Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of binary black holes in low-mass young star clusters

Young star clusters are dynamically active stellar systems and are a common birthplace for massive stars. Low-mass star clusters (∼300–103 M⊙) are more numerous than massive systems and are characterized by a two-body relaxation time-scale of a few Myr: the most massive stars sink to the cluster core and dynamically interact with each other even before they give birth to compact objects. Here, we explore the properties of black holes (BHs) and binary black holes (BBHs) formed in low-mass young star clusters, by means of a suite of 105 direct N-body simulations with a high original binary fraction (100 per cent for stars with mass >5 M⊙). Most BHs are ejected in the first ∼20 Myr by dynamical interactions. Dynamical exchanges are the main formation channel of BBHs, accounting for ∼40–80 per cent of all the systems. Most BBH mergers in low-mass young star clusters involve primary BHs with mass <40 M⊙ and low-mass ratios are extremely more common than in the field. Comparing our data with those of more massive star clusters (103 − 3 × 104 M⊙), we find a strong dependence of the percentage of exchanged BBHs on the mass of the host star cluster. In contrast, our results show just a mild correlation between the mass of the host star cluster and the efficiency of BBH mergers.
Weakly Supervised Pseudo-Label assisted Learning for ALS Point Cloud Semantic Segmentation

Abstract. Competitive point cloud semantic segmentation results usually rely on a large amount of labeled data. However, data annotation is a time-consuming and labor-intensive task, particularly for three-dimensional point cloud data. Thus, obtaining accurate results with limited ground truth as training data is considerably important. As a simple and effective method, pseudo labels can use information from unlabeled data for training neural networks. In this study, we propose a pseudo-label-assisted point cloud segmentation method with very few sparsely sampled labels that are normally randomly selected for each class. An adaptive thresholding strategy was proposed to generate a pseudo-label based on the prediction probability. Pseudo-label learning is an iterative process, and pseudo labels were updated solely on ground-truth weak labels as the model converged to improve the training efficiency. Experiments using the ISPRS 3D sematic labeling benchmark dataset indicated that our proposed method achieved an equally competitive result compared to that using a full supervision scheme with only up to 2‰ of labeled points from the original training set, with an overall accuracy of 83.7% and an average F1 score of 70.2%.
Information Complexity and Generalization Bounds

We present a unifying picture of PAC-Bayesian and mutual information-based upper bounds on the generalization error of randomized learning algorithms. As we show, Tong Zhang's information exponential inequality (IEI) gives a general recipe for constructing bounds of both flavors. We show that several important results in the literature can be obtained as simple corollaries of the IEI under different assumptions on the loss function. Moreover, we obtain new bounds for data-dependent priors and unbounded loss functions. Optimizing the bounds gives rise to variants of the Gibbs algorithm, for which we discuss two examples for learning with neural networks, namely, Entropy- and PAC-Bayes-SGD. Further, we use an Occam's factor argument to show a PAC-Bayesian bound that incorporates second-order curvature information of the training loss.
DeepPlastic: A Novel Approach to Detecting Epipelagic Bound Plastic Using Deep Visual Models

The quantification of positively buoyant marine plastic debris is critical to understanding how concentrations of trash form across the world’s ocean and identifying high concentration garbage hotspots in dire need of trash removal. Currently, the most common monitoring method to quantify floating plastic requires the use of a manta trawl. Techniques requiring manta trawls (or similar surface collection devices) utilize physical removal of marine plastic debris as the first step and then analyze collected samples as a second step. The need for physical removal before analysis incurs high costs and requires intensive labor–preventing scalable deployment of a real-time marine plastic monitoring service across the entirety of Earth’s ocean bodies. Without better monitoring and sampling methods, the total impact of plastic pollution on the environment as a whole, and details of impact within specific oceanic regions, will remain unknown. This study presents a highly scalable workflow that utilizes images captured within the epipelagic layer of the ocean as an input. It produces real-time quantification of marine plastic debris captured in the video for accurate quantification and physical removal. The workflow includes creating and preprocessing a domain-specific dataset, building an object detection model utilizing a deep neural network, and evaluating the model’s performance. YOLOv5-S was the best performing model, which operates at a Mean Average Precision (mAP) of 0.851 and an F1-Score of 0.89 while maintaining nearreal-time speed.
Verifiable Computing Using Computation Fingerprints Within FHE

