Miracle-dz/UniFAR-FTUs huggingface dataset:
StyleLight: HDR Panorama Generation for Lighting Estimation and Editing

We present a new lighting estimation and editing framework to generate high-dynamic-range (HDR) indoor panorama lighting from a single limited field-of-view (LFOV) image captured by low-dynamic-range (LDR) cameras. Existing lighting estimation methods either directly regress lighting representation parameters or decompose this problem into LFOV-to-panorama and LDR-to-HDR lighting generation sub-tasks. However, due to the partial observation, the high-dynamic-range lighting, and the intrinsic ambiguity of a scene, lighting estimation remains a challenging task. To tackle this problem, we propose a coupled dual-StyleGAN panorama synthesis network (StyleLight) that integrates LDR and HDR panorama synthesis into a unified framework. The LDR and HDR panorama synthesis share a similar generator but have separate discriminators. During inference, given an LDR LFOV image, we propose a focal-masked GAN inversion method to find its latent code by the LDR panorama synthesis branch and then synthesize the HDR panorama by the HDR panorama synthesis branch. StyleLight takes LFOV-to-panorama and LDR-to-HDR lighting generation into a unified framework and thus greatly improves lighting estimation. Extensive experiments demonstrate that our framework achieves superior performance over state-of-the-art methods on indoor lighting estimation. Notably, StyleLight also enables intuitive lighting editing on indoor HDR panoramas, which is suitable for real-world applications. Code is available at https://style-light.github. io.
Real-time Continuous Uncertainty Annotation (RCUA) for Spatial Navigation Studies

This study introduces two methods for continuously measuring uncertainty during human navigation in complex buildings: one using a joystick (RCUA), and the other with annotations on videos of recent navigation activity (CUA). To evaluate the usability, reliability, and validity of both approaches, we conducted a study with 54 participants. We assessed the measures' reactivity during different sign-seeing events. We also evaluated the convergent validity of both measures by comparing their outcomes with a self-report questionnaire, and assessed their discriminative and predictive validity by comparing uncertain values between known groups and correlating those values with wayfinding performance. Our findings suggest that both approaches were valid at the task level, but RCUA was better at capturing fine-grained dynamics of human experience. These continuous uncertainty measures can provide valuable insights into the fleeting nature of human experience and help identify"problem spots"for wayfinding in complex buildings.
Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology

Traditional datasets for the radiological diagnosis tend to only provide the radiology image alongside the radiology report. However, radiology reading as performed by radiologists is a complex process, and information such as the radiologist’s eye-fixations over the course of the reading has the potential to be an invaluable data source to learn from. Nonetheless, the collection of such data is expensive and time-consuming. This leads to the question of whether such data is worth the investment to collect. This paper utilizes the recently published Eye-Gaze dataset to perform an exhaustive study on the impact on performance and explainability of deep learning (DL) classification in the face of varying levels of input features, namely: radiology images, radiology report text, and radiologist eye-gaze data. We find that the best classification performance of X-ray images is achieved with a combination of radiology report free-text and radiology image, with the eye-gaze data providing no performance boost. Nonetheless, eye-gaze data serving as secondary ground truth alongside the class label results in highly explainable models that generate better attention maps compared to models trained to do classification and attention map generation without eye-gaze data.
Enhance primordial black hole abundance through the non-linear processes around bounce point

The non-singular bouncing cosmology is an alternative paradigm to inflation, wherein the background energy density vanishes at the bounce point, in the context of Einstein gravity. Therefore, the non-linear effects in the evolution of density fluctuations (δρ) may be strong in the bounce phase, which potentially provides a mechanism to enhance the abundance of primordial black holes (PBHs). This article presents a comprehensive illustration for PBH enhancement due to the bounce phase. To calculate the non-linear evolution of δρ, the Raychaudhuri equation is numerically solved here. Since the non-linear processes may lead to a non-Gaussian probability distribution function for δρ after the bounce point, the PBH abundance is calculated in a modified Press-Schechter formalism. In this case, the criterion of PBH formation is complicated, due to complicated non-linear evolutionary behavior of δρ during the bounce phase. Our results indicate that the bounce phase indeed has potential to enhance the PBH abundance sufficiently. Furthermore, the PBH abundance is applied to constrain the parameters of bounce phase, providing a complementary to the surveys of cosmic microwave background and large scale structure.
Pair localization in dipolar systems with tunable positional disorder

