Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrastive Unpaired Translation using Focal Loss for Patch Classification

Image-to-image translation models transfer images from input domain to output domain in an endeavor to retain the original content of the image. Contrastive Unpaired Translation is one of the existing methods for solving such problems. Significant advantage of this method, compared to competitors, is the ability to train and perform well in cases where both input and output domains are only a single image. Another key thing that differentiates this method from its predecessors is the usage of image patches rather than the whole images. It also turns out that sampling negatives (patches required to calculate the loss) from the same image achieves better results than a scenario where the negatives are sampled from other images in the dataset. This type of approach encourages mapping of corresponding patches to the same location in relation to other patches (negatives) while at the same time improves the output image quality and significantly decreases memory usage as well as the time required to train the model compared to CycleGAN method used as a baseline. Through a series of experiments we show that using focal loss in place of cross-entropy loss within the PatchNCE loss can improve on the model's performance and even surpass the current state-of-the-art model for image-to-image translation.
Self-loop Compensation in Signed Networks

Stability of multi-agent systems on signed networks is intricate. To some extent, this is due to the associated signed Laplacian may lose its diagonal dominance property. This paper proposes a distributed self-loop compensation approach to rebuild the diagonal dominance of signed Laplacian, and subsequently, examine the stability and cluster consensus of the resultant compensated signed networks. Quantitative connections between the magnitude of self-loop compensation and the steady-state of the compensated signed network are analytically established, depending on the structural balance of signed networks. Some necessary and suﬃcient conditions for cluster consensus of compensated signed networks are provided as well as the explicit characterization of their steady-states. It turns out that structurally imbalanced networks need less self-loop compensation to be stable compared with the structurally balanced ones. The optimality of compensation magnitude is discussed. Both undirected and directed signed networks are examined. Simulation examples are provided to demonstrate the theoretical results.
A Biologically-Inspired Global Localization System for Mobile Robots Using LiDAR Sensor

Localization in the environment is an essential navigational capability for animals and indoor robotic vehicles. In indoor environments, it is still challenging to perfectly solve the global localization problem using probabilistic methods. However, animals are able to instinctively localize themselves with much less effort. Therefore, an intriguing and promising approach is to seek biological inspiration from animals. In this paper, we present a biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based relocalization approach. The experiment results show that the proposed method is competitive with Monte Carlo Localization, and the results demonstrate the high accuracy, applicability, and reliability of the proposed biologically-inspired localization system in various localization scenarios.
Automatic Map Update Using Dashcam Videos

Autonomous driving requires 3-D maps that provide accurate and up-to-date information about semantic landmarks. Since cameras present wider availability and lower cost compared with laser scanners, vision-based mapping solutions, especially, the ones using crowdsourced visual data, have attracted much attention from academia and industry. However, previous works have mainly focused on creating 3-D point clouds, leaving automatic change detection as open issue. We propose a pipeline for initiating and updating 3-D maps with dashcam videos, with a focus on automatic change detection based on comparison of metadata (e.g., the types and locations of traffic signs). To improve the performance of metadata generation, which depends on the accuracy of 3-D object detection and localization, we introduce a novel deep learning-based pixelwise 3-D localization algorithm. The algorithm, trained directly with Structure from Motion (SfM) point cloud data, accurately locates objects in 3-D space by estimating not only depth from monocular images but also lateral and height distances. In addition, we also propose a point clustering and thresholding algorithm to improve the robustness of the system to errors. We have performed experiments with different types of cameras, lighting, and weather conditions. The changes were detected with an average accuracy above 90%. The errors in the campus area were mainly due to traffic signs seen from a far distance to the vehicle and intended for pedestrians and cyclists only. We also conducted cause analysis of the detection and localization errors to measure the impact from the performance of the background technology in use.
Graph Encoder Embedding

—In this paper we propose a lightning fast graph embedding method called graph encoder embedding. The proposed method has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC — an unattainable feat for any existing graph embedding method. The speedup is achieved without sacriﬁcing embedding performance: the encoder embedding performs as good as, and can be viewed as a transformation of the more costly spectral embedding. The encoder embedding is applicable to either adjacency matrix or graph Laplacian, and is theoretically sound, i.e., under stochastic block model or random dot product graph, the graph encoder embedding asymptotically converges to the block probability or latent positions, and is approximately normally distributed. We showcase three important applications: vertex classiﬁcation, vertex clustering, and graph bootstrap; and the embedding performance is evaluated via a comprehensive set of synthetic and real data. In every case, the graph encoder embedding exhibits unrivalled computational advantages while delivering excellent numerical performance.
Pragmatic competence of pre-trained language models through the lens of discourse connectives

