Miracle-dz/UniFAR-FTUs huggingface dataset:
A Class of Models for Large Zero-inflated Spatial Data

Spatially correlated data with an excess of zeros, usually referred to as zero-inflated spatial data, arise in many disciplines. Examples include count data, for instance, abundance (or lack thereof) of animal species and disease counts, as well as semi-continuous data like observed precipitation. Spatial two-part models are a flexible class of models for such data. Fitting two-part models can be computationally expensive for large data due to high-dimensional dependent latent variables, costly matrix operations, and slow mixing Markov chains. We describe a flexible, computationally efficient approach for modeling large zero-inflated spatial data using the projection-based intrinsic conditional autoregression (PICAR) framework. We study our approach, which we call PICAR-Z, through extensive simulation studies and two environmental data sets. Our results suggest that PICAR-Z provides accurate predictions while remaining computationally efficient. An important goal of our work is to allow researchers who are not experts in computation to easily build computationally efficient extensions to zero-inflated spatial models; this also allows for a more thorough exploration of modeling choices in two-part models than was previously possible. We show that PICAR-Z is easy to implement and extend in popular probabilistic programming languages such as nimble and stan.
Terahertz multi-functional metamaterials based

The paper reports a multi-functional terahertz metamaterials with composable mortise and tenon connection structures. These structures are prepared using a hybrid 3D printing process based on fused deposition modeling and electro-hydrodynamic jet flow printing, greatly simplifying the processing process, optimizing processing speed, and reducing costs. The printed metamaterial infrastructures can achieve three functions through the mortise and tenon connection: 1) Narrowband transmission and broadband absorption; 2) Perfect reflection; 3) Narrowband reflection and broadband absorption. Among them, function 1 is realized by the concave double-layer cross structure A, function 2 is realized by the combination of structure A and complementary structure B, and function 3 is realized by the combination of structure A and silver cross structure C. Relying on flexible design and convenient processing, this kind of modular multifunctional metamaterials is expected to be widely used in fields such as electromagnetic shielding and stealth.
Self-supervised 3D Human Pose Estimation from a Single Image

We propose a new self-supervised method for predicting 3D human body pose from a single image. The prediction network is trained from a dataset of unlabelled images depicting people in typical poses and a set of unpaired 2D poses. By minimising the need for annotated data, the method has the potential for rapid application to pose estimation of other articulated structures (e.g. animals). The self-supervision comes from an earlier idea exploiting consistency between predicted pose under 3D rotation. Our method is a substantial advance on state-of-the-art self-supervised methods in training a mapping directly from images, without limb articulation constraints or any 3D empirical pose prior. We compare performance with state-of-the-art self-supervised methods using benchmark datasets that provide images and ground-truth 3D pose (Human3.6M, MPI-INF-3DHP). Despite the reduced requirement for annotated data, we show that the method outperforms on Human3.6M and matches performance on MPI-INF-3DHP. Qualitative results on a dataset of human hands show the potential for rapidly learning to predict 3D pose for articulated structures other than the human body. Project page: https://josesosajs.github. io/imagepose/
Modified strategy to inertia weight in PSO for searching threshold of Otsu rule

In order to searching the best threshold of Otsu rule rapidly,a modified strategy to the inertia weight in Particle Swarm Optimization(PSO) is introduced.The strategy correlates the inertia weight in PSO with the globally best position and the individual best position in particle swarm and modifies the inertial weight by the distance between the best individual position and the globally best position.With the reduction of the distance between the globally best position and the individual best position,the inertia weight of the individual particle will be reduced,vice versa.The experimental results indicate that this algorithm decreases computational work and improves average convergence rate by 21.072 6% as compared with that of traditional methods.It can speed up on-line image segmentation using this method.
Parametrizing positroid cells using bicolored tilings

Bicolored tilings are given by a collection of smooth curves in a disk with a coloring map on the tiles these curves form. Postnikov diagrams can be viewed as the image of certain bicolored tilings under the Scott map. We introduce a reduction technique on bicolored tilings, and show that a tiling maps to a Postnikov diagram if and only if it is reduced. We then use bicolored tilings to parametrise positroid cells in the Grassmannian, and use the reduction, along with another transform, to generate tilings associated to lower-dimensional positroids cells. We also show that the parametrisation of such a cell can be derived from the parametrisation of the higher-dimensional cell.
Modeling temperature-dependent population dynamics in the excited state of the nitrogen-vacancy center in diamond