Strongly interacting quantum systems subject to quenched disorder exhibit intriguing phenomena such as glassiness and many-body localization. Theoretical studies have mainly focused on disorder in the form of random potentials, while many experimental realizations naturally feature disorder in the interparticle interactions. Inspired by cold Rydberg gases, where such disorder can be engineered using the dipole blockade effect,we study a Heisenberg XXZ spin model where the disorder is exclusively due to random spin-spin couplings, arising from power-law interactions between randomly positioned spins. Using established spectral and eigenstate properties and entanglement entropy, we show that this system exhibits a localization crossover and identify strongly interacting pairs as emergent local conserved quantities in the system, leading to an intuitive physical picture consistent with our numerical results.
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update

Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e.g., organizations or enterprises) to collaboratively build machine learning models with privacy protection. In the training phase, VFL only exchanges the intermediate statistics, i.e., forward activations and backward derivatives, across parties to compute model gradients. Nevertheless, due to its geo-distributed nature, VFL training usually suffers from the low WAN bandwidth. In this paper, we introduce CELU-VFL, a novel and efficient VFL training framework that exploits the local update technique to reduce the cross-party communication rounds. CELU-VFL caches the stale statistics and reuses them to estimate model gradients without exchanging the ad hoc statistics. Significant techniques are proposed to improve the convergence performance. First, to handle the stochastic variance problem, we propose a uniform sampling strategy to fairly choose the stale statistics for local updates. Second, to harness the errors brought by the stateness, we devise an instance weighting mechanism that measures the reliability of the estimated gradients. Theoretical analysis proves that CELU-VFL achieves a similar sub-linear convergence rate as vanilla VFL training but requires much fewer communication rounds. Empirical results on both public and real-world workloads validate that CELU-VFL can be up to six times faster than the existing works.
Orbital Acceleration Using Product of Exponentials

The Product of Exponentials (PoE) formulation is most commonly used in the ﬁeld of robotics, but has recently been adapted for use in describing orbital motion. The PoE formula for orbital mechanics is an alternate method for deﬁning and drawing an orbit based on its orbital elements set. Currently the PoE formula for orbital mechanics has only been derived through the ﬁrst derivative (velocity). This work explores the second derivative of the adapted PoE formula for orbital mechanics, which gives a more complete description of the orbital motion of a satellite in a two-body system. This comprehensive approach employs a uniﬁed approach to account for all six time-varying orbital elements, therefore broadening the scope of the research and applications.
Comparison of refilling schemes in the free-surface lattice Boltzmann method

Simulating mobile liquid–gas interfaces with the free-surface lattice Boltzmann method (FSLBM) requires frequent re-initialization of fluid flow information in computational cells that convert from gas to liquid. The corresponding algorithm, here referred to as the refilling scheme, is crucial for the successful application of the FSLBM in terms of accuracy and numerical stability. This study compares five refilling schemes that extract information from the surrounding liquid and interface cells by averaging, extrapolating, or assuming one of the three different equilibrium states. Six numerical experiments were performed, covering a broad spectrum of possible scenarios. These include a standing gravity wave, a rectangular and cylindrical dam break, a Taylor bubble, a drop impact into liquid, and a bubbly plane Poiseuille flow. In some simulations, the averaging, extrapolation, and one equilibrium-based scheme were numerically unstable. Overall, the results have shown that the simplest equilibrium-based scheme should be preferred in terms of numerical stability, computational cost, accuracy, and ease of implementation.
Self-Organized Polygon Formation Control Based on Distributed Estimation

This article studies the problem of controlling a multirobot system to achieve a polygon formation in a self-organized manner. Different from the typical formation control strategies where robots are steered to satisfy the predefined control variables, such as pair-wise distances, relative positions and bearings, the foremost idea of this article is to achieve polygon formations by injecting control inputs randomly to a few robots (say, vertex robots) of the group, and the rest follow the simple principles of moving toward the midpoint of their two nearest neighbors in the ring graph without any external inputs. In our problem, a fleet of robots is initially distributed in the plane. The so-called vertex robots take the responsibility of determining the geometric shape of the entire formation and its overall size, while the others move so as to minimize the differences with two direct neighbors. In the first step, each vertex robot estimates the number of robots in its associated chain. Two types of control inputs that serve for the estimation are designed using the measurements from the latest and the last two time instants, respectively. In the second step, the self-organized formation control law is proposed where only vertex robots receive external information. Comparisons between the two estimation strategies are carried out in terms of the convergence speed and robustness. The effectiveness of the whole control framework is further validated in both simulation and physical experiments.
Not All Dependencies are Equal: An Empirical Study on Production Dependencies in NPM

