Miracle-dz/UniFAR-FTUs huggingface dataset:
DOSA-MO: Dual-stage Optimizer for Systematic overestimation Adjustment in Multi-Objective problems improves biomarker discovery

The challenge in biomarker discovery and validation using machine learning from omics data lies in the abundance of molecular features but scarcity of samples. Most machine learning-based feature selection methods necessitate of hyperparameter tuning, typically performed by evaluating numerous alternatives on a validation set. Every evaluation has a performance estimation error and when the selection takes place between many models the best ones are almost certainly overestimated. Biomarker identification is a typical multi-objective problem with trade-offs between the predictive ability and the parsimony in the number of molecular features. Genetic algorithms are a popular tool for multi-objective optimization but they evolve numerous solutions and are prone to overestimation. Methods have been proposed to reduce the overestimation after a model has already been selected in single-objective problems, but to the best of our knowledge no algorithm existed that was capable of reducing the overestimation during the optimization, leading to a better model selection, or that had been applied in the more general domain of multi-objective problems. We propose DOSA-MO, a novel multi-objective optimization wrapper algorithm that learns how the original estimation, its variance, and the feature set size of the solutions predict the overestimation, and adjusts the expectation of the performance during the optimization, improving the composition of the solution set. We verify that DOSA-MO improves the performance of a state-of-the-art genetic algorithm on left-out or external sample sets, when predicting cancer subtypes and/or patient overall survival, using three transcriptomics datasets for kidney and breast cancer. Since to the best of our knowledge there was no measure of estimation error for multi-objective solution sets, we propose two such measures. According to both of them, DOSA-MO provides more realistic performance estimates in all the considered scenarios.
Signatures of quantum phases in a dissipative system.

Lindbladian formalism, as tuned to dissipative and open systems, has been all-pervasive to in- terpret non-equilibrium steady states of quantum many-body systems. We study the fate of free fermionic and superconducting phases in a dissipative one-dimensional Kitaev model - where the bath acts both as a source and a sink of fermionic particles with different coupling rates. As a function of these two couplings, we investigate the steady state, its entanglement content, and its approach from varying initial states. Interestingly, we find that the steady state phase diagram retains decipherable signatures of ground state critical physics. We also show that early-time fi- delity is a useful marker to find a subclass of phase transitions in such situations. Moreover, we show that the survival of critical signatures at late-times, strongly depend on the thermal nature of the steady state. This connection hints at a correspondence between quantum observables and classical magnetism in the steady state of such systems. Our work uncovers interesting connections between dissipative quantum many-body systems, thermalization of a classical spin and many-body quantum critical phenomena.
Mass spectrum of spin-one hadrons in dense two-color QCD: Novel predictions by extended linear sigma model

We construct an extended version of the linear sigma model in such a way as to describe spin-$1$ hadrons as well as spin-$0$ hadrons in two-color QCD (QC$_2$D) by respecting the Pauli-G\"{u}rsey $SU(4)$ symmetry. Within a mean-field approximation, we therefrom examine a mass spectrum of the spin-$1$ hadrons at finite quark chemical potential ($\mu_q$) and zero temperature. Not only mean fields of scalar mesons and scalar-diquark baryons but also of vector mesons and vector-diquark baryons are incorporated. As a result, we find that, unless all of those four types of mean fields are taken into account, neither lattice result for the critical $\mu_q$ that corresponds to the onset of baryon superfluidity nor for $\mu_q$ dependence of the pion mass can be reproduced. We also find that a slight suppression of the $\rho$ meson mass in the superfluid phase, which was suggested by the lattice simulation, is reproduced by subtle mixing effects between spin-$0$ and spin-$1$ hadrons. Moreover, we demonstrate the emergence of an axialvector condensed phase and possibly of a vector condensed phase by identifying the values of $\mu_q$ at which the corresponding hadron masses vanish. The possible presence of iso-triplet $1^-$ diquarks that may be denoted by a tensor-type quark bilinear field is also discussed.
FairCompass: Operationalising Fairness in Machine Learning

As artificial intelligence (AI) increasingly becomes an integral part of our societal and individual activities, there is a growing imperative to develop responsible AI solutions. Despite a diverse assortment of machine learning fairness solutions is proposed in the literature, there is reportedly a lack of practical implementation of these tools in real-world applications. Industry experts have participated in thorough discussions on the challenges associated with operationalising fairness in the development of machine learning-empowered solutions, in which a shift toward human-centred approaches is promptly advocated to mitigate the limitations of existing techniques. In this work, we propose a human-in-the-loop approach for fairness auditing, presenting a mixed visual analytical system (hereafter referred to as 'FairCompass'), which integrates both subgroup discovery technique and the decision tree-based schema for end users. Moreover, we innovatively integrate an Exploration, Guidance and Informed Analysis loop, to facilitate the use of the Knowledge Generation Model for Visual Analytics in FairCompass. We evaluate the effectiveness of FairCompass for fairness auditing in a real-world scenario, and the findings demonstrate the system's potential for real-world deployability. We anticipate this work will address the current gaps in research for fairness and facilitate the operationalisation of fairness in machine learning systems.
Resummed heat kernel and effective action for Yukawa and QED

