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Integrating Google's Topics API into the digital advertising ecosystem represents a significant shift toward privacy-conscious advertising practices. This article analyses the implications of implementing Topics API on ad networks, focusing on competition dynamics and ad space accessibility. Through simulations based on extensive datasets capturing user behavior and market share data for ad networks, we evaluate metrics such as Ad Placement Eligibility, Low Competition Rate, and solo competitor. The findings reveal a noticeable impact on ad networks, with larger players strengthening their dominance and smaller networks facing challenges securing ad spaces and competing effectively. Moreover, the study explores the potential environmental implications of Google's actions, highlighting the need to carefully consider policy and regulatory measures to ensure fair competition and privacy protection. Overall, this research contributes valuable insights into the evolving dynamics of digital advertising and highlights the importance of balancing privacy with competition and innovation in the online advertising landscape.

We present the results from spectroscopic and photometric analysis of 17 globular cluster (GC) candidates in the Irr II galaxy NGC 3077. The GC candidates were selected on the Hubble Space Telescope (HST) images and were cleaned of foreground Galactic stars using the GAIA parameters. We carried out aperture photometry using the multi-band archival images from SDSS, and 2MASS of all candidates, and low resolution (R= 1000) spectroscopic observations of 12 GC candidates and three suspected foreground stars using the OSIRIS/MOS mode at the Gran Telescopio Canarias (GTC). Age, metallicity and extinction values were determined both using spectroscopic and photometric data, independently. We find three of the 17 candidates are old (age >10 Gyr), metal-poor ([Fe/H]<-1.0 dex), massive (mass>10^5 Msun) GCs with characteristics similar to the classical GCs in the Milky Way. The rest are intermediate-age clusters (IACs) with typical ages of 3 to 4 Gyr, and in general metal-rich clusters. The radial velocities of both populations are within 100 km/s of the recessional velocity of the host galaxy. A relatively large population of IACs and low value of GC specific frequency (Sn=0.7) suggest that the pre-interaction galaxy was actively forming stars and star clusters, and is unlikely to be a dE as suggested in some previous works., Comment: Accepted in Astronomical Journal

Reinforcement learning is a subfield of machine learning that is having a huge impact in the different conventional disciplines, including physical sciences. Here, we show how reinforcement learning methods can be applied to solve optimization problems in the context of radiative heat transfer. We illustrate their use with the optimization of the near-field radiative heat transfer between multilayer hyperbolic metamaterials. Specifically, we show how this problem can be formulated in the language of reinforcement learning and tackled with a variety of algorithms. We show that these algorithms allow us to find solutions that outperform those obtained using physical intuition. Overall, our work shows the power and potential of reinforcement learning methods for the investigation of a wide variety of problems in the context of radiative heat transfer and related topics., Comment: 21 pages, 9 figures

We present a near infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses $>10^4$ M$_\odot$. Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of $\approx$1 pc and have stellar masses up to $10^6$ M$_\odot$. By comparing the color-color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A$_{\rm V}\sim3-24$ mag, corresponding to A$_{\rm 2.5\mu m}\sim0.3-2.1$ mag. There is still appreciable dust extinction towards these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is $\beta=1.9\pm0.2$, in excellent agreement with studies of star clusters in other galaxies., Comment: Resubmitted to ApJL

While the influence of galaxy clusters on galaxy evolution is relatively well-understood, the impact of the dynamical states of these clusters is less clear. This paper series explores how the dynamical state of galaxy clusters affects their galaxy populations' physical and morphological properties. The primary aim of this first paper is to evaluate the dynamical state of 87 massive ($M_{500} \geq 1.5 \times 10^{14} M_{\odot}$) galaxy clusters at low redshifts ($0.10 \leq z \leq 0.35$). This will allow us to have a well-characterized sample for analyzing physical and morphological properties in our next work. We employ six dynamical state proxies utilizing optical and X-ray imaging data. Principal Component Analysis (PCA) is applied to integrate these proxies effectively, allowing for robust classification of galaxy clusters into relaxed, intermediate, and disturbed states based on their dynamical characteristics. The methodology successfully segregates the galaxy clusters into the three dynamical states. Examination of the galaxy distributions in optical wavelengths and gas distributions in X-ray further confirms the consistency of these classifications. The clusters' dynamical states are statistically distinguishable, providing a clear categorization for further analysis., Comment: 19 pages, 13 figures. Submitted to A&A

