Miracle-dz/UniFAR-FTUs huggingface dataset:
Lane-Level Street Map Extraction from Aerial Imagery

Digital maps with lane-level details are the foundation of many applications. However, creating and maintaining digital maps especially maps with lane-level details, are labor-intensive and expensive. In this work, we propose a mapping pipeline to extract lane-level street maps from aerial imagery automatically. Our mapping pipeline first extracts lanes at non-intersection areas, then it enumerates all the possible turning lanes at intersections, validates the connectivity of them, and extracts the valid turning lanes to complete the map. We evaluate the accuracy of our mapping pipeline on a dataset consisting of four U.S. cities, demonstrating the effectiveness of our proposed mapping pipeline and the potential of scalable mapping solutions based on aerial imagery.
Hard X-Ray Flares and Spectral Variability in NGC 4395 ULX1

We report the detection of flaring events in NGC 4395 ULX1, a nearby ultraluminous X-ray source (ULX), for the first time, using recent XMM-Newton observations. The flaring episodes are spectrally harder than the steady-emission intervals, resulting in higher fractional variability in the high-energy regime. A thin Keplerian and a slim accretion disk provide the best-fit continuum for XMM-Newton spectra. All observations show a broad hump-like feature around ∼0.9 keV, which can be associated with a collection of blended emission lines, and suggests the presence of a wind or outflow in this ULX through comparison with other ULXs that show a similar feature. The flaring spectra correspond to higher slim-disk temperatures due to a higher mass accretion rate under an advection-dominated accretion scenario. The luminosity–temperature (L-T) values in different flux states show a positive trend. When characterized with a power-law relation, the L-T profile is broadly consistent with both L ∝ T 2 and L ∝ T 4 relations for the analyzed data. The empirical predictions for a slim accretion disk in the case of super-Eddington accretion onto a stellar-mass compact object is L ∝ T 2, which is a possible scenario in ULX1. The origin of the flaring events is understood as an intrinsic change in accretion rate or presence of variable clumpy wind in the inner region of the accretion disk.
Dissecting BFT Consensus: In Trusted Components we Trust!

The growing interest in reliable multi-party applications has fostered widespread adoption of Byzantine Fault-Tolerant (bft) consensus protocols. Existing bft protocols need f more replicas than Paxos-style protocols to prevent equivocation attacks. trust-bft protocols seek to minimize this cost by making use of trusted components at replicas. This paper makes two contributions. First, we analyze the design of existing trust-bft protocols and uncover three fundamental limitations that preclude most practical deployments. Some of these limitations are fundamental, while others are linked to the state of trusted components today. Second, we introduce a novel suite of consensus protocols, FlexiTrust, that attempts to sidestep these issues. We show that our FlexiTrust protocols achieve up to 185% more throughput than their trust-bft counterparts.
Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones

The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of detecting irregularly shaped obstacles such as road cracks, debris on the streets, and water puddles. In this paper, we characterize a number of state-of-the-art Fully Convolutional Network (FCN) models on mobile embedded platforms for self-navigation at these sites containing extremely irregular obstacles. We evaluate the models in terms of accuracy, performance, and energy efficiency. We present a few optimizations for our designed vision system. Lastly, we discuss the trade-offs of these models for a couple of mobile platforms that can each perform self-navigation. To enable vehicles to safely navigate earthquake-struck zones, we compile a new annotated image database of various earthquake impacted regions that is different than traditional road damage databases. We train our database with a number of state-of-the-art semantic segmentation models in order to identify obstacles unique to earthquake-struck zones. Based on the statistics and tradeoffs, an optimal FCN model is selected and applied to the mobile vehicular platforms. To our best knowledge, this is the first study that identifies unique challenges and discusses the accuracy, performance, and energy impact of edge-based self-navigation mobile vehicles for earthquake-struck zones. Our proposed database and trained models are publicly available.
Efficient Memory Partitioning in Software Defined Hardware

As programmers turn to software-defined hardware (SDH) to maintain a high level of productivity while programming hardware to run complex algorithms, heavy-lifting must be done by the compiler to automatically partition on-chip arrays. In this paper, we introduce an automatic memory partitioning system that can quickly compute more efficient partitioning schemes than prior systems. Our system employs a variety of resource-saving optimizations and an ML cost model to select the best partitioning scheme from an array of candidates. We compared our system against various state-of-the-art SDH compilers and FPGAs on a variety of benchmarks and found that our system generates solutions that, on average, consume 40.3% fewer logic resources, 78.3% fewer FFs, 54.9% fewer Block RAMs (BRAMs), and 100% fewer DSPs.
A Family of Current References Based on 2T Voltage References: Demonstration in 0.18-μm With 0.1-nA PTAT and 1.1-μA CWT 38-ppm/°C Designs