We suggest using Fully Homomorphic Encryption (FHE) to be used, not only to keep the privacy of information but also, to verify computations with no additional significant overhead, using only part of the variables length for verification. This method supports the addition of encrypted values as well as multiplication of encrypted values by the addition of their logarithmic representations and is based on a separation between hardware functionalities. The computer/server performs blackbox additions and is based on the separation of server/device/hardware, such as the enclave, that may deal with additions of logarithmic values and exponentiation. The main idea is to restrict the computer operations and to use part of the variable for computation verification (computation fingerprints) and the other for the actual calculation. The verification part holds the FHE value, of which the calculated result is known (either due to computing locally once or from previously verified computations) and will be checked against the returned FHE value. We prove that a server with bit computation granularity can return consistent encrypted wrong results even when the public key is not provided. For the case of computer word granularity the verification and the actual calculation parts are separated, the verification part (the consecutive bits from the LSB to the MSB of the variables) is fixed across all input vectors. We also consider the case of Single Instruction Multiple Data (SIMD) where the computation fingerprints index in the input vectors is fixed across all vectors.
Stitch Fix for Mapper and Information Gains

The mapper construction is a powerful tool from topological data analysis that is designed for the analysis and visualization of multivariate data. In this paper, we investigate a method for stitching a pair of univariate mappers together into a bivariate mapper and study topological notions of information gains during such a process. We further provide implementations that visualize such information gains
Prompt acceleration of the $\mu^+$ beam in a donut wakefield driven by a shaped Laguerre-Gaussian laser pulse

The recent experimental data of anomalous magnetic moments strongly indicate the existence of new physics beyond the standard model. An energetic μ beam is a potential option to the expected neutrino factories, the future muon colliders and the μSR(the spin rotation, resonance and relaxation) technology. It is proposed a prompt acceleration scheme of the μ beam in a donut wakefield driven by a shaped Laguerre-Gaussian (LG) laser pulse. The forward part of the donut wakefield can accelerate and also focus positive particle beams effectively. The LG laser is shaped by a near-critical-density plasma. The shaped LG laser has the shorter rise time and can enlarge the acceleration field. The acceleration field driven by a shaped LG laser pulse is six times higher than that driven by a normal LG laser pulse. The simulation results show that the μ bunch can be accelerated from 200MeV to 2GeV and the transversal size of the μ bunch is also focused from initial ω0 = 5μm to ω = 1μm within several picoseconds.
Sampling Colorings and Independent Sets of Random Regular Bipartite Graphs in the Non-Uniqueness Region

For spin systems, such as the $q$-colorings and independent-set models, approximating the partition function in the so-called non-uniqueness region, where the model exhibits long-range correlations, is typically computationally hard for bounded-degree graphs. We present new algorithmic results for approximating the partition function and sampling from the Gibbs distribution for spin systems in the non-uniqueness region on random regular bipartite graphs. We give an $\mathsf{FPRAS}$ for counting $q$-colorings for even $q=O\big(\tfrac{\Delta}{\log{\Delta}}\big)$ on almost every $\Delta$-regular bipartite graph. This is within a factor $O(\log{\Delta})$ of the sampling algorithm for general graphs in the uniqueness region and improves significantly upon the previous best bound of $q=O\big(\tfrac{\sqrt{\Delta}}{(\log\Delta)^2}\big)$ by Jenssen, Keevash, and Perkins (SODA'19). Analogously, for the hard-core model on independent sets weighted by $\lambda>0$, we present an $\mathsf{FPRAS}$ for estimating the partition function when $\lambda=\Omega\big(\tfrac{\log{\Delta}}{\Delta}\big)$, which improves upon previous results by an $\Omega(\log \Delta)$ factor. Our results for the colorings and hard-core models follow from a general result that applies to arbitrary spin systems. Our main contribution is to show how to elevate probabilistic/analytic bounds on the marginal probabilities for the typical structure of phases on random bipartite regular graphs into efficient algorithms, using the polymer method. We further show evidence that our result for colorings is within a constant factor of best possible using current polymer-method approaches.
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora

In recent years, speech emotion recognition (SER) has been used in wide ranging applications, from healthcare to the commercial sector. In addition to signal processing approaches, methods for SER now also use deep learning techniques. However, generalizing over languages, corpora and recording conditions is still an open challenge in the field. Furthermore, due to the black-box nature of deep learning algorithms, a newer challenge is the lack of interpretation and transparency in the models and the decision making process. This is critical when the SER systems are deployed in applications that influence human lives. In this work we address this gap by providing an in-depth analysis of the decision making process of the proposed SER system. Towards that end, we present low-complexity SER based on undercomplete- and denoising- autoencoders that achieve an average classification accuracy of over 55\% for four-class emotion classification. Following this, we investigate the clustering of emotions in the latent space to understand the influence of the corpora on the model behavior and to obtain a physical interpretation of the latent embedding. Lastly, we explore the role of each input feature towards the performance of the SER.
Rabi-like splitting and refractive index sensing with hybrid Tamm plasmon-cavity modes

Rabi-like splitting and self-referenced refractive index sensing in hybrid plasmonic-1D photonic crystal structures have been theoretically demonstrated. The coupling between Tamm plasmon and cavity photon modes are tuned by incorporating a low refractive index spacer layer adjacent to the metallic layer to form their hybrid modes. Anticrossing of the modes observed at different values of spacer layer thickness validates the strong coupling between the two modes and causes Rabi-like splitting with different splitting energy. The modes coupling has been supported by coupled mode theory. Rabi-like splitting energy decreases with increasing number of periods (N) and refractive index contrast (η) of two dielectric materials used to make the 1D photonic crystals, and the observed variation is explained by an analytical model. Angular and polarization dependency of the hybrid modes shows that the polarization splitting of the lower hybrid mode is much stronger than that of the upper hybrid mode. On further investigation, it is seen that one of the hybrid modes remains unchanged while other mode undergoes significant change with varying the cavity medium. This nature of the hybrid modes has been utilized for designing self-referenced refractive index sensors for sensing different analytes. For η= 1.333 and N= 10 in a hybrid structure, the sensitivity increases from 51 to 201 nm RIU−1 with increasing cavity thickness from 170 to 892 nm. For the fixed cavity thickness of 892 nm, the sensitivity increases from 201 to 259 nm RIU−1 by increasing η from 1.333 to 1.605. The sensing parameters such as detection accuracy, quality factor, and figure of merit for two different hybrid structures [(η = 1.333, N = 10) and (η = 1.605, N = 6)] have been evaluated and compared. The value of resonant reflectivity of one of the hybrid modes changes considerably with varying analyte medium which can be used for refractive index sensing.
MaNGA galaxy properties – I. An extensive optical, mid-infrared photometric, and environmental catalogue

We present an extensive catalog of non-parametric structural properties derived from optical and mid-infrared imaging for 4585 galaxies from the MaNGA survey. DESI and WISE imaging are used to extract surface brightness profiles in the g, r, z,W1,W2 photometric bands. Our optical photometry takes advantage of the automated algorithm AutoProf and probes surface brightnesses that typically reach below 29 mag arcsec−2 in the r-band, while our WISE photometry achieves 28 mag arcsec−2 in theW1-band. Neighbour density measures and central/satellite classifications are also provided for a large sub-sample of the MaNGA galaxies. Highlights of our analysis of galaxy light profiles include: (i) an extensive comparison of galaxian structural properties that illustrates the robustness of non-parametric extraction of light profiles over parametric methods; (ii) the ubiquity of bimodal structural properties suggesting the existence of galaxy families in multiple dimensions; and, (iii) an appreciation that structural properties measured relative to total light are uncertain. We study galaxy scaling relations based on photometric parameters, and present detailed comparisons with the literature and theoretical expectations. Salient features of this analysis include the near-constancy of the slope and scatter of the size-luminosity and size-stellar mass relations for late-type galaxies with wavelength, and the saturation of the central surface density, measured within 1 kpc, for elliptical galaxies with M∗ > 10.7M (corresponding to Σ1 ' 1010 M kpc−2). The multi-band photometry, environmental parameters, and structural scaling relations presented here are useful constraints for stellar population and galaxy formation models.
Formally Justifying MDL-based Inference of Cause and Effect