Quantum conductors attached to metallic reservoirs have been demonstrated to overcome the thermodynamic uncertainty relation (TUR), a trade-off relation between the amount of dissipation and the absence of charge and heat current fluctuations. Here, we report large TUR violations when superconducting reservoirs replace metallic ones. The coexistence of different transport processes, namely (multiple) Andreev reflection, where electrons and their retro-reflected holes create Cooper pairs, in addition to the normal quasiparticle transport is identified as the source for such TUR breakdowns. The large TUR violation is a remarkable advantage for building low dissipative and highly stable quantum thermal machines., Comment: 6 pages, 2 figures

Two crucial performance criteria for the deployment of visual localization are speed and accuracy. Current research on visual localization with neural networks is limited to examining methods for enhancing the accuracy of networks across various datasets. How to expedite the re-localization process within deep neural network architectures still needs further investigation. In this paper, we present a novel approach for accelerating visual re-localization in practice. A tree-like search strategy, built on the keyframes extracted by a visual clustering algorithm, is designed for matching acceleration. Our method has been validated on two tasks across three public datasets, allowing for 50 up to 90 percent time saving over the baseline while not reducing location accuracy., Comment: 11 pages, 6 figures

The DArk Matter In CCDs at Modane (DAMIC-M) experiment is designed to search for light dark matter (m$_{\chi}$<10\,GeV/c$^2$) at the Laboratoire Souterrain de Modane (LSM) in France. DAMIC-M will use skipper charge-coupled devices (CCDs) as a kg-scale active detector target. Its single-electron resolution will enable eV-scale energy thresholds and thus world-leading sensitivity to a range of hidden sector dark matter candidates. A DAMIC-M prototype, the Low Background Chamber (LBC), has been taking data at LSM since 2022. The LBC provides a low-background environment, which has been used to characterize skipper CCDs, study dark current, and measure radiopurity of materials planned for DAMIC-M. It also allows testing of various subsystems like readout electronics, data acquisition software, and slow control. This paper describes the technical design and performance of the LBC.

The existence of breather type solutions, i.e., periodic in time, exponentially localized in space solutions, is a very unusual feature for continuum, nonlinear wave type equations. Following an earlier work [Comm. Math. Phys. {\bf 302}, 815-841 (2011)], establishing a theorem for the existence of such structures, we bring to bear a combination of analysis-inspired numerical tools that permit the construction of such wave forms to a desired numerical accuracy. In addition, this enables us to explore their numerical stability. Our computations show that for the spatially heterogeneous form of the $\phi^4$ model considered herein, the breather solutions are generically found to be unstable. Their instability seems to generically favor the motion of the relevant structures. We expect that these results may inspire further studies towards the identification of stable continuous breathers in spatially-heterogeneous, continuum nonlinear wave equation models.

Entangled photon sources (EPSs) are pivotal in advancing quantum communication, computing and sensing. The demand for deploying efficient, robust EPSs in the field, characterized by exceptional (phase) stability, has become increasingly apparent. This work introduces a polarization-entangled photon source, leveraging type-0 spontaneous parametric down-conversion, and constructed using commercial bulk optomechanical components. Our system is versatile, enabling the generation of N00N states for sensing applications or Bell states for quantum key distribution protocols. We attained a maximal Bell inequality violation, with the average entanglement visibility exceeding 99% . The potential for further performance enhancements is also explored., Comment: The following article has been submitted to APL Photonics. After it is published, it will be found at https://pubs.aip.org/aip/app

