Miracle-dz/UniFAR-FTUs huggingface dataset:
Can No-reference features help in Full-reference image quality estimation?

Development of perceptual image quality assessment (IQA) metrics has been of significant interest to computer vision community. The aim of these metrics is to model quality of an image as perceived by humans. Recent works in Full-reference IQA research perform pixelwise comparison between deep features corresponding to query and reference images for quality prediction. However, pixelwise feature comparison may not be meaningful if distortion present in query image is severe. In this context, we explore utilization of no-reference features in Full-reference IQA task. Our model consists of both full-reference and no-reference branches. Full-reference branches use both distorted and reference images, whereas No-reference branch only uses distorted image. Our experiments show that use of no-reference features boosts performance of image quality assessment. Our model achieves higher SRCC and KRCC scores than a number of state-of-the-art algorithms on KADID-10K and PIPAL datasets.
On the Configuration of More and Less Expressive Logic Programs

The decoupling between the representation of a certain problem, that is, its knowledge model, and the reasoning side is one of main strong points of model-based artificial intelligence (AI). This allows, for example, to focus on improving the reasoning side by having advantages on the whole solving process. Further, it is also well known that many solvers are very sensitive to even syntactic changes in the input. In this paper, we focus on improving the reasoning side by taking advantages of such sensitivity. We consider two well-known model-based AI methodologies, SAT and ASP, define a number of syntactic features that may characterise their inputs, and use automated configuration tools to reformulate the input formula or program. Results of a wide experimental analysis involving SAT and ASP domains, taken from respective competitions, show the different advantages that can be obtained by using input reformulation and configuration.
Personalized Federated Learning With Structure

Knowledge sharing and model personalization are two key components to impact the performance of personalized federated learning (PFL). Existing PFL methods simply treat knowledge sharing as an aggregation of all clients regardless of the hidden relations among them. This paper is to enhance the knowledge-sharing process in PFL by leveraging the structural information among clients. We propose a novel structured federated learning(SFL) framework to simultaneously learn the global model and personalized model using each client’s local relations with others and its private dataset. This proposed framework has been formulated to a new optimization problem to model the complex relationship among personalized models and structural topology information into a uni-ﬁed framework. Moreover, in contrast to a pre-deﬁned structure, our framework could be further enhanced by adding a structure learning component to automatically learn the structure using the sim-ilarities between clients’ models’ parameters. By conducting extensive experiments, we ﬁrst demonstrate how federated learning can be beneﬁted by introducing structural information into the server aggregation process with a real-world dataset, and then the effectiveness of the proposed method has been demonstrated in varying degrees of data non-iid settings.
Hierarchical team structure and multidimensional localization (or siloing) on networks

Knowledge silos emerge when structural properties of organizational interaction networks limit the diffusion of information. These structural barriers are known to take many forms at different scales—hubs in otherwise sparse organizations, large dense teams, or global core-periphery structure—but we lack an understanding of how these different structures interact and shape dynamics. Here we take a first theoretical step in bridging the gap between the mathematical literature on localization of spreading dynamics and the more applied literature on knowledge silos in organizational interaction networks. To do so, we introduce a new model that considers a layered structure of teams to unveil a new form of hierarchical localization (i.e. the localization of information at the top or center of an organization) and study its interplay with known phenomena of mesoscopic localization (i.e. the localization of information in large groups), k-core localization (i.e. around denser subgraphs) and hub localization (i.e. around high degree stars). We also include a complex contagion mechanism by considering a general infection kernel which can depend on hierarchical level (influence), degree (popularity), infectious neighbors (social reinforcement) or team size (importance). This very general model allows us to explore the multifaceted phenomenon of information siloing in complex organizational interaction networks and opens the door to new optimization problems to promote or hinder the emergence of different localization regimes.
Are Latent Factor Regression and Sparse Regression Adequate?

We propose the Factor Augmented sparse linear Regression Model (FARM) that not only encompasses both the latent factor regression and sparse linear regression as special cases but also bridges dimension reduction and sparse regression together. We provide theoretical guarantees for the estimation of our model under the existence of sub-Gaussian and heavy-tailed noises (with bounded (1+x)-th moment, for all x>0), respectively. In addition, the existing works on supervised learning often assume the latent factor regression or the sparse linear regression is the true underlying model without justifying its adequacy. To fill in such an important gap, we also leverage our model as the alternative model to test the sufficiency of the latent factor regression and the sparse linear regression models. To accomplish these goals, we propose the Factor-Adjusted de-Biased Test (FabTest) and a two-stage ANOVA type test respectively. We also conduct large-scale numerical experiments including both synthetic and FRED macroeconomics data to corroborate the theoretical properties of our methods. Numerical results illustrate the robustness and effectiveness of our model against latent factor regression and sparse linear regression models.
Flow-Based Control of Marine Robots in Gyre-Like Environments

