Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalization of the Landauer-B\"uttiker theory onto the case of dissipative contacts

We revisit the problem of two-terminal transport of non-interacting Fermi particles in a mesoscopic device. First, we generalize the problem by including into consideration relaxation processes in contacts (which are characterized by the contact self-thermalization rate $\gamma$) and then solve it by using the master equation approach. In the limit $\gamma\rightarrow0$ the obtained results are shown to reproduce those of the Landauer-B\"uttiker theory. Thus, the presented analysis proves analytical correspondence between the Landauer-B\"uttiker and master-equation approaches to quantum transport, -- the problem which resisted solution for decades.
Robust policy iteration for continuous-time stochastic $H_\infty$ control problem with unknown dynamics

In this article, we study a continuous-time stochastic $H_\infty$ control problem based on reinforcement learning (RL) techniques that can be viewed as solving a stochastic linear-quadratic two-person zero-sum differential game (LQZSG). First, we propose an RL algorithm that can iteratively solve stochastic game algebraic Riccati equation based on collected state and control data when all dynamic system information is unknown. In addition, the algorithm only needs to collect data once during the iteration process. Then, we discuss the robustness and convergence of the inner and outer loops of the policy iteration algorithm, respectively, and show that when the error of each iteration is within a certain range, the algorithm can converge to a small neighborhood of the saddle point of the stochastic LQZSG problem. Finally, we applied the proposed RL algorithm to two simulation examples to verify the effectiveness of the algorithm.
Giant piezoelectricity in group IV monochalcogenides with ferroelectric AA layer stacking

The piezoelectricity of group IV monochalcogenides (MXs, with M = Ge, Sn and X = S, Se) has attracted much attention due to their substantially higher piezoelectric coefficients compared to other 2D materials. However, with increasing layer number, their piezoelectricity rapidly disappears due to the antiferroelectric stacking order, severely limiting their practical applications. Using first-principles calculations, we investigated the piezoelectricity of MXs with the ferroelectric AA stacking configuration, which has recently been stabilized in experiments. We found that AA-stacked MXs have a ferroelectric ground state with the smallest lattice constant among other stacking configurations, resulting in a giant piezoelectric coefficient, which is the first demonstration of a strategy where the piezoelectric coefficients can increase with the number of layers. This can be attributed to a strong negative correlation between the lattice constant along the armchair direction and the piezoelectric coefficient, and spontaneous compressive strain stabilized in ferroelectric AA stacking configuration.
Spin Evolution of Neutron Stars

In this paper we review the basics of magneto-rotational properties of neutron stars focusing on spin-up/spin-down behavior at different evolutionary stages. The main goal is to provide equations for the spin frequency changes in various regimes (radio pulsar, propeller, accretor, etc.). Since presently the spin behavior of neutron stars at all stages remains a subject of many uncertainties, we review different suggestions made over the years in the literature.
Broken Symmetry in Ideal Chern Bands

Recent observations of the fractional anomalous quantum Hall effect in moir\'e materials have reignited the interest in fractional Chern insulators (FCIs). The chiral limit in which analytic Landau level-like single-particle states form an"ideal"Chern band and local interactions lead to Laughlin-like FCIs at $1/3$ filling, has been very useful for understanding these systems by relating them to the lowest Landau level. We show, however, that, even in the idealized chiral limit, a fluctuating quantum geometry is associated with strongly broken symmetries and a phenomenology very different from that of Landau levels. In particular, particle-hole symmetry is strongly violated and e.g. at $2/3$ filling an emergent interaction driven Fermi liquid state with no Landau level counterpart is energetically favoured. In fact, even the exact Laughlin-like zero modes at $1/3$ filling have a non-uniform density tracking the underlying quantum geometry. Moreover, by switching to a Coulomb interaction, the ideal Chern band features charge density wave states with no lowest Landau level counterpart.
Momentum-resolved resonant photoelectron spectroscopic study for 1T-TiSe$_2$: Observation of negative q in the Fano resonance due to inter-atomic interaction in the valence band

