Miracle-dz/UniFAR-FTUs huggingface dataset:
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization

The increasing reliance on ML models in high-stakes tasks has raised a major concern on fairness violations. Although there has been a surge of work that improves algorithmic fairness, most of them are under the assumption of an identical training and test distribution. In many real-world applications, however, such an assumption is often violated as previously trained fair models are often deployed in a different environment, and the fairness of such models has been observed to collapse. In this paper, we study how to transfer model fairness under distribution shifts, a widespread issue in practice. We conduct a fine-grained analysis of how the fair model is affected under different types of distribution shifts and find that domain shifts are more challenging than subpopulation shifts. Inspired by the success of self-training in transferring accuracy under domain shifts, we derive a sufficient condition for transferring group fairness. Guided by it, we propose a practical algorithm with a fair consistency regularization as the key component. A synthetic dataset benchmark, which covers all types of distribution shifts, is deployed for experimental verification of the theoretical findings. Experiments on synthetic and real datasets including image and tabular data demonstrate that our approach effectively transfers fairness and accuracy under various distribution shifts.
Multi-Access Distributed Computing

Coded distributed computing (CDC) is a new technique proposed with the purpose of decreasing the intense data exchange required for parallelizing distributed computing systems. Under the famous MapReduce paradigm, this coded approach has been shown to decrease this communication overhead by a factor that is linearly proportional to the overall computation load during the mapping phase. Nevertheless, it is widely accepted that this overhead remains a main bottleneck in distributed computing. To address this, we take a new approach and we explore a new system model which, for the same aforementioned overall computation load of the mapping phase, manages to provide astounding reductions of the communication overhead and, perhaps counterintuitively, a substantial increase of the computational parallelization. In particular, we propose multi-access distributed computing (MADC) as a novel generalization of the original CDC model, where now mappers and reducers are distinct computing nodes that are connected through a multi-access network topology. Focusing on the MADC setting with combinatorial topology, which implies $\Lambda$ mappers and $K$ reducers such that there is a unique reducer connected to any $\alpha$ mappers, we propose a novel coded scheme and a novel information-theoretic converse, which jointly identify the optimal inter-reducer communication load to within a constant gap of $1.5$. Additionally, a modified coded scheme and converse identify the optimal max-link communication load across all existing links to within a gap of $4$. The unparalleled coding gains reported here should not be simply credited to having access to more mapped data, but rather to the powerful role of topology in effectively aligning mapping outputs. This realization raises the open question of which multi-access network topology guarantees the best possible performance in distributed computing.
Time-dependent Schrieffer-Wolff-Lindblad perturbation theory: Measurement-induced dephasing and second-order Stark shift in dispersive readout

We develop a time-dependent Schrieﬀer-Wolﬀ-Lindblad perturbation theory to study eﬀective interactions for driven open quantum systems. The starting point of our analysis is a given Lindblad equation, based on which we obtain an eﬀective (averaged) map that describes the renormalization of both the Hamiltonian and collapse operators due to the drive. As a case study, we apply this method to the dispersive readout of a transmon qubit and derive an eﬀective disperive map that describes measurement-induced dephasing and Stark shift for the transmon. The eﬀective map we derive is completely positive and trace-preserving under adiabatic resonator response. To benchmark our method, we demonstrate good agreement with a numerical computation of the eﬀective rates via the Lindbladian spectrum. Our results are also in agreement with, and extend upon, an earlier derivation of such eﬀects by Gambetta et al. [1] (Phys. Rev. A 74, 042318) using the positive P-representation for the resonator ﬁeld.
Revisiting Architecture-aware Knowledge Distillation: Smaller Models and Faster Search

Knowledge Distillation (KD) has recently emerged as a popular method for compressing neural networks. In recent studies, generalized distillation methods that find parameters and architectures of student models at the same time have been proposed. Still, this search method requires a lot of computation to search for architectures and has the disadvantage of considering only convolutional blocks in their search space. This paper introduces a new algorithm, coined as Trust Region Aware architecture search to Distill knowledge Effectively (TRADE), that rapidly finds effective student architectures from several state-of-the-art architectures using trust region Bayesian optimization approach. Experimental results show our proposed TRADE algorithm consistently outperforms both the conventional NAS approach and pre-defined architecture under KD training.
A complex multiphase DLA associated with a compact group at z = 2.431 traces accretion, outflows, and tidal streams