As pre-trained language models (LMs) continue to dominate NLP, it is increasingly important that we understand the depth of language capabilities in these models. In this paper, we target pre-trained LMs’ competence in pragmatics, with a focus on pragmatics relating to discourse connectives. We formulate cloze-style tests using a combination of naturally-occurring data and controlled inputs drawn from psycholinguistics. We focus on testing models’ ability to use pragmatic cues to predict discourse connectives, models’ ability to understand implicatures relating to connectives, and the extent to which models show humanlike preferences regarding temporal dynamics of connectives. We find that although models predict connectives reasonably well in the context of naturally-occurring data, when we control contexts to isolate high-level pragmatic cues, model sensitivity is much lower. Models also do not show substantial humanlike temporal preferences. Overall, the findings suggest that at present, dominant pre-training paradigms do not result in substantial pragmatic competence in our models.
Preliminary analysis on the noise characteristics of MWISP data

Noise is a significant part within a millimeter-wave molecular line datacube. Analyzing the noise improves our understanding of noise characteristics, and further contributes to scientific discoveries. We measure the noise level of a single datacube from MWISP and perform statistical analyses. We identified major factors which increase the noise level of a single datacube, including bad channels, edge effects, baseline distortion and line contamination. Cleaning algorithms are applied to remove or reduce these noise components. As a result, we obtained the cleaned datacube in which noise follows a positively skewed normal distribution. We further analyzed the noise structure distribution of a 3D mosaicked datacube in the range l=40⋅°7 to 43⋅°3 and b=−2⋅°3 to 0⋅°3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.
Path Based Hierarchical Clustering on Knowledge Graphs

Knowledge graphs have emerged as a widely adopted medium for storing relational data, making methods for automatically reasoning with them highly desirable. In this paper, we present a novel approach for inducing a hierarchy of subject clusters, building upon our earlier work done in taxonomy induction. Our method first constructs a tag hierarchy before assigning subjects to clusters on this hierarchy. We quantitatively demonstrate our method's ability to induce a coherent cluster hierarchy on three real-world datasets.
NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs

Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in many-core CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases.
Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation

Data assimilation (DA) is integrated with machine learning in order to perform entirely data‐driven online state estimation. To achieve this, recurrent neural networks (RNNs) are implemented as pretrained surrogate models to replace key components of the DA cycle in numerical weather prediction (NWP), including the conventional numerical forecast model, the forecast error covariance matrix, and the tangent linear and adjoint models. It is shown how these RNNs can be initialized using DA methods to directly update the hidden/reservoir state with observations of the target system. The results indicate that these techniques can be applied to estimate the state of a system for the repeated initialization of short‐term forecasts, even in the absence of a traditional numerical forecast model. Further, it is demonstrated how these integrated RNN‐DA methods can scale to higher dimensions by applying domain localization and parallelization, providing a path for practical applications in NWP.
Comb-based photonic neural population for parallel and nonlinear processing

Received XX Month XXXX; revised XX Month, XXXX; accepted XX Month XXXX; posted XX Month XXXX (Doc. ID XXXXX); published XX Month XXXX It is believed that neural information representation and processing relies on the neural population instead of a single neuron. In neuromorphic photonics, photonic neurons in the form of nonlinear responses have been extensively studied in single devices and temporal nodes. However, to construct a photonic neural population (PNP), the process of scaling up and massive interconnections remain challenging considering the physical complexity and response latency. Here, we propose a comb-based PNP interconnected by carrier coupling with superior scalability. Two unique properties of neural population are theoretically and experimentally demonstrated in the comb-based PNP, including nonlinear response curves and population activities coding. A classification task of three input patterns with dual radio-frequency (RF) tones is successfully implemented in a real-time manner, which manifests the comb-based PNP can make effective use of the ultra-broad bandwidth of photonics for parallel and nonlinear processing. © 2021 Optical Society of America
Entanglement resonance in the asymmetric quantum Rabi model