Modern software systems are often built by leveraging code written by others in the form of libraries and packages to accelerate their development. While there are many benefits to using third-party packages, software projects often become dependent on a large number of software packages. Consequently, developers are faced with the difficult challenge of maintaining their project dependencies by keeping them up-to-date and free of security vulnerabilities. However, how often are project dependencies used in production where they could pose a threat to their project’s security? We conduct an empirical study on 100 JavaScript projects using the Node Package Manager (npm) to quantify how often project dependencies are released to production and analyze their characteristics and their impact on security. Our results indicate that less than 1% of the installed dependencies are released to production. Our analysis reveals that the functionality of a package is not enough to determine if it will be released to production or not. In fact, 59% of the installed dependencies configured as runtime dependencies are not used in production, and 28.2% of the dependencies configured as development dependencies are used in production, debunking two common assumptions of dependency management. Findings also indicate that most security alerts target dependencies not used in production, making them highly unlikely to be a risk for the security of the software. Our study unveils a more complex side of dependency management: not all dependencies are equal. Dependencies used in production are more sensitive to security exposure and should be prioritized. However, current tools lack the appropriate support in identifying production dependencies.
The refractive index of InP and its temperature dependence in the wavelength range from 1.2 /spl mu/m to 1.6 /spl mu/m

Measurements of the refractive index of InP in the wavelength range from 1.2 /spl mu/m to 1.6 /spl mu/m have been carried out with a relative accuracy of 2/spl times/10/sup -4/. The refractive indices are determined from the measured transmission wavelengths of an n/sup -//n/sup +/-InP demultiplexer and their temperature dependence. The refractive indices are found to vary linearly with temperature with a coefficient between 2.3 and 1.9/spl times/10/sup -4//K. The results are compared with data from literature.
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 Challenge

This paper presents our efforts to build a robust ASR model for the shared task Automatic Speech Recognition for spontaneous and prepared speech & Speech Emotion Recognition in Portuguese (SE&R 2022) . The goal of the challenge is to advance the ASR research for the Portuguese language, considering prepared and spontaneous speech in different dialects. Our method consist on ﬁne-tuning an ASR model in a domain-speciﬁc approach, applying gain normalization and selective noise insertion. The proposed method improved over the strong baseline provided on the test set in 3 of the 4 tracks available.
An Equity-Aware Recommender System for Curating Art Exhibits Based on Locally-Constrained Graph Matching

Public art shapes our shared spaces. Public art should speak to community and context, and yet, recent work has demonstrated numerous instances of art in prominent institutions favoring outdated cultural norms and legacy communities. Motivated by this, we develop a novel recommender system to curate public art exhibits with built-in equity objectives and a local value-based allocation of constrained resources. We develop a cost matrix by drawing on Schelling's model of segregation. Using the cost matrix as an input, the scoring function is optimized via a projected gradient descent to obtain a soft assignment matrix. Our optimization program allocates artwork to public spaces in a way that de-prioritizes"in-group"preferences, by satisfying minimum representation and exposure criteria. We draw on existing literature to develop a fairness metric for our algorithmic output, and we assess the effectiveness of our approach and discuss its potential pitfalls from both a curatorial and equity standpoint.
AlphaVC: High-Performance and Efficient Learned Video Compression

Recently, learned video compression has drawn lots of attention and show a rapid development trend with promising results. How-ever, the previous works still suffer from some criticial issues and have a performance gap with traditional compression standards in terms of widely used PSNR metric. In this paper, we propose several techniques to effectively improve the performance. First, to address the problem of accumulative error, we introduce a conditional-I-frame as the first frame in the GoP, which stabilizes the reconstructed quality and saves the bit-rate. Second, to efficiently improve the accuracy of inter prediction without increasing the complexity of decoder, we propose a pixel-to-feature motion prediction method at encoder side that helps us to obtain high-quality motion information. Third, we propose a probability-based entropy skipping method, which not only brings performance gain, but also greatly reduces the runtime of entropy coding. With these powerful techniques, this paper proposes AlphaVC, a high-performance and efficient learned video compression scheme. To the best of our knowledge, AlphaVC is the first E2E AI codec that exceeds the latest compression standard VVC on all common test datasets for both PSNR (-28.2% BD-rate saving) and MSSSIM (-52.2% BD-rate saving), and has very fast encoding (0.001x VVC) and decoding (1.69x VVC) speeds.
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions

Reinforcement Learning (RL) and continuous nonlinear control have been successfully deployed in multiple domains of complicated sequential decision-making tasks. However, given the exploration nature of the learning process and the presence of model uncertainty, it is challenging to apply them to safety-critical control tasks due to the lack of safety guarantee. On the other hand, while combining control-theoretical approaches with learning algorithms has shown promise in safe RL applications, the sample efficiency of safe data collection process for control is not well addressed. In this paper, we propose a \emph{provably} sample efficient episodic safe learning framework for online control tasks that leverages safe exploration and exploitation in an unknown, nonlinear dynamical system. In particular, the framework 1) extends control barrier functions (CBFs) in a stochastic setting to achieve provable high-probability safety under uncertainty during model learning and 2) integrates an optimism-based exploration strategy to efficiently guide the safe exploration process with learned dynamics for \emph{near optimal} control performance. We provide formal analysis on the episodic regret bound against the optimal controller and probabilistic safety with theoretical guarantees. Simulation results are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.
Experimental Demonstration of Quantum Overlapping Tomography.

Quantum tomography is one of the major challenges of large-scale quantum information research due to the exponential time complexity. In this Letter, we develop and apply a Bayesian state estimation method to experimentally demonstrate quantum overlapping tomography [Phys. Rev. Lett. 124, 100401 (2020)PRLTAO0031-900710.1103/PhysRevLett.124. 100401], a scheme intent on characterizing critical information of a many-body quantum system in logarithmic time complexity. By comparing the measurement results of full-state tomography and overlapping tomography, we show that overlapping tomography gives accurate information of the system with much fewer state measurements than full-state tomography.
Treatment Effect Estimation with Unobserved and Heterogeneous Confounding Variables

The estimation of the treatment eﬀect is often biased in the presence of unobserved confounding variables which are commonly referred to as hidden variables. Although a few methods have been recently proposed to handle the eﬀect of hidden variables, these methods often overlook the possibility of any interaction between the observed treatment variable and the unobserved covariates. In this work, we address this shortcoming by studying a multivariate response regression problem with both unobserved and heterogeneous confounding variables of the form Y = A T X + B T Z + (cid:80) pj =1 C Tj X j Z + E , where Y ∈ R m are m -dimensional response variables, X ∈ R p are observed covariates (including the treatment variable), Z ∈ R K are K -dimensional unobserved confounders, and E ∈ R m is the random noise. Allowing for the interaction between X j and Z induces the heterogeneous confounding eﬀect. Our goal is to estimate the unknown matrix A , the direct eﬀect of the observed covariates or the treatment on the responses. To this end, we propose a new debiased estimation approach via SVD to remove the eﬀect of unobserved confounding variables. The rate of convergence of the estimator is established under both the homoscedastic and heteroscedastic noises. We also present several simulation experiments and a real-world data application to substantiate our ﬁndings.
A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG)

In this article, we present a new EEG signal classification framework by integrating the complex-valued and real-valued Convolutional Neural Network(CNN) with discrete Fourier transform (DFT). The proposed neural network architecture consists of one complex-valued convolutional layer, two real-valued convolutional layers, and three fully connected layers. Our method can efficiently utilize the phase information contained in the DFT. We validate our approach using two simulated EEG signals and a benchmark data set and compare it with two widely used frameworks. Our method drastically reduces the number of parameters used and improves accuracy when compared with the existing methods in classifying benchmark data sets, and significantly improves performance in classifying simulated EEG signals.
The Generalized Causal Dantzig: A Unified Approach to Instruments and Environments

Many recent works have proposed regression models which are invariant across data collection environments [Rothenhäusler et al., 2019, Peters et al., 2016, Heinze-Deml et al., 2018, Meinshausen, 2018, Gimenez and Rothenhäusler, 2021]. Under conditions on the environments and type of invariance imposed, these estimators often have a causal interpretation. One recent example is the Causal Dantzig (CD). In this work we derive the CD as generalized method of moment (GMM) estimator. In this form, the environment plays a role nearly identical to the instrument in classical estimators such as Two Stage Least Squares (TSLS), illustrating a close connection between the concepts of instruments, environments, and invariance. We show that several of the conceptual motivations behind environments such as do–interventions can be modeled with instrumental variables. This uniﬁed treatment of environments and instruments produces many practical results including: 1) immediate generalization of the Causal Dantzig to problems with continuous instruments/environments 2) straightforward asymptotic results based on GMM theory and 3) new hybrid estimators which have properties superior to CD or TSLS alone. We illustrate these results in simulations and an application to a Flow Cytometry data set.