Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching

We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our prior arts treat the above task as pure image retrieval by selecting the most similar satellite reference image matching the ground-level query image. However, such an approach often produces coarse location estimates because the geotag of the retrieved satellite image only corresponds to the image center while the ground camera can be located at any point within the image. To further consolidate our prior research finding, we present a novel geometry-aware geo-localization method. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image, once its coarse location and orientation have been determined. Moreover, we propose a new geometry-aware image retrieval pipeline to improve the coarse localization accuracy. Apart from a polar transform in our conference work, this new pipeline also maps satellite image pixels to the ground-level plane in the ground-view via a geometry-constrained projective transform to emphasize informative regions, such as road structures, for cross-view geo-localization. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our newly proposed framework. We also significantly improve the performance of coarse localization results compared to the state-of-the-art in terms of location recalls.
Control-Oriented Modeling of Pipe Flow in Gas Processing Facilities

Pipe flow models are developed with a focus on their eventual use for feedback control design at the process control level, as opposed to the unit level, in gas processing facilities. Accordingly, linearized facility-scale models are generated to describe pressures, mass flows, and temperatures based on sets of nonlinear partial differential equations from fluid dynamics and thermodynamics together with constraints associated with their interconnection. As part of the treatment, the divergence of these simplified models from physics is assessed since robustness to these errors will be an objective for the eventual control system. The approach commences with a thorough analysis of pipe flow models and then proceeds to study their automated interconnection into network models, which subsume the algebraic constraints of bond graph or standard fluid modeling. The models are validated and their errors are quantified by referring them to operational data from a commercial gas compressor test facility. For linear time-invariant models, the interconnection method to generate network models is shown to coincide with automation of Mason’s gain formula. These pipe network models based on engineering data are the first part of the development of general facility process control tools.
Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge

Text-to-Speech (TTS) services that run on edge devices have many advantages compared to cloud TTS, e.g., latency and privacy issues. However, neural vocoders with a low complexity and small model footprint inevitably generate annoying sounds. This study proposes a Bunched LPCNet2, an improved LPCNet architecture that provides highly efficient performance in high-quality for cloud servers and in a low-complexity for low-resource edge devices. Single logistic distribution achieves computational efficiency, and insightful tricks reduce the model footprint while maintaining speech quality. A DualRate architecture, which generates a lower sampling rate from a prosody model, is also proposed to reduce maintenance costs. The experiments demonstrate that Bunched LPCNet2 generates satisfactory speech quality with a model footprint of 1.1MB while operating faster than real-time on a RPi 3B. Our audio samples are available at https://srtts.github. io/bunchedLPCNet2.
ON UNSUPERSTABLE THEORIES IN GDST

We study the $\kappa$-Borel-reducibility of isomorphism relations of complete first order theories by using coloured trees. Under some cardinality assumptions, we show the following: For all theories T and T', if T is classifiable and T' is unsuperstable, then the isomorphism of models of T' is strictly above the isomorphism of models of T with respect to $\kappa$-Borel-reducibility.
"This is Fake News": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information

False information spreads on social media, and fact-checking is a potential countermeasure. However, there is a severe shortage of fact-checkers; an efficient way to scale fact-checking is desperately needed, especially in pandemics like COVID-19. In this study, we focus on spontaneous debunking by social media users, which has been missed in existing research despite its indicated usefulness for fact-checking and countering false information. Specifically, we characterize the tweets with false information, or fake tweets, that tend to be debunked and Twitter users who often debunk fake tweets. For this analysis, we create a comprehensive dataset of responses to fake tweets, annotate a subset of them, and build a classification model for detecting debunking behaviors. We find that most fake tweets are left undebunked, spontaneous debunking is slower than other forms of responses, and spontaneous debunking exhibits partisanship in political topics. These results provide actionable insights into utilizing spontaneous debunking to scale conventional fact-checking, thereby supplementing existing research from a new perspective.
Digital quantum simulation of non-perturbative dynamics of open systems with orthogonal polynomials