In this letter, we prove the existence of resummed expressions for the diagonal of the heat kernel and the effective action of a quantum field which interacts with a scalar or an electromagnetic background. Working in an arbitrary number of spacetime dimensions, we propose an Ansatz beyond the Schwinger--DeWitt proposal, effectively resumming an infinite number of invariants which can be constructed from powers of the background, as well as its first and second derivatives in the Yukawa case. This provides a proof of the recent conjecture that all terms containing the invariants $F_{\mu\nu}F^{\mu\nu}$ and $\widetilde F_{\mu\nu}F^{\mu\nu}$ in the proper-time series expansion of the SQED effective action can be resummed. Possible generalizations and several applications are also discussed -- in particular, the existence of an analogue of the Schwinger effect for Yukawa couplings.
QUANTITATIVE SURVEY OF SEVERAL IMPORTANT ISSUES CONCERNING THE FORMATION OF THE DANXIA LANDFORMS

Fluvial deposits on the palaeo-fluvial terraces of Danxia landform areas were dated by using TL technique. Tectonic uplift rates were obtained by dividing the heights of the paIaeo-fluvial terraces above current average water level. The geomorphic ages were then calculated from the tectonic uplift rates and the relative heights of Danxia strata. The cliff retreating rates were deduced from the geomorphic ages and the cliff valley width. Finally the continent erosional rates were estimated by surveying the sediment volume that had been eroded out with the geomorphic ages. This study is a new attempt in geomorphology to step from qualitative description to quantitative determination of earth surface landform processes.
Knowledge Enhanced Conditional Imputation for Healthcare Time-series

This study presents a novel approach to addressing the challenge of missing data in multivariate time series, with a particular focus on the complexities of healthcare data. Our Conditional Self-Attention Imputation (CSAI) model, grounded in a transformer-based framework, introduces a conditional hidden state initialization tailored to the intricacies of medical time series data. This methodology diverges from traditional imputation techniques by specifically targeting the imbalance in missing data distribution, a crucial aspect often overlooked in healthcare datasets. By integrating advanced knowledge embedding and a non-uniform masking strategy, CSAI adeptly adjusts to the distinct patterns of missing data in Electronic Health Records (EHRs).
Existence and spectrality of infinite convolutions generated by infinitely many admissible pairs

In this paper, we study the spectrality of infinite convolutions generated by infinitely many admissible pairs which may not be compactly supported, where the spectrality means the corresponding square integrable function space admits a family of exponential functions as an orthonormal basis. First, we prove that the infinite convolution exists and is a spectral measure if the sequence of admissible pairs satisfies the remainder bounded condition, and it has a subsequence consisting of general consecutive sets. Then we show that the subsequence of general consecutive sets may be replaced by a general assumption, named $\theta$-bounded condition. Finally, we investigate the infinite convolutions generated by special subsequences, and give a sufficient condition for the spectrality of such infinite convolutions.
Replication-proof Bandit Mechanism Design

We study a problem of designing replication-proof bandit mechanisms when agents strategically register or replicate their own arms to maximize their payoff. We consider Bayesian agents who are unaware of ex-post realization of their own arms' mean rewards, which is the first to study Bayesian extension of Shin et al. (2022). This extension presents significant challenges in analyzing equilibrium, in contrast to the fully-informed setting by Shin et al. (2022) under which the problem simply reduces to a case where each agent only has a single arm. With Bayesian agents, even in a single-agent setting, analyzing the replication-proofness of an algorithm becomes complicated. Remarkably, we first show that the algorithm proposed by Shin et al. (2022), defined H-UCB, is no longer replication-proof for any exploration parameters. Then, we provide sufficient and necessary conditions for an algorithm to be replication-proof in the single-agent setting. These results centers around several analytical results in comparing the expected regret of multiple bandit instances, which might be of independent interest. We further prove that exploration-then-commit (ETC) algorithm satisfies these properties, whereas UCB does not, which in fact leads to the failure of being replication-proof. We expand this result to multi-agent setting, and provide a replication-proof algorithm for any problem instance. The proof mainly relies on the single-agent result, as well as some structural properties of ETC and the novel introduction of a restarting round, which largely simplifies the analysis while maintaining the regret unchanged (up to polylogarithmic factor). We finalize our result by proving its sublinear regret upper bound, which matches that of H-UCB.
Clustering Sets of Functional Data by Similarity in Law

