Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmented Semantic Signatures of Airborne LiDAR Point Clouds for Comparison

LiDAR point clouds provide rich geometric information, which is particularly useful for the analysis of complex scenes of urban regions. Finding structural and semantic differences between two different three-dimensional point clouds, say, of the same region but acquired at different time instances is an important problem. A comparison of point clouds involves computationally expensive registration and segmentation. We are interested in capturing the relative differences in the geometric uncertainty and semantic content of the point cloud without the registration process. Hence, we propose an orientation-invariant geometric signature of the point cloud, which integrates its probabilistic geometric and semantic classifications. We study different properties of the geometric signature, which are an image-based encoding of geometric uncertainty and semantic content. We explore different metrics to determine differences between these signatures, which in turn compare point clouds without performing point-to-point registration. Our results show that the differences in the signatures corroborate with the geometric and semantic differences of the point clouds.
DeepHealth: Review and challenges of artificial intelligence in health informatics

Artificial intelligence has provided us with an exploration of a whole new research era. As more data and better computational power become available, the approach is being implemented in various fields. The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its applications in healthcare. It can help clinicians diagnose disease, identify drug effects for each patient, understand the relationship between genotypes and phenotypes, explore new phenotypes or treatment recommendations, and predict infectious disease outbreaks with high accuracy. In contrast to traditional models, recent artificial intelligence approaches do not require domain-specific data pre-processing, and it is expected that it will ultimately change life in the future. Despite its notable advantages, there are some key challenges on data (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, lack of label, bias) and model (reliability, interpretability, feasibility, security, scalability) for practical use. This article presents a comprehensive review of research applying artificial intelligence in health informatics, focusing on the last seven years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research. We highlight ongoing popular approaches' research and identify several challenges in building models.
Path integrals for causal diamonds and the covariant entropy principle

We study causal diamonds in Minkowski, Schwarzschild, (anti) de Sitter, and Schwarzschild-de Sitter spacetimes using Euclidean methods. The null boundaries of causal diamonds are shown to map to isolated punctures in the Euclidean continuation of the parent manifold. Boundary terms around these punctures decrease the Euclidean action by $A_\diamond/4$, where $A_\diamond$ is the area of the holographic screen around the diamond. We identify these boundary contributions with the maximal entropy of gravitational degrees of freedom associated with the diamond.
Implicit Multiagent Coordination at Unsignalized Intersections via Multimodal Inference Enabled by Topological Braids

We focus on navigation among rational, non-communicating agents at unsignalized street intersections. Following collision-free motion under such settings demands nuanced implicit coordination among agents. Often, the structure of these domains constrains multiagent trajectories to belong to a finite set of modes. Our key insight is that empowering agents with a model of these modes can enable effective coordination, realized implicitly via intent signals encoded in agents' actions. In this paper, we represent modes of joint behavior in a compact and interpretable fashion using the formalism of topological braids. We design a decentralized planning algorithm that generates actions aimed at reducing the uncertainty over the mode of the emerging multiagent behavior. This mechanism enables agents that individually run our algorithm to collectively reject unsafe intersection crossings. We validate our approach in a simulated case study featuring challenging multiagent scenarios at a four-way unsignalized intersection. Our model is shown to reduce frequency of collisions by >65% over a set of baselines explicitly reasoning over trajectories, while maintaining comparable time efficiency.
Direct photon emission and influence of dynamical wave packets in an extended quantum molecular dynamics model

Direct photon produced from first proton-neutron ($p\text{\ensuremath{-}}n$) collision during the early stage of heavy ion reaction is a sensitive probe to reflect energy and momentum distribution of nucleons. In this work, we embedded the hard photon production channel in an extended quantum molecular dynamics (EQMD) model, and took the direct photon as a possible probe to improve namely the Fermi motion in the EQMD model. A possible scheme is offered to handle the dynamical wave packet width within incoherent bremsstrahlung process. Direct photons calculated by our modified EQMD were compared with data of $^{14}\mathrm{N}+^{12}\mathrm{C}$ at beam energies $E/A$ = 20, 30, and 40 MeV, and it is found that the yield, inverse slope, and angular distribution of direct photons could be reasonably reproduced. In addition, asymmetric reaction systems of $^{4}\mathrm{He} +$ C and $^{4}\mathrm{He} +$ Zn at $E/A$ = 53 MeV are also simulated in this work. It is found that the symmetric angular distribution in the nucleon-nucleon ($N\text{\ensuremath{-}}N$) center-of-mass (c.m.) frame and the velocity of the $\ensuremath{\gamma}$-emission source can be reasonably obtained from our method although there is some quantitative difference.
Using PSPNet and UNet to analyze the internal parameter relationship and visualization of the convolutional neural network

