Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite-time thermodynamics of fluctuations in microscopic heat engines

Fluctuations of thermodynamic quantities become non-negligible and play an important role when the system size is small. We develop finite-time thermodynamics of fluctuations in microscopic heat engines whose environmental temperature and mechanical parameter are driven periodically in time. Within the slow-driving regime, this formalism universally holds in a coarse-grained time scale whose resolution is much longer than the correlation time of the fluctuations, and is shown to be consistent with the relation analogous to the fluctuationdissipation relation. Employing a geometric argument, a scenario to simultaneously minimize both the average and fluctuation of the dissipation in the Carnot cycle is identified. For this simultaneous optimization, the existence of a zero eigenvalue of the singular metric for the scale invariant equilibrium state is found to be essential. Furthermore, we demonstrate that our optimized protocol can improve the dissipation and its fluctuation over the current experiment.
Publisher References in Bibliographic Entity Descriptions

This paper describes a method for improved access to publisher references in linked data RDF editors using data mining techniques and a large corpus of library metadata encoded in the MARC21 standard. The corpus is comprised of clustered sets of publishers and publisher locations from the library MARC21 records found in the Platform for Open Data (POD). POD is a data aggregation project involving member institutions of the IvyPlus Library Confederation and contains seventy million MARC21 records, forty million of which are unique. The discovery of publisher entity sets described forms the basis for the streamlined description of BIBFRAME Instance entities. The result of this work includes a database of association rules and RDF editor improvements. The association rules are the basis of a prototype autosuggestion feature of BIBFRAME Instance entity description properties designed specifically to support the auto‐population of publisher entities in linked data RDF editors.
A comparison of sports-related head accelerations with and without direct head impacts

Concussion and repeated exposure to mild traumatic brain injury are risks for athletes in many sports. Typically, direct head impacts are catalogued and analyzed to improve the detection and awareness of head acceleration events so that an athlete's brain health can be appropriately monitored and treated. However, head accelerations can also be induced by impacts with little or no head involvement. In this work we evaluated if impacts that do not involve direct head contact, such as being pushed in the torso, can be sufficient in collegiate American football to induce head accelerations comparable to direct head impacts. Datasets of impacts with and without direct head contact were collected and compared. These datasets were gathered using a state-of-theart impact detection algorithm embedded in an instrumented mouthguard to record head kinematics. Video analysis was used to differentiate between impact types. In total, 15 impacts of each type were used in comparison, with clear video screenshots available to distinguish each impact type. Analysis of the kinematics showed that the impacts without direct head contact achieved similar levels of linear and angular accelerations during impact compared to those from direct head impacts. Finite element analyses using the median and peak kinematic signals were used to calculate maximum principal strain of the brain. Statistical analysis revealed that no significant difference was found between the two datasets based on a Bonferroni-adjusted pvalue threshold of 0.017, with the exception of peak linear acceleration. Impacts without direct head contact showed higher mean values of peak linear acceleration values of 17.6 g compared to the direct-head impact mean value of 6.1g. These results indicated that impacts other than direct head impacts could still produce meaningful kinematic loads in the head and as such should be included in athlete health monitoring.
Multiple Stellar Populations at Less-evolved Stages-II: No Evidence of Significant Helium Spread among NGC 1846 Dwarfs

The detection of star-to-star chemical variations in star clusters older than 2 Gyr has changed the traditional view of star clusters as canonical examples of “simple stellar populations” into the so-called “multiple stellar populations” (MPs). Although the significance of MPs seems to correlate with cluster total mass, it seems that the presence of MPs is determined by cluster age. In this article, we use deep photometry from the Hubble Space Telescope to investigate whether the FG-type dwarfs in the ∼1.7 Gyr old cluster NGC 1846, have helium spread. By comparing the observation with the synthetic stellar populations, we estimate a helium spread of ΔY ∼ 0.01 ± 0.01 among the main-sequence stars in NGC 1846. The maximum helium spread would not exceed ΔY ∼ 0.02, depending on the adopted fraction of helium-enriched stars. To mask the color variation caused by such a helium enrichment, a nitrogen enrichment of at least Δ[N/Fe] = 0.8 dex is required, which is excluded by previous analyses of the red-giant branch in this cluster. We find that our result is consistent with the ΔY–mass relationship for Galactic globular clusters. To examine whether or not NGC 1846 harbors MPs, higher photometric accuracy is required. We conclude that under the adopted photometric quality, there is no extreme helium variation among NGC 1846 dwarfs.
F-PKI: Enabling Innovation and Trust Flexibility in the HTTPS Public-Key Infrastructure