We introduce a new clustering method for the classification of functional data sets by their probabilistic law, that is, a procedure that aims to assign data sets to the same cluster if and only if the data were generated with the same underlying distribution. This method has the nice virtue of being non-supervised and non-parametric, allowing for exploratory investigation with few assumptions about the data. Rigorous finite bounds on the classification error are given along with an objective heuristic that consistently selects the best partition in a data-driven manner. Simulated data has been clustered with this procedure to show the performance of the method with different parametric model classes of functional data.
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible at https://dl3dv-10k.github. io/DL3DV-10K/.
Going Green in RAN Slicing

Network slicing is essential for transforming future telecommunication networks into versatile service platforms, but it also presents challenges for sustainable network operations. While meeting the requirements of network slices incurs additional energy consumption compared to non-sliced networks, operators strive to offer diverse 5G and beyond services while maintaining energy efficiency. In this study, we address the issue of slice activation/deactivation to reduce energy consumption while maintaining the user quality of service (QoS). We employ Deep Contextual Multi-Armed Bandit and Thompson Sampling Contextual Multi-Armed Bandit agents to make activation/deactivation decisions for individual clusters. Evaluations are performed using the NetMob23 dataset, which captures the spatio-temporal consumption of various mobile services in France. Our simulation results demonstrate that our proposed solutions provide significant reductions in network energy consumption while ensuring the QoS remains at a similar level compared to a scenario where all slice instances are active.
Dawn and fall of non-Gaussianity in the quantum parametric oscillator

Systems of coupled optical parametric oscillators (OPOs) forming an Ising machine are emerging as large-scale simulators of the Ising model. The advances in computer science and nonlinear optics have triggered not only the physical realization of hybrid (electro-optical) or all-optical Ising machines, but also the demonstration of quantum-inspired algorithms boosting their performances. To date, the use of the quantum nature of parametrically generated light as a further resource for computation represents a major open issue. A key quantum feature is the non-Gaussian character of the system state across the oscillation threshold. In this paper, we perform an extensive analysis of the emergence of non-Gaussianity in the single quantum OPO with an applied external field. We model the OPO by a Lindblad master equation, which is numerically solved by an ab initio method based on exact diagonalization. Non-Gaussianity is quantified by means of three different metrics: Hilbert-Schmidt distance, quantum relative entropy, and photon distribution. Our findings reveal a nontrivial interplay between parametric drive and applied field: (i) Increasing pump monotonously enhances non-Gaussianity, and (ii) Increasing field first sharpens non-Gaussianity, and then restores the Gaussian character of the state when above a threshold value.
Factoring Expertise, Workload, and Turnover into Code Review Recommendation

Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turnover tend to disrupt and increase workloads for developers. In this work, we suggest that through code review recommendation we can distribute knowledge and mitigate turnover while more evenly distributing review workload. We conduct historical analyses to understand the natural concentration of review workload and the degree of knowledge spreading that is inherent in code review. Even though review workload is highly concentrated, we show that code review natural spreads knowledge thereby reducing the files at risk to turnover. Using simulation, we evaluate existing code review recommenders and develop novel recommenders to understand their impact on the level of expertise during review, the workload of reviewers, and the files at risk to turnover. Our simulations use seeded random replacement of reviewers to allow us to compare the reviewer recommenders without the confounding variation of different reviewers being replaced for each recommender. Combining recommenders, we develop the SofiaWL recommender that suggests experts with low active review workload when none of the files under review are known by only one developer. In contrast, when knowledge is concentrated on one developer, it sends the review to other reviewers to spread knowledge. For the projects we study, we are able to globally increase expertise during reviews, +3%, reduce workload concentration, -12%, and reduce the files at risk, -28%. We make our scripts and data available in our replication package. Developers can optimize for a particular outcome measure based on the needs of their project, or use our GitHub bot to automatically balance the outcomes.
Achieving 100% amplitude modulation depth in a graphene-based tuneable capacitance metamaterial