Convolutional neural network(CNN) has achieved great success in many fields, but due to the huge number of parameters, it is very difficult to study. Then, can we start from the parameters themselves to explore the relationship between the internal parameters of CNN? This paper proposes to use the convolution layer parameters substitution with the same convolution kernel setting to explore the relationship between the internal parameters of CNN and proposes to use the CNN visualization method to check the relationship. Using the visualization method, the forward propagation process of CNN is visualized. It is an intuitive representation of how CNN learns. According to the experiments, this paper believes that 1. Residual layer parameters of ResNet are correlated, and some layers can be substituted for each other; 2. Image segmentation is a process of first learning image texture features and then locating and segmentation.
Improving Deep Learning for Defect Prediction (using the GHOST Hyperparameter Optimizer)

There has been much recent interest in the application of deep learning neural networks in software engineering. Some researchers are worried that deep learning is being applied with insufficient critical ssessment. Hence, for one well-studied software analytics task (defect prediction), this paper compares deep learning versus prior-state-of-the-art results. Deep learning will outperform those prior results, but only after adjusting its hyperparameters using GHOST (Goal-oriented Hyperparameter Optimization for Scalable Training). For defect prediction, GHOST terminates in just a few minutes and scales to larger data sets; i.e. it is practical to tune deep learning tuning for defect prediction. Hence this paper recommends deep learning for defect prediction, but only adjusting its goal predicates and tuning its hyperparameters (using some hyperparameter optimization tool, like GHOST)
Estimation of Attenuation Coefficients for Simultaneous PET/MRI Using Both MRI and PET Data Combining Bayesian Deep Learning pseudo-CT and Maximum Likelihood Estimation of Activity and Attenuation

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of MRI artifacts (e.g. implants, motion) as well as uncertainties due to the limitations of MRI contrast (e.g. accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that, in addition to generating an initial pseudo-CT from MR data, also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with an MLAA reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without metal implants, UpCT-MLAA had acceptable but slightly higher RMSE than ZeDD-CT for bone lesions (3.4% and 2.7%, p=0.037 respectively) and soft tissue lesions (4.8% and 4.1%, p=0.16, respectively) when compared to CTAC. In patients with metal implants, standard MLAA was able to extract the attenuation coefficients of the metal implant; however, the anatomy outside the implant region was heavily obscured by noise and cross-talk artifacts. BpCT-AC was able to estimate the attenuation coefficients in normal anatomy; however, the metal implant was estimated to have attenuation coefficients of air due to the metal artifact appearing as a signal void on the MRI. UpCT-MLAA estimated attenuation coefficients of the metal implants while still providing qualitatively and quantitatively accurate anatomic depiction outside of metal implant regions.
Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency

Recursive linear structural equation models are widely used to postulate causal mechanisms underlying observational data. In these models, each variable equals a linear combination of a subset of the remaining variables plus an error term. When there is no unobserved confounding or selection bias, the error terms are assumed to be independent. We consider estimating a total causal effect in this setting. The causal structure is assumed to be known only up to a maximally oriented partially directed acyclic graph (MPDAG), a general class of graphs that can represent a Markov equivalence class of directed acyclic graphs (DAGs) with added background knowledge. We propose a simple estimator based on recursive least squares, which can consistently estimate any identified total causal effect, under point or joint intervention. We show that this estimator is the most efficient among all regular estimators that are based on the sample covariance, which includes covariate adjustment and the estimators employed by the joint-IDA algorithm. Notably, our result holds without assuming Gaussian errors.
Point or Generate Dialogue State Tracker

