Miracle-dz/UniFAR-FTUs huggingface dataset:
THEORY AND APPLICATION TO THE US MILITARY

An ever‐growing share of defence R&D expenditures is being dedicated to the development and fielding of integrative technologies that enable separate individual systems to work in a coordinated and synergistic fashion as a single system. This study explores the optimal defence budget allocation to the development and acquisition of weapon systems and to the development of integrative technologies. We develop a suitable optimization framework, and then use it to derive the optimal budget allocation and analyse its properties. Finally, we use US defence budget data to calibrate the parameters of the model and provide a quantitative measure for the apparent US military supremacy.
The Compactness of Galaxy Groups in the Sloan Digital Sky Survey

We use an updated version of the halo-based galaxy group catalog of Yang et al., and take the surface brightness of the galaxy group (μ lim) based on projected positions and luminosities of galaxy members as a compactness proxy to divide groups into subsystems with different compactness. By comparing various properties, including galaxy conditional luminosity function, stellar population, active galactic nuclei (AGN) activity, and X-ray luminosity of the intracluster medium of carefully controlled high (HC) and low compactness (LC) group samples, we find that group compactness plays an essential role in characterizing the detailed physical properties of the group themselves and their group members, especially for low-mass groups with M h ≲ 1013.5 h −1 M ⊙. We find that the low-mass HC groups have a systematically lower magnitude gap Δm 12 and X-ray luminosity than their LC counterparts, indicating that the HC groups are probably in the early stage of group merging. On the other hand, a higher fraction of passive galaxies is found in the HC group, which however is a result of systematically smaller halo-centric distance distribution of their satellite population. After controlling for both M h and halo-centric distance, we did not find any differences in both the quenching fraction and AGN activity of the member galaxies between the HC and LC groups. Therefore, we conclude that the halo quenching effect, which results in the halo-centric dependence of a galaxy population, is a faster process compared to the dynamical relaxed timescale of galaxy groups.
Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution

In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Likewise, when humans observe a low-resolution text image, they will inherently use partial stroke-level details to recover the appearance of holistic characters. Inspired by Gestalt Psychology, we put forward a Stroke-Aware Scene Text Image Super-Resolution method containing a Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures of characters in text images. Specifically, we attempt to design rules for decomposing English characters and digits at stroke-level, then pre-train a text recognizer to provide stroke-level attention maps as positional clues with the purpose of controlling the consistency between the generated super-resolution image and high-resolution ground truth. The extensive experimental results validate that the proposed method can indeed generate more distinguishable images on TextZoom and manually constructed Chinese character dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to provide stroke-level guidance when training, it will not bring any time overhead during the test phase. Code is available at https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt.
Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data

Deep learning techniques for separating audio into different sound sources face several challenges. Standard architectures require training separate models for different types of audio sources. Although some universal separators employ a single model to target multiple sources, they have difficulty generalizing to unseen sources. In this paper, we propose a three-component pipeline to train a universal audio source separator from a large, but weakly-labeled dataset: AudioSet. First, we propose a transformer-based sound event detection system for processing weakly-labeled training data. Second, we devise a query-based audio separation model that leverages this data for model training. Third, we design a latent embedding processor to encode queries that specify audio targets for separation, allowing for zero-shot generalization. Our approach uses a single model for source separation of multiple sound types, and relies solely on weakly-labeled data for training. In addition, the proposed audio separator can be used in a zero-shot setting, learning to separate types of audio sources that were never seen in training. To evaluate the separation performance, we test our model on MUSDB18, while training on the disjoint AudioSet. We further verify the zero-shot performance by conducting another experiment on audio source types that are held-out from training. The model achieves comparable Source-to-Distortion Ratio (SDR) performance to current supervised models in both cases.
Interscript: A dataset for interactive learning of scripts through error feedback

How can an end-user provide feedback if a deployed structured prediction model generates inconsistent output, ignoring the structural complexity of human language? This is an emerging topic with recent progress in synthetic or constrained settings, and the next big leap would require testing and tuning models in real-world settings. We present a new dataset, Interscript, containing user feedback on a deployed model that generates complex everyday tasks. Interscript contains 8,466 data points -- the input is a possibly erroneous script and a user feedback, and the output is a modified script. We posit two use-cases of \ours that might significantly advance the state-of-the-art in interactive learning. The dataset is available at: https://github.com/allenai/interscript.
Mechanisms of Sinoatrial Node Dysfunction in Heart Failure With Preserved Ejection Fraction

