Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonunitary gate operations by dissipation engineering

Irreversible logic is at odds with unitary quantum evolution. Emulating such operations by classical measurements can result in disturbances and high resource demands. To overcome these limitations, we propose protocols that harness dissipation to realize the nonunitary evolution required for irreversible gate operations. Using additional excited states subject to decay, we engineer effective decay processes that perform the desired gate operations on the smallest stable Hilbert space. These operate deterministically and in an autonomous fashion, without the need for measurements. We exemplify our approach considering several classical logic operations, such as the OR, NOR, and XOR gates. Towards experimental realization, we discuss a possible implementation in quantum dots. Our study shows that irreversible logic operations can be efficiently performed on realistic quantum systems and that dissipation engineering is an essential tool for obtaining nonunitary evolutions. The proposed operations expand the quantum engineers’ toolbox and have promising applications in NISQ algorithms and quantum machine learning.
Investigation of lasing in highly strained germanium at the crossover to direct band gap

Efﬁcient and cost-effective Si-compatible lasers are a longstanding wish of the optoelectronic industry. In principle, there are two options. For many applications, lasers based on III-V compounds provide compelling solutions, even if the integration is complex and therefore costly. However, where low costs and also high integration density are crucial, group-IV-based lasers—made of Ge and GeSn, for example—could be an alternative, provided their performance can be improved. Such progress will come with better materials but also with the development of a more profound understanding of their optical properties. In this work, we demonstrate, using Ge microbridges with strain up to 6.6%, a powerful method for determining the population inversion gain and the material and optical losses of group IV lasers. This is done by deriving the values for the injection carrier densities and the cavity losses from the measurement of the change of the refractive index and the mode linewidth, respectively. We observe a laser threshold consistent with optical gain. Material loss values are obtained from a tight-binding calculation. Lasing in Ge—at steady-state—is found to be limited to low temperatures in a narrow regime of tensile strain at the crossover to the direct-band-gap band structure. We explain this observation by parasitic inter-valence-band absorption that increases rapidly with higher injection densities and temperature. N -doping seems to reduce the material loss at low excitation, but it does not extend the lasing regime. We also discuss the impact of the optically inactive carriers in the L-valley on the linewidth of group IV lasers.
Detecting Discussions of Technical Debt

Technical debt (TD) refers to suboptimal choices during software development that achieve short-term goals at the expense of long-term quality. Although developers often informally discuss TD, the concept has not yet crystalized into a consistently applied label when describing issues in most repositories. We apply machine learning to understand developer insights into TD when discussing tickets in an issue tracker. We generate expert labels that indicate whether discussion of TD occurs in the free text associated with each ticket in a sample of more than 1,900 tickets in the Chromium issue tracker. We then use these labels to train a classifier that estimates labels for the remaining 475,000 tickets. We conclude that discussion of TD appears in about 16% of the tracked Chromium issues. If we can effectively classify TD-related issues, we can focus on what practices could be most useful for their timely resolution.
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning

Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level. Although many variants have been proposed, few provide a simple way to produce high fidelity explanations that are also stable and intuitive. In this work, we provide a novel perspective by proposing a model agnostic local explanation method inspired by the invariant risk minimization (IRM) principle -- originally proposed for (global) out-of-distribution generalization -- to provide such high fidelity explanations that are also stable and unidirectional across nearby examples. Our method is based on a game theoretic formulation where we theoretically show that our approach has a strong tendency to eliminate features where the gradient of the black-box function abruptly changes sign in the locality of the example we want to explain, while in other cases it is more careful and will choose a more conservative (feature) attribution, a behavior which can be highly desirable for recourse. Empirically, we show on tabular, image and text data that the quality of our explanations with neighborhoods formed using random perturbations are much better than LIME and in some cases even comparable to other methods that use realistic neighbors sampled from the data manifold. This is desirable given that learning a manifold to either create realistic neighbors or to project explanations is typically expensive or may even be impossible. Moreover, our algorithm is simple and efficient to train, and can ascertain stable input features for local decisions of a black-box without access to side information such as a (partial) causal graph as has been seen in some recent works.
Peculiar Radio–X-Ray Relationship in Active Stars