Dialogue state tracking is a key part of a task-oriented dialogue system, which estimates the user's goal at each turn of the dialogue. In this paper, we propose the Point-Or-Generate Dialogue State Tracker (POGD). POGD solves the dialogue state tracking task in two perspectives: 1) point out explicitly expressed slot values from the user's utterance, and 2) generate implicitly expressed ones based on slot-specific contexts. It also shares parameters across all slots, which achieves knowledge sharing and gains scalability to large-scale across-domain dialogues. Moreover, the training process of its submodules is formulated as a multi-task learning procedure to further promote its capability of generalization. Experiments show that POGD not only obtains state-of-the-art results on both WoZ 2.0 and MultiWoZ 2.0 datasets but also has good generalization on unseen values and new slots.
Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification

The effects of speaking-style variability on automatic speaker verification were investigated using the UCLA Speaker Variability database which comprises multiple speaking styles per speaker. An x-vector/PLDA (probabilistic linear discriminant analysis) system was trained with the SRE and Switchboard databases with standard augmentation techniques and evaluated with utterances from the UCLA database. The equal error rate (EER) was low when enrollment and test utterances were of the same style (e.g., 0.98% and 0.57% for read and conversational speech, respectively), but it increased substantially when styles were mismatched between enrollment and test utterances. For instance, when enrolled with conversation utterances, the EER increased to 3.03%, 2.96% and 22.12% when tested on read, narrative, and pet-directed speech, respectively. To reduce the effect of style mismatch, we propose an entropy-based variable frame rate technique to artificially generate style-normalized representations for PLDA adaptation. The proposed system significantly improved performance. In the aforementioned conditions, the EERs improved to 2.69% (conversation -- read), 2.27% (conversation -- narrative), and 18.75% (pet-directed -- read). Overall, the proposed technique performed comparably to multi-style PLDA adaptation without the need for training data in different speaking styles per speaker.
Stationary solutions for dyadic mixed model of the Euler equation.

Dyadic models of the Euler equations were introduced as toy models to study the behaviour of an inviscid fluid in turbulence theory. In 1974 Novikov proposed a generalized mixed dyadic model that preserves four main characteristic features derived from the spectral form of Navier-Stokes equation. This model extends both Katz-Pavlovic and Obukhov models giving birth to a more complex structure: no results were found in literature until 2015 where blow up in finite time for smooth solutions and existence of self-similar solution for particular values of the model parameters were proved within a local uniqueness theorem. We extend such results by giving a complete spectrum of existence and uniqueness results for two special classes of finite energy stationary solutions, namely constant and self-similar solutions. Such solutions satisfy the Kolmogorov K41 scaling law in turbulence theory.
Invertible Neural BRDF for Object Inverse Rendering

We introduce a novel neural network-based BRDF model and a Bayesian framework for object inverse rendering, i.e., joint estimation of reflectance and natural illumination from a single image of an object of known geometry. The BRDF is expressed with an invertible neural network, namely, normalizing flow, which provides the expressive power of a high-dimensional representation, computational simplicity of a compact analytical model, and physical plausibility of a real-world BRDF. We extract the latent space of real-world reflectance by conditioning this model, which directly results in a strong reflectance prior. We refer to this model as the invertible neural BRDF model (iBRDF). We also devise a deep illumination prior by leveraging the structural bias of deep neural networks. By integrating this novel BRDF model and reflectance and illumination priors in a MAP estimation formulation, we show that this joint estimation can be computed efficiently with stochastic gradient descent. We experimentally validate the accuracy of the invertible neural BRDF model on a large number of measured data and demonstrate its use in object inverse rendering on a number of synthetic and real images. The results show new ways in which deep neural networks can help solve challenging radiometric inverse problems.
Function completion in the time of massive data: A code embedding perspective

Code completion is an important feature of integrated development environments (IDEs). It allows developers to produce code faster, especially novice ones who are not fully familiar with APIs and others code. Previous works on code completion have mainly exploited static type systems of programming languages or code history of the project under development or of other projects using common APIs. In this work, we present a novel approach for improving current function-calls completion tools by learning from independent code repositories, using well-known natural language processing models that can learn vector representation of source code (code embeddings). Our models are not trained on historical data of specific projects. Instead, our approach allows to learn high-level concepts and their relationships present among thousands of projects. As a consequence, the resulting system is able to provide general suggestions that are not specific to particular projects or APIs. Additionally, by taking into account the context of the call to complete, our approach suggests function calls relevant to that context. We evaluated our approach on a set of open-source projects unseen during the training. The results show that the use of the trained model along with a code suggestion plug-in based on static type analysis improves significantly the correctness of the completion suggestions.
Variable Stiffness Control with Strict Frequency Domain Constraints for Physical Human-Robot Interaction