As part of our program to identify host galaxies of known z = 2–3 Mg ii absorbers with the Keck Cosmic Web Imager (KCWI), we discovered a compact group giving rise to a z = 2.431 DLA with ultrastrong Mg ii absorption in quasar field J234628+124859. The group consists of four star-forming galaxies within 8–28 kpc and v ∼ 40–340 km s−1 of each other, where tidal streams are weakly visible in deep HST imaging. The group geometric centre is D = 25 kpc from the quasar (D = 20–40 kpc for each galaxy). Galaxy G1 dominates the group (1.66L*, SFRFUV = 11.6 M⊙ yr−1) while G2, G3, and G4 are less massive (0.1–0.3L*, SFRFUV = 1.4–2.0 M⊙ yr−1). Using a VLT/UVES quasar spectrum covering the H i Lyman series and metal lines such as Mg ii, Si iii, and C iv, we characterized the kinematic structure and physical conditions along the line of sight with cloud-by-cloud multiphase Bayesian modelling. The absorption system has a total $\log (N({{{\rm H}\,\rm{\small I}}})/{\rm cm}^{-2})=20.53$ and an $N({{{\rm H}\,\rm{\small I}}})$-weighted mean metallicity of log (Z/Z⊙) = −0.68, with a very large Mg ii linewidth of Δv ∼ 700 km s−1. The highly kinematically complex profile is well modelled with 30 clouds across low- and intermediate-ionization phases with values ${13\lesssim \log (N({{{\rm H}\,\rm{\small I}}})/{\rm cm}^{-2})\lesssim 20}$ and −3 ≲ log (Z/Z⊙) ≲ 1. Comparing these properties to the galaxy properties, we infer a wide range of gaseous environments, including metal-rich outflows, metal-poor IGM accretion, and tidal streams from galaxy–galaxy interactions. This diversity of structures forms the intragroup medium around a complex compact group environment at the epoch of peak star formation activity. Surveys of low-redshift compact groups would benefit from obtaining a more complete census of this medium for characterizing evolutionary pathways.
Diffractive deep inelastic scattering at NLO in the dipole picture: The 
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We calculate the contribution from the $q \bar q g$ state production to the diffractive cross sections in deep inelastic scattering at high energy. The obtained cross section is finite by itself and a part of the full next-to-leading order result for the diffractive structure functions. We perform the calculation in exact kinematics in the eikonal limit, and show that the previously known high-$Q^2$ and large $M_X^2$ results for the structure functions can be extracted from our results in the appropriate limits. We furthermore discuss the steps required to obtain the full next-to-leading order results for the structure functions.
Guided Exploration in Reinforcement Learning via Monte Carlo Critic Optimization

The class of deep deterministic off-policy algorithms is effectively applied to solve challenging continuous control problems. However, current approaches use random noise as a common exploration method that has several weaknesses, such as a need for manual adjusting on a given task and the absence of exploratory calibration during the training process. We address these challenges by proposing a novel guided exploration method that uses a differential directional controller to incorporate scalable exploratory action correction. An ensemble of Monte Carlo Critics that provides exploratory direction is presented as a controller. The proposed method improves the traditional exploration scheme by changing exploration dynamically. We then present a novel algorithm exploiting the proposed directional controller for both policy and critic modification. The presented algorithm outperforms modern reinforcement learning algorithms across a variety of problems from DMControl suite.
CNN-aided Channel and Carrier Frequency Offset Estimation for HAPS-LEO Links

Low Earth orbit (LEO) satellite mega-constellation networks aim to address the high connectivity demands with a projected 50,000 satellites in less than a decade. To fully utilize such a large-scale dynamic network, an air network composed of stratospheric nodes, specifically high altitude platform station (HAPS), can help significantly with a number of aspects including mobility management. HAPS-LEO network will be subject to time-varying conditions, and in this paper, we introduce an artificial intelligence (AI)-based approach for the unique channel estimation and synchronization problems. First, channel equalization and carrier frequency offset with residual Doppler effects are minimized by using the proposed convolutional neural networks based estimator. Then, the data rate is compounded by increasing spectral efficiency using non-orthogonal multiple access method. We observed that the proposed AI-empowered HAPS-LEO network provides not only a high data throughput per second but also higher service quality thanks to the agile signal reconstruction process.
AFT-VO: Asynchronous Fusion Transformers for Multi-View Visual Odometry Estimation

Motion estimation approaches typically employ sensor fusion techniques, such as the Kalman Filter, to handle individual sensor failures. More recently, deep learning-based fusion approaches have been proposed, increasing the performance and requiring less model-specific implementations. However, current deep fusion approaches often assume that sensors are synchronised, which is not always practical, especially for low-cost hardware. To address this limitation, in this work, we propose AFT-VO, a novel transformer-based sensor fusion architecture to estimate VO from multiple sensors. Our framework combines predictions from asynchronous multi-view cameras and accounts for the time discrepancies of measurements coming from different sources. Our approach first employs a Mixture Density Network (MDN) to estimate the probability distributions of the 6-DoF poses for every camera in the system. Then a novel transformer-based fusion module, AFT-VO, is introduced, which combines these asynchronous pose estimations, along with their confidences. More specifically, we introduce Discretiser and Source Encoding techniques which enable the fusion of multi-source asynchronous signals. We evaluate our approach on the popular nuScenes and KITTI datasets. Our experiments demonstrate that multi-view fusion for VO estimation provides robust and accurate trajectories, outperforming the state of the art in both challenging weather and lighting conditions.
Quantum network medicine: rethinking medicine with network science and quantum algorithms