Supplemental Digital Content is available in the text. Background: The ability to increase heart rate during exercise and other stressors is a key homeostatic feature of the sinoatrial node (SAN). When the physiological heart rate response is blunted, chronotropic incompetence limits exercise capacity, a common problem in patients with heart failure with preserved ejection fraction (HFpEF). Despite its clinical relevance, the mechanisms of chronotropic incompetence remain unknown. Methods: Dahl salt-sensitive rats fed a high-salt diet and C57Bl6 mice fed a high-fat diet and an inhibitor of constitutive nitric oxide synthase (Nω-nitro-L-arginine methyl ester [L-NAME]; 2-hit) were used as models of HFpEF. Myocardial infarction was created to induce HF with reduced ejection fraction. Rats and mice fed with a normal diet or those that had a sham surgery served as respective controls. A comprehensive characterization of SAN function and chronotropic response was conducted by in vivo, ex vivo, and single-cell electrophysiologic studies. RNA sequencing of SAN was performed to identify transcriptomic changes. Computational modeling of biophysically-detailed human HFpEF SAN was created. Results: Rats with phenotypically-verified HFpEF exhibited limited chronotropic response associated with intrinsic SAN dysfunction, including impaired β-adrenergic responsiveness and an alternating leading pacemaker within the SAN. Prolonged SAN recovery time and reduced SAN sensitivity to isoproterenol were confirmed in the 2-hit mouse model. Adenosine challenge unmasked conduction blocks within the SAN, which were associated with structural remodeling. Chronotropic incompetence and SAN dysfunction were also found in rats with HF with reduced ejection fraction. Single-cell studies and transcriptomic profiling revealed HFpEF-related alterations in both the “membrane clock” (ion channels) and the “Ca2+ clock” (spontaneous Ca2+ release events). The physiologic impairments were reproduced in silico by empirically-constrained quantitative modeling of human SAN function. Conclusions: Chronotropic incompetence and SAN dysfunction were seen in both models of HF. We identified that intrinsic abnormalities of SAN structure and function underlie the chronotropic response in HFpEF.
Quasi-resonant dc-link soft-switching PWM inverter with active feedback clamp circuit for motor drive applications

A novel soft-switched quasi-resonant dc-link (QRDCL) inverter topology is derived using the concept of an auxiliary switch-assisted soft-switcher (AASS) employing the flying capacitor. This concept is known for its high efficiency at high power levels, and is used for soft-switching the boost switch in zero-voltage transition (ZVT) power factor correction (PFC) boost rectifiers. Also, an active clamp circuit configuration to clamp the dc-link and recover the energy stored in the clamp capacitor is introduced and is applied to the proposed inverter topology. The resultant inverter configuration gives high efficiency and is suitable for high power motor drive applications. The maximum dc-link voltage is clamped at around (1.05–1.1) times the input dc-source voltage. The proposed inverter is suitable for pulse width modulation (PWM) operation. A detailed analysis along with the design procedure of the proposed inverter is presented. Simulation and laboratory experiments are carried out to validate the analysis.
Novel transport phenomena in graphene induced by strong spin-orbit interaction

Graphene is known to have small intrinsic spin-orbit Interaction (SOI). In this review, we demonstrate that SOIs in graphene can be strongly enhanced by proximity effect when graphene is deposited on the top of transition metal dichalcogenides. We discuss the symmetry of the induced SOIs and differences between TMD underlayers in the capacity of inducing strong SOIs in graphene. The strong SOIs contribute to bring novel phenomena to graphene, exemplified by robust supercurrents sustained even under tesla-range magnetic fields.
Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval

This paper tackles the Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) problem from the viewpoint of cross-modality metric learning. This task has two characteristics: 1) the zero-shot setting requires a metric space with good within-class compactness and the between-class discrepancy for recognizing the novel classes and 2) the sketch query and the photo gallery are in different modalities. The metric learning viewpoint benefits ZS-SBIR from two aspects. First, it facilitates improvement through recent good practices in deep metric learning (DML). By combining two fundamental learning approaches in DML, e.g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR. Without bells and whistles, this baseline achieves competitive retrieval accuracy. Second, it provides an insight that properly suppressing the modality gap is critical. To this end, we design a novel method named Modality-Aware Triplet Hard Mining (MATHM). MATHM enhances the baseline with three types of pairwise learning, e.g., a cross-modality sample pair, a within-modality sample pair, and their combination. \We also design an adaptive weighting method to balance these three components during training dynamically. Experimental results confirm that MATHM brings another round of significant improvement based on the strong baseline and sets up new state-of-the-art performance. For example, on the TU-Berlin dataset, we achieve 47.88+2.94% mAP@all and 58.28+2.34% Prec@100. Code will be publicly available at: https://github.com/huangzongheng/MATHM.
On cusps of caustics by reflection: a billiard variation on Jacobi's Last Geometric Statement