We present a flow-based control strategy that enables resource-constrained marine robots to patrol gyre-like flow environments on an orbital trajectory with a periodicity in a given range. The controller does not require a detailed model of the flow field and relies only on the robot's location relative to the center of the gyre. Instead of precisely tracking a pre-defined trajectory, the robots are tasked to stay in between two bounding trajectories with known periodicity. Furthermore, the proposed strategy leverages the surrounding flow field to minimize control effort. We prove that the proposed strategy enables robots to cycle in the flow satisfying the desired periodicity requirements. Our method is tested and validated both in simulation and in experiments using a low-cost, underactuated, surface swimming robot, i.e. the Modboat.
FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry

To achieve accurate and robust pose estimation in Simultaneous Localization and Mapping (SLAM) task, multisensor fusion is proven to be an effective solution and thus provides great potential in robotic applications. This paper proposes FAST-LIVO, a fast LiDAR-Inertial-Visual Odometry system, which builds on two tightly-coupled and direct odometry subsystems: a VIO subsystem and a LIO subsystem. The LIO subsystem registers raw points (instead of feature points on e.g., edges or planes) of a new scan to an incrementally-built point cloud map. The map points are additionally attached with image patches, which are then used in the VIO subsystem to align a new image by minimizing the direct photometric errors without extracting any visual features (e.g., ORB or FAST corner features). To further improve the VIO robustness and accuracy, a novel outlier rejection method is proposed to reject unstable map points that lie on edges or are occluded in the image view. Experiments on both open data sequences and our customized device data are conducted. The results show our proposed system outperforms other counterparts and can handle challenging environments at reduced computation cost. The system supports both multi-line spinning LiDARs and emerging solid-state LiDARs with completely different scanning patterns, and can run in real-time on both Intel and ARM processors. We open source our code and dataset of this work on Github22https://github.com/hku-mars/FAST-LIVO to benefit the robotics community.
Chance-Constrained Iterative Linear-Quadratic Stochastic Games

Dynamic game arises as a powerful paradigm for multi-robot planning, for which safety constraint satisfaction is crucial. Constrained stochastic games are of particular interest, as real-world robots need to operate and satisfy constraints under uncertainty. Existing methods for solving stochastic games handle chance constraints using exponential penalties with hand-tuned weights. However, finding a suitable penalty weight is nontrivial and requires trial and error. In this letter, we propose the chance-constrained iterative linear-quadratic stochastic games (CCILQGames) algorithm. CCILQGames solves chance-constrained stochastic games using the augmented Lagrangian method. We evaluate our algorithm in three autonomous driving scenarios, including merge, intersection, and roundabout. Experimental results and Monte Carlo tests show that CCILQGames can generate safe and interactive strategies in stochastic environments.
A Radial Velocity Study of the Planetary System of π Mensae: Improved Planet Parameters for π Mensae c and a Third Planet on a 125 Day Orbit

π Men hosts a transiting planet detected by the Transiting Exoplanet Survey Satellite space mission and an outer planet in a 5.7 yr orbit discovered by radial velocity (RV) surveys. We studied this system using new RV measurements taken with the HARPS spectrograph on ESO’s 3.6 m telescope, as well as archival data. We constrain the stellar RV semiamplitude due to the transiting planet, π Men c, as K c = 1.21 ± 0.12 m s−1, resulting in a planet mass of M c = 3.63 ± 0.38 M ⊕. A planet radius of R c = 2.145 ± 0.015 R ⊕ yields a bulk density of ρ c = 2.03 ± 0.22 g cm−3. The precisely determined density of this planet and the brightness of the host star make π Men c an excellent laboratory for internal structure and atmospheric characterization studies. Our HARPS RV measurements also reveal compelling evidence for a third body, π Men d, with a minimum mass M d sin i d = 13.38 ± 1.35 M ⊕ orbiting with a period of P orb,d = 125 days on an eccentric orbit (e d = 0.22). A simple dynamical analysis indicates that the orbit of π Men d is stable on timescales of at least 20 Myr. Given the mutual inclination between the outer gaseous giant and the inner rocky planet and the presence of a third body at 125 days, π Men is an important planetary system for dynamical and formation studies.
PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling

We propose a generative adversarial network for point cloud upsampling, which can not only make the upsampled points evenly distributed on the underlying surface but also efficiently generate clean high frequency regions. The generator of our network includes a dynamic graph hierarchical residual aggregation unit and a hierarchical residual aggregation unit for point feature extraction and upsampling, respectively. The former extracts multiscale point-wise descriptive features, while the latter captures rich feature details with hierarchical residuals. To generate neat edges, our discriminator uses a graph filter to extract and retain high frequency points. The generated high resolution point cloud and corresponding high frequency points help the discriminator learn the global and high frequency properties of the point cloud. We also propose an identity distribution loss function to make sure that the upsampled points remain on the underlying surface of the input low resolution point cloud. To assess the regularity of the upsampled points in high frequency regions, we introduce two evaluation metrics. Objective and subjective results demonstrate that the visual quality of the upsampled point clouds generated by our method is better than that of the state-of-the-art methods.
IS OUR RESEARCH PRODUCTIVITY IN DECLINE? A New Approach in Resolving the Controversy

This contribution examines the current controversy over research productivity. There are two sides in this controversy. Using extensive data from several industries and areas of research, one side argues that research productivity is currently in decline. The other side disputes this conclusion. It contends that the data used in making this argument are selective and limited; they do not reflect the overall state of research. The conclusion that follows from this critique is that the indicators of research productivity we currently use are not reliable and do not warrant a definitive answer to the problem. The article agrees that we need a new set of indicators in assessing research productivity. It proposes that we should look at global indicators related to knowledge production in general, rather than look at selective data that are inevitably limited in their scope. The article argues that the process of creation plays the essential role in knowledge production. Therefore, the perspective that uses the process of creation as its central organizing principle offers a unique and global view on the production of knowledge and makes a definitive resolution of the controversy possible. The article also outlines some steps for improving research productivity and realizing the full potential of the human capacity to produce knowledge.
Video is All You Need: Attacking PPG-based Biometric Authentication

Unobservable physiological signals enhance biometric authentication systems. Photoplethysmography (PPG) signals are convenient owing to its ease of measurement and are usually well protected against remote adversaries in authentication. Any leaked PPG signals help adversaries compromise the biometric authentication systems, and the advent of remote PPG (rPPG) enables adversaries to acquire PPG signals through restoration. While potentially dangerous, rPPG-based attacks are overlooked because existing methods require the victim's PPG signals. This paper proposes a novel spoofing attack approach that uses the waveforms of rPPG signals extracted from video clips to fool the PPG-based biometric authentication. We develop a new PPG restoration model to accomplish adversarial attacks without leaked PPG signals. Empirical study results on state-of-art PPG-based biometric authentication show that the signals recovered through rPPG pose a severe threat to PPG-based biometric authentication.
An Analysis of Ensemble Sampling

Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In this paper, we establish a regret bound that ensures desirable behavior when ensemble sampling is applied to the linear bandit problem. This represents the first rigorous regret analysis of ensemble sampling and is made possible by leveraging information-theoretic concepts and novel analytic techniques that may prove useful beyond the scope of this paper.
Sensitive Pictures: Emotional Interpretation in the Museum

Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional visitor experience co-created with the Munch art museum. Visitors choose emotions, locate associated paintings in the museum, experience an emotional story while viewing them, and self-report their response. A subsequent interview with a portrayal of the artist employs computer vision to estimate emotional responses from facial expressions. Visitors are given a souvenir postcard visualizing their emotional data. A study of 132 members of the public (39 interviewed) illuminates key themes: designing emotional provocations; capturing emotional responses; engaging visitors with their data; a tendency for them to align their views with the system's interpretation; and integrating these elements into emotional trajectories. We consider how Affective Computing can hold up a mirror to our emotions during Affective Interaction
Computationally Efficient and Statistically Optimal Robust Low-rank Matrix Estimation