Classical non-perturbative simulations of open quantum systems' dynamics face several scalability problems, namely, exponential scaling of the computational effort as a function of either the time length of the simulation or the size of the open system. In this work, we propose the use of the Time Evolving Density operator with Orthogonal Polynomials Algorithm (TEDOPA) on a quantum computer, which we term as Quantum TEDOPA (Q-TEDOPA), to simulate non-perturbative dynamics of open quantum systems linearly coupled to a bosonic environment (continuous phonon bath). By performing a change of basis of the Hamiltonian, the TEDOPA yields a chain of harmonic oscillators with only local nearest-neighbour interactions, making this algorithm suitable for implementation on quantum devices with limited qubit connectivity such as superconducting quantum processors. We analyse in detail the implementation of the TEDOPA on a quantum device and show that exponential scalings of computational resources can potentially be avoided for time-evolution simulations of the systems considered in this work. We applied the proposed method to the simulation of the exciton transport between two light-harvesting molecules in the regime of moderate coupling strength to a non-Markovian harmonic oscillator environment on an IBMQ device. Applications of the Q-TEDOPA span problems which can not be solved by perturbation techniques belonging to different areas, such as the dynamics of quantum biological systems and strongly correlated condensed matter systems.
Towards Privacy-Preserving Speech Representation for Client-Side Data Sharing

Privacy and security are major concerns when sharing and collecting speech data for cloud services such as automatic speech recognition (ASR) and speech emotion recognition (SER). Existing solutions for client-side privacy mainly focus on voice conversion or voice modiﬁcation to convert a raw utterance into another one with similar content but different, or no, identity-related information. However, an alternative approach to share speech data under the form of privacy-preserving representations has been largely under-explored. To ﬁll this gap, we propose a speech anonymization framework that provides formal privacy guarantees via noise perturbation to a selected subset of the high-utility representations extracted us-ing a pre-trained speech encoder. The subset is chosen with a Transformer-based privacy-risk saliency estimator. We validate our framework on four tasks, namely, Automatic Speaker Ver-iﬁcation (ASV), ASR, SER and Intent Classiﬁcation (IC) for privacy and utility assessment. Experimental results show that our approach is able to achieve a competitive, or even better, utility compared to the baselines that use voice conversion and voice modiﬁcation, providing the same level of privacy. More-over, the easily-controlled amount of perturbation allows our framework to have a ﬂexible range of privacy-utility trade-offs without re-training any components.
UTIL: An Ultra-wideband Time-difference-of-arrival Indoor Localization Dataset

Ultra-wideband (UWB) time-difference-of-arrival (TDOA)-based localization has emerged as a promising, low-cost, and scalable indoor localization solution, which is especially suited for multi-robot applications. However, there is a lack of public datasets to study and benchmark UWB TDOA positioning technology in cluttered indoor environments. We fill in this gap by presenting a comprehensive dataset using Decawave’s DWM1000 UWB modules. To characterize the UWB TDOA measurement performance under various line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, we collected signal-to-noise ratio (SNR), power difference values, and raw UWB TDOA measurements during the identification experiments. We also conducted a cumulative total of around 150 min of real-world flight experiments on a customized quadrotor platform to benchmark the UWB TDOA localization performance for mobile robots. The quadrotor was commanded to fly with an average speed of 0.45 m/s in both obstacle-free and cluttered environments using four different UWB anchor constellations. Raw sensor data including UWB TDOA, inertial measurement unit (IMU), optical flow, time-of-flight (ToF) laser altitude, and millimeter-accurate ground truth robot poses were collected during the flights. The dataset and development kit are available at https://utiasdsl.github. io/util-uwb-dataset/ .
VLSI Architecture of FM0/Manchester Encoding Technique for DSRC

The dedicated short-range communication (DSRC) is an evolving technology to push the smart transport system into our daily life. The DSRC technique usually adopt FM0 and Manchester encodings to reach dc-balance, boosting the signal quality. Nevertheless, the coding coordination between the FM0 and Manchester codes limits the potential to design a fully used VLSI architecture for Fm0 and Manchester. In this project, the (SOLS) technique is presented to overcome this drawback. The SOLS technique improves the hardware consumption rated from 57.14% to 100% for FM0 and Manchester codes. The projected method is capable of generating Manchester and Fm0 coding for DSRC applications .
Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds

Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape representation. To capture geometry details, current methods usually learn DIF using local latent codes, which discretize the space into a regular 3D grid (or octree) and store local codes in grid points (or octree nodes). Given a query point, the local feature is computed by interpolating its neighboring local codes with their positions. However, the local codes are constrained at discrete and regular positions like grid points, which makes the code positions difficult to be optimized and limits their representation ability. To solve this problem, we propose to learn DIF with Dynamic Code Cloud, named DCC-DIF. Our method explicitly associates local codes with learnable position vectors, and the position vectors are continuous and can be dynamically optimized, which improves the representation ability. In addition, we propose a novel code position loss to optimize the code positions, which heuristically guides more local codes to be distributed around complex geometric details. In contrast to previous methods, our DCC-DIF represents 3D shapes more efficiently with a small amount of local codes, and improves the reconstruction quality. Experi-ments demonstrate that DCC-DIF achieves better performance over previous methods. Code and data are available at https://github.com/lity20/DCCDIF.
Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application

Taking medicines is a fundamental aspect to cure illnesses. However, studies have shown that it can be hard for patients to remember the correct posology. More aggravating, a wrong dosage generally causes the disease to worsen. Although, all relevant instructions for a medicine are summarized in the corresponding patient information leaflet, the latter is generally difficult to navigate and understand. To address this problem and help patients with their medication, in this paper we introduce an augmented reality mobile application that can present to the user important details on the framed medicine. In particular, the app implements an inference engine based on a deep neural network, i.e., a densenet, fine-tuned to recognize a medicinal from its package. Subsequently, relevant information, such as posology or a simplified leaflet, is overlaid on the camera feed to help a patient when taking a medicine. Extensive experiments to select the best hyperparameters were performed on a dataset specifically collected to address this task; ultimately obtaining up to 91.30\% accuracy as well as real-time capabilities.
MiSeRTrace: Kernel-level Request Tracing for Microservice Visibility

With the evolution of microservice applications, the underlying architectures have become increasingly complex compared to their monolith counterparts. This mainly brings in the challenge of observability. By providing a deeper understanding into the functioning of distributed applications, observability enables improving the performance of the system by obtaining a view of the bottlenecks in the implementation. The observability provided by currently existing tools that perform dynamic tracing on distributed applications is limited to the user-space and requires the application to be instrumented to track request flows. In this paper, we present a new open-source framework MiSeRTrace that can trace the end-to-end path of requests entering a microservice application at the kernel space without requiring instrumentation or modification of the application. Observability at the comprehensiveness of the kernel space allows breaking down of various steps in activities such as network transfers and IO tasks, thus enabling root cause based performance analysis and accurate identification of hotspots. MiSeRTrace supports tracing user-enabled kernel events provided by frameworks such as bpftrace or ftrace and isolates kernel activity associated with each application request with minimal overheads. We then demonstrate the working of the solution with results on a benchmark microservice application.
Multi-phase image segmentation by the Allen-Cahn Chan-Vese model

This paper proposes an Allen-Cahn Chan-Vese model to settle the multi-phase image segmentation. We first integrate the Allen--Cahn term and the Chan--Vese fitting energy term to establish an energy functional, whose minimum locates the segmentation contour. The subsequent minimization process can be attributed to variational calculation on fitting intensities and the solution approximation of several Allen-Cahn equations, wherein $n$ Allen-Cahn equations are enough to partition $m = 2^n$ segments. The derived Allen-Cahn equations are solved by efficient numerical solvers with exponential time integrations and finite difference space discretization. The discrete maximum bound principle and energy stability of the proposed numerical schemes are proved. Finally, the capability of our segmentation method is verified in various experiments for different types of images.
Is the mm/submm dust polarization a robust tracer of the magnetic field topology in protostellar envelopes? A model exploration

Context. High-resolution millimeter and submillimeter (mm and submm) polarization observations have opened a new era in the understanding of how magnetic ﬁelds are organized in star forming regions, unveiling an intricate interplay between the magnetic ﬁelds and the gas in protostellar cores. However, to assess the role of the magnetic ﬁeld in the process of solar-type star formation, it is important to understand to what extent these polarized dust emission are good tracers of the magnetic ﬁeld in the youngest protostellar objects. Aims. In this paper, we present a thorough investigation of the ﬁdelity and limitations of using dust polarized emission to map the magnetic ﬁeld topologies in low-mass protostars. Methods. To assess the importance of these e ﬀ ects, we performed an analysis of magnetic ﬁeld properties in 27 realizations of magnetohydrodynamics (MHD) models following the evolution of physical properties in star-forming cores. Assuming a uniform population of dust grains the sizes of which follow the standard MRN size distribution, we analyzed the synthetic polarized dust emission maps produced when these grains align with the local B -ﬁeld because of radiative torques (B-RATs). Results. We ﬁnd that mm and submm polarized dust emission is a robust tracer of the magnetic ﬁeld topologies in inner protostellar envelopes and is successful at capturing the details of the magnetic ﬁeld spatial distribution down to radii ∼ 100 au. Measurements of the line-of-sight-averaged magnetic ﬁeld line orientation using the polarized dust emission are precise to < 15 ◦ (typical of the error on polarization angles obtained with observations from large mm polarimetric facilities such as ALMA) in about 75% − 95% of the independent lines of sight that pass through protostellar envelopes. Large discrepancies between the integrated B -ﬁeld mean orientation and the orientation reconstructed from the polarized dust emission are mostly observed in (i) lines of sight where the magnetic ﬁeld is highly disorganized and (ii) those that probe large column densities. Our analysis shows that the high opacity of the thermal dust emission and low polarization fractions could be used to avoid using the small fraction of measurements a ﬀ ected by large errors.
What Does Nature Minimize In Every Incompressible Flow?