The empirical relationship between the nonthermal 5 GHz radio luminosity and the soft X-ray luminosity of active stellar coronae, canonically called the Güdel–Benz relationship, has been a cornerstone of stellar radio astronomy, as it explicitly ties the radio emission to the coronal heating mechanisms. The relationship extends from microflares on the Sun to the coronae of the most active stars suggesting that active coronae are heated by a flare-like process. The relationship is thought to originate from a consistent partition of the available flare energy into relativistic charges, which emit in the radio-band via the incoherent gyrosynchrotron mechanism, and heating of the bulk coronal plasma, which emits in the X-ray band via the Bremsstrahlung mechanism. Consequently, coherent emission from stellar and substellar objects is not expected to adhere to this empirical relationship, as it is observed in ultracool dwarf stars and brown dwarfs. Here we report a population of radio-detected chromospherically active stars that surprisingly follow the Güdel–Benz relationship despite their radio emission being classified as coherent emission by virtue of its high circularly polarized fraction and high brightness temperature. Our results prompt a reexamination of the physics behind the Güdel–Benz relationship, its implication for the mechanism of coronal heating and particle acceleration in active stars, and the phenomenological connection between solar and stellar flares.
Recursive Decoding: A Situated Cognition Approach to Compositional Generation in Grounded Language Understanding

Compositional generalization is a troubling blind spot for neural language models. Recent efforts have presented techniques for improving a model's ability to encode novel combinations of known inputs, but less work has focused on generating novel combinations of known outputs. Here we focus on this latter"decode-side"form of generalization in the context of gSCAN, a synthetic benchmark for compositional generalization in grounded language understanding. We present Recursive Decoding (RD), a novel procedure for training and using seq2seq models, targeted towards decode-side generalization. Rather than generating an entire output sequence in one pass, models are trained to predict one token at a time. Inputs (i.e., the external gSCAN environment) are then incrementally updated based on predicted tokens, and re-encoded for the next decoder time step. RD thus decomposes a complex, out-of-distribution sequence generation task into a series of incremental predictions that each resemble what the model has already seen during training. RD yields dramatic improvement on two previously neglected generalization tasks in gSCAN. We provide analyses to elucidate these gains over failure of a baseline, and then discuss implications for generalization in naturalistic grounded language understanding, and seq2seq more generally.
Cognitive-Based Metrics to Evaluate Collaboration Effectiveness

Abstract : Effective collaboration within culturally diverse multinational coalitions is essential in many military operations, especially in Operations Other Than War (OOTW). Unfortunately, effective collaboration is sometimes difficult to achieve within any collaboration team. Because methods to improve collaboration, including selecting the right team members, creating the right type of organization, providing the right kind of training, and selecting the right types of collaboration tools are not fully understood, identifying effective interventions requires experimentation. Metrics, and especially cognitive-oriented metrics that focus on team member understandings, are critical to such experimentation. Such cognitive-focused metrics can measure not only whether particular interventions are improving team effectiveness, but also can illuminate the cognitive reasons for the improvement. This paper reports on a three-year research effort to develop, test, and apply such metrics. It describes a model-based strategy for selecting metrics, several models useful for metrics generation, eight classes of metrics for measuring collaboration effectiveness and the factors that contribute to this effectiveness, and the results of two metrics evaluations that demonstrate the practicality of applying the metrics in military experiments. Five different collaboration models have contributed to the development of the cognitive-focused collaboration metrics: teamwork/taskwork model, planning/execution feedback model, individual-team interplay model, cognition-behavior-product model, and the transactive memory model. The handling of human and organizational issues, scenario development, selection of metrics, and use of models follows the recommendations of the Code of Best Practice. The paper is followed by 28 briefing charts that summarize the presentation. (6 tables, 6 figures, 7 refs.)
Autonomous, Mobile Manipulation in a Wall-building Scenario: Team LARICS at MBZIRC 2020

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, autonomously, to detect, manipulate, and transport bricks in an unstructured, outdoor environment. Our control approach is based on a state machine that dictates which controllers are active at each stage of the Challenge. In the first stage our UGV uses visual servoing and local controllers to approach the target object without considering its orientation. The second stage consists of detecting the object’s global pose using OpenCV-based processing of RGB-D image and point-cloud data, and calculating an alignment goal within a global map. The map is built with Google Cartographer and is based on onboard LIDAR, IMU, and GPS data. Motion control in the second stage is realized using the ROS Move Base package with Time-Elastic Band trajectory optimization. Visual servo algorithms guide the vehicle in local object-approach movement and the arm in manipulating bricks. To ensure a stable grasp of the brick’s magnetic patch, we developed a passively-compliant, electromagnetic gripper with tactile feedback. Our fully-autonomous UGV performed well in Challenge 2 and in post-competition evaluations of its brick pick-and-place algorithms.
Eigenvalues of Autoencoders in Training and at Initialization

