Miracle-dz/UniFAR-FTUs huggingface dataset:
A Generalizable Artificial Intelligence Model for COVID-19 Classification Task Using Chest X-ray Radiographs: Evaluated Over Four Clinical Datasets with 15, 097 Patients

Purpose: To answer the long-standing question of whether a model trained from a single clinical site can be generalized to external sites. Materials and Methods: 17,537 chest x-ray radiographs (CXRs) from 3,264 COVID-19-positive patients and 4,802 COVID-19-negative patients were collected from a single site for AI model development. The generalizability of the trained model was retrospectively evaluated using four different real-world clinical datasets with a total of 26,633 CXRs from 15,097 patients (3,277 COVID-19-positive patients). The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance. Results: The AI model trained using a single-source clinical dataset achieved an AUC of 0.82 (95% CI: 0.80, 0.84) when applied to the internal temporal test set. When applied to datasets from two external clinical sites, an AUC of 0.81 (95% CI: 0.80, 0.82) and 0.82 (95% CI: 0.80, 0.84) were achieved. An AUC of 0.79 (95% CI: 0.77, 0.81) was achieved when applied to a multi-institutional COVID-19 dataset collected by the Medical Imaging and Data Resource Center (MIDRC). A power-law dependence, N^(k )(k is empirically found to be -0.21 to -0.25), indicates a relatively weak performance dependence on the training data sizes. Conclusion: COVID-19 classification AI model trained using well-curated data from a single clinical site is generalizable to external clinical sites without a significant drop in performance.
A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning

Proportional-integral-derivative (PID) controllers have been widely used in the process industry. However, the satisfactory control performance of a PID controller depends strongly on the tuning parameters. Conventional PID tuning methods require extensive knowledge of the system model, which is not always known especially in the case of complex dynamical systems. In contrast, reinforcement learning-based PID tuning has gained popularity since it can treat PID tuning as a black-box problem and deliver the optimal PID parameters without requiring explicit process models. In this paper, we present a novel entropy-maximizing twin-delayed deep deterministic policy gradient (EMTD3) method for automating the PID tuning. In the proposed method, an entropy-maximizing stochastic actor is employed at the beginning to encourage the exploration of the action space. Then a deterministic actor is deployed to focus on local exploitation and discover the optimal solution. The incorporation of the entropy-maximizing term can significantly improve the sample efficiency and assist in fast convergence to the global solution. Our proposed method is applied to the PID tuning of a second-order system to verify its effectiveness in improving the sample efficiency and discovering the optimal PID parameters compared to traditional TD3.
Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning

We study the problem of episodic reinforcement learning in continuous state-action spaces with unknown rewards and transitions. Specifically, we consider the setting where the rewards and transitions are modeled using parametric bilinear exponential families. We propose an algorithm, that a) uses penalized maximum likelihood estimators to learn the unknown parameters, b) injects a calibrated Gaussian noise in the parameter of rewards to ensure exploration, and c) leverages linearity of the bilinear exponential family transitions with respect to an underlying RKHS to perform tractable planning. We provide a frequentist regret upper-bound for our algorithm which, in the case of tabular MDPs, is order-optimal with respect to H and K, where H is the episode length and K is the number of episodes. Our analysis improves the existing bounds for the bilinear exponential family of MDPs by square root of H and removes the handcrafted clipping deployed in existing RLSVI-type algorithms.
Comparative Study of Blockchain Development Platforms: Features and Applications

Many developers have ideas to create blockchain applications but do not know where to begin. Often these developers default to using the first blockchain development platform they discover, which may not be the best platform for their project. Over 8000 new blockchain-related projects are added to GitHub a year. The near-constant influx of new projects can make it difficult for developers to search through existing projects and platforms to find the best platform for their projects. We considered 65 blockchain development platforms for this work and provided a brief yet comprehensive summary of the 23 most popular platforms. Our aim for this work is to assist developers in selecting the most appropriate platform for their blockchain projects.
Impact of substrates and quantum effects on exciton line shapes of 2D semiconductors at room temperature

