; in particular, it is the exponential growth rate of the expected number of good models over a uniform random homomorphism. In this paper we present an analogous formula for the relative f-invariant and use it to prove a formula for the exponential growth rate of the expected number of good models over a random sofic approximation which is a type of stochastic block model.
Optimal Feature Manipulation Attacks Against Linear Regression

In this paper, we investigate how to manipulate the coefficients obtained via linear regression by adding carefully designed poisoning data points to the dataset or modifying the original data points. Given the energy budget, we first provide the closed-form solution of the optimal poisoning data point when our target is modifying one designated regression coefficient. We then extend the analysis to a more challenging scenario where the attacker aims to change one particular regression coefficient while making others to be changed as small as possible. For this scenario, we introduce a semidefinite relaxation method to design the best attack scheme. Finally, we study a more powerful adversary who can perform a rank-one modification on the feature matrix. We propose an alternating optimization method to find the optimal rank-one modification matrix. Numerical examples are provided to illustrate the analytical results obtained in this paper.
Recent Advances in Scalable Network Generation

Random graph models are frequently used as a controllable and versatile data source for experimental campaigns in various research fields. Generating such data-sets at scale is a non-trivial task as it requires design decisions typically spanning multiple areas of expertise. Challenges begin with the identification of relevant domain-specific network features, continue with the question of how to compile such features into a tractable model, and culminate in algorithmic details arising while implementing the pertaining model. In the present survey, we explore crucial aspects of random graph models with known scalable generators. We begin by briefly introducing network features considered by such models, and then discuss random graphs alongside with generation algorithms. Our focus lies on modelling techniques and algorithmic primitives that have proven successful in obtaining massive graphs. We consider concepts and graph models for various domains (such as social network, infrastructure, ecology, and numerical simulations), and discuss generators for different models of computation (including shared-memory parallelism, massive-parallel GPUs, and distributed systems).
Gaussian Process Policy Optimization

We propose a novel actor-critic, model-free reinforcement learning algorithm which employs a Bayesian method of parameter space exploration to solve environments. A Gaussian process is used to learn the expected return of a policy given the policy's parameters. The system is trained by updating the parameters using gradient descent on a new surrogate loss function consisting of the Proximal Policy Optimization 'Clipped' loss function and a bonus term representing the expected improvement acquisition function given by the Gaussian process. This new method is shown to be comparable to and at times empirically outperform current algorithms on environments that simulate robotic locomotion using the MuJoCo physics engine.
Learning Contact-Rich Manipulation Tasks with Rigid Position-Controlled Robots: Learning to Force Control

To fully realize industrial automation, it is indispensable to give the robot manipulators the ability to adapt by themselves to their surroundings and to learn to handle novel manipulation tasks. Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using rigid position-controlled manipulators. These challenges include the need for a robust controller to avoid undesired behavior, that risk damaging the robot and its environment, and constant supervision from a human operator. The main contributions of this work are, first, we propose a framework for safely training an RL agent on manipulation tasks using a rigid robot. Second, to enable a position-controlled manipulator to perform contact-rich manipulation tasks, we implemented two different force control schemes based on standard force feedback controllers; one is a modified hybrid position-force control, and the other one is an impedance control. Third, we empirically study both control schemes when used as the action representation of an RL agent. We evaluate the trade-off between control complexity and performance by comparing several versions of the control schemes, each with a different number of force control parameters. The proposed methods are validated both on simulation and a real robot, a UR3 e-series robotic arm when executing contact-rich manipulation tasks.
Dynamic nuclear polarization by optical Stark effect in periodically pumped gallium arsenide

Optical pump-probe time- and magnetic-field-resolved Kerr rotation measurements provide a window into the mechanisms that generate dynamic nuclear polarization in bulk gallium arsenide. Previously, we have reported an unexpected dependence of the direction of the nuclear polarization on the sweep direction of the applied external magnetic field. In this paper, we present numerical calculations based on a model for this nuclear polarization due to the optical orientation and optical Stark effect produced by a train of ultrafast optical pulses. We demonstrate the correspondence of the model to our experimental measurements for different laser wavelengths and magnetic field sweep rates. Finally, we show that the model reproduces the sweep direction dependence and provides an explanation for this behavior.
Grounded and Controllable Image Completion by Incorporating Lexical Semantics