We present F-PKI, an enhancement to the HTTPS public-key infrastructure (or web PKI) that gives trust flexibility to both clients and domain owners, and enables certification authorities (CAs) to enforce stronger security measures. In today's web PKI, all CAs are equally trusted, and security is defined by the weakest link. We address this problem by introducing trust flexibility in two dimensions: with F-PKI, each domain owner can define a domain policy (specifying, for example, which CAs are authorized to issue certificates for their domain name) and each client can set or choose a validation policy based on trust levels. F-PKI thus supports a property that is sorely needed in today's Internet: trust heterogeneity. Different parties can express different trust preferences while still being able to verify all certificates. In contrast, today's web PKI only allows clients to fully distrust suspicious/misbehaving CAs, which is likely to cause collateral damage in the form of legitimate certificates being rejected. Our contribution is to present a system that is backward compatible, provides sensible security properties to both clients and domain owners, ensures the verifiability of all certificates, and prevents downgrade attacks. Furthermore, F-PKI provides a ground for innovation, as it gives CAs an incentive to deploy new security measures to attract more customers, without having these measures undercut by vulnerable CAs.
Can primordial black holes as all dark matter explain fast radio bursts?

Primordial black holes (PBHs) are one of the most interesting non-particle dark matter (DM) candidates. They may explain all the DM content in the Universe in the mass regime about 10−14M − 10−11M . We study PBHs as the source of Fast Radio Bursts via magnetic reconnection in the event of collisions between them and neutron stars (NSs) in galaxies. We investigate the energy-loss of PBHs during PBH-NS encounters to model their capture by NSs. To an order-ofmagnitude estimation, we conclude that the parameter space of PBHs being all DM is accidentally consistent with that to produce FRBs with a rate which is the order of the observed FRB rate.
Resonant Tidal Responses in Rotating Fluid Bodies: Global Modes Hidden beneath Localized Wave Beams

In rotating stars and planets, excitation of inertial waves in convective envelopes provides an important channel for tidal dissipation, but the dissipation rate due to inertial waves depends erratically on the tidal frequency. Tidal dissipation is significantly enhanced at some frequencies, suggesting possible resonances between the tidal forcing and some eigenmodes. However, the nature of these resonances remains enigmatic owing to the singularity of the eigenvalue problem of inertial waves, and the resonances are often mistakenly attributed to wave attractors in the literature. In this Letter, we reveal that resonant tidal responses correspond to inertial modes with large-scale flows hidden beneath localized wave beams. Strong couplings between the tidal forcing and the hidden large-scale flows intensify the localized wave beams emanating from the critical latitudes, leading to enhanced tidal dissipation. This study resolves a longstanding puzzle regarding the frequency dependence of tidal dissipation due to inertial waves in convective envelopes.
Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs

Controllable scene synthesis consists of generating 3D information that satisfy underlying specifications. Thereby, these specifications should be abstract, i.e. allowing easy user interaction, whilst providing enough interface for detailed control. Scene graphs are representations of a scene, composed of objects (nodes) and inter-object relationships (edges), proven to be particularly suited for this task, as they allow for semantic control on the generated content. Previous works tackling this task often rely on synthetic data, and retrieve object meshes, which naturally limits the generation capabilities. To circumvent this issue, we instead propose the first work that directly generates shapes from a scene graph in an end-to-end manner. In addition, we show that the same model supports scene modification, using the respective scene graph as interface. Leveraging Graph Convolutional Networks (GCN) we train a variational Auto-Encoder on top of the object and edge categories, as well as 3D shapes and scene layouts, allowing latter sampling of new scenes and shapes.
Lifelong Computing

Computing systems form the backbone of many aspects of our life, hence they are becoming as vital as water, electricity, and road infrastructures for our society. Yet, engineering long running computing systems that achieve their goals in ever-changing environments pose significant challenges. Currently, we can build computing systems that adjust or learn over time to match changes that were anticipated. However, dealing with unanticipated changes, such as anomalies, novelties, new goals or constraints, requires system evolution, which remains in essence a human-driven activity. Given the growing complexity of computing systems and the vast amount of highly complex data to process, this approach will eventually become unmanageable. To break through the status quo, we put forward a new paradigm for the design and operation of computing systems that we coin"lifelong computing. "The paradigm starts from computing-learning systems that integrate computing/service modules and learning modules. Computing warehouses offer such computing elements together with data sheets and usage guides. When detecting anomalies, novelties, new goals or constraints, a lifelong computing system activates an evolutionary self-learning engine that runs online experiments to determine how the computing-learning system needs to evolve to deal with the changes, thereby changing its architecture and integrating new computing elements from computing warehouses as needed. Depending on the domain at hand, some activities of lifelong computing systems can be supported by humans. We motivate the need for lifelong computing with a future fish farming scenario, outline a blueprint architecture for lifelong computing systems, and highlight key research challenges to realise the vision of lifelong computing.
Harnack inequality for fractional Laplacian-type operators on hyperbolic spaces