Effective control of terahertz radiation requires the development of efficient and fast modulators with a large modulation depth. This challenge is often tackled by using metamaterials, artificial sub-wavelength optical structures engineered to resonate at the desired terahertz frequency. Metamaterial-based devices exploiting graphene as the active tuneable element have been proven to be a highly effective solution for THz modulation. However, whilst the graphene conductivity can be tuned over a wide range, it cannot be reduced to zero due to the gapless nature of graphene, which directly limits the maximum achievable modulation depth for single-layer metamaterial modulators. Here, we demonstrate two novel solutions to circumvent this restriction: Firstly, we excite the modulator from the back of the substrate, and secondly, we incorporate air gaps into the graphene patches. This results in a ground-breaking graphene-metal metamaterial terahertz modulator, operating at 2.0-2.5 THz, which demonstrates a 99.01 % amplitude and a 99.99 % intensity modulation depth at 2.15 THz, with a reconfiguration speed in excess of 3 MHz. Our results open up new frontiers in the area of terahertz technology.
Dealing with missing angular sections in nanoCT reconstructions of low contrast polymeric samples employing a mechanical in situ loading stage

While in situ experiments are gaining importance for the (mechanical) assessment of metamaterials or materials with complex microstructures, imaging conditions in such experiments are often challenging. The lab-based computed tomography system Xradia 810 Ultra allows for the in situ (time lapsed) mechanical testing of samples. However, the in situ loading setup from this system limits the image acquisition angle to 140$^\circ$. For low contrast polymeric materials, this limited acquisition angle leads to regions of low information gain, thus preventing an accurate reconstruction of the data using a filtered back projection algorithm. Here we demonstrate how the information gain can be improved by selecting an appropriate position of the sample. A low contrast polymeric tetrahedral microlattice sample and a specifically structured sample, both scanned over 140$^\circ$ and 180$^\circ$, demonstrate that the missing structural details in the 140$^\circ$ reconstruction are limited to an angular wedge of about 20$^\circ$. Depending on the sample geometry and structure, applying simple strategies for the in situ experiments allows accurate reconstruction of the data. For the tetrahedral microlattice, a simple rotation of the sample by 90$^\circ$ provides enough X-ray absorption for an accurate reconstruction of the geometry.
The asymptotic mean action and the asymptotic linking number for pseudo-rotations

We establish that the asymptotic mean action and the asymptotic linking number of irrational pseudo-rotations remain well-defined everywhere and constant for every $C^{1}$ irrational pseudo-rotation that behaves as a rotation on the boundary. As a consequence, we demonstrate that the isotopy of irrational pseudo-rotations with a positive rotation number is a right-handed isotopy in the sense of Ghys.
Soft synchronous gauge in the perturbative gravity

An attempt to directly use the synchronous gauge ($g_{0 \lambda} = - \delta_{0 \lambda}$) in perturbative gravity leads to a singularity at $p_0 = 0$ in the graviton propagator. This is similar to the singularity in the propagator for Yang-Mills fields $A^a_\lambda$ in the temporal gauge ($A^a_0 = 0$). There the singularity was softened, obtaining this gauge as the limit at $\varepsilon \to 0$ of the gauge $n^\lambda A^a_\lambda = 0$, $n^\lambda = (1, - \varepsilon (\partial^j \partial_j )^{- 1} \partial^k ) $. Then the singularities at $p_0 = 0$ are replaced by negative powers of $p_0 \pm i \varepsilon$, and thus we bypass these poles in a certain way. Now consider a similar condition on $n^\lambda g_{\lambda \mu}$ in perturbative gravity, which becomes the synchronous gauge at $\varepsilon \to 0$. Unlike the Yang-Mills case, the contribution of the Faddeev-Popov ghosts to the effective action is nonzero, and we calculate it. In this calculation, an intermediate regularization is needed, and we assume the discrete structure of the theory at short distances for that. The effect of this contribution is, in particular, to add non-pole terms to the propagator. In overall, this contribution vanishes at $\varepsilon \to 0$. Thus, we effectively have the synchronous gauge with the resolved singularities at $p_0 = 0$, where only the physical components $g_{j k}$ are active and there is no need to calculate the ghost contribution.
Spatial Dynamics of Higher Order Rock-Paper-Scissors and Generalisations

We introduce and study the spatial replicator equation with higher order interactions and both infinite (spatially homogeneous) populations and finite (spatially inhomogeneous) populations. We show that in the special case of three strategies (rock-paper-scissors) higher order interaction terms allow travelling waves to emerge in non-declining finite populations. We show that these travelling waves arise from diffusion stabilisation of an unstable interior equilibrium point that is present in the aspatial dynamics. Based on these observations and prior results, we offer two conjectures whose proofs would fully generalise our results to all odd cyclic games, both with and without higher order interactions, assuming a spatial replicator dynamic. Intriguingly, these generalisations for $N \geq 5$ strategies seem to require declining populations, as we show in our discussion.