Miracle-dz/UniFAR-FTUs huggingface dataset:
Your Consent Is Worth 75 Euros A Year - Measurement and Lawfulness of Cookie Paywalls

Most websites offer their content for free, though this gratuity often comes with a counterpart: personal data is collected to finance these websites by resorting, mostly, to tracking and thus targeted advertising. Cookie walls and paywalls, used to retrieve consent, recently generated interest from EU DPAs and seemed to have grown in popularity. However, they have been overlooked by scholars. We present in this paper 1) the results of an exploratory study conducted on 2800 Central European websites to measure the presence and practices of cookie paywalls, and 2) a framing of their lawfulness amidst the variety of legal decisions and guidelines.
Deep Learning for Medical Image Segmentation: Tricks, Challenges and Future Directions

—Over the past few years, the rapid development of deep learning technologies for computer vision has greatly promoted the performance of medical image segmentation (MedISeg). However, the recent MedISeg publications usually focus on presentations of the major contributions ( e . g ., network architectures, training strategies, and loss functions) while unwittingly ignoring some marginal implementation details (also known as “tricks”), leading to a potential problem of the unfair experimental result comparisons. In this paper, we collect a series of MedISeg tricks for different model implementation phases ( i . e ., pre-training model, data pre-processing, data augmentation, model implementation, model inference, and result post-processing), and experimentally explore the effectiveness of these tricks on the consistent baseline models. Compared to paper-driven surveys that only blandly focus on the advantages and limitation analyses of segmentation models, our work provides a large number of solid experiments and is more technically operable. With the extensive experimental results on both the representative 2D and 3D medical image datasets, we explicitly clarify the effect of these tricks. Moreover, based on the surveyed tricks, we also open-sourced a strong MedISeg repository, where each of its components has the advantage of plug-and-play. We believe that this milestone work not only completes a comprehensive and complementary survey of the state-of-the-art MedISeg approaches, but also offers a practical guide for addressing the future medical image processing challenges including but not limited to small dataset learning, class imbalance learning, multi-modality learning, and domain adaptation. The code has been released at: MedISeg.
HAZE-Net: High-Frequency Attentive Super-Resolved Gaze Estimation in Low-Resolution Face Images

Although gaze estimation methods have been developed with deep learning techniques, there has been no such approach as aim to attain accurate performance in low-resolution face images with a pixel width of 50 pixels or less. To solve a limitation under the challenging low-resolution conditions, we propose a high-frequency attentive super-resolved gaze estimation network, i.e., HAZE-Net. Our network improves the resolution of the input image and enhances the eye features and those boundaries via a proposed super-resolution module based on a high-frequency attention block. In addition, our gaze estimation module utilizes high-frequency components of the eye as well as the global appearance map. We also utilize the structural location information of faces to approximate head pose. The experimental results indicate that the proposed method exhibits robust gaze estimation performance even in low-resolution face images with 28x28 pixels. The source code of this work is available at https://github.com/dbseorms16/HAZE_Net/.
A Survey of CO, CO2, and H2O in Comets and Centaurs

CO and CO2 are the two dominant carbon-bearing molecules in comae and have major roles in driving activity. Their relative abundances also provide strong observational constraints to models of solar system formation and evolution but have never been studied together in a large sample of comets. We carefully compiled and analyzed published measurements of simultaneous CO and CO2 production rates for 25 comets. Approximately half of the comae have substantially more CO2 than CO, about one-third are CO-dominated, and about 1/10 produce a comparable amount of both. There may be a heliocentric dependence to this ratio with CO dominating comae beyond 3.5 au. Eight out of nine of the Jupiter Family Comets in our study produce more CO2 than CO. The six dynamically new comets produce more CO2 relative to CO than the eight Oort Cloud comets that have made multiple passes through the inner solar system. This may be explained by long-term cosmic-ray processing of a comet nucleus’s outer layers. We find (Q CO/Q H2O )median = 3 ± 1% and (Q CO2 /Q H2O )median = 12 ± 2%. The inorganic volatile carbon budget was estimated to be (Q CO+Q CO2 )/Q H2O ∼ 18% for most comets. Between 0.7 and 4.6 au, CO2 outgassing appears to be more intimately tied to the water production in a way that the CO is not. The volatile carbon/oxygen ratio for 18 comets is C/Omedian ∼ 13%, which is consistent with a comet formation environment that is well within the CO snow line.
SDA-xNet: Selective Depth Attention Networks for Adaptive Multi-scale Feature Representation