Abstract Exciton resonances in monolayer transition-metal dichalcogenides (TMDs) provide exceptionally strong light–matter interaction at room temperature. Their spectral line shape is critical in the design of a myriad of optoelectronic devices, ranging from solar cells to quantum information processing. However, disorder resulting from static inhomogeneities and dynamical fluctuations can significantly impact the line shape. Many recent works experimentally evaluate the optical properties of TMD monolayers placed on a substrate and the line shape is typically linked directly to the material’s quality. Here, we highlight that the interference of the substrate and TMD reflections can strongly influence the line shape. We further show how basic, room-temperature reflection measurements allow investigation of the quantum mechanical exciton dynamics by systematically controlling the substrate reflection with index-matching oils. By removing the substrate contribution with properly chosen oil, we can extract the excitonic decay rates including the quantum mechanical dephasing rate. The results provide valuable guidance for the engineering of exciton line shapes in layered nanophotonic systems.
TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagation

Temporal graph is an abstraction for modeling dynamic systems that consist of evolving interaction elements. In this paper, we aim to solve an important yet neglected problem -- how to learn information from high-order neighbors in temporal graphs? -- to enhance the informativeness and discriminativeness for the learned node representations. We argue that when learning high-order information from temporal graphs, we encounter two challenges, i.e., computational inefficiency and over-smoothing, that cannot be solved by conventional techniques applied on static graphs. To remedy these deficiencies, we propose a temporal propagation-based graph neural network, namely TPGNN. To be specific, the model consists of two distinct components, i.e., propagator and node-wise encoder. The propagator is leveraged to propagate messages from the anchor node to its temporal neighbors within $k$-hop, and then simultaneously update the state of neighborhoods, which enables efficient computation, especially for a deep model. In addition, to prevent over-smoothing, the model compels the messages from $n$-hop neighbors to update the $n$-hop memory vector preserved on the anchor. The node-wise encoder adopts transformer architecture to learn node representations by explicitly learning the importance of memory vectors preserved on the node itself, that is, implicitly modeling the importance of messages from neighbors at different layers, thus mitigating the over-smoothing. Since the encoding process will not query temporal neighbors, we can dramatically save time consumption in inference. Extensive experiments on temporal link prediction and node classification demonstrate the superiority of TPGNN over state-of-the-art baselines in efficiency and robustness.
Three-Torsion Subgroups and Conductors of Genus 3 Hyperelliptic Curves

We give a practical method for computing the 3-torsion subgroup of the Jacobian of a genus 3 hyperelliptic curve. We define a scheme for the 3-torsion points of the Jacobian and use complex approximations, homotopy continuation and lattice reduction to find precise expression for the 3-torsion. In the latter stages of the paper, we explain how the 3-torsion subgroup can be used to compute the wild part of the local exponent of the conductor at 2.
Reactive fungal insoles

Mycelium bound composites are promising materials for a diverse range of applications including wearables and building elements. Their functionality surpasses some of the capabilities of traditionally passive materials, such as synthetic fibres, reconstituted cellulose fibres and natural fibres. Thereby, creating novel propositions including augmented functionality (sensory) and aesthetic (personal fashion). Biomaterials can offer multiple modal sensing capability such as mechanical loading (compressive and tensile) and moisture content. To assess the sensing potential of fungal insoles we undertook laboratory experiments on electrical response of bespoke insoles made from capillary matting colonised with oyster fungi Pleurotus ostreatus to compressive stress which mimics human loading when standing and walking. We have shown changes in electrical activity with compressive loading. The results advance the development of intelligent sensing insoles which are a building block towards more generic reactive fungal wearables. Using FitzhHugh-Nagumo model we numerically illustrated how excitation wave-fronts behave in a mycelium network colonising an insole and shown that it may be possible to discern pressure points from the mycelium electrical activity.
Quantum fluctuation effects on the quench dynamics of thermal quasicondensates