In this paper, we discover the fundamental quantity that Nature minimizes in almost all ﬂows encountered in everyday life: river, rain, ﬂow in a pipe, blood ﬂow, airﬂow over an airplane, etc. We show that the norm of the pressure gradient over the ﬁeld is minimum at every instant of time! We call it the principle of minimum pressure gradient (PMPG). The principle is deeply rooted in classical mechanics via Gauss’ principle of least constraint . Therefore, while we prove mathematically that Navier-Stokes’ equation represents the necessary condition for minimization of the pressure gradient, the PMPG stands on its own philosophy independent of Navier-Stokes’. It turns any ﬂuid mechanics problem into a minimization one. We demonstrate this intriguing property by solving three of the classical problems in ﬂuid mechanics using the PMPG without resorting to Navier-Stokes’ equation. In fact, the inviscid version of the PMPG allowed solving the long-standing problem of the aerohydrodynamic lift over smooth cylindrical shapes where Euler’s equation fails to provide a unique answer. Moreover, the result challenges the accepted wisdom about lift generation on an airfoil, which has prevailed over a century. The PMPG is expected to be transformative for theoretical modeling of ﬂuid mechanics as it encodes a complicated nonlinear partial diﬀerential equation into a simple minimization problem. The principle even transcends Navier-Stokes’ equations in its applicability to non-Newtonian ﬂuids with arbitrary constitutive relations and ﬂuids subject to arbitrary forcing (e.g. electric or magnetic).
Health Promotion, Health Literacy and Vaccine Hesitancy: The Role of Humanoid Robots

The use of humanoid robot technologies within global healthcare settings is rapidly evolving; however, the potential of robots in health promotion and health education is not established. The aim of this study was to explore the impact of a social humanoid robot on individuals’ knowledge of influenza (flu) prevention and attitudes towards influenza vaccination. A multi-methods approach involving pre and post-test questions and interviews was used. The study was undertaken in a publicly funded tertiary level hospital in northern Queensland, Australia. Of the 995 participants, the majority were visitors (53.07%). The mean age of the participants was 42.25 (SD=19.54) years. Based on the three knowledge questions that were posed at the two-point interactions of participants with the humanoid robot ‘Pepper’, the results showed that there was a significant difference in the correct responses pre- and post-test regarding the best way to avoid getting the flu (Exact McNemar significance probability <.0001), how long the flu virus can live outside the human body (p <.0001) and the length of time for handwashing to be effective against spreading germs (p <.0001). The results also showed that there was a significant difference in attitudes associated with influenza vaccination when pre-test was compared to post-test (p=.0019). Interaction of the participants with the humanoid robot demonstrated immediate knowledge gains and attitudinal change that suggests that humanoid robots may be an important intervention for health promotion in prevention of influenza and other respiratory viruses.
The value perspective in the analysis and design of service systems

The thesis proposes an interdisciplinary framework for service research in information systems that goes beyond the technological concerns and integrates the business concerns about service processes and models and the user’s concerns about service usage and value. Based on this framework, the thesis analyzes further the concept of service value and suggests the value perspective in the analysis and design of service systems. In addition, the thesis analyses the role of the user in service systems and suggests a method for service analysis and design that emphasises on the use of service by the user. The research outcomes can provide several research implications and insights and can inspire various potential uses, especially in research projects that seek to integrate a variety of technologies and services with the purpose to facilitate and enable people in their daily life practices.