Scientific and technological advances in medicine and systems biology have unequivocally shown that health and disease must be viewed in the context of the interplay among multiple molecular and environmental factors. Understanding the effects of cellular interconnection on disease progression may lead to the identification of novel disease genes and pathways, and hence influence precision diagnostics and therapeutics. To accomplish this goal, the emerging field of network medicine applies network science approaches to investigate disease pathogenesis, integrating information from relevant Omics databases, including protein-protein interaction, correlation-based, gene regulatory, and Bayesian networks. However, this requires analysing and computing large amounts of data. Moreover, if we are to efficiently search for new drugs and new drug combinations, there is a pressing need for computational methods that could allow us to access the immense chemical compound space until now largely unexplored. Finally, at the microscopic level, drug-target chemistry simulation is ultimately a quantum problem, and hence it requires a quantum solution. As we will discuss, quantum computing may be a key ingredient in enabling the full potential of network medicine. We propose to combine network medicine and quantum algorithms in a novel research field, quantum network medicine, to lay the foundations of a new era of disease prevention and drug design.
Spatiotemporal Data Mining: A Survey

Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science, etc. This problem is challenging because of the high societal cost of spurious patterns and exorbitant computational cost. Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.
RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments.

We propose RAPid-Learn (Learning to Recover and Plan Again), a hybrid planning and learning method, to tackle the problem of adapting to sudden and unexpected changes in an agent’s environment (i.e., novelties). RAPid-Learn is designed to formulate and solve modifications to a task’s Markov Decision Process (MDPs) on-the-fly. It is capable of exploiting the domain knowledge to learn action executors which can be further used to resolve execution impasses, leading to a successful plan execution. We demonstrate its efficacy by introducing a wide variety of novelties in a gridworld environment inspired by Minecraft, and compare our algorithm with transfer learning baselines from the literature. Our method is (1) effective even in the presence of multiple novelties, (2) more sample efficient than transfer learning RL baselines, and (3) robust to incomplete model information, as opposed to pure symbolic planning approaches.
An Efficient Global Optimality Certificate for Landmark-Based SLAM

Modern state estimation is often formulated as an optimization problem and solved using efficient local search methods. These methods at best guarantee convergence to local minima, but, in some cases, global optimality can also be certified. Although such global optimality certificates have been well established for 3D pose-graph optimization, the details have yet to be worked out for the 3D landmark-based SLAM problem, in which estimated states include both robot poses and map landmarks. In this letter, we address this gap by using a graph-theoretic approach to cast the subproblems of landmark-based SLAM into a form that yields a sufficient condition for global optimality. Efficient methods of computing the optimality certificates for these subproblems exist, but first require the construction of a large data matrix. We show that this matrix can be constructed with complexity that remains linear in the number of landmarks and does not exceed the state-of-the-art computational complexity of one local solver iteration. We demonstrate the efficacy of the certificate on simulated and real-world landmark-based SLAM problems. We also integrate our method into the state-of-the-art SE-Sync pipeline to efficiently solve landmark-based SLAM problems to global optimality. Finally, we study the robustness of the global optimality certificate to measurement noise, taking into consideration the effect of the underlying measurement graph.
The Tidal Disruption Event AT2021ehb: Evidence of Relativistic Disk Reflection, and Rapid Evolution of the Disk–Corona System

We present X-ray, UV, optical, and radio observations of the nearby (≈78 Mpc) tidal disruption event AT2021ehb/ZTF21aanxhjv during its first 430 days of evolution. AT2021ehb occurs in the nucleus of a galaxy hosting a≈107 M ⊙ black hole (M BH inferred from host galaxy scaling relations). High-cadence Swift and Neutron Star Interior Composition Explorer (NICER) monitoring reveals a delayed X-ray brightening. The spectrum first undergoes a gradual soft → hard transition and then suddenly turns soft again within 3 days at δ t≈272 days during which the X-ray flux drops by a factor of 10. In the joint NICER+NuSTAR observation (δ t = 264 days, harder state), we observe a prominent nonthermal component up to 30 keV and an extremely broad emission line in the iron K band. The bolometric luminosity of AT2021ehb reaches a maximum of 6.0−3.8+10.4%LEdd when the X-ray spectrum is the hardest. During the dramatic X-ray evolution, no radio emission is detected, the UV/optical luminosity stays relatively constant, and the optical spectra are featureless. We propose the following interpretations: (i) the soft → hard transition may be caused by the gradual formation of a magnetically dominated corona; (ii) hard X-ray photons escape from the system along solid angles with low scattering optical depth (∼a few) whereas the UV/optical emission is likely generated by reprocessing materials with much larger column density—the system is highly aspherical; and (iii) the abrupt X-ray flux drop may be triggered by the thermal–viscous instability in the inner accretion flow, leading to a much thinner disk.
Low temperature competing magnetic energy scales in the topological ferrimagnet TbMn6Sn6