Existing multi-scale solutions lead to a risk of just increasing the receptive field sizes while neglecting small receptive fields. Thus, it is a challenging problem to effectively construct adaptive neural networks for recognizing various spatial-scale objects. To tackle this issue, we first introduce a new attention dimension, i.e., depth, in addition to existing attention dimensions such as channel, spatial, and branch, and present a novel selective depth attention network to symmetrically handle multi-scale objects in various vision tasks. Specifically, the blocks within each stage of a given neural network, i.e., ResNet, output hierarchical feature maps sharing the same resolution but with different receptive field sizes. Based on this structural property, we design a stage-wise building module, namely SDA, which includes a trunk branch and a SE-like attention branch. The block outputs of the trunk branch are fused to globally guide their depth attention allocation through the attention branch. According to the proposed attention mechanism, we can dynamically select different depth features, which contributes to adaptively adjusting the receptive field sizes for the variable-sized input objects. In this way, the cross-block information interaction leads to a long-range dependency along the depth direction. Compared with other multi-scale approaches, our SDA method combines multiple receptive fields from previous blocks into the stage output, thus offering a wider and richer range of effective receptive fields. Moreover, our method can be served as a pluggable module to other multi-scale networks as well as attention networks, coined as SDA-xNet. Their combination further extends the range of the effective receptive fields towards small receptive fields, enabling interpretable neural networks. Extensive experiments demonstrate that the proposed SDA method achieves state-of-theart (SOTA) performance, outperforming other multi-scale and attention counterparts on numerous computer vision tasks, e.g., image classification, object detection, and instance segmentation. Our source code is available at https://github.com/QingbeiGuo/ SDA-xNet.git.
On the Bousfield-Kan spectral sequence for $Q(2)_{(3)}$

We compute the $E_2$-term of the Bousfield-Kan spectral sequence converging to the homotopy groups of the semi-cosimplicial $E_{\infty}$ ring spectrum $Q(2)_{(3)}$. This 3-local spectrum was constructed by M. Behrens using degree 2 isogenies of elliptic curves, and its localization with respect to the 2nd Morava $K$-theory $K(2)$ is "one half" of the $K(2)$-local sphere at the prime 3. The computation in this paper uses techniques developed in the author's previous work on the Adams-Novikov spectral sequence for $Q(2)_{(3)}$, and provides another gateway to the homotopy ring $\pi_*Q(2)_{(3)}$.
Hyper-runaway and hypervelocity white dwarf candidates in Gaia Data Release 3: Possible remnants from Ia/Iax supernova explosions or dynamical encounters

Type Ia and other peculiar supernovae (SNe) are thought to originate from the thermonuclear explosions of white dwarfs (WDs). Some of the proposed channels involve the ejection of a partly exploded WD (e.g. Iax SN remnant) or the companion of an exploding WD at extremely high velocities (>400 km s−1). Characterization of such hyper-runaway/hypervelocity (HVS) WDs might therefore shed light on the physics and origins of SNe. Here we analyse the Gaia DR3 data to search for HVS WDs candidates and peculiar sub-main-sequence (sub-MS) objects. We retrieve the previously identified HVSs and find 46 new HVS candidates. Among these we identify two new unbound WDs and two new unbound sub-MS candidates. The remaining stars are hyper-runaway WDs and hyper-runaway sub-MS stars. The numbers and properties of the HVS WD and sub-MS candidates suggest that extreme velocity ejections (>1000 km s−1) can accompany at most a small fraction of type Ia SNe, disfavouring a significant contribution of the D6-scenario to the origin of Ia SNe. The rate of HVS ejections following the hybrid WD reverse-detonation channel could be consistent with the identified HVSs. The numbers of lower-velocity HVS WDs could be consistent with type Iax SNe origin and/or contribution from dynamical encounters. We also searched for HVS WDs related to known SN remnants but identified only one such candidate.
Design of the monodomain model by artificial neural networks

