Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-Scale and Efficient Texture Mapping Algorithm via Loopy Belief Propagation

Texture mapping (TM) as a fundamental task in 3-D modeling has been well established for well-acquired aerial assets under consistent illumination, yet it remains a challenge when it is scaled to large datasets with images under varying views and illuminations. A well-performed TM algorithm must be able to efficiently select views, fuse and map textures from these views to mesh models, at the same time, achieve consistent radiometry over the entire model. Existing approaches achieve efficiency either by limiting the number of images to one view per face, or simplifying global inferences to only achieve local color consistency. In this article, we break this tie by proposing a novel and efficient TM framework that allows the use of multiple views of texture per face, at the same time to achieve global color consistency. The proposed method leverages a loopy belief propagation algorithm to perform an efficient and global-level probabilistic inferences to rank candidate views per face, which enables face-level multiview texture fusion and blending. The texture fusion algorithm, being nonparametric, brings another advantage over typical parametric post color correction methods, due to its improved robustness to nonlinear illumination differences. The experiments on three different types of datasets (i.e., satellite dataset, unmanned-aerial vehicle (UAV) dataset, and close-range dataset) show that the proposed method has produced visually pleasant and texturally consistent results in all scenarios, with an added advantage of consuming less running time as compared to the state-of-the-art methods, especially for large-scale dataset such as satellite-derived models.
Joint Task Offloading and Resource Allocation for Streaming Application in Cooperative Mobile Edge Computing

Mobile edge computing (MEC) enables resource-limited IoT devices to complete computation-intensive or delay-sensitive task by offloading the task to adjacent edge server deployed at the base station (BS), thus becoming an important technology in 5G and beyond. Due to channel occlusion, some users may not be able to access the computation capability directly from the BS. Confronted with this issue, many other devices in the MEC system can serve as cooperative nodes to collect the tasks of these users and further forward them to the BS. In this paper, we study a MEC system in which multiple users continuously generate the tasks and offload the tasks to the BS through a cooperative node. As the tasks are continuously generated, users should simultaneously execute the task generation in the current time frame and the task offloading of the last time frame, i.e. the task is processed in a streaming model. To optimize the power consumption of the users and the cooperative node for finishing these streaming tasks, we investigate the duration of each step in finishing the tasks together with multiuser offloading ratio and bandwidth allocation within two cases: the BS has abundant computation capacity (Case I) and the BS has limited computation capacity (Case II). For both cases, the formulated optimization problems are nonconvex due to fractional structure of the objective function and complicated variable coupling. To address this issue, we propose optimal solution algorithm with low complexity. Finally, simulation is carried out to verify the effectiveness of the proposed methods and reveal the performance of the considered system.
No Surviving SN Ia Companion in SNR 0509-67.5: Stellar Population Characterization and Comparison to Models

The community agrees that Type Ia supernovae arise from carbon/oxygen white dwarfs undergoing thermonuclear runaway. However, the full progenitor system and the process that prompts the white dwarf to explode remain unknown. Most current models suggest that the white dwarf explodes because of interaction with a binary companion that may survive the process and remain within the resulting remnant of the exploded star. Furthermore, both the pre-supernova interaction process and the explosion of the primary are expected to imprint a significant departure from ordinary stellar radii and temperatures onto the secondary, making the star identifiable against the unrelated stellar population. Identification of a surviving companion inside an SN Ia remnant might confirm a specific corresponding SN Ia progenitor channel based on the identity of the companion. We conducted a surviving companion search of the Type Ia remnant SNR 0509−67.5 based in the Large Magellanic Cloud. The well-constrained distance to and foreground extinction of the Large Magellanic Cloud allow for Bayesian inference of stellar parameters with low correlation and uncertainties. We present a deep catalog of fully characterized stars interior to SNR 0509−67.5 with radii, effective temperatures, and metallicities inferred using combined Hubble Space Telescope photometric observations across multiple visits. We then compile a list of surviving companion models appropriate for the age of the remnant (roughly 400 yr after the explosion). We compare these predictions with the inferred stellar parameters and conclude that none of the stars are consistent with the predicted signatures of a surviving companion.
Hele-Shaw flow as a singular limit of a Keller-Segel system with nonlinear diffusion

We study a singular limit of the classical parabolic-elliptic Patlak-Keller-Segel (PKS) model for chemotaxis with non linear diffusion. The main result is the $\Gamma$ convergence of the corresponding energy functional toward the perimeter functional. Following recent work on this topic, we then prove that under an energy convergence assumption, the solution of the PKS model converges to a solution of the Hele-Shaw free boundary problem with surface tension, which describes the evolution of the interface separating regions with high density from those with low density. This result complements a recent work by the author with I. Kim and Y. Wu, in which the same free boundary problem is derived from the incompressible PKS model (which includes a density constraint $\rho\leq 1$ and a pressure term): It shows that the incompressibility constraint is not necessary to observe phase separation and surface tension phenomena.
Rise of escitalopram and the fall of citalopram

