Miracle-dz/UniFAR-FTUs huggingface dataset:
Power-Aperture Resource Allocation for a MPAR with Communications Capabilities

Multifunction phased array radars (MPARs) exploit the intrinsic flexibility of their active electronically steered array (ESA) to perform, at the same time, a multitude of operations, such as search, tracking, fire control, classification, and communications. This paper aims at addressing the MPAR resource allocation so as to satisfy the quality of service (QoS) demanded by both line of sight (LOS) and reflective intelligent surfaces (RIS)-aided non line of sight (NLOS) search operations along with communications tasks. To this end, the ranges at which the cumulative detection probability and the channel capacity per bandwidth reach a desired value are introduced as task quality metrics for the search and communication functions, respectively. Then, to quantify the satisfaction level of each task, for each of them a bespoke utility function is defined to map the associated quality metric into the corresponding perceived utility. Hence, assigning different priority weights to each task, the resource allocation problem, in terms of radar power aperture (PAP) specification, is formulated as a constrained optimization problem whose solution optimizes the global radar QoS. Several simulations are conducted in scenarios of practical interest to prove the effectiveness of the approach.
Volumetric Occupancy Detection: A Comparative Analysis of Mapping Algorithms

Despite the growing interest in innovative functionalities for collaborative robotics, volumetric detection remains indispensable for ensuring basic security. However, there is a lack of widely used volumetric detection frameworks specifically tailored to this domain, and existing evaluation metrics primarily focus on time and memory efficiency. To bridge this gap, the authors present a detailed comparison using a simulation environment, ground truth extraction, and automated evaluation metrics calculation. This enables the evaluation of state-of-the-art volumetric mapping algorithms, including OctoMap, SkiMap, and Voxblox, providing valuable insights and comparisons through the impact of qualitative and quantitative analyses. The study not only compares different frameworks but also explores various parameters within each framework, offering additional insights into their performance.
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations

Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. However, implementing RL in hardware-efficient and bio-inspired ways remains a challenge. This paper presents a novel Spiking Neural Network (SNN) architecture for solving RL problems with real-valued observations. The proposed model incorporates multi-layered event-based clustering, with the addition of Temporal Difference (TD)-error modulation and eligibility traces, building upon prior work. An ablation study confirms the significant impact of these components on the proposed model's performance. A tabular actor-critic algorithm with eligibility traces and a state-of-the-art Proximal Policy Optimization (PPO) algorithm are used as benchmarks. Our network consistently outperforms the tabular approach and successfully discovers stable control policies on classic RL environments: mountain car, cart-pole, and acrobot. The proposed model offers an appealing trade-off in terms of computational and hardware implementation requirements. The model does not require an external memory buffer nor a global error gradient computation, and synaptic updates occur online, driven by local learning rules and a broadcasted TD-error signal. Thus, this work contributes to the development of more hardware-efficient RL solutions.
A rich structure related to the construction of holomorphic matrix functions

The problem of designing controllers that are robust with respect to uncertainty leads to questions that are in the areas of operator theory and several complex variables. One direction is the engineering problem of μ-synthesis, which has led to the study of certain inhomogeneous domains such as the symmetrised polydisc and the tetrablock. The μsynthesis problem involves the construction of holomorphic matrix valued functions on the disc, subject to interpolation conditions and a boundedness condition. In more detail, let λ1, . , λn be distinct points in the disc, and let W1, . ,Wn be 2×2 matrices. The μ-synthesis problem related to the symmetrised bidisc involves finding a holomorphic 2× 2 matrix function F on the disc such that F (λj) = Wj for all j, and the spectral radius of F (λ) is less than or equal to 1 for all λ in the disc. The μ-synthesis problem related to the tetrablock involves finding a holomorphic 2 × 2 matrix function F on the disc such that F (λj) = Wj for all j, and the structured singular value (for the diagonal matrices with entries in C) of F (λ) is less than or equal to 1 for all λ in the disc. For the symmetrised bidisc and for the tetrablock, we study the structure of interconnections between the matricial Schur class, the Schur class of the bidisc, the set of pairs of positive kernels on the bidisc subject to a boundedness condition, and the set of holomorphic functions from the disc into the given inhomogeneous domain. We use the theory of reproducing kernels and Hilbert function spaces in these connections. We give a solvability criterion for the interpolation problem that arises from the μ-synthesis problem related to the tetrablock. Our strategy for this problem is the following: (i) reduce the μ-synthesis problem to an interpolation problem in the set of holomorphic functions from the disc into the tetrablock; (ii) induce a duality between this set and the Schur class of the bidisc; and then (iii) use Hilbert space models for this Schur class to obtain necessary and sufficient conditions for solvability.
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration

U-Net style networks are commonly utilized in unsupervised image registration to predict dense displacement fields, which for high-resolution volumetric image data is a resource-intensive and time-consuming task. To tackle this challenge, we first propose Fourier-Net, which replaces the costly U-Net style expansive path with a parameter-free model-driven decoder. Instead of directly predicting a full-resolution displacement field, our Fourier-Net learns a low-dimensional representation of the displacement field in the band-limited Fourier domain which our model-driven decoder converts to a full-resolution displacement field in the spatial domain. Expanding upon Fourier-Net, we then introduce Fourier-Net+, which additionally takes the band-limited spatial representation of the images as input and further reduces the number of convolutional layers in the U-Net style network's contracting path. Finally, to enhance the registration performance, we propose a cascaded version of Fourier-Net+. We evaluate our proposed methods on three datasets, on which our proposed Fourier-Net and its variants achieve comparable results with current state-of-the art methods, while exhibiting faster inference speeds, lower memory footprint, and fewer multiply-add operations. With such small computational cost, our Fourier-Net+ enables the efficient training of large-scale 3D registration on low-VRAM GPUs. Our code is publicly available at \url{https://github.com/xi-jia/Fourier-Net}.
Amplitude phase‐field crystal model for the hexagonal close‐packed lattice

The phase‐field crystal model allows the study of materials on atomic length and diffusive time scales. It accounts for elastic and plastic deformation in crystal lattices, including several processes such as growth, dislocation dynamics, and microstructure evolution. The amplitude expansion of the phase‐field crystal model (APFC) describes the atomic density by a small set of Fourier modes with slowly‐varying amplitudes characterizing lattice deformations. This approach allows for tackling large, three‐dimensional systems. However, it has been used mostly for modeling basic lattice symmetries. In this work, we present a coarse‐grained description of the hexagonal closed‐packed (HCP) lattice that supports lattice deformation and defects. It builds on recent developments of the APFC model and introduces specific modeling aspects for this crystal structure. After illustrating the general modeling framework, we show that the proposed approach allows for simulating relatively large three‐dimensional HCP systems hosting complex defect networks.
Dynamic Factor Analysis with Dependent Gaussian Processes for High-Dimensional Gene Expression Trajectories

The increasing availability of high-dimensional, longitudinal measures of genetic expression can facilitate analysis of the biological mechanisms of disease and prediction of future trajectories, as required for precision medicine. Biological knowledge suggests that it may be best to describe complex diseases at the level of underlying pathways, which may interact with one another. We propose a Bayesian approach that allows for characterising such correlation among different pathways through Dependent Gaussian Processes (DGP) and mapping the observed high-dimensional gene expression trajectories into unobserved low-dimensional pathway expression trajectories via Bayesian Sparse Factor Analysis. Compared to previous approaches that model each pathway expression trajectory independently, our model demonstrates better performance in recovering the shape of pathway expression trajectories, revealing the relationships between genes and pathways, and predicting gene expressions (closer point estimates and narrower predictive intervals), as demonstrated in the simulation study and real data analysis. To fit the model, we propose a Monte Carlo Expectation Maximization (MCEM) scheme that can be implemented conveniently by combining a standard Markov Chain Monte Carlo sampler and an R package GPFDA (Konzen and others, 2021), which returns the maximum likelihood estimates of DGP parameters. The modular structure of MCEM makes it generalizable to other complex models involving the DGP model component. An R package has been developed that implements the proposed approach.
D YNAMIC O BSERVATION P OLICIES IN O BSERVATION C OST -S ENSITIVE R EINFORCEMENT L EARNING

Reinforcement learning (RL) has been shown to learn sophisticated control policies for complex tasks including games, robotics, heating and cooling systems and text generation. The action-perception cycle in RL, however, generally assumes that a measurement of the state of the environment is available at each time step without a cost. In applications such as deep-sea and planetary robot exploration, materials design and medicine, however, there can be a high cost associated with measuring, or even approximating, the state of the environment. In this paper, we survey the recently growing literature that adopts the perspective that an RL agent might not need, or even want, a costly measurement at each time step. Within this context, we propose the Deep Dynamic Multi-Step Observationless Agent (DMSOA), contrast it with the literature and empirically evaluate it on OpenAI gym and Atari Pong environments. Our results, show that DMSOA learns a better policy with fewer decision steps and measurements than the considered alternative from the literature.
JSONoid: Monoid-based Enrichment for Configurable and Scalable Data-Driven Schema Discovery

