Miracle-dz/UniFAR-FTUs huggingface dataset:
A practitioner’s guide to quantum algorithms for optimisation problems

Quantum computing is gaining popularity across a wide range of scientific disciplines due to its potential to solve long-standing computational problems that are considered intractable with classical computers. One promising area where quantum computing has potential is in the speed-up of NP-hard optimisation problems that are common in industrial areas such as logistics and finance. Newcomers to the field of quantum computing who are interested in using this technology to solve optimisation problems do not have an easily accessible source of information on the current capabilities of quantum computers and algorithms. This paper aims to provide a comprehensive overview of the theory of quantum optimisation techniques and their practical application, focusing on their near-term potential for noisy intermediate scale quantum devices. The paper starts by drawing parallels between classical and quantum optimisation problems, highlighting their conceptual similarities and differences. Two main paradigms for quantum hardware are then discussed: analogue and gate-based quantum computers. While analog devices such as quantum annealers are effective for some optimisation problems, they have limitations and cannot be used for universal quantum computation. In contrast, gate-based quantum computers offer the potential for universal quantum computation, but they face challenges with hardware limitations and accurate gate implementation. The paper provides a detailed mathematical discussion with references to key works in the field, as well as a more practical discussion with relevant examples. The most popular techniques for quantum optimisation on gate-based quantum computers, the quantum approximate optimisation algorithm and the quantum alternating operator ansatz framework, are discussed in detail. However, it is still unclear whether these techniques will yield quantum advantage, even with advancements in hardware and noise reduction. The paper concludes with a discussion of the challenges facing quantum optimisation techniques and the need for further research and development to identify new, effective methods for achieving quantum advantage.
Simple blow-up solutions of singular Liouville equations

In a recent series of important works \cite{wei-zhang-1,wei-zhang-2,wei-zhang-3}, Wei-Zhang proved several vanishing theorems for non-simple blow-up solutions of singular Liouville equations. It is well known that a non-simple blow-up situation happens when the spherical Harnack inequality is violated near a quantized singular source. In this article, we further strengthen the conclusions of Wei-Zhang by proving that if the spherical Harnack inequality does hold, there exist blow-up solutions with non-vanishing coefficient functions.
Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis

Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking data to inform a collaborative filtering model for movie recommendation providing an improvement over the click-based implementations and additionally analyze the area of interest (AOI) duration as related to the known information of click data and movies seen previously, showing AOI information consistently coincides with these items of interest.
Versatile Audio-Visual Learning for Handling Single and Multi Modalities in Emotion Regression and Classification Tasks

Most current audio-visual emotion recognition models lack the flexibility needed for deployment in practical applications. We envision a multimodal system that works even when only one modality is available and can be implemented interchangeably for either predicting emotional attributes or recognizing categorical emotions. Achieving such flexibility in a multimodal emotion recognition system is difficult due to the inherent challenges in accurately interpreting and integrating varied data sources. It is also a challenge to robustly handle missing or partial information while allowing direct switch between regression and classification tasks. This study proposes a \emph{versatile audio-visual learning} (VAVL) framework for handling unimodal and multimodal systems for emotion regression and emotion classification tasks. We implement an audio-visual framework that can be trained even when audio and visual paired data is not available for part of the training set (i.e., audio only or only video is present). We achieve this effective representation learning with audio-visual shared layers, residual connections over shared layers, and a unimodal reconstruction task. Our experimental results reveal that our architecture significantly outperforms strong baselines on both the CREMA-D and MSP-IMPROV corpora. Notably, VAVL attains a new state-of-the-art performance in the emotional attribute prediction task on the MSP-IMPROV corpus. Code available at: https://github.com/ilucasgoncalves/VAVL
Matching Statistics speed up BWT construction