Practical parameter identifiability in ODE-based epidemiological models is a known issue, yet one that merits further study. It is essentially ubiquitous due to noise and errors in real data. In this study, to avoid uncertainty stemming from data of unknown quality, simulated data with added noise are used to investigate practical identifiability in two distinct epidemiological models. Particular emphasis is placed on the role of initial conditions, which are assumed unknown, except those that are directly measured. Instead of just focusing on one method of estimation, we use and compare results from various broadly used methods, including maximum likelihood and Markov Chain Monte Carlo (MCMC) estimation. Among other findings, our analysis revealed that the MCMC estimator is overall more robust than the point estimators considered. Its estimates and predictions are improved when the initial conditions of certain compartments are fixed so that the model becomes globally identifiable. For the point estimators, whether fixing or fitting the that are not directly measured improves parameter estimates is model-dependent. Specifically, in the standard SEIR model, fixing the initial condition for the susceptible population S(0) improved parameter estimates, while this was not true when fixing the initial condition of the asymptomatic population in a more involved model. Our study corroborates the change in quality of parameter estimates upon usage of pre-peak or post-peak time-series under consideration. Finally, our examples suggest that in the presence of significantly noisy data, the value of structural identifiability is moot.

As natural access points to the subsurface, lava tubes and other caves have become premier targets of planetary missions for astrobiological analyses. Few existing robotic paradigms, however, are able to explore such challenging environments. ReachBot is a robot that enables navigation in planetary caves by using extendable and retractable limbs to locomote. This paper outlines the potential science return and mission operations for a notional mission that deploys ReachBot to a martian lava tube. In this work, the motivating science goals and science traceability matrix are provided to guide payload selection. A Concept of Operations (ConOps) is also developed for ReachBot, providing a framework for deployment and activities on Mars, analyzing mission risks, and developing mitigation strategies, Comment: In International Conference on Space Robotics 2024

In honor of the great Russian mathematician A. N. Kolmogorov, we would like to draw attention in the present paper to a curious mathematical observation concerning fractional differential equations describing physical systems, whose time evolution for integer derivatives has a time-honored conservative form. This observation, although known to the general mathematical community, has not, in our view, been satisfactorily addressed. More specifically, we follow the recent exploration of Caputo-Riesz time-space-fractional nonlinear wave equation, in which two of the present authors introduced an energy-type functional and proposed a finite-difference scheme to approximate the solutions of the continuous model. The relevant Klein-Gordon equation considered here has the form: \begin{equation} \frac {\partial ^\beta \phi (x , t)} {\partial t ^\beta} - \Delta ^\alpha \phi (x , t) + F ^\prime (\phi (x , t)) = 0, \quad \forall (x , t) \in (-\infty,\infty) \end{equation} where we explore the sine-Gordon nonlinearity $F(\phi)=1-\cos(\phi)$ with smooth initial data. For $\alpha=\beta=2$, we naturally retrieve the exact, analytical form of breather waves expected from the literature. Focusing on the Caputo temporal derivative variation within $1< \beta < 2$ values for $\alpha=2$, however, we observe artificial dissipative effects, which lead to complete breather disappearance, over a time scale depending on the value of $\beta$. We compare such findings to single degree-of-freedom linear and nonlinear oscillators in the presence of Caputo temporal derivatives and also consider anti-damping mechanisms to counter the relevant effect. These findings also motivate some interesting directions for further study, e.g., regarding the consideration of topological solitary waves, such as kinks/antikinks and their dynamical evolution in this model.

Spatiotemporal optical vortices (STOVs) are short pulses that present a vortex whose axis is perpendicular to the main propagation direction. We present analytic expressions for these pulses that satisfy exactly Maxwell's equation, by applying appropriate differential operators to complex focus pulses with Poisson-like frequency spectrum. We also provide a simple ray picture for understanding the deformation of these pulses under propagation. Finally, we use these solutions to propose a type of pulse with sagittal skyrmionic polarization distribution covering all states of transverse polarization., Comment: 12 pages

An analysis of the Cosmological Constant $\Omega_\Lambda$ fitted to subsamples of the Pantheon+ Type Ia SN sample spanning 2$\pi$ sterradians for a grid of 432 pole positions covering the whole sky reveals two large scale asymmetries. One of them is closely aligned with the Galactic North-South direction and the other points approximately towards RA$\sim 217.5^\circ$ Dec$\sim -26.4^\circ$, $\sim$50.9 degrees from the CMB dipole Apex. The signal to noise ratio (S/N) of the multiple $\Omega_\Lambda$ measurements in these directions is $3.2\lesssim $ S/N $\lesssim 8.4$. The first asymmetry is puzzling, and would indicate a systematic effect related with the distribution of Pantheon+ SNe on the sky and, probably, how the correction for reddening in the Galaxy is calculated. The second one, which entails a 2.8-$\sigma$ tension between $\Omega_\Lambda$ measure in opposite directions, bears strong implications on its interpretation as Dark Energy: It is consistent with the prediction for tilted observers located in a Friedmann-Robertson-Walker universe who could measure an acceleration or a deceleration with a dipolar asymmetry, irrespective of what the universe as a whole is doing. In this case, $\Omega_\Lambda$ would not be a physical entity, a real Dark Energy, but an apparent effect associated with the relativistic frame of reference transformation., Comment: 13 pages, 8 figures