We investigate the entanglement features in the interacting system of a quantized optical ﬁeld and a two-level system which is statically driven, known as the asymmetric quantum Rabi model (AsymQRM). Intriguing entanglement resonance valleys with the increase of the photon-atom coupling strength and peaks with the increase of the driving amplitude are found. It is revealed that both of these two kinds of entanglement resonance are caused by the avoided level crossing of the associated eigenenergies. In sharp contrast to the quantum Rabi model, the entanglement of the AsymQRM collapses to zero in the strong coupling regime except when the driving amplitude equals to mω/ 2, with m being an integer and ω being the photon frequency. Our analysis demonstrates that such entanglement reappearance is induced by the hidden symmetry of the AsymQRM. Sup-plying an insightful understanding on the AsymQRM, our results will be helpful in exploring the hidden symmetry and in preparing photon-atom entanglement in light-matter coupled systems.
Effect of ZnS on Morphological and Optical Properties of CH3NH3PbI3 Micro Rods

Organometallic perovskite is one of the potential materials in the various optoelectronic research fields. This research work demonstrates the synthesis of CH3NH3PbI3 perovskite micro rods with ZnS composite for optoelectronic applications. Incorporating ZnS in the perovskite material caused bandgap variation in the visible wavelength energy range. Structural and chemical compositions have been observed using X-ray diffraction (XRD), Scanning electron microscope (SEM), and Fourier transform infrared spectroscopy (FTIR). The optical properties were observed via Uv-Vis spectroscopy which showed an absorption coefficient of the order of 10 cm. The bandgap of the thin films was calculated using the Tauc relation and a preferable bandgap was obtained for solar cell application.
The edge labeling of higher order Voronoi diagrams

We present an edge labeling of order-$k$ Voronoi diagrams, $V_k(S)$, of point sets $S$ in the plane, and study properties of the regions defined by them. Among them, we show that $V_k(S)$ has a small orientable cycle and path double cover, and we identify configurations that cannot appear in $V_k(S)$ for small values of $k$. This paper also contains a systematic study of well-known and new properties of $V_k(S)$, all whose proofs only rely on elementary geometric arguments in the plane. The maybe most comprehensive study of structural properties of $V_k(S)$ was done by D.T. Lee (On k-nearest neighbor Voronoi diagrams in the plane) in 1982. Our work reviews and extends the list of properties of higher order Voronoi diagrams.
Theory of Rashba coupling mediated superconductivity in incipient ferroelectrics

Maria N. Gastiasoro, ∗ Maria Eleonora Temperini, Paolo Barone, and Jose Lorenzana ISC-CNR and Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 2, 00185, Rome, Italy ISC-CNR Institute for Complex Systems, via dei Taurini 19, 00185 Rome, Italy SPIN-CNR Institute for superconductors, oxides and other innovative materials, Area della Ricerca di Tor Vergata, Via del Fosso del Cavaliere 100, 00133 Rome, Italy (Dated: September 28, 2021)
AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia

No single Automatic Differentiation (AD) system is the optimal choice for all problems. This means informed selection of an AD system and combinations can be a problem-specific variable that can greatly impact performance. In the Julia programming language, the major AD systems target the same input and thus in theory can compose. Hitherto, switching between AD packages in the Julia Language required end-users to familiarize themselves with the user-facing API of the respective packages. Furthermore, implementing a new, usable AD package required AD package developers to write boilerplate code to define convenience API functions for end-users. As a response to these issues, we present AbstractDifferentiation.jl for the automatized generation of an extensive, unified, user-facing API for any AD package. By splitting the complexity between AD users and AD developers, AD package developers only need to implement one or two primitive definitions to support various utilities for AD users like Jacobians, Hessians and lazy product operators from native primitives such as pullbacks or pushforwards, thus removing tedious -- but so far inevitable -- boilerplate code, and enabling the easy switching and composing between AD implementations for end-users.
A Biologically-Inspired Simultaneous Localization and Mapping System Based on LiDAR Sensor