Due to the exponential growth of genomic data, constructing dedicated data structures has become the principal bottleneck in common bioinformatics applications. In particular, the Burrows-Wheeler Transform (BWT) is the basis of some of the most popular self-indexes for genomic data, due to its known favourable behaviour on repetitive data. Some tools that exploit the intrinsic repetitiveness of biological data have risen in popularity, due to their speed and low space consumption. We introduce a new algorithm for computing the BWT, which takes advantage of the redundancy of the data through a compressed version of matching statistics, the $\textit{CMS}$ of [Lipt\'ak et al., WABI 2022]. We show that it suffices to sort a small subset of suffixes, lowering both computation time and space. Our result is due to a new insight which links the so-called insert-heads of [Lipt\'ak et al., WABI 2022] to the well-known run boundaries of the BWT. We give two implementations of our algorithm, called $\texttt{CMS}$-$\texttt{BWT}$, both competitive in our experimental validation on highly repetitive real-life datasets. In most cases, they outperform other tools w.r. t. running time, trading off a higher memory footprint, which, however, is still considerably smaller than the total size of the input data.
Deep Reinforcement Learning for Interference Management in UAV-Based 3D Networks: Potentials and Challenges

Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This challenge is growing as cellular networks become three-dimensional with the adoption of unmanned aerial vehicles (UAVs). This is because the strength and number of interference links rapidly increase due to the line-of-sight channels in UAV communications. Existing interference management solutions require each transmitter to know the channel information of interfering signals, rendering them impractical due to excessive signaling overhead. In this article, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming. In particular, we show that interference can still be effectively mitigated even without knowing its channel information. We then discuss novel approaches to scale the algorithms with linear/sublinear complexity and decentralize them using multi-agent reinforcement learning. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution

Current MRI super-resolution (SR) methods only use existing contrasts acquired from typical clinical sequences as input for the neural network (NN). In turbo spin echo sequences (TSE) the sequence parameters can have a strong influence on the actual resolution of the acquired image and have consequently a considera-ble impact on the performance of the NN. We propose a known-operator learning approach to perform an end-to-end optimization of MR sequence and neural net-work parameters for SR-TSE. This MR-physics-informed training procedure jointly optimizes the radiofrequency pulse train of a proton density- (PD-) and T2-weighted TSE and a subsequently applied convolutional neural network to predict the corresponding PDw and T2w super-resolution TSE images. The found radiofrequency pulse train designs generate an optimal signal for the NN to perform the SR task. Our method generalizes from the simulation-based optimi-zation to in vivo measurements and the acquired physics-informed SR images show higher correlation with a time-consuming segmented high-resolution TSE sequence compared to a pure network training approach.
Stellar Characterization and Radius Inflation of Hyades M-dwarf Stars from the APOGEE Survey

We present a spectroscopic analysis of a sample of 48 M-dwarf stars (0.2 M ⊙ < M < 0.6 M ⊙) from the Hyades open cluster using high-resolution H-band spectra from the Sloan Digital Sky Survey/Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey. Our methodology adopts spectrum synthesis with LTE MARCS model atmospheres, along with the APOGEE Data Release 17 line list, to determine effective temperatures, surface gravities, metallicities, and projected rotational velocities. The median metallicity obtained for the Hyades M dwarfs is [M/H] = 0.09 ± 0.03 dex, indicating a small internal uncertainty and good agreement with optical results for Hyades red giants. Overall, the median radii are larger than predicted by stellar models by 1.6% ± 2.3% and 2.4% ± 2.3%, relative to a MIST and DARTMOUTH isochrone, respectively. We emphasize, however, that these isochrones are different, and the fractional radius inflation for the fully and partially convective regimes have distinct behaviors depending on the isochrone. Using a MIST isochrone there is no evidence of radius inflation for the fully convective stars, while for the partially convective M dwarfs the radii are inflated by 2.7% ± 2.1%, which is in agreement with predictions from models that include magnetic fields. For the partially convective stars, rapid rotators present on average higher inflation levels than slow rotators. The comparison with SPOTS isochrone models indicates that the derived M-dwarf radii can be explained by accounting for stellar spots in the photosphere of the stars, with 76% of the studied M dwarfs having up to 20% spot coverage, and the most inflated stars with ∼20%–40% spot coverage.
How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology

Predictions are a central part of water resources research. Historically, physically-based models have been preferred; however, they have largely failed at modeling hydrological processes at a catchment scale and there are some important prediction problems that cannot be modeled physically. As such, machine learning (ML) models have been seen as a valid alternative in recent years. In spite of their availability, well-optimized state-of-the-art ML strategies are not being widely used in water resources research. This is because using state-of-the-art ML models and optimizing hyperparameters requires expert mathematical and statistical knowledge. Further, some analyses require many model trainings, so sometimes even expert statisticians cannot properly optimize hyperparameters. To leverage data and use it effectively to drive scientific advances in the field, it is essential to make ML models accessible to subject matter experts by improving automated machine learning resources. ML models such as XGBoost have been recently shown to outperform random forest (RF) models which are traditionally used in water resources research. In this study, based on over 150 water-related datasets, we extensively compare XGBoost and RF. This study provides water scientists with access to quick user-friendly RF and XGBoost model optimization.
Shielding collisions of ultracold CaF molecules with static electric fields

We study collisions of ultracold CaF molecules in strong static electric fields. These fields allow the creation of long-range barriers in the interaction potential, effectively preventing the molecules from reaching the short-range region where inelastic and other loss processes are likely to occur. We carry out coupled-channel calculations of rate coefficients for elastic scattering and loss. We develop an efficient procedure for including energetically well-separated rotor functions in the basis set via a Van Vleck transformation. We show that shielding is particularly efficient for CaF and allows the rate of two-body loss processes to be reduced by a factor of $10^7$ or more at a field of 23 kV/cm. The loss rates remain low over a substantial range of fields. Electron and nuclear spins cause strong additional loss in some small ranges of field, but have little effect elsewhere. These results pave the way for evaporative cooling of CaF towards quantum degeneracy.
Linking UV spectral properties of MUSE Ly 𝛼 emitters at 𝑧 > 3 to Lyman continuum escape

The physical conditions giving rise to high escape fractions of ionising radiation (LyC 𝑓 esc ) in star-forming galaxies – most likely protagonists of cosmic reionisation – are not yet fully understood. Using the properties of the Lyman-𝛼 line proﬁle associated with LyC escape, we select potential LyC leakers and non-leakers from a compiled sample of 1422 MUSE-Wide and MUSE HUDF Lyman-𝛼 emitters (LAEs) in the redshift range 2 . 9 < 𝑧 < 6 . 7. We perform spectral stacking to obtain high signal-to-noise detections of rest-frame UV absorption and emission lines, and ﬁnd that the stacks with LyC-leaker candidates show (i) strong nebular O iii] 𝜆 1666, [Si iii] 𝜆 1883 and [C iii] 𝜆 1907 + C iii] 𝜆 1909 emission, suggesting high ionisation parameters due to an elevated production rate of ionising photons coming from young, metal-poor stars; (ii) high equivalent widths of He ii 𝜆 1640 ( ∼ 1 − 3 Å), possibly indicating a hard ionising spectrum alongside with a high ionising photon production eﬃciency; (iii) Si ii ∗ 𝜆 1533 emission, revealing the presence of neutral hydrogen oﬀ the line of sight, thus implying a highly anisotropic interstellar medium (ISM); (iv) high C iv 𝜆𝜆 1548,1550 to [C iii] 𝜆 1907 + C iii] 𝜆 1909 ratios (C iv / C iii] (cid:38) 0 . 75), partly associated with the increased ISM transparency. In contrast, the stacks with non-leakers show weaker nebular emission lines, low He ii 𝜆 1640 equivalent widths ( (cid:46) 1 Å), and low C iv / C iii] ( (cid:46) 0 . 25), suggesting a low ionisation state of the ISM and a high neutral hydrogen content. Consequently, our results substantiate that the C iv / C iii] ratio can be used as an indirect tracer of 𝑓 esc , providing a promising tool for identiﬁcation of ionising sources among star-forming galaxies in the epoch of reionisation.
Neural Responses to Political Words in Natural Speech Diﬀer by Political Orientation

Worldviews may diﬀer signiﬁcantly according to political orientation. Even a single word can have a completely diﬀerent meaning depending on political orientation. However, direct evidence indicating diﬀerences in the neural responses to words, between conservative- and liberal-leaning individuals, has not been obtained. The present study aimed to investigate whether neural responses related to semantic processing of political words in natural speech diﬀer according to political orientation. We measured electroencephalographic signals while participants with diﬀerent political orientations listened to natural speech. Responses for moral-, ideology-, and policy-related words between and within the participant groups were then compared. Within-group comparisons showed that right-leaning participants reacted more to moral-related words than to policy-related words, while left-leaning participants reacted more to policy-related words than to moral-related words. In addition, between-group comparisons also showed that neural responses for moral-related words were greater in right-leaning participants than in left-leaning participants and those for policy-related words were lesser in right-leaning participants than in neutral participants. There was a signiﬁcant correlation between the predicted and self-reported political orientations. In summary, the study found that people with diﬀerent political orientations diﬀer in semantic processing at the level of a single word. These ﬁndings have implications for understanding the mechanisms of political polarization and for making policy messages more eﬀective.
Drop in the Hard Pulsed Fraction and a Candidate Cyclotron Line in IGR J16320–4751 Seen by NuSTAR