A point source of light is placed inside an oval. The $n$-th caustic by reflection is the envelope of the light rays emanating from the light source after $n$ reflections off the curve. We show that each of these caustics, for a generic point light source, has at least 4 cusps. This is a billiard variation on Jacobi's Last Geometric Statement, concerning the number of cusps of the conjugate locus of a point on a convex surface. We present various proofs, using different ideas, including the curve shortening flow and Legendrian knot theory.
Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies

Affinity maturation is crucial for improving the binding affinity of antibodies to antigens. This process is mainly driven by point substitutions caused by somatic hypermutations of the immunoglobulin gene. It also includes deletions and insertions of genomic material known as indels. While the landscape of point substitutions has been extensively studied, a detailed statistical description of indels is still lacking. Here we present a probabilistic inference tool to learn the statistics of indels from repertoire sequencing data, which overcomes the pitfalls and biases of standard annotation methods. The model includes antibody-specific maturation ages to account for variable mutational loads in the repertoire. After validation on synthetic data, we applied our tool to a large dataset of human immunoglobulin heavy chains. The inferred model allows us to identify universal statistical features of indels in heavy chains. We report distinct insertion and deletion hotspots, and show that the distribution of lengths of indels follows a geometric distribution, which puts constraints on future mechanistic models of the hypermutation process.
Blockchain-enabled Server-less Federated Learning

—Motivated by the heterogeneous nature of devices participating in large-scale Federated Learning (FL) optimization, we focus on an asynchronous server-less FL solution empowered by Blockchain (BC) technology. In contrast to mostly adopted FL approaches, which assume synchronous operation, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates. The asynchronous setting ﬁts well with the federated optimization idea in practical large-scale settings with heterogeneous clients. Thus, it potentially leads to higher efﬁciency in terms of communication overhead and idle periods. To evaluate the learning completion delay of BC-enabled FL, we provide an analytical model based on batch service queue theory. Furthermore, we provide simulation results to assess the performance of both synchronous and asynchronous mechanisms. Important aspects involved in the BC-enabled FL optimization, such as the network size, link capacity, or user requirements, are put together and analyzed. As our results show, the synchronous setting leads to higher prediction accuracy than the asynchronous case. Nevertheless, asynchronous federated optimization provides much lower latency in many cases, thus becoming an appealing FL solution when dealing with large data sets, tough timing constraints (e.g., near-real-time applications), or highly varying training data.
Theory of versatile fidelity estimation with confidence

Estimating the fidelity with a target state is important in quantum information tasks. Many fidelity estimation techniques present a suitable measurement scheme to perform the estimation. In contrast, we present techniques that allow the experimentalist to choose a convenient measurement setting. Our primary focus lies on a method that constructs an estimator with nearly minimax optimal confidence interval for any specified measurement setting. We demonstrate, through a combination of theoretical and numerical results, various desirable properties for the method: robustness against experimental imperfections, competitive sample complexity, and accurate estimates in practice. We compare this method with Maximum Likelihood Estimation and the associated Profile Likelihood method, a Semi-Definite Programming based approach, as well as a popular direct fidelity estimation technique.
AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

We use artiﬁcial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8 , that include modes up to (cid:96) ≤ 4 and (5 , 5), except for (4 , 0) and (4 , 1), that describe binaries with mass-ratios q ≤ 8 and individual spins s z { 1 , 2 } ∈ [ − 0 . 8 , 0 . 8]. We use our AI models to obtain deterministic and probabilistic estimates of the mass-ratio, individual spins, eﬀective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. Our studies indicate that AI provides informative estimates for these physical parameters. This work marks the ﬁrst time AI is capable of characterizing this high-dimensional signal manifold. Our AI models were trained within 3.4 hours using distributed training on 256 nodes (1,536 NVIDIA V100 GPUs) in the Summit supercomputer.
Dynamics of Vertical Axis Wind Turbines