The remarkable properties of (1T-)TiSe$_2$ among the transition metal dichalcogenides have attracted the attention of many researchers due to its peculiar behavior during the charge density wave (CDW) transition. Therefore, it is highly desirable to study its electronic structure down to the atomic orbitals. In the present research, we applied momentum-resolved resonant photoelectron spectroscopy to study TiSe$_2$ at the Ti2p-Ti3d absorption edge by using a momentum microscope, which can simultaneously detect the electronic states in a wide $(k_x,k_y)$ range. We have also used constant initial state (CIS) spectroscopy and density functional theory (DFT) calculations to reveal the hybridization between the Ti3d and Se4p orbitals within the valence band at the Gamma point at room temperature. In addition, an interesting result comes from our analysis of the CIS spectrum for the energy band located at a binding energy of 2 eV at the M-point. This band, mainly composed of the Se4p orbital, exhibited a Fano line profile at the Ti2p edge, with a negative value of the parameter"$q$". This is the first clear evidence of the inter-atomic interaction during the valence band photoelectron emission process. This behavior differs significantly from the standard resonant photoelectron emission, which usually involves intra-atomic interactions. It also differs from the multi-atom resonant photoelectron emission (MARPE) observed in the core-level photoelectron emission, as we focus on the photoelectron emission from the valence band in this research.
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart without sacrificing the performance of Graph Neural Networks (GNNs) trained on it, which has shed light on reducing the computational cost for training GNNs. Nevertheless, existing methods often fall short of accurately replicating the original graph for certain datasets, thereby failing to achieve the objective of lossless condensation. To understand this phenomenon, we investigate the potential reasons and reveal that the previous state-of-the-art trajectory matching method provides biased and restricted supervision signals from the original graph when optimizing the condensed one. This significantly limits both the scale and efficacy of the condensed graph. In this paper, we make the first attempt toward \textit{lossless graph condensation} by bridging the previously neglected supervision signals. Specifically, we employ a curriculum learning strategy to train expert trajectories with more diverse supervision signals from the original graph, and then effectively transfer the information into the condensed graph with expanding window matching. Moreover, we design a loss function to further extract knowledge from the expert trajectories. Theoretical analysis justifies the design of our method and extensive experiments verify its superiority across different datasets. Code is released at https://github.com/NUS-HPC-AI-Lab/GEOM.
Observation of scale-free localized states induced by non-Hermitian defects

Wave localization is a fundamental phenomenon that appears universally in both natural materials and artificial structures and plays a crucial role in understanding the various physical properties of a system. Usually, a localized state has an exponential profile with a localization length independent of the system size. Here, we experimentally demonstrate a new class of localized states called scale-free localized states, which has an unfixed localization length scaling linearly with the system size. Using circuit lattices, we observe that a non-Hermitian defect added to a Hermitian lattice induces an extensive number of states with scale-free localization. Furthermore, we demonstrate that, in a lattice with a parity-time-symmetric non-Hermitian defect, the scale-free localization emerges because of spontaneous parity-time symmetry breaking. Our results uncover a new type of localized states and extend the study of defect physics to the non-Hermitian regime.
Boosting Reinforcement Learning Algorithms in Continuous Robotic Reaching Tasks using Adaptive Potential Functions

In reinforcement learning, reward shaping is an efficient way to guide the learning process of an agent, as the reward can indicate the optimal policy of the task. The potential-based reward shaping framework was proposed to guarantee policy invariance after reward shaping, where a potential function is used to calculate the shaping reward. In former work, we proposed a novel adaptive potential function (APF) method to learn the potential function concurrently with training the agent based on information collected by the agent during the training process, and examined the APF method in discrete action space scenarios. This paper investigates the feasibility of using APF in solving continuous-reaching tasks in a real-world robotic scenario with continuous action space. We combine the Deep Deterministic Policy Gradient (DDPG) algorithm and our proposed method to form a new algorithm called APF-DDPG. To compare APF-DDPG with DDPG, we designed a task where the agent learns to control Baxter's right arm to reach a goal position. The experimental results show that the APF-DDPG algorithm outperforms the DDPG algorithm on both learning speed and robustness.
Edge-Parallel Graph Encoder Embedding

New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations. One-Hot Graph Encoder Embedding (GEE) uses a single, linear pass over edges and produces an embedding that converges asymptotically to the spectral embedding. The scaling and performance benefits of this approach have been limited by a serial implementation in an interpreted language. We refactor GEE into a parallel program in the Ligra graph engine that maps functions over the edges of the graph and uses lock-free atomic instrutions to prevent data races. On a graph with 1.8B edges, this results in a 500 times speedup over the original implementation and a 17 times speedup over a just-in-time compiled version.
Learning Communication Policies for Different Follower Behaviors in a Collaborative Reference Game

Albrecht and Stone (2018) state that modeling of changing behaviors remains an open problem"due to the essentially unconstrained nature of what other agents may do". In this work we evaluate the adaptability of neural artificial agents towards assumed partner behaviors in a collaborative reference game. In this game success is achieved when a knowledgeable Guide can verbally lead a Follower to the selection of a specific puzzle piece among several distractors. We frame this language grounding and coordination task as a reinforcement learning problem and measure to which extent a common reinforcement training algorithm (PPO) is able to produce neural agents (the Guides) that perform well with various heuristic Follower behaviors that vary along the dimensions of confidence and autonomy. We experiment with a learning signal that in addition to the goal condition also respects an assumed communicative effort. Our results indicate that this novel ingredient leads to communicative strategies that are less verbose (staying silent in some of the steps) and that with respect to that the Guide's strategies indeed adapt to the partner's level of confidence and autonomy.
Progressive Unsupervised Domain Adaptation for ASR Using Ensemble Models and Multi-Stage Training