Schema discovery is an important aspect to working with data in formats such as JSON. Unlike relational databases, JSON data sets often do not have associated structural information. Consumers of such datasets are often left to browse through data in an attempt to observe commonalities in structure across documents to construct suitable code for data processing. However, this process is time-consuming and error-prone. Existing distributed approaches to mining schemas present a significant usability advantage as they provide useful metadata for large data sources. However, depending on the data source, ad hoc queries for estimating other properties to help with crafting an efficient data pipeline can be expensive. We propose JSONoid, a distributed schema discovery process augmented with additional metadata in the form of monoid data structures that are easily maintainable in a distributed setting. JSONoid subsumes several existing approaches to distributed schema discovery with similar performance. Our approach also adds significant useful additional information about data values to discovered schemas with linear scalability.
A Comprehensive Optical Search for Pre-explosion Outbursts from the Quiescent Progenitor of SN 2023ixf

We perform a comprehensive search for optical precursor emission at the position of SN 2023ixf using data from the DLT40, ZTF, and ATLAS surveys. By comparing the current data set with precursor outburst hydrodynamical model light curves, we find that the probability of a significant outburst within 5 yr of explosion is low, and the circumstellar material (CSM) ejected during any possible precursor outburst is likely smaller than ∼0.015M ⊙. By comparing to a set of toy models, we find that, if there was a precursor outburst, the duration must have been shorter than ∼100 days for a typical brightness of M r ≃ −9 mag or shorter than 200 days for M r ≃ −8 mag; brighter, longer outbursts would have been discovered. Precursor activity like that observed in the normal Type II SN 2020tlf (M r ≃ −11.5) can be excluded in SN 2023ixf. If the dense CSM inferred by early flash spectroscopy and other studies is related to one or more precursor outbursts, then our observations indicate that any such outburst would have to be faint and only last for days to months, or it occurred more than 5 yr prior to the explosion. Alternatively, any dense, confined CSM may not be due to eruptive mass loss from a single red supergiant progenitor. Taken together, the results of SN 2023ixf and SN 2020tlf indicate that there may be more than one physical mechanism behind the dense CSM inferred around some normal Type II supernovae.
KoRC: Knowledge oriented Reading Comprehension Benchmark for Deep Text Understanding

Deep text understanding, which requires the connections between a given document and prior knowledge beyond its text, has been highlighted by many benchmarks in recent years. However, these benchmarks have encountered two major limitations. On the one hand, most of them require human annotation of knowledge, which leads to limited knowledge coverage. On the other hand, they usually use choices or spans in the texts as the answers, which results in narrow answer space. To overcome these limitations, we build a new challenging benchmark named KoRc in this paper. Compared with previous benchmarks, KoRC has two advantages, i.e., broad knowledge coverage and flexible answer format. Specifically, we utilize massive knowledge bases to guide annotators or large language models (LLMs) to construct knowledgable questions. Moreover, we use labels in knowledge bases rather than spans or choices as the final answers. We test state-of-the-art models on KoRC and the experimental results show that the strongest baseline only achieves 68.3% and 30.0% F1 measure in the in-distribution and out-of-distribution test set, respectively. These results indicate that deep text understanding is still an unsolved challenge. The benchmark dataset, leaderboard, and baseline methods are released in https://github.com/THU-KEG/KoRC.
CFSum Coarse-to-Fine Contribution Network for Multimodal Summarization

Multimodal summarization usually suffers from the problem that the contribution of the visual modality is unclear. Existing multimodal summarization approaches focus on designing the fusion methods of different modalities, while ignoring the adaptive conditions under which visual modalities are useful. Therefore, we propose a novel Coarse-to-Fine contribution network for multimodal Summarization (CFSum) to consider different contributions of images for summarization. First, to eliminate the interference of useless images, we propose a pre-filter module to abandon useless images. Second, to make accurate use of useful images, we propose two levels of visual complement modules, word level and phrase level. Specifically, image contributions are calculated and are adopted to guide the attention of both textual and visual modalities. Experimental results have shown that CFSum significantly outperforms multiple strong baselines on the standard benchmark. Furthermore, the analysis verifies that useful images can even help generate non-visual words which are implicitly represented in the image.
Characterization of the photon emission statistics in nitrogen-vacancy centers