Simultaneous localization and mapping (SLAM) is one of the essential techniques and functionalities used by robots to perform autonomous navigation tasks. Inspired by the rodent hippocampus, this paper presents a biologically inspired SLAM system based on a LiDAR sensor using a hippocampal model to build a cognitive map and estimate the robot pose in indoor environments. Based on the biologically inspired models mimicking boundary cells, place cells, and head direction cells, the SLAM system using LiDAR point cloud data is capable of leveraging the self-motion cues from the LiDAR odometry and the boundary cues from the LiDAR boundary cells to build a cognitive map and estimate the robot pose. Experiment results show that with the LiDAR boundary cells the proposed SLAM system greatly outperforms the camera-based brain-inspired method in both simulation and indoor environments, and is competitive with the conventional LiDAR-based SLAM methods.
Joint Multimedia Event Extraction from Video and Article

Visual and textual modalities contribute complementary information about events described in multimedia documents. Videos contain rich dynamics and detailed unfoldings of events, while text describes more high-level and abstract concepts. However, existing event extraction methods either do not handle video or solely target video while ignoring other modalities. In contrast, we propose the first approach to jointly extract events from video and text articles. We introduce the new task of Video MultiMedia Event Extraction (Video M2E2) and propose two novel components to build the first system towards this task. First, we propose the first self-supervised multimodal event coreference model that can determine coreference between video events and text events without any manually annotated pairs. Second, we introduce the first multimodal transformer which extracts structured event information jointly from both videos and text documents. We also construct and will publicly release a new benchmark of video-article pairs, consisting of 860 video-article pairs with extensive annotations for evaluating methods on this task. Our experimental results demonstrate the effectiveness of our proposed method on our new benchmark dataset. We achieve 6.0% and 5.8% absolute F-score gain on multimodal event coreference resolution and multimedia event extraction.
Provable Low Rank Plus Sparse Matrix Separation Via Nonconvex Regularizers

This paper considers a large class of problems where we seek to recover a low rank matrix and/or sparse vector from some set of measurements. While methods based on convex relaxations suffer from a (possibly large) estimator bias, and other nonconvex methods require the rank or sparsity to be known a priori, we use nonconvex regularizers to minimize the rank and $l_0$ norm without the estimator bias from the convex relaxation. We present a novel analysis of the alternating proximal gradient descent algorithm applied to such problems, and bound the error between the iterates and the ground truth sparse and low rank matrices. The algorithm and error bound can be applied to sparse optimization, matrix completion, and robust principal component analysis as special cases of our results.
Insights from an adaptive variational wave function study of the Fermi-Hubbard Model

Approximating the ground states of strongly interacting electron systems in quantum chemistry and condensed matter physics is expected to be one of the earliest applications of quantum computers. In this paper, we prepare highly accurate ground states of the Fermi-Hubbard model for small grids up to 6 sites (12 qubits) by using an interpretable, adaptive variational quantum eigensolver(VQE) called ADAPT-VQE [1]. In contrast with non-adaptive VQE, this algorithm builds a system-specific ansatz by adding an optimal gate built from one-body or two-body fermionic operators at each step. We show this adaptive method outperforms the non-adaptive counterpart in terms of fewer variational parameters, short gate depth, and scaling with the system size. The fidelity and energy of the prepared state appear to improve asympotically with ansatz depth.We also demonstrate the application of adaptive variational methods by preparing excited states and Green functions using a proposed ADAPT-SSVQE algorithm. Lower depth, asymptotic convergence, noise tolerance of a variational approach[2–4] and a highly controllable, system specific ansatz make the adaptive variational methods particularly well-suited for NISQ devices.
Locally Sparse Function-on-Function Regression

Abstract In functional data analysis, functional linear regression has attracted significant attention recently. Herein, we consider the case where both the response and covariates are functions. There are two available approaches for addressing such a situation: concurrent and nonconcurrent functional models. In the former, the value of the functional response at a given domain point depends only on the value of the functional regressors evaluated at the same domain point, whereas, in the latter, the functional covariates evaluated at each point of their domain have a non null effect on the response at any point of its domain. To balance these two extremes, we propose a locally sparse functional regression model in which the functional regression coefficient is allowed (but not forced) to be exactly zero for a subset of its domain. This is achieved using a suitable basis representation of the functional regression coefficient and exploiting an overlapping group-Lasso penalty for its estimation. We introduce efficient computational strategies based on majorization-minimization algorithms and discuss appealing theoretical properties regarding the model support and consistency of the proposed estimator. We further illustrate the empirical performance of the method through simulations and two applications related to human mortality and bidding in energy markets. Supplementary materials for this article are available online.