We propose an optimal control approach in order to identify the nonlinearity in the monodomain model, from given data. This data-driven approach gives an answer to the problem of selecting the model when studying phenomena related to cardiac electrophysiology. Instead of determining coefficients of a prescribed model (like the FitzHugh-Nagumo model for instance) from empirical observations, we design the model itself, in the form of an artificial neural network. The relevance of this approach relies on the approximation capacities of neural networks. We formulate this inverse problem as an optimal control problem, and provide mathematical analysis and derivation of optimality conditions. One of the difficulties comes from the lack of smoothness of activation functions which are classically used for training neural networks. Numerical simulations demonstrate the feasibility of the strategy proposed in this work.
Multipolar ordering from dynamical mean field theory with application to CeB6

Magnetic and multipolar ordering in f electron systems takes place at low temperatures of order 1-10 Kelvin. Combinations of first-principles with many-body calculations for such low-energy properties of correlated materials are challenging problems. We address multipolar ordering in f electron systems based on the dynamical mean-field theory (DMFT) combined with density functional theory. We derive the momentum-dependent multipolar susceptibilities and interactions by solving the Bethe-Salpeter (BS) equation of the two-particle Green's function. We apply the formalism to the prototypical example of multipolar ordering CeB6, and demonstrate that the experimental quadrupole transition is correctly reproduced. This first-principles formalism based on DMFT and BS equation has applications which are beyond the reach of the traditional RKKY formula. In particular, more itinerant electron systems including 5f, 4d and 5d electrons can be addressed.
NuSTAR Observations of a Heavily X-Ray-obscured AGN in the Dwarf Galaxy J144013+024744

We present a multiwavelength analysis of the dwarf Seyfert 2 galaxy J144013+024744, a candidate obscured active galactic nucleus (AGN) thought to be powered by an intermediate-mass black hole (IMBH, M • ≈ 104−106 M ⊙) of mass M • ∼ 105.2 M ⊙. To study its X-ray properties, we targeted J144013+024744 with NuSTAR for ≈100 ks. The X-ray spectrum was fitted with an absorbed power law, Pexmon, and a physical model (RXTorus). A Bayesian X-ray analysis was performed to estimate the posteriors. The phenomenological and the physical models suggest the AGN to be heavily obscured by a column density of N H = (3.4–7.0) × 1023 cm−2. In particular, the RXTorus model with a subsolar metallicity suggests the obscuring column to be almost Compton-thick. We compared the 2–10 keV intrinsic X-ray luminosity with the inferred X-ray luminosities based on empirical scaling relations for unobscured AGNs using L [Oiv] 25.89 μm, L [Oiii] λ5007, and L 6μm and found that the high-excitation [Oiv] line provides a better estimate of the intrinsic 2–10 keV X-ray luminosity ( L2–10int∼1041.41 erg s−1). Our results suggest that J144013+024744 is the first type 2 dwarf galaxy that shows X-ray spectroscopic evidence for obscuration. The column density that we estimated is among the highest measured to date for IMBH-powered AGNs, implying that a typical AGN torus geometry might extend to the low-mass end. This work has implications for constraining the BH occupation fraction in dwarf galaxies using X-ray observations.
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies

Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize policy performance. However, the gradient conflict problem is still poorly understood in Meta-RL, which may lead to performance degradation when encountering distinct tasks. To tackle this challenge, this paper proposes a novel personalized Meta-RL (pMeta-RL) algorithm, which aggregates task-specific personalized policies to update a meta-policy used for all tasks, while maintaining personalized policies to maximize the average return of each task under the constraint of the meta-policy. We also provide the theoretical analysis under the tabular setting, which demonstrates the convergence of our pMeta-RL algorithm. Moreover, we extend the proposed pMeta-RL algorithm to a deep network version based on soft actor-critic, making it suitable for continuous control tasks. Experiment results show that the proposed algorithms outperform other previous Meta-RL algorithms on Gym and MuJoCo suites.
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL

Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving decentralised decision-making problems at scale. Research in the field has been growing steadily with many breakthrough algorithms proposed in recent years. In this work, we take a closer look at this rapid development with a focus on evaluation methodologies employed across a large body of research in cooperative MARL. By conducting a detailed meta-analysis of prior work, spanning 75 papers accepted for publication from 2016 to 2022, we bring to light worrying trends that put into question the true rate of progress. We further consider these trends in a wider context and take inspiration from single-agent RL literature on similar issues with recommendations that remain applicable to MARL. Combining these recommendations, with novel insights from our analysis, we propose a standardised performance evaluation protocol for cooperative MARL. We argue that such a standard protocol, if widely adopted, would greatly improve the validity and credibility of future research, make replication and reproducibility easier, as well as improve the ability of the field to accurately gauge the rate of progress over time by being able to make sound comparisons across different works. Finally, we release our meta-analysis data publicly on our project website for future research on evaluation: https://sites.google. com/view/marl-standard-protocol
Arithmetic Average Density Fusion - Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion

As a fundamental information fusion approach, the arithmetic average (AA) fusion has recently been investigated for various random finite set (RFS) filter fusion in the context of multi-sensor multi-target tracking. It is not a straightforward extension of the ordinary density-AA fusion to the RFS distribution but has to preserve the form of the fusing multi-target density. In this work, we first propose a statistical concept, probability hypothesis density (PHD) consistency, and explain how it can be achieved by the PHD-AA fusion and lead to more accurate and robust detection and localization of the present targets. This forms a both theoretically sound and technically meaningful reason for performing inter-filter PHD AA-fusion/consensus, while preserving the form of the fusing RFS filter. Then, we derive and analyze the proper AA fusion formulations for most existing unlabeled/labeled RFS filters basing on the (labeled) PHD-AA/consistency. These derivations are theoretically unified, exact, need no approximation and greatly enable heterogenous unlabeled and labeled RFS density fusion which is separately demonstrated in two consequent companion papers.
Extreme nesting in the conformal loop ensemble

The conformal loop ensemble CLEκ with parameter 8/3<κ<8 is the canonical conformally invariant measure on countably infinite collections of noncrossing loops in a simply connected domain. Given κ and ν, we compute the almost-sure Hausdorff dimension of the set of points z for which the number of CLE loops surrounding the disk of radius e centered at z has asymptotic growth νlog(1/e) as e→0. By extending these results to a setting in which the loops are given i.i. d. weights, we give a CLE-based treatment of the extremes of the Gaussian free field.
Model-Free Reinforcement Learning for Asset Allocation

Asset allocation (or portfolio management) is the task of determining how to optimally allocate funds of a finite budget into a range of financial instruments/assets such as stocks. This study investigated the performance of reinforcement learning (RL) when applied to portfolio management using model-free deep RL agents. We trained several RL agents on real-world stock prices to learn how to perform asset allocation. We compared the performance of these RL agents against some baseline agents. We also compared the RL agents among themselves to understand which classes of agents performed better. From our analysis, RL agents can perform the task of portfolio management since they significantly outperformed two of the baseline agents (random allocation and uniform allocation). Four RL agents (A2C, SAC, PPO, and TRPO) outperformed the best baseline, MPT, overall. This shows the abilities of RL agents to uncover more profitable trading strategies. Furthermore, there were no significant performance differences between value-based and policy-based RL agents. Actor-critic agents performed better than other types of agents. Also, on-policy agents performed better than off-policy agents because they are better at policy evaluation and sample efficiency is not a significant problem in portfolio management. This study shows that RL agents can substantially improve asset allocation since they outperform strong baselines. On-policy, actor-critic RL agents showed the most promise based on our analysis.
Interpolation Formulas for Asymptotically Safe Cosmology