Introduction: Citalopram and escitalopram are among the most used medications and are key treatments for many psychiatric disorders. Previous findings suggest citalopram and escitalopram prescription rates are changing because of the patent for citalopram ending as opposed to evidence of a clear therapeutic advantage, which is called evergreening. This retrospective study focuses on characterizing the chronologic and geographic variation in the use of citalopram and escitalopram among US Medicaid and Medicare patients. We hypothesized that prescription rates of citalopram will decrease with a concurrent increase in escitalopram, consistent with evergreening. Methods: Citalopram and escitalopram prescription rates and costs per state were obtained from the Medicaid State Drug Utilization Database and Medicare Provider Utilization and Payment Data. Annual prescription rates outside a 95% confidence interval were considered significantly different from the average. Results: Overall, a decreasing trend for citalopram and an increasing trend for escitalopram prescription rates were noted in both Medicare and Medicaid patients. Cost differences between generic and brand were noted for both drugs, with generic forms being cheaper compared to the brand version. Discussion: Despite limited evidence suggesting that citalopram and escitalopram have any meaningful differences in therapeutic or adverse effects, there exists a noticeable decline in the use of citalopram that cooccurred with an increase in escitalopram prescribing, consistent with our hypothesis. Moreover, among these general pharmacoepidemiologic trends exists significant geographic variability. There was disproportionate spending (relative to their use) on the brand versions of these medicines compared to their generic forms.
Lipidomic Profiling Unveils Sex Differences in Diabetes Risk: Implications for Precision Medicine

Type 2 diabetes (T2D) is a multifactorial condition whose greatest impact comes from its complications. Not only impaired glucose homeostasis, but also lipid alterations have a relevant role, with insulin derived mechanisms behind this milieu, i.e., glycemia and lipidemia. Thus, we hypothesized that a) distinct glucose and lipid profiles and b) sex differences, particularly in lipids patterns, may be used to identify subjects at higher risk to develop T2D. The PREVADIAB2 study evaluated metabolic alterations after 5 years in subjects without T2D when participating to PREVADIAB1. Herein, 953 subjects from the PREVADIAB2 cohort were stratified using a hierarchical clustering algorithm, informed by HOMA-IR, IGI, fISR and fIC. The resulting clusters were profiled and the lipidome of a subset (n=488) was assessed by LC/MS-QTOF. We identified four clusters, named according to their main metabolic features: Liver Sensitive (LS); Pancreas Glucose Sensitive (PGS); Insulin Deficient (ID); and Insulin Resistant (IR). These cluster metabolic patterns were similar between sexes. However, men and women had distinct parameters cut-offs and lipidomic profiles. Overall, women presented higher long chain ceramides. Nonetheless, men had higher ceramide to sphingomyelin ratio and higher lysophosphatidylcholine to phosphatidylcholine ratio. For both genders, the LS cluster had the most advantageous lipid profile, whereas the other clusters presented lipid specificities towards dysmetabolism. This work shows that clustering individuals by distinct insulin-related metabolic features and sex identifies different phenotypes with distinct lipidome profile, thus demonstrating the importance of placing diabetes in a broader context of metabolism beyond glucose.
Sound and Complete Bisimilarities for Call-by-Name and Call-by-Value Lambda-mu Calculus

We propose the first sound and complete bisimilarities for the call-by-name and call-by-value untyped lambda-mu calculus. We define applicative bisimilarities for both semantics and environmental bisimilarity for call-by-name. We give equivalence examples to illustrate how our relations can be used; in particular, we prove David and Py's counter-example, which cannot be proved with Lassen's preexisting normal form bisimilarities for the lambda-mu calculus.
Synthesizing Dexterous Nonprehensile Pregrasp for Ungraspable Objects

Daily objects embedded in a contextual environment are often ungraspable initially. Whether it is a book sandwiched by other books on a fully packed bookshelf or a piece of paper lying flat on the desk, a series of nonprehensile pregrasp maneuvers is required to manipulate the object into a graspable state. Humans are proficient at utilizing environmental contacts to achieve manipulation tasks that are otherwise impossible, but synthesizing such nonprehensile pregrasp behaviors is challenging to existing methods. We present a novel method that combines graph search, optimal control, and a learning-based objective function to synthesize physically realistic and diverse nonprehensile pre-grasp motions that leverage the external contacts. Since the “graspability” of an object in context with its surrounding is difficult to define, we utilize a dataset of dexterous grasps to learn a metric which implicitly takes into account the exposed surface of the object and the finger tip locations. Our method can efficiently discover hand and object trajectories that are certified to be physically feasible by the simulation and kinematically achievable by the dexterous hand. We evaluate our method on eight challenging scenarios where nonprehensile pre-grasps are required to succeed. We also show that our method can be applied to unseen objects different from those in the training dataset. Finally, we report quantitative analyses on generalization and robustness of our method, as well as an ablation study.
Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints

Convex optimization with equality and inequality constraints is a ubiquitous problem in several optimization and control problems in large-scale systems. Recently there has been a lot of interest in establishing accelerated convergence of the loss function. A class of high-order tuners was recently proposed in an effort to lead to accelerated convergence for the case when no constraints are present. In this paper, we propose a new high-order tuner that can accommodate the presence of equality constraints. In order to accommodate the underlying box constraints, time-varying gains are introduced in the high-order tuner which leverage convexity and ensure anytime feasibility of the constraints. Numerical examples are provided to support the theoretical derivations.
We Need to Talk: Identifying and Overcoming Communication-Critical Scenarios for Self-Driving

In this work, we consider the task of collision-free trajectory planning for connected self-driving vehicles. We specifically consider communication-critical situations--situations where single-agent systems have blindspots that require multi-agent collaboration. To identify such situations, we propose a method which (1) simulates multi-agent perspectives from real self-driving datasets, (2) finds scenarios that are challenging for isolated agents, and (3) augments scenarios with adversarial obstructions. To overcome these challenges, we propose to extend costmap-based trajectory evaluation to a distributed multi-agent setting. We demonstrate that our bandwidth-efficient, uncertainty-aware method reduces collision rates by up to 62.5% compared to single agent baselines.
Koopman-type inverse operator for linear non-minimum phase systems with disturbances

In this paper, a novel Koopman-type inverse operator for linear time-invariant non-minimum phase systems with stochastic disturbances is proposed. This operator employs functions of the desired output to directly calculate the input. Furthermore, it can be applied as a data-driven approach for systems with unknown parameters yet a known relative degree, which is a departure from the majority of existing data-driven methods that are only applicable to minimum phase systems. Based on this foundation, we use the Monte Carlo approach to develop an improved Koopman-type method for addressing the issue of inaccurate parameter estimation in data-driven systems with large disturbances. The simulation results justify the tracking accuracy of Koopman-type operator.
From Unstructured to Structured: Transforming Chatbot Dialogues into Data Mart Schema for Visualization

Schools are among the primary avenues for public healthcare interventions. With resource limitations posing challenges to the routine conduct of health and wellness checks in Philippine public schools, the deployment of a chatbot-assisted health monitoring system may provide an alternative method. However, deriving insights from raw conversations is not straightforward due to the expressiveness of natural language that causes variances in the input. In this paper, we present a process for transforming unstructured dialogues into a structured schema. The process comprises four stages: (i) processing the dialogues through entity extraction and data aggregation, (ii) storing them as NoSQL documents on the cloud, (iii) transforming them into a star schema for online analytical processing and building an extract-transform-load workflow, and (iv) creating a web-based dashboard for visualizing summarized data and reports. Performance evaluation of this dashboard showed that increasing the number of stored dialogues by a factor of 100,000 increased the loading time for the display of roll-up, drill-down, and filter results by around only one second.
Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems

Data-driven modeling and machine learning are widely used to model the behavior of dynamic systems. One application is the residual evaluation of technical systems where model predictions are compared with measurement data to create residuals for fault diagnosis applications. While recurrent neural network models have been shown capable of modeling complex non-linear dynamic systems, they are limited to fixed steps discrete-time simulation. Modeling using neural ordinary differential equations, however, make it possible to evaluate the state variables at specific times, compute gradients when training the model and use standard numerical solvers to explicitly model the underlying dynamic of the time-series data. Here, the effect of solver selection on the performance of neural ordinary differential equation residuals during training and evaluation is investigated. The paper includes a case study of a heavy-duty truck's after-treatment system to highlight the potential of these techniques for improving fault diagnosis performance.
A Census of WISE-selected Dual and Offset AGNs Across the Sky: New Constraints on Merger-driven Triggering of Obscured AGNs