A computing package that combines finite element methods for evaluating the resonance frequencies and modes of turbine subcomponents (blade, tower and shaft) together with the aerodynamic calculations for forces and moments taking into consideration the dynamic stall as well as the dynamic response is developed. This method was applied to a realistic VAWT; namely; the PIONEER built in the Netherlands by Fokker company. A reasonable agreement between the calculated and field results was predicted.
Programming Knowledge Tracing: A Comprehensive Dataset and A New Model

In this paper, we study knowledge tracing in the domain of programming education and make two important contributions. First, we harvest and publish so far the most comprehensive dataset, namely BePKT, which covers various online behaviors in an online judge (OJ) system, including programming text problems, knowledge annotations, user-submitted code and system-logged events. Second, we propose a new model PDKT to exploit the enriched context for accurate student behavior prediction. More specifically, we construct a bipartite graph for programming problem embedding, and design an improved pre-training model PLCodeBERT for code embedding, as well as a double-sequence RNN model with exponential decay attention for effective feature fusion. Experimental results on the new dataset BePKT show that our proposed model establishes state-of-the-art performance in programming knowledge tracing. In addition, we verify that our code embedding strategy based on PLCodeBERT is complementary to existing knowledge tracing models to further enhance their accuracy. As a side product, PLCodeBERT also results in better performance in other programming-related tasks such as code clone detection.
Energy-Efficient Real-Time Heart Monitoring on Edge–Fog–Cloud Internet of Medical Things

The recent developments in wearable devices and the Internet of Medical Things (IoMT) allow real-time monitoring and recording of electrocardiogram (ECG) signals. However, continuous monitoring of ECG signals is challenging in low-power wearable devices due to energy and memory constraints. Therefore, in this article, we present a novel and energy-efficient methodology for continuously monitoring the heart for low-power wearable devices. The proposed methodology is composed of three different layers: 1) a noise/artifact detection layer to grade the quality of the ECG signals; 2) a normal/abnormal beat classification layer to detect the anomalies in the ECG signals; and 3) an abnormal beat classification layer to detect diseases from ECG signals. Moreover, a distributed multioutput convolutional neural network (CNN) architecture is used to decrease the energy consumption and latency between the edge–fog/cloud. Our methodology reaches an accuracy of 99.2% on the well-known MIT-BIH Arrhythmia Data Set. Evaluation on real hardware shows that our methodology is suitable for devices having a minimum RAM of 32 kb. Moreover, the proposed methodology achieves $7\times $ more energy efficiency compared to state-of-the-art works.
The Apogee to Apogee Path Sampler

Abstract Among Markov chain Monte Carlo algorithms, Hamiltonian Monte Carlo (HMC) is often the algorithm of choice for complex, high-dimensional target distributions; however, its efficiency is notoriously sensitive to the choice of the integration-time tuning parameter. When integrating both forward and backward in time using the same leapfrog integration step as HMC, the set of apogees, local maxima in the potential along a path, is the same whatever point (position and momentum) along the path is chosen to initialize the integration. We present the Apogee to Apogee Path Sampler (AAPS), which uses this invariance to create a simple yet generic methodology for constructing a path, proposing a point from it and accepting or rejecting that proposal so as to target the intended distribution. We demonstrate empirically that AAPS has a similar efficiency to HMC but is much more robust to the setting of its equivalent tuning parameter, the number of apogees that the path crosses. Supplementary materials for this article are available online.
H.E.S.S. Follow-up Observations of Binary Black Hole Coalescence Events during the Second and Third Gravitational-wave Observing Runs of Advanced LIGO and Advanced Virgo

We report on the observations of four well-localized binary black hole (BBH) mergers by the High Energy Stereoscopic System (H.E.S.S.) during the second and third observing runs of Advanced LIGO and Advanced Virgo, O2 and O3. H.E. S.S. can observe 20 deg2 of the sky at a time and follows up gravitational-wave (GW) events by “tiling” localization regions to maximize the covered localization probability. During O2 and O3, H.E. S.S. observed large portions of the localization regions, between 35% and 75%, for four BBH mergers (GW170814, GW190512_180714, GW190728_064510, and S200224ca). For these four GW events, we find no significant signal from a pointlike source in any of the observations, and we set upper limits on the very high energy (>100 GeV) γ-ray emission. The 1–10 TeV isotropic luminosity of these GW events is below 1045 erg s−1 at the times of the H.E. S.S. observations, around the level of the low-luminosity GRB 190829A. Assuming no changes are made to how follow-up observations are conducted, H.E. S.S. can expect to observe over 60 GW events per year in the fourth GW observing run, O4, of which eight would be observable with minimal latency.