The robustness of current and voltage references to process, voltage and temperature (PVT) variations is paramount to the operation of integrated circuits in real-world conditions. However, while recent voltage references can meet most of these requirements with a handful of transistors, current references remain rather complex, requiring significant design time and silicon area. In this paper, we present a family of simple current references consisting of a two-transistor (2T) ultra-low-power voltage reference, buffered onto a voltage-to-current converter by a single transistor. Two topologies are fabricated in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> partially-depleted silicon-on-insulator (SOI) technology and measured over 10 dies. First, a 7T nA-range proportional-to-absolute-temperature (PTAT) reference intended for constant-<inline-formula> <tex-math notation="LaTeX">$g_{m}$ </tex-math></inline-formula> biasing of subthreshold operational amplifiers demonstrates a 0.096-nA current with a line sensitivity (LS) of 1.48 %/V, a temperature coefficient (TC) of 0.75 %/°C, and a variability <inline-formula> <tex-math notation="LaTeX">$(\sigma /\mu)$ </tex-math></inline-formula> of 1.66 %. Then, two 4T+1R <inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula>-range constant-with-temperature (CWT) references with (resp. without) TC calibration exhibit a 1.09-<inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> (resp. 0.99-<inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula>) current with a 0.21-%/V (resp. 0.20-%/V) LS, a 38-ppm/°C (resp. 290-ppm/°C) TC, and a 0.87-% (resp. 0.65-%) <inline-formula> <tex-math notation="LaTeX">$(\sigma /\mu)$ </tex-math></inline-formula>. In addition, portability to common scaled CMOS technologies, such as 65-nm bulk and 28-nm fully-depleted SOI, is discussed and validated through post-layout simulations.
Non-spectroscopic sensing enabled byelectro-optical reservoir computer

A fully bio-inspired apparatus to perform non-spectroscopic sensing to discriminate, and determine the constituent concentrations of a chemical mixture is proposed. Here, fully bio-inspired means that it is comprised of a biomimetic sensor and a neuromorphic signal processor. The sensor is similar to the human eye in terms of its trichromat architecture and overlapping spectral response. The information processor is a neuromorphic system based on an electro-optical implementation of a reservoir computer. A time-stepping signal algorithm based on theZ-bilinear transformation has been developed to realistically simulate the electrooptical reservoir computer; this demonstrates the discrimination and chemical concentration determination tasks. We believe such an apparatus offers potential benefits in areas in which chemical composition needs to be monitored in real time, for example in chemical processing, and food-beverage (fermentation/brewing) and environmental monitoring. © 2022 Optica Publishing Group under the terms of the Optica Publishing Group Publishing Agreement
ExPoSe: Combining State-Based Exploration with Gradient-Based Online Search

Online tree-based search algorithms iteratively simulate trajectories and update action-values for a set of states stored in a tree structure. It works reasonably well in practice but fails to effectively utilise the information gathered from similar states. Depending upon the smoothness of the action-value function, one approach to overcoming this issue is through online learning, where information is interpolated among similar states; Policy Gradient Search provides a practical algorithm to achieve this. However, Policy Gradient Search lacks an explicit exploration mechanism, which is a key feature of tree-based online search algorithms. In this paper, we propose an efficient and effective online search algorithm called Exploratory Policy Gradient Search (ExPoSe), which leverages information sharing among states by updating the search policy parameters directly, while incorporating a well-defined exploration mechanism during the online search process. We evaluate ExPoSe on a range of decision-making problems, including Atari games, Sokoban, and Hamiltonian cycle search in sparse graphs. The results demonstrate that ExPoSe consistently outperforms other popular online search algorithms across all domains. The ExPoSe source code is available at \textit{\url{https://github.com/dixantmittal/ExPoSe}}.
How to build a cognitive map: insights from models of the hippocampal formation

Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviours for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unravelling the learning and neural representation of such a map has become a central focus of neuroscience. While experimentalists are providing a detailed picture of the neural substrate of cognitive maps in hippocampus and beyond, theorists have been busy building models to bridge the divide between neurons, computation, and behaviour. These models can account for a variety of known representations and neural phenomena, but often provide a differing understanding of not only the underlying principles of cognitive maps, but also the respective roles of hippocampus and cortex. In this Perspective, we bring many of these models into a common language, distil their underlying principles of constructing cognitive maps, provide novel (re)interpretations for neural phenomena, suggest how the principles can be extended to account for prefrontal cortex representations and, finally, speculate on the role of cognitive maps in higher cognitive capacities.
Security of Microservice Applications: A Practitioners' Perspective on Challenges and Best Practices