Pairs of galaxies hosting active galactic nuclei (AGNs) are powerful probes of merger-driven supermassive black hole (SMBH) growth as they can resolve individual AGNs and trace mergers over a large range of physical separations. To exploit this on a large scale for the first time for both obscured and unobscured AGNs, we use the photometric redshifts of AGNs selected by the Wide-field Infrared Survey Explorer (WISE) to find probabilistic pairs (<100 kpc separations) across the sky, along with a comparison sample of inactive galaxy pairs. Our final sample of integrated pair probabilities yields 198 AGN–AGN pairs (dual AGNs) and 2767 AGN–galaxy pairs (offset AGNs) with uniformly measured AGN and host galaxy physical properties. We find the fraction of galaxy pairs hosting WISE AGNs is dominated by offset AGNs and is significantly elevated above that of inactive galaxies for large host stellar masses. We show how the AGN merger fraction directly increases with AGN extinction for both offset and dual AGNs, with up to ∼40% of heavily obscured AGNs found in galaxy pairs. Elevated AGN merger fractions coincide with increased host specific star formation rates that suggest merger-driven coevolution of galaxies and SMBHs. Among dual AGNs, the most rapid SMBH growth may occur within the less-massive galaxy. Relative to stochastic mechanisms, mergers produce an excess of AGNs at increasingly smaller separations, especially for obscured AGNs (up to a factor of ∼5), and are augmented by correlated triggering. Finally, this excess is stronger than for lower-luminosity optically selected AGNs, regardless of the AGN obscuration level.
Hierarchical Relaxation of Safety-critical Controllers: Mitigating Contradictory Safety Conditions with Application to Quadruped Robots

The safety-critical control of robotic systems often must account for multiple, potentially conflicting, safety constraints. This paper proposes novel relaxation techniques to address safety-critical control problems in the presence of conflicting safety conditions. In particular, Control Barrier Functions (CBFs) provide a means to encode safety as constraints in a Quadratic Program (QP), wherein multiple safety conditions yield multiple constraints. However, the QP problem becomes infeasible when the safety conditions cannot be simultaneously satisfied. To resolve this potential infeasibility, we introduce a hierarchy between the safety conditions and employ an additional variable to relax the less important safety conditions (Relaxed-CBF-QP). We also formulate a cascaded structure to achieve smaller violations of lower-priority safety conditions (Hierarchical-CBF-QP). The proposed approach, therefore, ensures the existence of at least one solution to the QP problem with the CBFs while dynamically balancing enforcement of additional safety constraints. Importantly, this paper evaluates the impact of different weighting factors in the Hierarchical-CBF-QP and, due to the sensitivity of these weightings in the observed behavior, proposes a method to determine the weighting factors via a sampling-based technique. The validity of the proposed approach is demonstrated through simulations and experiments on a quadrupedal robot navigating to a goal through regions with different levels of danger.
Regularity of the Siciak-Zaharjuta extremal function on compact K\"ahler manifolds

We prove that the regularity of the extremal function of a compact subset of a compact K\"ahler manifold is a local property, and that the continuity and H\"older continuity are equivalent to classical notions of the local $L$-regularity and the locally H\"older continuous property in pluripolential theory. As a consequence we give an effective characterization of the $(\Cc^\al, \Cc^{\al'})$-regularity of compact sets, the notion introduced by Dinh, Ma and Nguyen. Using this criterion all compact fat subanalytic subsets in $\bR^n$ are shown to be regular in this sense.
Relativistic bulk-viscous dynamics far from equilibrium

We show that a two-component, reactive relativistic fluid mixture is dual to bulk-viscous Israel-Stewart theory in the fully nonlinear regime. This implies that such mixtures can be rigorously rewritten as Israel-Stewart-like fluids even far from equilibrium, namely when the bulk viscous stress is comparable to the equilibrium pressure. Using this duality, we construct the first consistent example of a bulk-viscous Israel-Stewart-type fluid whose field equations do not rely on a near-equilibrium expansion. We discuss how this result sheds new light on open questions concerning bulk viscous effects in neutron star mergers and in cosmological models for the expanding universe.
Goal-oriented inference of environment from redundant observations

The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that events necessary for learning are only partially observable, called as Partially Observable Markov Decision Process (POMDP). However, the real-world environment also gives many events irrelevant to reward delivery and an optimal behavioral strategy. The conventional methods in POMDP, which attempt to infer transition rules among the entire observations, including irrelevant states, are ineffective in such an environment. Supposing Redundantly Observable Markov Decision Process (ROMDP), here we propose a method for goal-oriented reinforcement learning to efficiently learn state transition rules among reward-related "core states" from redundant observations. Starting with a small number of initial core states, our model gradually adds new core states to the transition diagram until it achieves an optimal behavioral strategy consistent with the Bellman equation. We demonstrate that the resultant inference model outperforms the conventional method for POMDP. We emphasize that our model only containing the core states has high explainability. Furthermore, the proposed method suits online learning as it suppresses memory consumption and improves learning speed.