We model and experimentally demonstrate the full time-dependent counting statistics of photons emitted by a single nitrogen-vacancy (NV) center in diamond under non-resonant laser excitation and resonant microwave control. A generalization of the quantum jump formalism for the seven electronic states involved in the fast intrinsic dynamics of an NV center provides a self-contained model that allows for the characterization of its emission and clarifies the relation between the quantum system internal states and the measurable detected photon counts. The model allows the elaboration of detection protocols to optimize the energy and time resources while maximizing the system sensitivity to magnetic-field measurements.
Origin-Destination Travel Time Oracle for Map-based Services

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.
Mapping the present-day chemical abundance structure of the Solar neighbourhood: O & Si

Context. Large scale chemical abundance gradients in the Galactic disks, small-scale abundance structures, and the mean chemical abundance values in the Solar vicinity, are important constraints to Galactic chemo-dynamical formation and evolution models. The formation and evolution of stars, and interstellar gas and dust depends on the distribution and evolution of matter in the Galaxy. Therefore, metallicity structures within the Milky Way can be mapped from the abundance analysis of its gas and stars. Data. A sample of 379 mostly spectral type B main sequence stars within 1.5 kpc radius from the Sun, was observed with the MIKE high-resolution spectrograph on the Magellan 6.5-m telescope on Las Campanas in Chile in 2007. Projected rotational velocities and photometric effective temperatures were determined for these stars by Braganca et al. (2012). B-stars are good indicators for present-day cosmic abundances due to their short lifetimes. They preserve the interstellar medium abundances, which they were born from, in the photosphere, and they do not migrate far from their birth environment. Aims. The purpose of this Master’s project was to start mapping the present-day silicon and oxygen abundance structure of the Solar neighbourhood, by determining the stellar atmospheric parameters, and Si and O abundances, for the low-rotating (< 22 km/s ) subsample. The thesis at hand is part of an international study aimed to determine the elemental abundances of B-type main sequence stars near the Solar vicinity and in the outer Galactic disk (Braganca et al. 2012, 2015; Garmany et al. 2015, Braganca et al. in prep.). Methods. Line-profile fitting of Si, O and Balmer lines, with full NLTE synthetic spectra, and an iterative analysis methodology, was used to constrain stellar atmospheric parameters and elemental abundances at high accuracy and precision. Results & Conclusions. With this Master’s thesis, an extensive mapping the Solar neighbourhood has begun: stellar parameters, and Si and O abundances were established for 17 stars. The mean elemental abundance of the Solar neighbourhood of a 1.25 kpc radius, was found to be 7.42±0.13 dex for silicon and 8.73±0.06 dex for oxygen. The results are consistent with the cosmic abundance standard from Nieva & Przybilla (2012) within error-bars, and provide reliable present-day anchor points for Galactic chemical evolution models. However, the stars in our sample have on average smaller abundance values and the Solar neighbourhood seems to be more heterogeneous, than determined by Nieva & Przybilla (2012) study. Small scale abundance structures were not discovered, due to yet small sample size. In the future, other low-rotating stars in the observed stellar sample will be analysed, up to a v sin i = 80 km/s limit. This would amount to about 100 stars. Combined with Gaia DR2 positions, and chemical abundances for the fast rotating B-type stars in the Solar vicinity (Cazorla et al. 2017), would provide an unprecedented view of the present-day Solar neighbourhood.
Analysis of a Programmable Quantum Annealer as a Random Number Generator

Quantum devices offer a highly useful function - that is generating random numbers in a non-deterministic way since the measurement of a quantum state is not deterministic. This means that quantum devices can be constructed that generate qubits in a uniform superposition and then measure the state of those qubits. If the preparation of the qubits in a uniform superposition is unbiased, then quantum computers can be used to create high entropy, secure random numbers. Quantum annealing (QA) is a type of analog quantum computation that is a relaxed form of adiabatic quantum computation and uses quantum fluctuations in order to search for ground state solutions of a programmable Ising model. Here we present extensive experimental random number results from a D-Wave 2000Q quantum annealer, totaling over 20 billion bits of QA measurements, which is significantly larger than previous D-Wave QA random number generator studies. Current quantum annealers are susceptible to noise from environmental sources and calibration errors, and are not in general unbiased samplers. Therefore, it is of interest to quantify whether noisy quantum annealers can effectively function as an unbiased QRNG. The amount of data that was collected from the quantum annealer allows a comprehensive analysis of the random bits to be performed using the NIST SP 800-22 Rev 1a testsuite, as well as min-entropy estimates from NIST SP 800-90B. The randomness tests show that the generated random bits from the D-Wave 2000Q are biased, and not unpredictable random bit sequences. With no server-side sampling post-processing, the $1$ microsecond annealing time measurements had a min-entropy of $0.824$.