The nitrogen-vacancy (NV) center in diamond is well known in quantum metrology and quantum information for its favorable spin and optical properties, which span a wide temperature range from near zero to over 600 K. Despite its prominence, the NV center's photo-physics is incompletely understood, especially at intermediate temperatures between 10-100 K where phonons become activated. In this work, we present a rate model able to describe the cross-over from the low-temperature to the high-temperature regime. Key to the model is a phonon-driven hopping between the two orbital branches in the excited state (ES), which accelerates spin relaxation via an interplay with the ES spin precession. We extend our model to include magnetic and electric fields as well as crystal strain, allowing us to simulate the population dynamics over a wide range of experimental conditions. Our model recovers existing descriptions for the low- and high-temperature limits, and successfully explains various sets of literature data. Further, the model allows us to predict experimental observables, in particular the photoluminescence (PL) emission rate, spin contrast, and spin initialization fidelity relevant for quantum applications. Lastly, our model allows probing the electron-phonon interaction of the NV center and reveals a gap between the current understanding and recent experimental findings.
DUALITY FOR RABINOWITZ FUKAYA CATEGORY

In this paper, we prove that the derived Rabinowitz Fukaya category of a Liouville domain M of dimension 2 n is ( n − 1)-Calabi–Yau assuming the wrapped Fukaya category of M admits an at most countable set of Lagrangians that generate it and satisfy some ﬁniteness condition on morphism spaces between them.
PWESuite: Phonetic Word Embeddings and Tasks They Facilitate

Mapping words into a fixed-dimensional vector space is the backbone of modern NLP. While most word embedding methods successfully encode semantic information, they overlook phonetic information that is crucial for many tasks. We develop three methods that use articulatory features to build phonetically informed word embeddings. To address the inconsistent evaluation of existing phonetic word embedding methods, we also contribute a task suite to fairly evaluate past, current, and future methods. We evaluate both (1) intrinsic aspects of phonetic word embeddings, such as word retrieval and correlation with sound similarity, and (2) extrinsic performance on tasks such as rhyme and cognate detection and sound analogies. We hope our task suite will promote reproducibility and inspire future phonetic embedding research.
Constraining Horndeski theory with gravitational waves from coalescing binaries

In the broad subclass of Horndeski theories with a luminal speed of gravitational waves, we derive gravitational waveforms emitted from a compact binary by considering the wave propagation on a spatially flat cosmological background. A scalar field nonminimally coupled to gravity gives rise to hairy neutron star (NS) solutions with a nonvanishing scalar charge, whereas black holes (BHs) do not have scalar hairs in such theories. A binary system containing at least one hairy neutron star modifies the gravitational waveforms in comparison to those of the BH-BH binary. Using the tensor gravitational waveforms, we forecast the constraints on a parameter characterizing the difference of scalar charges of NS-BH or NS-NS binaries for Advanced LIGO and Einstein Telescope. We illustrate how these constraints depend on redshift and signal-to-noise ratio, and on different possible priors. We show that in any case it is possible to constrain the scalar charge precisely, so that some scalarized NS solutions known in the literature can be excluded.
Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition

People can innately recognize human facial expressions in unnatural forms, such as when depicted on the unusual faces drawn in cartoons or when applied to an animal's features. However, current machine learning algorithms struggle with out-of-domain transfer in facial expression recognition (FER). We propose a biologically-inspired mechanism for such transfer learning, which is based on norm-referenced encoding, where patterns are encoded in terms of difference vectors relative to a domain-specific reference vector. By incorporating domain-specific reference frames, we demonstrate high data efficiency in transfer learning across multiple domains. Our proposed architecture provides an explanation for how the human brain might innately recognize facial expressions on varying head shapes (humans, monkeys, and cartoon avatars) without extensive training. Norm-referenced encoding also allows the intensity of the expression to be read out directly from neural unit activity, similar to face-selective neurons in the brain. Our model achieves a classification accuracy of 92.15\% on the FERG dataset with extreme data efficiency. We train our proposed mechanism with only 12 images, including a single image of each class (facial expression) and one image per domain (avatar). In comparison, the authors of the FERG dataset achieved a classification accuracy of 89.02\% with their FaceExpr model, which was trained on 43,000 images.
Three Long-period Transiting Giant Planets from TESS

We report the discovery and orbital characterization of three new transiting warm giant planets. These systems were initially identified as presenting single-transit events in the light curves generated from the full-frame images of the Transiting Exoplanet Survey Satellite. Follow-up radial velocity measurements and additional light curves were used to determine the orbital periods and confirm the planetary nature of the candidates. The planets orbit slightly metal-rich late F- and early G-type stars. We find that TOI 4406b has a mass of M P = 0.30 ± 0.04 M J, a radius of R P = 1.00 ± 0.02 R J, and a low-eccentricity orbit (e = 0.15 ± 0.05) with a period of P = 30.08364 ±0.00005 days. TOI 2338b has a mass of M P = 5.98 ± 0.20 M J, a radius of R P = 1.00 ± 0.01 R J, and a highly eccentric orbit (e = 0.676 ± 0.002) with a period of P = 22.65398 ± 0.00002 days. Finally, TOI 2589b has a mass of M P = 3.50 ± 0.10 M J, a radius of R P = 1.08 ± 0.03 R J, and an eccentric orbit (e = 0.522 ± 0.006) with a period of P = 61.6277 ± 0.0002 days. TOI 4406b and TOI 2338b are enriched in metals compared to their host stars, while the structure of TOI 2589b is consistent with having similar metal enrichment to its host star.
Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System