In 2021, Mexico introduced "Pasaporte al Aprendizaje," a localized version of the Learning Passport digital learning platform, to mitigate learning loss after widespread school closures due to the COVID-19 pandemic. Between October 2021 and February 2023, almost 100,000 students utilized it for upper-secondary courses. The research explores the key design and implementation steps undertaken for the successful deployment and use of the "Pasaporte al Aprendizaje." In addition, analysis of data from the digital learning platform shows that students assessment scores improved as they progressed through courses, in subjects such as mathematics, Spanish, chemistry and physics. The overall goal of this research is to inform improvements in the "Pasaporte al Aprendizaje" and provide key lessons learned for other countries implementing national digital learning programmes.

This paper outlines potential actions and roles for intermediaries to facilitate the development and implementation of truly equitable education-to-career pathways systems. Specifically, it could guide the broader field of intermediary organizations and efforts to engage in policy and advocacy by outlining how intermediaries can influence the design and implementation of policy solutions to address systemic inequity. Recognizing that intermediaries range widely in size, capacity, and purpose, we offer suggestions that organizations can apply wherever they sit in the policy space. The resource also highlights a set of Building Equitable Pathways (BEP) intermediaries and their work in policy and advocacy.

The equitable distribution of resources in a network is a complex process, considering that not all nodes have the same requirements, and the In-Band On-Channel (IBOC) hybrid transmission system is no exception. The IBOC system utilizes a hybrid in-band transmission to simultaneously broadcast analog and digital audio over the FM band. This article proposes the use of a Max-Min Fairness (MMF) algorithm, with a strategy to optimize resource allocation for IBOC FM transmission in a multiservice scenario. Additionally, the MMF algorithm offers low computational complexity for implementation in low-cost embedded systems, aiming to achieve fair resource distribution and provide adequate Quality of Service (QoS) levels for each node in the RF network, considering channel conditions and traffic types. The article explores a scenario under saturated traffic conditions to assess the optimization capabilities of the MMF algorithm under well-defined traffic and channel conditions. The evaluation process yielded highly favorable results, indicating that theMMF algorithm can be considered a viable alternative for bandwidth optimization in digital broadcasting over IBOC on FM with 95% confidence, and it holds potential for implementation in other digital broadcasting system., Comment: 10 pages, 3 table

Electron spin resonance (ESR) spectroscopy in scanning tunneling microscopy (STM) has enabled probing the electronic structure of single magnetic atoms and molecules on surfaces with unprecedented energy resolution, as well as demonstrating coherent manipulation of single spins. Despite this remarkable success, the field could still be greatly advanced by a more quantitative understanding of the ESR-STM physical mechanisms. Here, we present a theory of ESR-STM which quantitatively models not only the ESR signal itself, but also the full background tunneling current, from which the ESR signal is derived. Our theory is based on a combination of Green's function techniques to describe the electron tunneling and a quantum master equation for the dynamics of the spin system along with microwave radiation interacting with both the tunneling current and the spin system. We show that this theory is able to quantitatively reproduce the experimental results for a spin-1/2 system (TiH molecules on MgO) across many orders of magnitude in tunneling current, providing access to the relaxation and decoherence rates that govern the spin dynamics due to intrinsic mechanisms and to the applied bias voltage. More importantly, our work establishes that: (i) sizable ESR signals, which are a measure of microwave-induced changes in the junction magnetoresistance, require surprisingly high tip spin polarizations, (ii) the coupling of the magnetization dynamics to the microwave field gives rise to the asymmetric ESR spectra often observed in this spectroscopy. Additionally, our theory provides very specific predictions for the dependence of the relaxation and decoherence times on the bias voltage and the tip-sample distance. Finally, with the help of electromagnetic simulations, we find that the transitions in our ESR-STM experiments can be driven by the ac magnetic field at the junction., Comment: 17 pages, 6 figures