The algorithmic independence of conditionals, which postulates that the causal mechanism is algorithmically independent of the cause, has recently inspired many highly successful approaches to distinguish cause from effect given only observational data. Most popular among these is the idea to approximate algorithmic independence via two-part Minimum Description Length (MDL). Although intuitively sensible, the link between the original postulate and practical two-part MDL encodings is left vague. In this work, we close this gap by deriving a two-part formulation of this postulate, in terms of Kolmogorov complexity, which directly links to practical MDL encodings. To close the cycle, we prove that this formulation leads on expectation to the same inference result as the original postulate.
Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization

Various post-training uniform quantization methods have usually been studied based on convex optimization. As a result, most previous ones rely on the quantization error minimization and/or quadratic approximations. Such approaches are computationally efficient and reasonable when a large number of quantization bits are employed. When the number of quantization bits is relatively low, however, non-convex optimization is unavoidable to improve model accuracy. In this paper, we propose a new post-training uniform quantization technique considering non-convexity. We empirically show that hyper-parameters for clipping and rounding of weights and activations can be explored by monitoring task loss. Then, an optimally searched set of hyper-parameters is frozen to proceed to the next layer such that an incremental non-convex optimization is enabled for post-training quantization. Throughout extensive experimental results using various models, our proposed technique presents higher model accuracy, especially for a low-bit quantization.
Associativity of fusion products of $C_1$-cofinite ${\mathbb N}$-gradable modules of vertex operator algebra

We prove an associative law of the fusion products ⊠ of C1-cofinite N-gradable modules for a vertex operator algebra V . To be more precise, for C1-cofinite Ngradable V -modules A,B,C and their fusion products (A⊠B,Y), ((A⊠B)⊠ C,Y(AB)C ), (B⊠C,Y ), (A⊠(B⊠C),YA(BC)) with logarithmic intertwining operators Y , . ,YA(BC) satisfying the universal properties for N-gradable modules, we prove that four-point correlation functions 〈θ,YA(BC)(v, x)Y (u, y)w〉 and 〈θ′,Y(AB)C (Y(v, x− y)u, y)w〉 are locally normally convergent over {(x, y) ∈ C2 | 0< |x−y|< |y|< |x|}. We then take their respective principal branches F̃ (〈θ,YA(BC)(v, x)Y (u, y)w〉) and F̃ (〈θ,Y(AB)C (YAB)(v, x− y)u, y)w〉) on D2 = {(x, y) ∈ C2 | 0< |x−y|< |y|< |x|, and x, y, x−y 6∈ R≤0} and then show that there is an isomorphism φ[AB]C : (A⊠B)⊠ C → A⊠ (B ⊠ C) such that F̃ (〈θ,Y(v, x)Y (u, y)w〉) = F̃ (〈φ[AB]C(θ),Y (AB)C (Y(v, x− y)u, y)w)〉 on D2 for θ ∈ (A ⊠ (B ⊠ C))∨, v ∈ A, u ∈ B, and w ∈ C, where W∨ denotes the contragredient module of W and φ[AB]C denotes the dual of φ[AB]C . We also prove the pentagon identity.
Don't forget your classics: Systematizing 45 years of Ancestry for Security API Usability Recommendations