Low-rank matrix estimation under heavy-tailed noise is challenging, both computationally and statistically. Convex approaches have been proven statistically optimal but suﬀer from high computational costs, especially since robust loss functions are usually non-smooth. More recently, computationally fast non-convex approaches via sub-gradient descent are proposed, which, unfortunately, fail to deliver a statistically consistent estimator even under sub-Gaussian noise. In this paper, we introduce a novel Riemannian sub-gradient (RsGrad) algorithm which is not only computationally eﬃcient with linear convergence but also is statistically optimal, be the noise Gaussian or heavy-tailed with a ﬁnite 1+ ε moment. Convergence theory is established for a general framework and speciﬁc applications to absolute loss, Huber loss and quantile loss are investigated. Compared with existing non-convex methods, ours reveals a surprising phenomenon of dual-phase convergence . In phase one, RsGrad behaves as in a typical non-smooth optimization that requires gradually decaying stepsizes. However, phase one only delivers a statistically sub-optimal estimator which is already observed in existing literature. Interestingly, during phase two, RsGrad converges linearly as if minimizing a smooth and strongly convex objective function and thus a constant stepsize suﬃces. Underlying the phase-two convergence is the smoothing eﬀect of random noise to the non-smooth robust losses in an area close but not too close to the truth. Lastly, RsGrad is applicable for low-rank tensor estimation under heavy-tailed noise where statistically optimal rate is attainable with the same phenomenon of dual-phase convergence, and a novel shrinkage-based second-order moment method is guaranteed to deliver a warm initialization. Numerical simulations conﬁrm our theoretical discovery and showcase the superiority of RsGrad over prior methods.
First-order logic axiomatization of metric graph theory

The main goal of this note is to provide a First-Order Logic with Betweenness (FOLB) axiomatization of the main classes of graphs occurring in Metric Graph Theory, in analogy to Tarski's axiomatization of Euclidean geometry. We provide such an axiomatization for weakly modular graphs and their principal subclasses (median and modular graphs, bridged graphs, Helly graphs, dual polar graphs, etc), basis graphs of matroids and even $\Delta$-matroids, partial cubes and their subclasses (ample partial cubes, tope graphs of oriented matroids and complexes of oriented matroids, bipartite Pasch and Peano graphs, cellular and hypercellular partial cubes, almost-median graphs, netlike partial cubes), and Gromov hyperbolic graphs. On the other hand, we show that some classes of graphs (including chordal, planar, Eulerian, and dismantlable graphs), closely related with Metric Graph Theory, but defined in a combinatorial or topological way, do not allow such an axiomatization.
Information Gain Propagation: a new way to Graph Active Learning with Soft Labels

Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort. GNN-based Active Learning (AL) methods are proposed to improve the labeling efficiency by selecting the most valuable nodes to label. Existing methods assume an oracle can correctly categorize all the selected nodes and thus just focus on the node selection. However, such an exact labeling task is costly, especially when the categorization is out of the domain of individual expert (oracle). The paper goes further, presenting a soft-label approach to AL on GNNs. Our key innovations are: i) relaxed queries where a domain expert (oracle) only judges the correctness of the predicted labels (a binary question) rather than identifying the exact class (a multi-class question), and ii) new criteria of maximizing information gain propagation for active learner with relaxed queries and soft labels. Empirical studies on public datasets demonstrate that our method significantly outperforms the state-of-the-art GNN-based AL methods in terms of both accuracy and labeling cost.
Framework for Network-Constrained Target Tracking

—The increase in perception capabilities of connected mobile sensor platforms ( e.g. , self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the trafﬁc ﬂows in a larger area. This paper proposes a new method for advanced trafﬁc applications, tracking an unknown and varying number of moving targets ( e.g. , pedestrians or cyclists) constrained by a road network, using mobile ( e.g. , vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT ﬁlter in free space. Results particularly highlight network-constraint effects for more efﬁcient target predictions over extended periods of time, and in the simpliﬁcation of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications.
High Photoabsorption and Photostability of Photosesitizers for Kaolinite-Bixin Solar Cells

Natural pigments have low photostability due to the nature of their molecular structures. The conjugated structures lead the pigment molecules to be degraded quickly by the UV radiation. In this work, bixin is used as natural pigments for photosensitizers. Kaolinite is employed as a barrier to protect the bixin from degradation. Impregnating bixin into kaolinite forms more stable molecular structures which improve photostability of the bixin. Moreover, kaolinite can serve as a carrier material which allows the transfer of electrons from bixin to kaolinite molecules through the hydrogen bonds that form between them. As results, kaolinite does not only slow down the bixin photodegradation rate, but also improve bixin photoabsorption capability
Presymplectic integrators for optimal control problems via retraction maps

Retractions maps are used to define a discretization of the tangent bundle of the configuration manifold as two copies of the configuration manifold where the dynamics take place. Such discretization maps can be conveniently lifted to the cotangent bundle so that symplectic integrators are constructed for Hamilton’s equations. Optimal control problems are provided with a Hamiltonian framework by Pontryagin’s Maximum Principle. That is why we use discretization maps and the integrability algorithm to obtain presymplectic integrators for optimal control problems.