In Automatic Speech Recognition (ASR), teacher-student (T/S) training has shown to perform well for domain adaptation with small amount of training data. However, adaption without ground-truth labels is still challenging. A previous study has shown the effectiveness of using ensemble teacher models in T/S training for unsupervised domain adaptation (UDA) but its performance still lags behind compared to the model trained on in-domain data. This paper proposes a method to yield better UDA by training multi-stage students with ensemble teacher models. Initially, multiple teacher models are trained on labelled data from read and meeting domains. These teachers are used to train a student model on unlabelled out-of-domain telephone speech data. To improve the adaptation, subsequent student models are trained sequentially considering previously trained model as their teacher. Experiments are conducted with three teachers trained on AMI, WSJ and LibriSpeech and three stages of students on SwitchBoard data. Results shown on eval00 test set show significant WER improvement with multi-stage training with an absolute gain of 9.8%, 7.7% and 3.3% at each stage.
The VampPrior Mixture Model

Current clustering priors for deep latent variable models (DLVMs) require defining the number of clusters a-priori and are susceptible to poor initializations. Addressing these deficiencies could greatly benefit deep learning-based scRNA-seq analysis by performing integration and clustering simultaneously. We adapt the VampPrior (Tomczak&Welling, 2018) into a Dirichlet process Gaussian mixture model, resulting in the VampPrior Mixture Model (VMM), a novel prior for DLVMs. We propose an inference procedure that alternates between variational inference and Empirical Bayes to cleanly distinguish variational and prior parameters. Using the VMM in a Variational Autoencoder attains highly competitive clustering performance on benchmark datasets. Augmenting scVI (Lopez et al., 2018), a popular scRNA-seq integration method, with the VMM significantly improves its performance and automatically arranges cells into biologically meaningful clusters.
Model Predictive Trajectory Optimization With Dynamically Changing Waypoints for Serial Manipulators

Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from superordinate task planning, has been challenging for online model predictive trajectory optimization with short planning horizons. This paper presents a novel waypoint model predictive control (wMPC) concept for online replanning tasks. The main idea is to split the planning horizon at the waypoint when it becomes reachable within the current planning horizon and reduce the horizon length towards the waypoints and goal points. This approach keeps the computational load low and provides flexibility in adapting to changing conditions in real time. The presented approach achieves competitive path lengths and trajectory durations compared to (global) offline RRT-type planners in a multi-waypoint scenario. Moreover, the ability of wMPC to dynamically replan tasks online is experimentally demonstrated on a KUKA LBR iiwa 14 R820 robot in a dynamic pick-and-place scenario.
What Values Do ImageNet-trained Classifiers Enact?

We identify"values"as actions that classifiers take that speak to open questions of significant social concern. Investigating a classifier's values builds on studies of social bias that uncover how classifiers participate in social processes beyond their creators' forethought. In our case, this participation involves what counts as nutritious, what it means to be modest, and more. Unlike AI social bias, however, a classifier's values are not necessarily morally loathsome. Attending to image classifiers' values can facilitate public debate and introspection about the future of society. To substantiate these claims, we report on an extensive examination of both ImageNet training/validation data and ImageNet-trained classifiers with custom testing data. We identify perceptual decision boundaries in 118 categories that address open questions in society, and through quantitative testing of rival datasets we find that ImageNet-trained classifiers enact at least 7 values through their perceptual decisions. To contextualize these results, we develop a conceptual framework that integrates values, social bias, and accuracy, and we describe a rhetorical method for identifying how context affects the values that a classifier enacts. We also discover that classifier performance does not straightforwardly reflect the proportions of subgroups in a training set. Our findings bring a rich sense of the social world to ML researchers that can be applied to other domains beyond computer vision.
Hyperbolic Space Spectral Characteristics in a Network of Mechanical Linkages