Cloud-based application deployment is becoming increasingly popular among businesses, thanks to the emergence of microser-vices. However, securing such architectures is a challenging task since traditional security concepts cannot be directly applied to microservice architectures due to their distributed nature. The situation is exacerbated by the scattered nature of guidelines and best practices advocated by practitioners and organizations in this ﬁeld. This research paper we aim to shay light over the current microservice security discussions hidden within Grey Literature (GL) sources. Particularly, we identify the challenges that arise when securing microservice architectures, as well as solutions recommended by practitioners to address these issues. For this, we conducted a systematic GL study on the challenges and best practices of microservice security present in the Internet with the goal of capturing relevant discussions in blogs, white papers, and standards. We collected 312 GL sources from which 57 were rigorously classiﬁed and analyzed. This analysis on the one hand validated past academic literature studies in the area of microservice security, but it also identiﬁed improvements to existing methodologies pointing towards future research directions.
Mathematical Content Browsing for Print-disabled Readers Based on Virtual-world Exploration and Audio-visual Sensory Substitution

Documents containing mathematical content remain largely inaccessible to blind and visually impaired readers because they are predominantly published as untagged PDFs, which do not include the semantic data necessary for effective accessibility. Equations in such documents consist of text interlaced with lines and other graphical elements and cannot be interpreted using a screen reader. We present a browsing approach for print-disabled readers specifically aimed at such mathematical content. This approach draws on the navigational mechanisms often used to explore the virtual worlds of text adventure games with audio-visual sensory substitution for graphical content. The relative spatial placement of the elements of an equation are represented as a virtual world so the reader can navigate between elements. Text elements are announced conventionally using synthesised speech, while graphical elements, such as roots and fraction lines, are rendered using a modification of the vOICe algorithm. The virtual world allows the reader to interactively discover the spatial structure of the equation, while the rendition of graphical elements as sound allows the shape and identity of elements that cannot be synthesised as speech to be discovered and recognised. The browsing approach was evaluated by 11 blind and 14 sighted participants in a user trial that included identifying twelve equations extracted from PDF documents. Overall, equations were identified completely correctly in 78% of cases (74% and 83%, respectively, for blind and sighted subjects). If partial correctness is considered, then the performance is substantially higher. Feedback from the blind subjects indicated that the technique allows spatial information and graphical detail to be discovered. We conclude that the integration of a spatial model represented as a virtual world in conjunction with audio-visual sensory substitution for non-textual elements can be an effective way for blind and visually impaired readers to read currently inaccessible mathematical content in PDF documents.
WatchAuth: User Authentication and Intent Recognition in Mobile Payments using a Smartwatch

In this paper, we show that the tap gesture, performed when a user ‘taps’ a smartwatch onto an NFC-enabled terminal to make a payment, is a biometric capable of implicitly authenticating the user and simultaneously recognising intent-to-pay. The proposed system can be deployed purely in software on the watch without requiring updates to payment terminals. It is agnostic to terminal type and position and the intent recognition portion does not require any training data from the user. To validate the system, we conduct a user study (n=16) to collect wrist motion data from users as they interact with payment terminals and to collect long-term data from a subset of them ($\mathrm{n}=9$) as they perform daily activities. Based on this data, we identify optimum gesture parameters and develop authentication and intent recognition models, for which we achieve EERs of 0.08 and 0.04, respectively.
Comparative assessment of federated and centralized machine learning

Federated Learning (FL) is a privacy preserving machine learning scheme, where training happens with data federated across devices and not leaving them to sustain user privacy. This is ensured by making the untrained or partially trained models to reach directly the individual devices and getting locally trained"on-device"using the device owned data, and the server aggregating all the partially trained model learnings to update a global model. Although almost all the model learning schemes in the federated learning setup use gradient descent, there are certain characteristic differences brought about by the non-IID nature of the data availability, that affects the training in comparison to the centralized schemes. In this paper, we discuss the various factors that affect the federated learning training, because of the non-IID distributed nature of the data, as well as the inherent differences in the federating learning approach as against the typical centralized gradient descent techniques. We empirically demonstrate the effect of number of samples per device and the distribution of output labels on federated learning. In addition to the privacy advantage we seek through federated learning, we also study if there is a cost advantage while using federated learning frameworks. We show that federated learning does have an advantage in cost when the model sizes to be trained are not reasonably large. All in all, we present the need for careful design of model for both performance and cost.
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming

Interior point methods (IPMs) are a common approach for solving linear programs (LPs) with strong theoretical guarantees and solid empirical performance. The time complexity of these methods is dominated by the cost of solving a linear system of equations at each iteration. In common applications of linear programming, particularly in machine learning and scientific computing, the size of this linear system can become prohibitively large, requiring the use of iterative solvers, which provide an approximate solution to the linear system. However, approximately solving the linear system at each iteration of an IPM invalidates the theoretical guarantees of common IPM analyses. To remedy this, we theoretically and empirically analyze (slightly modified) predictor-corrector IPMs when using approximate linear solvers: our approach guarantees that, when certain conditions are satisfied, the number of IPM iterations does not increase and that the final solution remains feasible. We also provide practical instantiations of approximate linear solvers that satisfy these conditions for special classes of constraint matrices using randomized linear algebra.
Deep Hierarchy in Bandits

Mean rewards of actions are often correlated. The form of these correlations may be complex and unknown a priori, such as the preferences of a user for recommended products and their categories. To maximize statistical efficiency, it is important to leverage these correlations when learning. We formulate a bandit variant of this problem where the correlations of mean action rewards are represented by a hierarchical Bayesian model with latent variables. Since the hierarchy can have multiple layers, we call it deep. We propose a hierarchical Thompson sampling algorithm (HierTS) for this problem, and show how to implement it efficiently for Gaussian hierarchies. The efficient implementation is possible due to a novel exact hierarchical representation of the posterior, which itself is of independent interest. We use this exact posterior to analyze the Bayes regret of HierTS in Gaussian bandits. Our analysis reflects the structure of the problem, that the regret decreases with the prior width, and also shows that hierarchies reduce the regret by non-constant factors in the number of actions. We confirm these theoretical findings empirically, in both synthetic and real-world experiments.
An all Mach number finite volume method for isentropic two-phase flow

Abstract We present an implicit–explicit finite volume scheme for isentropic two phase flow in all Mach number regimes. The underlying model belongs to the class of symmetric hyperbolic thermodynamically compatible models. The key element of the scheme consists of a linearisation of pressure and enthalpy terms at a reference state. The resulting stiff linear parts are integrated implicitly, whereas the non-linear higher order and transport terms are treated explicitly. Due to the flux splitting, the scheme is stable under a CFL condition which is determined by the resolution of the slow material waves and allows large time steps even in the presence of fast acoustic waves. Further the singular Mach number limits of the model are studied and the asymptotic preserving property of the scheme is proven. In numerical simulations the consistency with single phase flow, accuracy and the approximation of material waves in different Mach number regimes are assessed.
Quantifying knowledge synchronisation in the 21st century

Humans acquire and accumulate knowledge through language usage and eagerly exchange their knowledge for advancement. Although geographical barriers had previously limited communication, the emergence of information technology has opened new avenues for knowledge exchange. However, it is unclear which communication pathway is dominant in the 21st century. Here, we explore the dominant path of knowledge diffusion in the 21st century using Wikipedia, the largest communal dataset. We evaluate the similarity of shared knowledge between population groups, distinguished based on their language usage. When population groups are more engaged with each other, their knowledge structure is more similar, where engagement is indicated by socioeconomic connections, such as cultural, linguistic, and historical features. Moreover, geographical proximity is no longer a critical requirement for knowledge dissemination. Furthermore, we integrate our data into a mechanistic model to better understand the underlying mechanism and suggest that the knowledge"Silk Road"of the 21st century is based online.
Developer Load Normalization Using Iterative Kuhn-Munkres Algorithm: An Optimization Triaging Approach

Bug triage can be defined as the process of assigning a developer to a bug report. The duty of the bug triage team is to study the developers profiles well in order to make an appropriate match between the developers and the incoming bug reports. Thus, this process is a vital step in issue management system. In fact, the number of bug reports submitted every day is gradually increasing which affects the developer workload. Thus, the triage team should consider this factor in distributing the bugs and because of the manual approach, many developers are burden. In particular, triaging bug reports without considering the workload does not only affect the developers workload but also leads to an increase in the number of unaddressed bug reports. As a result, the fixing time of the reported bugs will relatively increase. Unlike other researchers who focus on automating the bug triage and ignoring the developer workload, in this work, we handle the triaging process from a different perspective. The proposed approach focuses on how to optimize the bug fixing time by normalizing the developer load in an automating system. To evaluate our work, we use 26,317 bug reports from different bug repositories. Results shows that our work outperforms other systems in terms of optimizing the bug total fixing time and normalizing developer load.