In this paper, we present an approach, namely Lexical Semantic Image Completion (LSIC), that may have potential applications in art, design, and heritage conservation, among several others. Existing image completion procedure is highly subjective by considering only visual context, which may trigger unpredictable results which are plausible but not faithful to a grounded knowledge. To permit both grounded and controllable completion process, we advocate generating results faithful to both visual and lexical semantic context, i.e., the description of leaving holes or blank regions in the image (e.g., hole description). One major challenge for LSIC comes from modeling and aligning the structure of visual-semantic context and translating across different modalities. We term this process as structure completion, which is realized by multi-grained reasoning blocks in our model. Another challenge relates to the unimodal biases, which occurs when the model generates plausible results without using the textual description. This can be true since the annotated captions for an image are often semantically equivalent in existing datasets, and thus there is only one paired text for a masked image in training. We devise an unsupervised unpaired-creation learning path besides the over-explored paired-reconstruction path, as well as a multi-stage training strategy to mitigate the insufficiency of labeled data. We conduct extensive quantitative and qualitative experiments as well as ablation studies, which reveal the efficacy of our proposed LSIC.
CALVIS: chest, waist and pelvis circumference from 3D human body meshes as ground truth for deep learning

In this paper we present CALVIS, a method to calculate $\textbf{C}$hest, w$\textbf{A}$ist and pe$\textbf{LVIS}$ circumference from 3D human body meshes. Our motivation is to use this data as ground truth for training convolutional neural networks (CNN). Previous work had used the large scale CAESAR dataset or determined these anthropometrical measurements $\textit{manually}$ from a person or human 3D body meshes. Unfortunately, acquiring these data is a cost and time consuming endeavor. In contrast, our method can be used on 3D meshes automatically. We synthesize eight human body meshes and apply CALVIS to calculate chest, waist and pelvis circumference. We evaluate the results qualitatively and observe that the measurements can indeed be used to estimate the shape of a person. We then asses the plausibility of our approach by generating ground truth with CALVIS to train a small CNN. After having trained the network with our data, we achieve competitive validation error. Furthermore, we make the implementation of CALVIS publicly available to advance the field.
Evidence of the polaronic character of excitons in a two-dimensional semiconducting magnet $\mathrm{CrI_3}$

Exciton dynamics can be strongly affected by lattice vibrations through electron-phonon (e-ph) coupling, which can be described by the formation of exciton-polarons -- composite quasiparticles with excitons dressed by a cloud of polar phonons. The recently discovered 2D magnetic semiconductor, $\mathrm{CrI_3}$, provides a new platform to explore exciton physics beyond the well-studied 2D transition metal dichalcogenide semiconductors because of its localized orbitals and intrinsic long-range magnetic order. However, the nature of excitons as well as the roles of magnetic ordering and e-ph coupling in the exciton dynamics remain elusive in 2D $\mathrm{CrI_3}$. Here in bilayer $\mathrm{CrI_3}$, we first performed temperature-dependent photoluminescence and linear absorption measurements to show the presence of strong e-ph coupling. We then carried out careful temperature and magnetic field-dependent resonant micro-Raman spectroscopy measurements and discovered a rare Raman feature of periodic broad modes up to as high as the $8^{th}$ order, providing direct evidence of the polaronic character of excitons. We establish that the formation of polarons in $\mathrm{CrI_3}$ is dominated by the strong Fr\"{o}hlich interactions between the bright exciton at $\sim$1.96 eV and a longitudinal optical phonon at 120 $\mathrm{cm}^{-1}$. We further show that the emergence of long-range magnetic order below $T_C$ = 45 K enhances the e-ph coupling and that the transition from layered antiferromagnetic to ferromagnetic order across $B_C$ = 0.7 T tunes the polaron spectra, suggesting a strong coupling among the lattice, charge and spin degrees of freedom in 2D $\mathrm{CrI_3}$. Our study opens up new avenues for tailoring light-matter interactions in 2D semiconductors with long-range orders and strong correlations.
Deep-MAPS: Machine-Learning-Based Mobile Air Pollution Sensing

Mobile and ubiquitous sensing of urban air quality (AQ) has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial–temporal resolution. This article proposes a machine-learning-based mobile air pollution sensing framework, coined Deep-MAPS, and demonstrates its scientific and financial values in the following aspects: 1) based on a combination of fixed and mobile AQ sensors, we perform spatial inference of PM2.5 concentrations in Beijing (3025 km2, June 19–July 16, 2018) for a spatial–temporal resolution of 1 km $\times $ 1 km and 1 h, with under 15% SMAPE; 2) we leverage urban big data to generate insights regarding the potential cause of pollution, which facilitates evidence-based sustainable urban management; and 3) to achieve such spatial–temporal coverage and accuracy, Deep-MAPS can save up to 90% hardware investment, compared with ubiquitous sensing that relies primarily on fixed sensors.
Generalized Rental Harmony