In the realm of search systems, multi-stage cascade architecture is a prevalent method, typically consisting of sequential modules such as matching, pre-ranking, and ranking. It is generally acknowledged that the model used in the pre-ranking stage must strike a balance between efficacy and efficiency. Thus, the most commonly employed architecture is the representation-focused vector product based model. However, this architecture lacks effective interaction between the query and document, resulting in a reduction in the effectiveness of the search system. To address this issue, we present a novel pre-ranking framework called RankDFM. Our framework leverages DeepFM as the backbone and employs a pairwise training paradigm to learn the ranking of videos under a query. The capability of RankDFM to cross features provides significant improvement in offline and online A/B testing performance. Furthermore, we introduce a learnable feature selection scheme to optimize the model and reduce the time required for online inference, equivalent to a tree model. Currently, RankDFM has been deployed in the search system of a shortvideo App, providing daily services to hundreds of millions users.
Digital Shadows of Safety for Human Robot Collaboration in the World-Wide Lab

The World Wide Lab (WWL) connects the Digital Shadows (DSs) of processes, products, companies, and other entities allowing the exchange of information across company boundaries. Since DSs are context- and purpose-specific representations of a process, as opposed to Digital Twins (DTs) which offer a full simulation, the integration of a process into the WWL requires the creation of DSs representing different aspects of the process. Human-Robot Collaboration (HRC) for assembly processes was recently studied in the context of the WWL where Behaviour Trees (BTs) were proposed as a standard task-level representation of these processes. We extend previous work by proposing to standardise safety functions that can be directly integrated into these BTs. This addition uses the WWL as a communication and information exchange platform allowing industrial and academic practitioners to exchange, reuse, and experiment with different safety requirements and solutions in the WWL.
The Right Variety: Improving Expressive Range Analysis with Metric Selection Methods

Expressive Range Analysis (ERA), an approach for visualising the output of Procedural Content Generation (PCG) systems, is widely used within PCG research to evaluate and compare generators, often to make comparative statements about their relative performance in terms of output diversity and search space exploration. Producing a standard ERA visualisation requires the selection of two metrics which can be calculated for all generated artefacts to be visualised. However, to our knowledge there are no methodologies or heuristics for justifying the selection of a specific metric pair over alternatives. Prior work has typically either made a selection based on established but unjustified norms, designer intuition, or has produced multiple visualisations across all possible pairs. This work aims to contribute to this area by identifying valuable characteristics of metric pairings, and by demonstrating that pairings that have these characteristics have an increased probability of producing an informative ERA projection of the underlying generator. We introduce and investigate three quantifiable selection criteria for assessing metric pairs, and demonstrate how these criteria can be operationalized to rank those available. Though this is an early exploration of the concept of quantifying the utility of ERA metric pairs, we argue that the approach explored in this paper can make ERA more useful and usable for both researchers and game designers.
Visualizing Quantum Circuit Probability - estimating computational action for quantum program synthesis

This research applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits. A tutorial-style introduction to states and various notions of the complexity of states are presented. Thereafter, the probability of states in the circuit model of computation is defined. Classical and quantum gate sets are compared to select some characteristic sets. The reachability and expressibility in a space-time-bounded setting for these gate sets are enumerated and visualized. These results are studied in terms of computational resources, universality and quantum behavior. The article suggests how applications like geometric quantum machine learning, novel quantum algorithm synthesis and quantum artificial general intelligence can benefit by studying circuit probabilities.
Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search

Approximate nearest neighbour (ANN) search is an essential component of search engines, recommendation systems, etc. Many recent works focus on learning-based data-distribution-dependent hashing and achieve good retrieval performance. However, due to increasing demand for users' privacy and security, we often need to remove users' data information from Machine Learning (ML) models to satisfy specific privacy and security requirements. This need requires the ANN search algorithm to support fast online data deletion and insertion. Current learning-based hashing methods need retraining the hash function, which is prohibitable due to the vast time-cost of large-scale data. To address this problem, we propose a novel data-dependent hashing method named unfolded self-reconstruction locality-sensitive hashing (USR-LSH). Our USR-LSH unfolded the optimization update for instance-wise data reconstruction, which is better for preserving data information than data-independent LSH. Moreover, our USR-LSH supports fast online data deletion and insertion without retraining. To the best of our knowledge, we are the first to address the machine unlearning of retrieval problems. Empirically, we demonstrate that USR-LSH outperforms the state-of-the-art data-distribution-independent LSH in ANN tasks in terms of precision and recall. We also show that USR-LSH has significantly faster data deletion and insertion time than learning-based data-dependent hashing.
Dynamical theory of topological defects I: the multivalued solution of the diffusion equation