In this paper, we investigate the evolution of autoencoders near their initialization. In particular, we study the distribution of the eigenvalues of the Jacobian matrices of autoencoders early in the training process, training on the MNIST data set. We find that autoencoders that have not been trained have eigenvalue distributions that are qualitatively different from those which have been trained for a long time ($>$100 epochs). Additionally, we find that even at early epochs, these eigenvalue distributions rapidly become qualitatively similar to those of the fully trained autoencoders. We also compare the eigenvalues at initialization to pertinent theoretical work on the eigenvalues of random matrices and the products of such matrices.
Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods

Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains. However, it has proven difficult to leverage knowledge that is not manually tailored to the environment. We propose to use the subclass relationships present in open-source knowledge graphs to abstract away from specific objects. We develop a residual policy gradient method that is able to integrate knowledge across different abstraction levels in the class hierarchy. Our method results in improved sample efficiency and generalisation to unseen objects in commonsense games, but we also investigate failure modes, such as excessive noise in the extracted class knowledge or environments with little class structure.
Generative Coarse-Graining of Molecular Conformations

Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation. However, such CG procedure induces information losses, which makes accurate backmapping, i.e., restoring fine-grained (FG) coordinates from CG coordinates, a long-standing challenge. Inspired by the recent progress in generative models and equivariant networks, we propose a novel model that rigorously embeds the vital probabilistic nature and geometric consistency requirements of the backmapping transformation. Our model encodes the FG uncertainties into an invariant latent space and decodes them back to FG geometries via equivariant convolutions. To standardize the evaluation of this domain, we provide three comprehensive benchmarks based on molecular dynamics trajectories. Experiments show that our approach always recovers more realistic structures and outperforms existing data-driven methods with a significant margin.
Global and microlocal aspects of Dirac operators: propagators and Hadamard states

We propose a geometric approach to construct the Cauchy evolution operator for the Lorentzian Dirac operator on Cauchy-compact globally hyperbolic 4-manifolds. We realise the Cauchy evolution operator as the sum of two invariantly defined oscillatory integrals -- the positive and negative Dirac propagators -- global in space and in time, with distinguished complex-valued geometric phase functions. As applications, we relate the Cauchy evolution operators with the Feynman propagator and construct Cauchy surfaces covariances of quasifree Hadamard states.
Improving pre-movement pattern detection with filter bank selection

Objective. Pre-movement decoding plays an important role in detecting the onsets of actions using low-frequency electroencephalography (EEG) signals before the movement of an upper limb. In this work, a binary classification method is proposed between two different states. Approach. The proposed method, referred to as filter bank standard task-related component analysis (FBTRCA), is to incorporate filter bank selection into the standard task-related component analysis (STRCA) method. In FBTRCA, the EEG signals are first divided into multiple sub-bands which start at specific fixed frequencies and end frequencies that follow in an arithmetic sequence. The STRCA method is then applied to the EEG signals in these bands to extract CCPs. The minimum redundancy maximum relevance feature selection method is used to select essential features from these correlation patterns in all sub-bands. Finally, the selected features are classified using the binary support vector machine classifier. A convolutional neural network (CNN) is an alternative approach to select canonical correlation patterns. Main Results. Three methods were evaluated using EEG signals in the time window from 2 s before the movement onset to 1 s after the movement onset. In the binary classification between a movement state and the resting state, the FBTRCA achieved an average accuracy of 0.8968 ± 0.0847 while the accuracies of STRCA and CNN were 0.8228 ± 0.1149 and 0.8828 ± 0.0917, respectively. In the binary classification between two actions, the accuracies of STRCA, CNN, and FBTRCA were 0.6611 ± 0.1432, 0.6993 ± 0.1271, 0.7178 ± 0.1274, respectively. Feature selection using filter banks, as in FBTRCA, produces comparable results to STRCA. Significance. The proposed method provides a way to select filter banks in pre-movement decoding, and thus it improves the classification performance. The improved pre-movement decoding of single upper limb movements is expected to provide people with severe motor disabilities with a more natural, non-invasive control of their external devices.
Monolithically integrated waveguide-coupled single-frequency microlaser on erbium-doped thin film lithium niobate