Abstract Rental harmony is the problem of assigning rooms in a rented house to tenants with different preferences, and simultaneously splitting the rent among them, such that no tenant envies the bundle (room and price) given to another tenant. Various researchers have studied this problem mainly under two incompatible assumptions: the miserly tenants assumption—each tenant prefers a free room to a room with a positive price; and the quasilinear tenants assumption—each tenant attributes a monetary value to each room, and prefers a room of which the difference between value and price is maximum. This article shows that the main technique used for rental harmony with miserly tenants, using Sperner’s lemma, can be adapted to a much more general class of preferences, one that contains both miserly tenants and quasilinear tenants as special cases. As a corollary, some recent results derived for miserly tenants are found to be applicable to this more general class, too.
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Interpretation of common-yet-challenging inter- action scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction patterns of the ego vehicle with other nearby vehicles. Our model relaxes dependency on the number of surrounding vehicles by developing an acceleration-sensitive velocity field based on Gaussian processes. The experiment results demonstrate that the velocity field can represent the spatial interactions between the ego vehicle and its surroundings. A discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering the sequential interaction data into interpretable granular patterns automatically. We then evaluate our approach in the highway discretionary lane-change scenarios using the highD dataset collected from real-world settings. Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated discretionary lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian interactions. View associated demos via: https://chengyuan-zhang.github. io/Multivehicle-Interaction.
DriverMHG: A Multi-Modal Dataset for Dynamic Recognition of Driver Micro Hand Gestures and a Real-Time Recognition Framework

The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. However, real-time recognition of dynamic micro hand gestures from video streams is challenging for in-vehicle scenarios since (i) the gestures should be performed naturally without distracting the driver, (ii) micro hand gestures occur within very short time intervals at spatially constrained areas, (iii) the performed gesture should be recognized only once, and (iv) the entire architecture should be designed lightweight as it will be deployed to an embedded system. In this work, we propose an HCI system for dynamic recognition of driver micro hand gestures, which can have a crucial impact in automotive sector especially for safety related issues. For this purpose, we initially collected a dataset named Driver Micro Hand Gestures (DriverMHG), which consists of RGB, depth and infrared modalities. The challenges for dynamic recognition of micro hand gestures have been addressed by proposing a lightweight convolutional neural network (CNN) based architecture which operates online efficiently with a sliding window approach. For the CNN model, several 3-dimensional resource efficient networks are applied and their performances are analyzed. Online recognition of gestures has been performed with 3D-MobileNetV2, which provided the best offline accuracy among the applied networks with similar computational complexities. The final architecture is deployed on a driver simulator operating in real-time. We make DriverMHG dataset and our source code publicly available 1.1https://www.mmk. ei.tum. de/DriverMHG/
Expression Recognition in the Wild Using Sequence Modeling

As we exceed upon the procedures for modelling the different aspects of behaviour, expression recognition has become a key field of research in Human Computer Interactions. Expression recognition in the wild is a very interesting problem and is challenging as it involves detailed feature extraction and heavy computation. This paper presents the methodologies and techniques used in our contribution to recognize different expressions i.e., neutral, anger, disgust, fear, happiness, sadness, surprise in ABAW competition on Aff-Wild2 database. Aff-Wild2 database consists of videos in the wild labelled for seven different expressions at frame level. We used a bi-modal approach by fusing audio and visual features and train a sequence-to-sequence model that is based on Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM) network. We show experimental results on validation data. The overall accuracy of the proposed approach is 41.5 \%, which is better than the competition baseline of 37\%.
Tensor Networks for Language Modeling

The tensor network formalism has enjoyed over two decades of success in modeling the behavior of complex quantum-mechanical systems, but has only recently and sporadically been leveraged in machine learning. Here we introduce a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We identify several distinctive features of this recurrent generative model, notably the ability to condition or marginalize sampling on characters at arbitrary locations within a sequence, with no need for approximate sampling methods. Despite the sequential architecture of u-MPS, we show that a recursive evaluation algorithm can be used to parallelize its inference and training, with a string of length n only requiring parallel time $\mathcal{O}(\log n)$ to evaluate. Experiments on a context-free language demonstrate a strong capacity to learn grammatical structure from limited data, pointing towards the potential of tensor networks for language modeling applications.