We report on a timing and spectral analysis of a 50 ks NuSTAR observation of IGR J16320−4751 (= AX J1631.9−4752), a high-mass X-ray binary hosting a slowly rotating neutron star. In this observation from 2015, the spin period was 1308.8 ± 0.4 s giving a period derivative Ṗ∼2×10−8 s s−1 when compared with the period measured in 2004. In addition, the pulsed fraction decreased as a function of energy, as opposed to the constant trend that was seen previously. This suggests a change in the accretion geometry of the system during the intervening 11 yr. The phase-averaged spectra were fit with the typical model for accreting pulsars: a power law with an exponential cutoff. This left positive residuals at 6.4 keV attributable to the known iron Kα line, as well as negative residuals around 14 keV from a candidate cyclotron line detected at a significance of 5σ. We found no significant differences in the spectral parameters across the spin period, other than the expected changes in flux and component normalizations. A flare lasting around 5 ks was captured during the first half of the observation, where the X-ray emission hardened and the local column density decreased. Finally, the binary orbital period was refined to 8.9912 ± 0.0078 days thanks to Swift/BAT monitoring data from 2005–2022.
An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this article, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multimap matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.
Exploration of differentiability in a proton computed tomography simulation framework

Objective. Gradient-based optimization using algorithmic derivatives can be a useful technique to improve engineering designs with respect to a computer-implemented objective function. Likewise, uncertainty quantification through computer simulations can be carried out by means of derivatives of the computer simulation. However, the effectiveness of these techniques depends on how ‘well-linearizable’ the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications. Approach. This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques. Main results. The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. However, the observed high density and magnitude of jumps was likely to preclude most meaningful uses of the derivatives. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path, and could be reduced in magnitude by a ‘fuzzy voxels’ approach. The investigated jumps in the MC function arose from local changes in the control flow that affected the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem. Significance. Besides the technical challenges of merely applying AD to existing software projects, the MC and MBIR codes must be adapted to compute smoother functions. For the MBIR code, we presented one possible approach for this while for the MC code, this will be subject to further research. For the tracking subprocedure, further research on surrogate models is necessary.
Rapid radiation of ant parasitic butterflies during the Miocene aridification of Africa

Abstract Africa has undergone a progressive aridification during the last 20 My that presumably impacted organisms and fostered the evolution of life history adaptations. We test the hypothesis that shift to living in ant nests and feeding on ant brood by larvae of phyto‐predaceous Lepidochrysops butterflies was an adaptive response to the aridification of Africa that facilitated the subsequent radiation of butterflies in this genus. Using anchored hybrid enrichment we constructed a time‐calibrated phylogeny for Lepidochrysops and its closest, non‐parasitic relatives in the Euchrysops section (Poloyommatini). We estimated ancestral areas across the phylogeny with process‐based biogeographical models and diversification rates relying on time‐variable and clade‐heterogeneous birth‐death models. The Euchrysops section originated with the emerging Miombo woodlands about 22 million years ago (Mya) and spread to drier biomes as they became available in the late Miocene. The diversification of the non‐parasitic lineages decreased as aridification intensified around 10 Mya, culminating in diversity decline. In contrast, the diversification of the phyto‐predaceous Lepidochrysops lineage proceeded rapidly from about 6.5 Mya when this unusual life history likely first evolved. The Miombo woodlands were the cradle for diversification of the Euchrysops section, and our findings are consistent with the hypothesis that aridification during the Miocene selected for a phyto‐predaceous life history in species of Lepidochrysops, with ant nests likely providing caterpillars a safe refuge from fire and a source of food when vegetation was scarce.