1State Key Laboratory of Precision Spectroscopy, East China Normal University, Shanghai 200062, China 2The Extreme Optoelectromechanics Laboratory (XXL), School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China 3State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-intense Laser Science, Shanghai Institute of Optics and Fine Mechanics (SIOM), Chinese Academy of Sciences (CAS), Shanghai 201800, China 4Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China 5Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China. 6Collaborative Innovation Center of Light Manipulations and Applications, Shandong Normal University, Jinan 250358, People’s Republic of China 7Shanghai Research Center for Quantum Sciences, Shanghai 201315, China 8 rbwu@siom.ac. cn 9 zwfang@phy.ecnu. edu.cn 10ya.cheng@siom.ac. cn
Jet Formation in 3D GRMHD Simulations of Bondi–Hoyle–Lyttleton Accretion

A black hole (BH) traveling through a uniform, gaseous medium is described by Bondi–Hoyle–Lyttleton (BHL) accretion. If the medium is magnetized, then the black hole can produce relativistic outflows. We performed the first 3D, general-relativistic magnetohydrodynamic simulations of BHL accretion onto rapidly rotating black holes using the H-AMR code, where we mainly varied the strength of a background magnetic field that threads the medium. We found that the ensuing accretion continuously drags the magnetic flux to the BH, which accumulates near the event horizon until it becomes dynamically important. Depending on the strength of the background magnetic field, the BHs can sometimes launch relativistic jets with high enough power to drill out of the inner accretion flow, become bent by the headwind, and escape to large distances. For stronger background magnetic fields, the jets are continuously powered, while at weaker field strengths they are intermittent, turning on and off depending on the fluctuating gas and magnetic flux distributions near the event horizon. We find that our jets reach extremely high efficiencies of ∼100%–300%, even in the absence of an accretion disk. We also calculated the drag forces exerted by the gas onto to the BH and found that the presence of magnetic fields causes the drag forces to be much less efficient than in unmagnetized BHL accretion. They can even sometimes become negative, accelerating the BH rather than slowing it down. Our results extend classical BHL accretion to rotating BHs moving through magnetized media, and demonstrate that accretion and drag are significantly altered in this environment.
Approximate Bayesian Computation with Domain Expert in the Loop

Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions. As ABC methods usually rely on comparing summary statistics of observed and simulated data, the choice of the statistics is crucial. This choice involves a trade-off between loss of information and dimensionality reduction, and is often determined based on domain knowledge. However, handcrafting and selecting suitable statistics is a laborious task involving multiple trial-and-error steps. In this work, we introduce an active learning method for ABC statistics selection which reduces the domain expert's work considerably. By involving the experts, we are able to handle misspecified models, unlike the existing dimension reduction methods. Moreover, empirical results show better posterior estimates than with existing methods, when the simulation budget is limited.
Guided Bug Crush: Assist Manual GUI Testing of Android Apps via Hint Moves

Mobile apps are indispensable for people’s daily life. Complementing with automated GUI testing, manual testing is the last line of defence for app quality. However, the repeated actions and easily missing of functionalities make manual testing time-consuming and inefficient. Inspired by the game candy crush with flashy candies as hint moves for players, we propose an approach named NaviDroid for navigating testers via highlighted next operations for more effective and efficient testing. Within NaviDroid, we construct an enriched state transition graph with the triggering actions as the edges for two involved states. Based on it, we utilize the dynamic programming algorithm to plan the exploration path, and augment the GUI with visualized hints for testers to quickly explore untested activities and avoid duplicate explorations. The automated experiments demonstrate the high coverage and efficient path planning of NaviDroid and a user study further confirms its usefulness. The NaviDroid can help us develop more robust software that works in more mission-critical settings, not only by performing more thorough testing with the same effort that has been put in before, but also by integrating these techniques into different parts of development pipeline.
Rethinking Learning Dynamics in RL using Adversarial Networks

We present a learning mechanism for reinforcement learning of closely related skills parameterized via a skill embedding space. Our approach is grounded on the intuition that nothing makes you learn better than a coevolving adversary. The main contribution of our work is to formulate an adversarial training regime for reinforcement learning with the help of entropy-regularized policy gradient formulation. We also adapt existing measures of causal attribution to draw insights from the skills learned. This would facilitate easier re-purposing of skills for adaptation to different environments and tasks. Our experiments demonstrate that the adversarial process leads to a better exploration of multiple solutions and understanding the minimum number of different skills necessary to solve a given set of tasks.