We study the influence of quantum fluctuations on the phase, density, and pair correlations in a trapped quasicondensate after a quench of the interaction strength. To do so, we derive a description similar to the stochastic Gross–Pitaevskii equation (SGPE) but keeping a fully quantum description of the low-energy fields using the positive-P representation. This allows us to treat both the quantum and thermal fluctuations together in an integrated way. A plain SGPE only allows for thermal fluctuations. The approach is applicable to such situations as finite temperature quantum quenches, but not equilibrium calculations due to the time limitations inherent in positive-P descriptions of interacting gases. One sees the appearance of antibunching, the generation of counter-propagating atom pairs, and increased phase fluctuations. We show that the behavior can be estimated by adding the T = 0 quantum fluctuation contribution to the thermal fluctuations described by the plain SGPE.
Tabletop potentials for inflation from f(R) gravity

We show that a large class of modified gravity theories (MOG) with the Jordan-frame Lagrangian f(R) translate into scalar-field (scalaron) models with hilltop potentials in the Einstein frame. (A rare exception to this rule is provided by the Starobinsky model for which the corresponding scalaron potential is plateau-like for ϕ > 0.) We find that MOG models featuring two distinct mass scales lead to scalaron potentials that have a flattened hilltop, or tabletop. Inflationary evolution in tabletop models agrees very well with CMB observations. Tabletop potentials therefore provide a new and compelling class of MOG-based inflationary models. By contrast, MOG models with a single mass scale generally correspond to steep hilltop potentials and fail to reproduce the CMB power spectrum. Inflationary evolution in hilltop/tabletop models can proceed in two alternative directions: towards the stable point at small R describing the observable universe, or towards the asymptotic region at large R. The MOG models which we examine have several new properties including the fact that gravity can become asymptotically vanishing, with G eff → 0, at infinite or large finite values of the scalar curvature R. A universe evolving towards the asymptotically vanishing gravity region at large R will either run into a 'Big-Rip' singularity, or inflate eternally.
Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics

Measuring Sentence Textual Similarity (STS) is a classic task that can be applied to many downstream NLP applications such as text generation and retrieval. In this paper, we focus on unsupervised STS that works on various domains but only requires minimal data and computational resources. Theoretically, we propose a light-weighted Expectation-Correction (EC) formulation for STS computation. EC formulation unifies unsupervised STS approaches including the cosine similarity of Additively Composed (AC) sentence embeddings, Optimal Transport (OT), and Tree Kernels (TK). Moreover, we propose the Recursive Optimal Transport Similarity (ROTS) algorithm to capture the compositional phrase semantics by composing multiple recursive EC formulations. ROTS finishes in linear time and is faster than its predecessors. ROTS is empirically more effective and scalable than previous approaches. Extensive experiments on 29 STS tasks under various settings show the clear advantage of ROTS over existing approaches. Detailed ablation studies prove the effectiveness of our approaches.
Upper bounds for the secure key rate of the decoy-state quantum key distribution

The use of decoy states in quantum key distribution (QKD) has provided a method for substantially increasing the secret key rate and distance that can be covered by QKD protocols with practical signals. The security analysis of these schemes, however, leaves open the possibility that the development of better proof techniques, or better classical post-processing methods, might further improve their performance in realistic scenarios. In this paper, we derive upper bounds on the secure key rate for decoy state QKD. These bounds are based basically only on the classical correlations established by the legitimate users during the quantum communication phase of the protocol. The only assumption about the possible post-processing methods is that double click events are randomly assigned to single click events. Further we consider only secure key rates based on the uncalibrated device scenario which assigns imperfections such as detection inefficiency to the eavesdropper. Our analysis relies on two preconditions for secure two-way and one-way QKD: The legitimate users need to prove that there exists no separable state (in the case of two-way QKD), or that there exists no quantum state having a symmetric extension (one-way QKD), that is compatible with the available measurements results. Both criteria have been previously applied to evaluate single-photon implementations of QKD. Here we use them to investigate a realistic source of weak coherent pulses. The resulting upper bounds can be formulated as a convex optimization problem known as a semidefinite program which can be efficiently solved. For the standard four-state QKD protocol, they are quite close to known lower bounds, thus showing that there are clear limits to the further improvement of classical post-processing techniques in decoy state QKD.
Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective

Visual-Semantic Embedding (VSE) aims to learn an embedding space where related visual and semantic instances are close to each other. Recent VSE models tend to design complex structures to pool visual and semantic features into fixed-length vectors and use hard triplet loss for optimization. However, we find that: (1) combining simple pooling methods is no worse than these sophisticated methods; and (2) only considering the most difficult-to-distinguish negative sample leads to slow convergence and poor Recall@K improvement. To this end, we propose an adaptive pooling strategy that allows the model to learn how to aggregate features through a combination of simple pooling methods. We also introduce a strategy to dynamically select a group of negative samples to make the optimization converge faster and perform better. Experimental results on Flickr30K and MS-COCO demonstrate that a standard VSE using our pooling and optimization strategies outperforms current state-of-the-art systems (at least 1.0{% on the metrics of recall) in image-to-text and text-to-image retrieval. Source code of our experiments is available at https://github.com/96-Zachary/vse_2ad .
Stable keypoints selection for 2D LiDAR based place recognition with map data reduction

Abstract This paper presents a new feature based approach for place recognition using 2D LiDAR (Light Detection And Ranging) data. The main contribution lies in the mapping process. It includes a keypoint selection strategy to model places with persistent keypoints from concatenated LiDAR scans. Our objective is to achieve map data reduction while maintaining good place recognition performace. LiDAR scans are concatenated with a registration algorithm and keypoints are extracted from each scan. Based on a regular grid, our approach measures the occurrence of similar keypoints in each region of interest defined by a grid cell. Only keypoints with occurrences beyond a threshold, qualified as stable keypoints, are kept in the place model called submap. The environment is therefore modeled as a collection of submaps, which constitutes the global map. Place recognition consists in submap matching followed by a two steps geometric verification. In the first stage, optimal parameters are set using corrected data. Mapping parameters satisfy six criteria among which is the spatial distribution distance, which represents another contribution of our work. It gives a measure on how well keypoints are distributed in space. Place recognition optimal parameters are set through global localization. In the second stage, the approach is evaluated using raw data in the contexts of global localization, and loop closure detection in a SLAM framework. The results obtained using popular real data sets show that our approach achieves significant map data reduction (up to $92\%$ ) while maintaining good place recognition performance, comparable to state-of-the-art methods.
Computational Electromagnetism: Variational Formulations, Complementarity, Edge Elements

Maxwell Equations: Overview. Magnetostatics: "Scalar Potential" Approach. Solving for the Scalar Magnetic Potential. The Approximate Magnetic Potential: Properties and Shortcomings. New Tools: Whitney Elements, Symmetry. Complementary in Magnetostatistics. Magnetostatistics in Infinite Domains. Eddy-Current Problems. Mathematical Background. Appendices. Subject Index.
Energy and Time Based Topology Control Approach to Enhance the Lifetime of WSN in an economic zone