Point-like topological defects are singular configurations that manifest in and out of various equilibrium systems with two-dimensional orientational order. Because they are associated with a nonzero circuitation condition, the presence of defects induces a long-range perturbation of the orientation landscape around them. The effective dynamics of defects is thus generally described in terms of quasi-particles interacting via the orientation field they produce, whose evolution in the simplest setting is governed by the diffusion equation. Because of the multivalued nature of the orientation field, its expression for a defect moving with an arbitrary trajectory cannot be determined straightforwardly and is often evaluated in the quasi-static approximation. Here, we instead derive the exact expression for the orientation created by multiple moving defects, which we find to depend on their past trajectories and thus to be nonlocal in time. Performing various expansions in relevant regimes, we demonstrate how improved approximations with respect to the quasi-static defect solution can be obtained. Moreover, our results lead to so far unnoticed structures in the orientation field of moving defects, which we discuss in light of existing experimental results.
AutoRL Hyperparameter Landscapes

Although Reinforcement Learning (RL) has shown to be capable of producing impressive results, its use is limited by the impact of its hyperparameters on performance. This often makes it difficult to achieve good results in practice. Automated RL (AutoRL) addresses this difficulty, yet little is known about the dynamics of the hyperparameter landscapes that hyperparameter optimization (HPO) methods traverse in search of optimal configurations. In view of existing AutoRL approaches dynamically adjusting hyperparameter configurations, we propose an approach to build and analyze these hyperparameter landscapes not just for one point in time but at multiple points in time throughout training. Addressing an important open question on the legitimacy of such dynamic AutoRL approaches, we provide thorough empirical evidence that the hyperparameter landscapes strongly vary over time across representative algorithms from RL literature (DQN, PPO, and SAC) in different kinds of environments (Cartpole, Bipedal Walker, and Hopper) This supports the theory that hyperparameters should be dynamically adjusted during training and shows the potential for more insights on AutoRL problems that can be gained through landscape analyses. Our code can be found at https://github.com/automl/AutoRL-Landscape
LogoNet: a fine-grained network for instance-level logo sketch retrieval

Sketch-based image retrieval, which aims to use sketches as queries to retrieve images containing the same query instance, receives increasing attention in recent years. Although dramatic progress has been made in sketch retrieval, few efforts are devoted to logo sketch retrieval which is still hindered by the following challenges: Firstly, logo sketch retrieval is more difficult than typical sketch retrieval problem, since a logo sketch usually contains much less visual contents with only irregular strokes and lines. Secondly, instance-specific sketches demonstrate dramatic appearance variances, making them less identifiable when querying the same logo instance. Thirdly, there exist several sketch retrieval benchmarking datasets nowadays, whereas an instance-level logo sketch dataset is still publicly unavailable. To address the above-mentioned limitations, we make twofold contributions in this study for instance-level logo sketch retrieval. To begin with, we construct an instance-level logo sketch dataset containing 2k logo instances and exceeding 9k sketches. To our knowledge, this is the first publicly available instance-level logo sketch dataset. Next, we develop a fine-grained triple-branch CNN architecture based on hybrid attention mechanism termed LogoNet for accurate logo sketch retrieval. More specifically, we embed the hybrid attention mechanism into the triple-branch architecture for capturing the key query-specific information from the limited visual cues in the logo sketches. Experimental evaluations both on our assembled dataset and public benchmark datasets demonstrate the effectiveness of our proposed network.
A Bayesian Collocation Integral Method for Parameter Estimation in Ordinary Differential Equations

Inferring the parameters of ordinary differential equations (ODEs) from noisy observations is an important problem in many scientific fields. Currently, most parameter estimation methods that bypass numerical integration tend to rely on basis functions or Gaussian processes to approximate the ODE solution and its derivatives. Due to the sensitivity of the ODE solution to its derivatives, these methods can be hindered by estimation error, especially when only sparse time-course observations are available. We present a Bayesian collocation framework that operates on the integrated form of the ODEs and also avoids the expensive use of numerical solvers. Our methodology has the capability to handle general nonlinear ODE systems. We demonstrate the accuracy of the proposed method through simulation studies, where the estimated parameters and recovered system trajectories are compared with other recent methods. A real data example is also provided.