Simple interpolation formulas are proposed for the description of the renormalization group (RG) scale dependences of the gravitational couplings in the framework of the 2-parameters Einstein-Hilbert (EH) theory of gravity and applied to a simple, analytically solvable, spatially homogeneous and isotropic, spatially flat model universe. The analytical solution is found in two schemes incorporating different methods of the determination of the conversion rule k(t) of the RG scale k to the cosmological time t. In the case of the discussed model these schemes turn out to yield identical cosmological evolution. Explicit analytical formulas are found for the conversion rule k(t) as well as for the characteristic time scales tG and tΛ>tG corresponding to the dynamical energy scales kG and kΛ, respectively, arising form the RG analysis of the EH theory. It is shown that there exists a model-dependent time scale td (tG≤td<tΛ) at which the accelerating expansion changes to the decelerating one. It is shown that the evolution runs from a well-identified cosmological fixed point to another one. As a by-product we show that the entropy of the system decreases monotonically in the interval 0<t≤tΛ due to the quantum effects.
Charge separation in the photosystem II reaction center resolved by multispectral two-dimensional electronic spectroscopy

The photosystem II reaction center (PSII RC) performs the primary energy conversion steps of oxygenic photosynthesis. While the PSII RC has been studied extensively, the similar time scales of energy transfer and charge separation and the severely overlapping pigment transitions in the Qy region have led to multiple models of its charge separation mechanism and excitonic structure. Here, we combine two-dimensional electronic spectroscopy (2DES) with a continuum probe and two-dimensional electronic vibrational spectroscopy (2DEV) to study the cyt b559-D1D2 PSII RC at 77 K. This multispectral combination correlates the overlapping Qy excitons with distinct anion and pigment-specific Qx and mid-infrared transitions to resolve the charge separation mechanism and excitonic structure. Through extensive simultaneous analysis of the multispectral 2D data, we find that charge separation proceeds on multiple time scales from a delocalized excited state via a single pathway in which PheoD1 is the primary electron acceptor, while ChlD1 and PD1 act in concert as the primary electron donor.
IoU-Enhanced Attention for End-to-End Task Specific Object Detection

Without densely tiled anchor boxes or grid points in the image, sparse R-CNN achieves promising results through a set of object queries and proposal boxes updated in the cascaded training manner. However, due to the sparse nature and the one-to-one relation between the query and its attending region, it heavily depends on the self attention, which is usually inaccurate in the early training stage. Moreover, in a scene of dense objects, the object query interacts with many irrelevant ones, reducing its uniqueness and harming the performance. This paper proposes to use IoU between different boxes as a prior for the value routing in self attention. The original attention matrix multiplies the same size matrix computed from the IoU of proposal boxes, and they determine the routing scheme so that the irrelevant features can be suppressed. Furthermore, to accurately extract features for both classification and regression, we add two lightweight projection heads to provide the dynamic channel masks based on object query, and they multiply with the output from dynamic convs, making the results suitable for the two different tasks. We validate the proposed scheme on different datasets, including MS-COCO and CrowdHuman, showing that it significantly improves the performance and increases the model convergence speed.
Far-off-equilibrium expansion trajectories in the QCD phase diagram

We consider the hydrodynamic evolution of a quark-gluon gas with non-zero quark masses and net baryon number in its phase diagram. For far-off-equilibrium initial conditions the expansion trajectories appear to violate simple rules based on the second law of thermodynamics that were previously established for ideal or weakly dissipative fluids. For Bjorken flow we present a detailed analysis within kinetic theory that provides a full microscopic understanding of these macroscopic phenomena and establishes their thermodynamic consistency. We point out that, for certain far-off-equilibrium initial conditions, the well-known phenomenon of"viscous heating"turns into"viscous cooling"where, driven by dissipative effects, the temperature decreases faster than in adiabatic expansion.