An economic zone requires continuous monitoring and controlling by an autonomous surveillance system for heightening its production competency and security. Wireless sensor network (WSN) has swiftly grown popularity over the world for uninterruptedly monitoring and controlling a system. Sensor devices, the main elements of WSN, are given limited amount of energy, which leads the network to limited lifespan. Therefore, the most significant challenge is to increase the lifespan of a WSN system. Topology control mechanism (TCM) is a renowned method to enhance the lifespan of WSN. This paper proposes an approach to extend the lifetime of WSN for an economic area, targeting an economic zone in Bangladesh. Observations are made on the performance of the network lifetime considering the individual combinations of the TCM protocols and comparative investigation between the time and energy triggering strategy of TCM protocols. Results reveal the network makes a better performance in the case of A3 protocol while using the topology maintenance protocols with both time and energy triggering methods. Moreover, the performance of the A3 and DGETRec is superior to the other combinations of TCM protocols. Hence, the WSN system can be able to serve better connectivity coverage in the target economic zone.
Effects of polyphenolic-rich extracts from Citrus hystrix on proliferation and oxidative stress in breast and colorectal cancer

Abstract Objectives The anti-proliferative effects of Citrus hystrix have been reported. However, information on breast and colorectal cancer is limited especially the mechanistic aspects. In this study, the antioxidant activities of hexane, ethyl acetate, methanol and water extracts of C. hystrix leaves and their growth inhibitory effects on colorectal (HCT 116) and breast cancer (MCF 7, MDA-MB 231 and HCC 1937) cells were analysed. Methods Antioxidant and oxidative stress status were measured using non-cellular and cellular assays. Caspase and gene expression were utilized to determine anti-proliferative effects. Polyphenolic content was analysed using LC-IT-TOF/MS. Results The water extract showed the highest polyphenolic content and antioxidant activities (FRAP, DPPH, ABTS, superoxide anion radical scavenging, ferrous ion chelation, cellular antioxidant assay). The ethyl acetate extracts of C. hystrix (CH-EA) demonstrated the highest anti-proliferative activity against all cancer cell lines (IC50<100 μg/mL). Increase in ROS was observed in CH-EA-treated HCT 116, MDA-MB 231 and HCC 1937 cells (p<0.05). Increase in caspase activities and upregulation of Bax, Bcl-2, Cdk-1, TP53 and TNF-α expression in HCT 116 cells indicated activation of apoptosis by CH-EA. LC-IT-TOF/MS analysis indicated presence of quercetin and rutin in CH-EA. Conclusions CH-EA showed anti-proliferative effects, possibly through modulation of oxidative stress and apoptosis.
Modelling stellar coronae from surface magnetograms: the role of missing magnetic flux

Recent advances in spectropolarimetry have allowed the reconstruction of stellar coronal magnetic fields. This uses Zeeman–Doppler magnetograms (ZDI) of the surface magnetic field as a lower boundary condition. The ZDI maps, however, suffer from the absence of information about the magnetic field over regions of the surface due to the presence of dark starspots and portions of the surface out of view due to a tilt in the rotation axis. They also suffer from finite resolution which leads to small-scale field structures being neglected. This paper explores the effects of this loss of information on the extrapolated coronal fields. For this, we use simulated stellar surface magnetic maps for two hypothetical stars. Using the potential field approximation, the coronal fields and emission measures are calculated. This is repeated for the cases of missing information due to, (i) starspots, (ii) a large area of the stellar surface out of view and (iii) a finite resolution. The largest effect on the magnetic field structure arises when a significant portion of the stellar surface remains out of view. This changes the nature of the field lines that connect to this obscured hemisphere. None the less, the field structure in the visible hemisphere is reliably reproduced. Thus, the calculation of the locations and surface filling factors of accretion funnels is reasonably well reproduced for the observed hemisphere. The decrease with height of the magnetic pressure, which is important in calculating disc truncation radii for accreting stars, is also largely unaffected in the equatorial plane. The fraction of surface flux that is open and therefore able to supply angular momentum loss in a wind, however, is often overestimated in the presence of missing flux. The magnitude and rotational modulation of the calculated emission measures is consistently decreased by the loss of magnetic flux in dark starspots. For very inactive stars, this may make it impossible to recover a magnetic cycle in the coronal emission. Finite resolution has little effect on those quantities, such as the emission measure and the average coronal electron density, that can currently be observed.