TbMn 6 Sn 6 is a metallic ferrimagnet displaying signatures of both topological electrons and topological magnons arising from ferromagnetism and spin-orbit coupling within its Mn kagome layers. Inelastic neutron scattering measurements ﬁnd strong ferromagnetic (FM) interactions within the Mn kagome layer and reveal a magnetic bandwidth of ∼ 230 meV. The low-energy magnetic excitations are characterized by strong FM Mn-Mn and antiferromagnetic (AFM) Mn-Tb interlayer magnetic couplings. We observe weaker, competing long-range FM and AFM Mn-Mn interlayer interactions similar to those driving helical magnetism in the YMn 6 Sn 6 system. Combined with density-functional theory calculations, we ﬁnd that competing Mn-Mn interlayer magnetic interactions occur in all R Mn 6 Sn 6 compounds with R = Y, Gd − Lu, resulting in magnetic instabilities and tunability when Mn- R interactions are weak. In the case of TbMn 6 Sn 6 , strong AFM Mn-Tb coupling ensures a highly stable three-dimensional ferrimagnetic network.
Pediatric Dog Bite Injuries: A 5-Year Nationwide Study and Implications of the COVID-19 Pandemic

Introduction: Although single institution studies have analyzed various animal attacks, there has not been multicenter investigation into dog bites in children. The purpose of this study was to characterize national trends and investigate the characteristics of pediatric dog bites. Methods: Aretrospective cohort study was conducted of pediatric dog bite injuries in the United States from 2015 to 2020 using the Pediatric Health Information System national database. Patient characteristics, injury locations, and need for intervention were analyzed. Mann–Whitney U test, Pearson chi-square, and Fisher exact test, and linear multivariate regressions were performed for statistical analysis of data values; statistical significance was maintained at P < 0.05. Results: A total of 56,106 patients were included, majority male (55.1%) with a median age 6.8 years (interquartile range 3.5–10.6). Incidence peaked in July (median =1217) with nadirs in February (median = 760). A substantial increase in bites was seen per overall Emergency Department presentations during the pandemic. Most common bite location was the head (62.1%), followed by the upper extremity (25.1%). Relative proportions of dog bites to the face gradually decreased with age (B = –3.4%/year, P< 0.001), whereas proportions to the upper extremities (B = + 1.9%/year, P < 0.001) and lower extremities (B = + 1.6%/year, P = 0.002) gradually increased with age. Overall, 8.0% patients required repair in the operating suite. injuries isolated to the head (OR= 2.6, P< 0.001) and those to multiple anatomic regions were more likely to require operative intervention [operating room (OR= 2.6, P< 0.001)]. Conclusions: Dog bites most commonly occur during the summer in school-aged boys. Toddlers disproportionately suffer injuries to the head, with a trend towards upper extremity bites in teenagers. The coronavirus disease 2019 pandemic ushered a spike in dog bite presentations among Emergency Department visits, further underscoring the need for targeted educational initiatives to halt the persistence of these preventable injuries.
The orphan G protein‐coupled receptor GPR149 is a negative regulator of myelination and remyelination

Myelin sheath, formed by oligodendrocytes (OLs) in the central nervous system (CNS) and Schwann cells in periphery, plays a critical role in supporting neuronal functions. OLs, differentiated from oligodendrocyte precursor cells (OPCs), are important for myelination during development and myelin repair in CNS demyelinating disease. To identify mechanisms of myelin development and remyelination after myelin damage is of great clinical interest. Here we show that the orphan G protein‐coupled receptor GPR149, enriched in OPCs, negatively regulate OPC to OL differentiation, myelination, as well as remyelination. The expression of GPR149 is downregulated during OPCs differentiation into OLs. GPR149 deficiency does not affect the number of OPCs, but promotes OPC to OL differentiation which results in earlier development of myelin. In cuprizone‐induced demyelination model, GPR149 deficiency significantly enhances myelin regeneration. Further study indicates that GPR149 may regulate OL differentiation and myelin formation via MAPK/ERK pathway. Our study suggests that deleting or blocking GPR149 might be an intriguing way to promote myelin repair in demyelinating diseases.