Abstract. The Krylov–Safonov theory for fully nonlinear nonlocal operators on hyperbolic spaces of dimension three is established. Since the operators on hyperbolic spaces exhibit qualitatively different behavior than those on manifolds with nonnegative curvature, new scale functions are introduced which take the effect of negative curvature into account. The regularity theory in this work provides unified regularity results for fractional-order and second-order operators in the sense that the regularity estimates stay uniform as the fractional-order approaches 2. In the unified regularity theory, the asymptotic behavior of the normalizing constant for the fractional Laplacian plays a fundamental role. The dimension restriction has been imposed to compute the explicit value of this constant by using the Fourier analysis on hyperbolic spaces.
Radiative Turbulent Mixing Layers and the Survival of Magellanic Debris

The Magellanic Stream is sculpted by its infall through the Milky Way’s circumgalactic medium, but the rates and directions of mass, momentum, and energy exchange through the stream-halo interface are relative unknowns critical for determining the origin and fate of the Stream. Complementary to large-scale simulations of LMC-SMC interactions, we apply new insights derived from idealized, high-resolution cloud-crushing and radiative turbulent mixing layer simulations to the Leading Arm and Trailing Stream. Contrary to classical expectations of fast cloud breakup, we predict that the Leading Arm and much of the Trailing Stream should be surviving infall and even gaining mass due to strong radiative cooling. Provided a sufficiently supersonic tidal swing-out from the Clouds, the present-day Leading Arm could be a series of high-density clumps in the cooling tail behind the progenitor cloud. We back up our analytic framework with a suite of converged wind-tunnel simulations, finding that previous results on cloud survival and mass growth can be extended to high Mach number (  ) flows with a modified drag time tdrag∝1+ and longer growth time. We also simulate the Trailing Stream; we find that the growth time is long (approximately gigayears) compared to the infall time, and approximate Hα emission is low on average (∼ a few milliRayleigh) but can be up to tens of milliRayleigh in bright spots. Our findings also have broader extragalactic implications, e.g., galactic winds, which we discuss.
An Innovative Attack Modelling and Attack Detection Approach for a Waiting Time-based Adaptive Traffic Signal Controller

An adaptive traffic signal controller (ATSC) combined with a connected vehicle (CV) concept uses real-time vehicle trajectory data to regulate green time and has the ability to reduce intersection waiting time significantly and thereby improve travel time in a signalized corridor. However, the CV-based ATSC increases the size of the surface vulnerable to potential cyber-attack, allowing an attacker to generate disastrous traffic congestion in a roadway network. An attacker can congest a route by generating fake vehicles by maintaining traffic and car-following rules at a slow rate so that the signal timing and phase change without having any abrupt changes in number of vehicles. Because of the adaptive nature of ATSC, it is a challenge to model this kind of attack and also to develop a strategy for detection. This paper introduces an innovative"slow poisoning"cyberattack for a waiting time based ATSC algorithm and a corresponding detection strategy. Thus, the objectives of this paper are to: (i) develop a"slow poisoning"attack generation strategy for an ATSC, and (ii) develop a prediction-based"slow poisoning"attack detection strategy using a recurrent neural network -- i.e., long short-term memory model. We have generated a"slow poisoning"attack modeling strategy using a microscopic traffic simulator -- Simulation of Urban Mobility (SUMO) -- and used generated data from the simulation to develop both the attack model and detection model. Our analyses revealed that the attack strategy is effective in creating a congestion in an approach and detection strategy is able to flag the attack.
Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility

We present a novel framework for mesh reconstruction from unstructured point clouds by taking advantage of the learned visibility of the 3D points in the virtual views and traditional graph-cut based mesh generation. Specifically, we first propose a three-step network that explicitly employs depth completion for visibility prediction. Then the visibility information of multiple views is aggregated to generate a 3D mesh model by solving an optimization problem considering visibility in which a novel adaptive visibility weighting in surface determination is also introduced to suppress line of sight with a large incident angle. Compared to other learning-based approaches, our pipeline only exercises the learning on a 2D binary classification task, i.e., points visible or not in a view, which is much more generalizable and practically more efficient and capable to deal with a large number of points. Experiments demonstrate that our method with favorable transferability and robustness, and achieve competing performances w.r. t. state-of-the-art learning-based approaches on small complex objects and outperforms on large indoor and outdoor scenes. Code is available at https://github.com/GDAOSU/vis2mesh.
Deep Contrastive Learning for Multi-View Network Embedding

Multi-view network embedding aims at projecting nodes in the network to low-dimensional vectors, while preserving their multiple relations and attribute information. Contrastive learning-based methods have preliminarily shown promising performance in this task. However, most contrastive learning-based methods mostly rely on high-quality graph embedding and explore less on the relationships between diﬀerent graph views. To deal with these deﬁciencies, we design a novel node-to-node Contrastive leaRning framEwork for Multi-view network Embedding (CREME), which mainly contains two contrastive objectives: Multi-view fusion InfoMax and Inter-view InfoMin . The former objective distills information from embeddings generated from diﬀerent graph views, while the latter distinguishes diﬀerent graph views better to capture the complementary information between them. Speciﬁcally, we ﬁrst apply a view encoder to generate each graph view representation and utilize a multi-view aggregator to fuse these representations. Then, we unify the two contrastive objectives into one learning objective for training. Extensive experiments on three real-world datasets show that CREME outperforms existing methods consistently.
Byzantine Cluster-Sending in Expected Constant Communication

Traditional resilient systems operate on fully-replicated fault-tolerant clusters, which limits their scalability and performance. One way to make the step towards resilient high-performance systems that can deal with huge workloads, is by enabling independent fault-tolerant clusters to efficiently communicate and cooperate with each other, as this also enables the usage of high-performance techniques such as sharding and parallel processing. Recently, such inter-cluster communication was formalized as the Byzantine cluster-sending problem, and worst-case optimal protocols have been proposed that solve this problem. Unfortunately, these protocols have an all-case linear complexity in the size of the clusters involved. In this paper, we propose probabilistic cluster-sending techniques that can reliably send messages from one Byzantine fault-tolerant cluster to another with only an expected constant message complexity, this independent of the size of the clusters involved. Depending on the robustness of the clusters involved, our techniques require only two-to-four message round-trips. Furthermore, our protocols can support worst-case linear communication between clusters, which is optimal, and deal with asynchronous and unreliable communication. As such, our work provides a strong foundation for the further development of resilient high-performance systems.
Medical Image Segmentation using 3D Convolutional Neural Networks: A Review

Computer-aided medical image analysis plays a signiﬁcant role in assisting medical practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present, convolutional neural networks (CNNs) are the preferred choice for medical image analysis. In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods are gaining popularity in medical image analysis. Here, we present an extensive review of the recently evolved 3D deep learning methods in medical image segmentation. Furthermore, the research gaps and future directions in 3D medical image segmentation are discussed.
End-to-End License Plate Recognition Pipeline for Real-time Low Resource Video Based Applications

Automatic License Plate Recognition systems aim to provide an end-to-end solution towards detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames. However, deploying such systems in the real world requires real-time performance in low-resource environments. In our paper, we propose a novel two-stage detection pipeline paired with Vision API that aims to provide real-time inference speed along with consistently accurate detection and recognition performance. We used a haar-cascade classifier as a filter on top of our backbone MobileNet SSDv2 detection model. This reduces inference time by only focusing on high confidence detections and using them for recognition. We also impose a temporal frame separation strategy to identify multiple vehicle license plates in the same clip. Furthermore, there are no publicly available Bangla license plate datasets, for which we created an image dataset and a video dataset containing license plates in the wild. We trained our models on the image dataset and achieved an AP(0.5) score of 86% and tested our pipeline on the video dataset and observed reasonable detection and recognition performance (82.7% detection rate, and 60.8% OCR F1 score) with real-time processing speed (27.2 frames per second).
Image Inpainting using Partial Convolution

Image Inpainting is one of the very popular tasks in the field of image processing with broad applications in computer vision. In various practical applications, images are often deteriorated by noise due to the presence of corrupted, lost, or undesirable information. There have been various restoration techniques used in the past with both classical and deep learning approaches for handling such issues. Some traditional methods include image restoration by filling gap pixels using the nearby known pixels or using the moving average over the same. The aim of this paper is to perform image inpainting using robust deep learning methods that use partial convolution layers.