Variable impedance control is advantageous for physical human-robot interaction to improve safety, adaptability and many other aspects. This paper presents a gain-scheduled variable stiffness control approach under strict frequency-domain constraints. Firstly, to reduce conservativeness, we characterize and constrain the impedance rendering, actuator saturation, disturbance/noise rejection and passivity requirements into their specific frequency bands. This relaxation makes sense because of the restricted frequency properties of the interactive robots. Secondly, a gain-scheduled method is taken to regulate the controller gains with respect to the desired stiffness. Thirdly, the scheduling function is parameterized via a nonsmooth optimization method. Finally, the proposed approach is validated by simulations, experiments and comparisons with a gain-fixed passivity-based PID method.
The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self-consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean-led broker run by an interdisciplinary team of astronomers and engineers working to become intermediaries between survey and follow-up facilities. ALeRCE uses a pipeline that includes the real-time ingestion, aggregation, cross-matching, machine-learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp-based classifier, designed for rapid classification, and a light curve–based classifier, which uses the multiband flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools, and services, which are made public for the community (see https://alerce.science). Since we began operating our real-time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real-time processing of 1.5 × 108 alerts, the stamp classification of 3.4 × 107 objects, the light-curve classification of 1.1 × 106 objects, the report of 6162 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead in going from a single stream of alerts such as ZTF to a multistream ecosystem dominated by LSST.
The Classical Capacity of Additive Quantum Queue-Channels

We consider a setting where a stream of qubits is processed sequentially. We derive fundamental limits on the rate at which classical information can be transmitted using qubits that decohere as they wait to be processed. Specifically, we model the sequential processing of qubits using a single server queue, and derive expressions for the classical capacity of such a quantum ‘queue-channel. ’ Focusing on two important noise models, namely the erasure channel and the depolarizing channel, we obtain explicit single-letter capacity formulas in terms of the stationary waiting time of qubits in the queue. Our capacity proof also implies that a ‘classical’ coding/decoding strategy is optimal, i.e., an encoder which uses only orthogonal product states, and a decoder which measures in a fixed product basis, are sufficient to achieve the classical capacity of both queue-channels. Our proof technique for the converse theorem generalizes readily — in particular, whenever the underlying quantum noise channel is additive, we can obtain a single-letter upper bound on the classical capacity of the corresponding quantum queue-channel. More broadly, our work begins to quantitatively address the impact of decoherence on the performance limits of quantum information processing systems.
Symbolic Music Playing Techniques Generation as a Tagging Problem

Music generation has always been a hot topic. When discussing symbolic music, melody or harmonies are usually seen as the only generating targets. But in fact, playing techniques are also quite an important part of the music. In this paper, we discuss the playing techniques generation problem by seeing it as a tagging problem. We propose a model that can use both the current data and external knowledge. Experiments were carried out by applying the proposed model in Chinese bamboo flute music, and results show that our method can make generated music more lively.
Programmable generation of terahertz bursts in chirped-pulse laser amplification

Amplified bursts of laser pulses are sought for various machining, deposition, spectroscopic and strong-field applications. Standard frequency- and time-domain techniques for pulse division become inadequate when intraburst repetition rates reach the terahertz (THz) range as a consequence of inaccessible spectral resolution, requirement for interferometric stability, and collapse of the chirped pulse amplification (CPA) concept due to the loss of usable bandwidth needed for safe temporal stretching. Avoiding the burst amplification challenge and resorting to a lossy post-division of an isolated laser pulse after CPA leaves the limitations of frequency- and time-domain techniques unsolved. In this letter, we demonstrate an approach that successfully combines amplitude and phase shaping of THz bursts, formed using the Vernier effect, with active stabilization of spectral modes and efficient energy extraction from a CPA regenerative amplifier. As proof of concept, the amplified bursts of femtosecond near-infrared pulses are down-converted into tunable THz-frequency pulses via optical rectification.