We investigate the dynamic properties of elastic lattices defined by tessellations of a curved hyperbolic space. The lattices are obtained by projecting nodes of a regular hyperbolic tessellation onto a flat disk and then connecting those sites with simple linkages. Numerical and experimental investigations illustrate how their vibrational spectral properties are characterized by a high density of modes that are localized at the domain boundaries. Such properties govern the propagation of waves induced by broadband inputs. This suggests the potential for applications seeking the protection of bulk media from boundary-incident perturbations. We uncover the boundary-dominated nature of an exemplary hyperbolic lattice through the evaluation and analysis of its integrated density of states and vibrational spectrum. The dynamics of the lattice are also contextualized by comparing them with those of continuous disks characterized by Euclidean and hyperbolic property distributions, which confirms that the lattice retains the boundary-dominated spectrum observed in the hyperbolic plane. We then numerically investigate the response of the lattice to transient pulses incident on the boundary and find that edge-confined wave propagation occurs. The modal and transient pulse propagation behavior of the lattice is experimentally validated in a milled aluminum sample. By leveraging hyperbolic geometry, our mechanical lattice ushers in a novel class of mechanical metamaterials with boundary-dominated wave phenomena reminiscent of topologically protected systems suitable for applications in advanced wave control.
Detection Schemes with Low-Resolution ADCs and Spatial Oversampling for Transmission with Higher-Order Constellations in the Terahertz Band

In this work, we consider Terahertz (THz) communications with low-resolution uniform quantization and spatial oversampling at the receiver side. We compare different analog-to-digital converter (ADC) parametrizations in a fair manner by keeping the ADC power consumption constant. Here, 1-, 2-, and 3-bit quantization is investigated with different oversampling factors. We analytically compute the statistics of the detection variable, and we propose the optimal as well as several suboptimal detection schemes for arbitrary quantization resolutions. Then, we evaluate the symbol error rate (SER) of the different detectors for a 16- and a 64-ary quadrature amplitude modulation (QAM) constellation. The results indicate that there is a noticeable performance degradation of the suboptimal detection schemes compared to the optimal scheme when the constellation size is larger than the number of quantization levels. Furthermore, at low signal-to-noise ratios (SNRs), 1-bit quantization outperforms 2- and 3-bit quantization, respectively, even when employing higher-order constellations. We confirm our analytical results by Monte Carlo simulations. Both a pure line-of-sight (LoS) and a more realistically modeled indoor THz channel are considered. Then, we optimize the input signal constellation with respect to SER for 1-bit quantization. The results show that the minimum SER can be lowered significantly for 16-QAM by increasing the distance between the inner and outer points of the input constellation. For larger constellations, however, the achievable reduction of the minimum SER is much smaller compared to 16-QAM.
Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification

We introduce a method to construct a stochastic surrogate model from the results of dimensionality reduction in forward uncertainty quantification. The hypothesis is that the high-dimensional input augmented by the output of a computational model admits a low-dimensional representation. This assumption can be met by numerous uncertainty quantification applications with physics-based computational models. The proposed approach differs from a sequential application of dimensionality reduction followed by surrogate modeling, as we"extract"a surrogate model from the results of dimensionality reduction in the input-output space. This feature becomes desirable when the input space is genuinely high-dimensional. The proposed method also diverges from the Probabilistic Learning on Manifold, as a reconstruction mapping from the feature space to the input-output space is circumvented. The final product of the proposed method is a stochastic simulator that propagates a deterministic input into a stochastic output, preserving the convenience of a sequential"dimensionality reduction + Gaussian process regression"approach while overcoming some of its limitations. The proposed method is demonstrated through two uncertainty quantification problems characterized by high-dimensional input uncertainties.
InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior

Comprehending natural language instructions is a charming property for 3D indoor scene synthesis systems. Existing methods directly model object joint distributions and express object relations implicitly within a scene, thereby hindering the controllability of generation. We introduce InstructScene, a novel generative framework that integrates a semantic graph prior and a layout decoder to improve controllability and fidelity for 3D scene synthesis. The proposed semantic graph prior jointly learns scene appearances and layout distributions, exhibiting versatility across various downstream tasks in a zero-shot manner. To facilitate the benchmarking for text-driven 3D scene synthesis, we curate a high-quality dataset of scene-instruction pairs with large language and multimodal models. Extensive experimental results reveal that the proposed method surpasses existing state-of-the-art approaches by a large margin. Thorough ablation studies confirm the efficacy of crucial design components. Project page: https://chenguolin.github. io/projects/InstructScene.
STAR: Shape-focused Texture Agnostic Representations for Improved Object Detection and 6D Pose Estimation

Recent advances in machine learning have greatly benefited object detection and 6D pose estimation for robotic grasping. However, textureless and metallic objects still pose a significant challenge due to fewer visual cues and the texture bias of CNNs. To address this issue, we propose a texture-agnostic approach that focuses on learning from CAD models and emphasizes object shape features. To achieve a focus on learning shape features, the textures are randomized during the rendering of the training data. By treating the texture as noise, the need for real-world object instances or their final appearance during training data generation is eliminated. The TLESS and ITODD datasets, specifically created for industrial settings in robotics and featuring textureless and metallic objects, were used for evaluation. Texture agnosticity also increases the robustness against image perturbations such as imaging noise, motion blur, and brightness changes, which are common in robotics applications. Code and datasets are publicly available at github.com/hoenigpeter/randomized_texturing.