Producing secure software is challenging. The poor usability of security APIs makes this even harder. Many recommendations have been proposed to support developers by improving the usability of cryptography libraries and APIs; rooted in wider best practice guidance in software engineering and API design. In this SLR, we systematize knowledge regarding these recommendations. We identify and analyze 65 papers spanning 45 years, offering a total of 883 recommendations.We undertake a thematic analysis to identify 7 core ways to improve usability of APIs. We find that most of the recommendations focus on helping API developers to construct and structure their code and make it more usable and easier for programmers to understand. There is less focus, however, on documentation, writing requirements, code quality assessment and the impact of organizational software development practices. By tracing and analyzing paper ancestry, we map how this knowledge becomes validated and translated over time.We find evidence that less than a quarter of all API usability recommendations are empirically validated, and that recommendations specific to usable security APIs lag even further behind in this regard.
First demonstration of robust tri-gate β-Ga2O3 nano-membrane field-effect transistors

Nano-membrane tri-gate β-gallium oxide (β-Ga2O3) field-effect transistors (FETs) on SiO2/Si substrate fabricated via exfoliation have been demonstrated for the first time. By employing electron beam lithography, the minimum-sized features can be defined with the footprint channel width of 50 nm. For high-quality interface between β-Ga2O3 and gate dielectric, atomic layer-deposited 15 nm thick aluminum oxide (Al2O3) was utilized with tri-methyl-aluminum (TMA) self-cleaning surface treatment. The fabricated devices demonstrate extremely low subthreshold slope (SS) of 61 mV dec−1, high drain current (I DS) ON/OFF ratio of 1.5 × 109, and negligible transfer characteristic hysteresis. We also experimentally demonstrated robustness of these devices with current–voltage (I–V) characteristics measured at temperatures up to 400 °C.
Comparing Field Trips, VR Experiences and Video Representations on Spatial Layout Learning in Complex Buildings

This study aimed to compare and investigate the efficacy of the real-world experiences, immersive virtual reality (IVR) experiences, and video walkthrough representations on layout-learning in a complex building. A quasiexperimental, intervention, and delayed post-test research design was used among three groups: real-world, IVR, and video walkthrough representation. A total of 41 first-year design students from architecture, and game design departments were attended the study. Design students were selected as they already know how to communicate graphically the layout of a building on paper. IVR and video walkthrough groups experienced the representations of the building by themselves, but real-world group experienced the building within a group as imitating a design education field trip. After 10 days, a total of 26 participants out of 41 tested for spatial recall performance. Recall performances were measured as an indicator of layout learning by analysing the plan sketches drawn by the participants. Results showed IVR group recalled significantly more spatial memories compared with the real-world and video walkthrough representation groups. Real-world group performed the worst among three groups. This result was interpreted to three conclusions. First, the layout learning tends to be overly sensitive to distractions and lower levels of distraction may lead to better levels of layout learning. Second, for the domain of layout knowledge learning, a remarkably simple IVR model is enough. Third, although real-world experiences give direct and richer sensory information about the spaces, layout knowledge acquisition from the surrounding environment requires psychologically active wayfinding decisions.
Multi-Modal Loop Closing in Unstructured Planetary Environments with Visually Enriched Submaps

Future planetary missions will rely on rovers that can autonomously explore and navigate in unstructured environments. An essential element is the ability to recognize places that were already visited or mapped. In this work, we leverage the ability of stereo cameras to provide both visual and depth information, guiding the search and validation of loop closures from a multi-modal perspective. We propose to augment submaps that are created by aggregating stereo point clouds, with visual keyframes. Point clouds matches are found by comparing CSHOT descriptors and validated by clustering, while visual matches are established by comparing keyframes using Bag-of-Words (BoW) and ORB descriptors. The relative transformations resulting from both keyframe and point cloud matches are then fused to provide pose constraints between submaps in our graph-based SLAM framework. Using the LRU rover, we performed several tests in both an indoor laboratory environment as well as a challenging planetary analog environment on Mount Etna, Italy, consisting of areas where either keyframes or point clouds alone failed to provide adequate matches demonstrating the benefit of the proposed multi-modal approach.