Three-dimensional data registration is an established yet challenging problem that is key in many different applications, such as mapping the environment for autonomous vehicles, and modeling objects and people for avatar creation, among many others. Registration refers to the process of mapping multiple data into the same coordinate system by means of matching correspondences and transformation estimation. Novel proposals exploit the benefits of deep learning architectures for this purpose, as they learn the best features for the data, providing better matches and hence results. However, the state of the art is usually focused on cases of relatively small transformations, although in certain applications and in a real and practical environment, large transformations are very common. In this paper, we present ReLaTo (Registration for Large Transformations), an architecture that faces the cases where large transformations happen while maintaining good performance for local transformations. This proposal uses a novel Softmax pooling layer to find correspondences in a bilateral consensus manner between two point sets, sampling the most confident matches. These matches are used to estimate a coarse and global registration using weighted Singular Value Decomposition (SVD). A target-guided denoising step is then applied to both the obtained matches and latent features, estimating the final fine registration considering the local geometry. All these steps are carried out following an end-to-end approach, which has been shown to improve 10 state-of-the-art registration methods in two datasets commonly used for this task (ModelNet40 and KITTI), especially in the case of large transformations.

We use the kinematics of field OB stars to estimate the frequencies of runaway stars generated by the dynamical ejection scenario (DES), the binary supernova scenario (BSS), and the combined two-step mechanism. We update the proper motions for field OB and OBe stars in the Small Magellanic Cloud (SMC) using Gaia DR3. Our sample now contains 336 stars from the Runaways and Isolated O-Type Star Spectroscopic Survey of the SMC (RIOTS4), and we update our algorithm to calculate more accurate velocities compared to those obtained previously from DR2. We find a decrease in median velocity from 39 to 29 km/s, implying that the proper motions from our previous work were systematically overestimated. We present the velocity distribution for OBe stars and quantitatively compare it to those of non-compact binaries and high-mass X-ray binaries. We confirm that OBe stars appear to be dominated by the BSS and are likely post-SN binary systems, further supporting the mass-transfer model to explain the origin of their emission-line disks. In contrast, normal OB stars may show a bimodal velocity distribution, as may be expected from different processes that occur with dynamical ejections. The kinematics of fast-rotating OB stars are similar to those of normal OB stars rather than OBe stars, suggesting that the origin of their high v_r*sin(i) is different from that of OBe stars. We update our model parameters describing the kinematic origins of the SMC field population, still confirming that for runaway stars, the DES mechanism dominates, and two-step ejections seem comparable in frequency to pure BSS ejections., Comment: 12 pages, 7 figures (figures 5 and 7 contain 2 files each), and 4 tables

Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on observations of the Galactic centre region as well as of selected dwarf spheroidal galaxies. We find that current limits and detection prospects for dark matter masses above 300 GeV will be significantly improved, by up to an order of magnitude in the multi-TeV range. This demonstrates that CTA will set a new standard for gamma-ray astronomy also in this respect, as the world's largest and most sensitive high-energy gamma-ray observatory, in particular due to its exquisite energy resolution at TeV energies and the adopted observational strategy focussing on regions with large dark matter densities. Throughout our analysis, we use up-to-date instrument response functions, and we thoroughly model the effect of instrumental systematic uncertainties in our statistical treatment. We further present results for other potential signatures with sharp spectral features, e.g.~box-shaped spectra, that would likewise very clearly point to a particle dark matter origin., Comment: 44 pages JCAP style (excluding author list and references), 19 figures; minor changes to match published version

Resource-constrained robotic platforms are particularly useful for tasks that require low-cost hardware alternatives due to the risk of losing the robot, like in search-and-rescue applications, or the need for a large number of devices, like in swarm robotics. For this reason, it is crucial to find mechanisms for adapting reinforcement learning techniques to the constraints imposed by lower computational power and smaller memory capacities of these ultra low-cost robotic platforms. We try to address this need by proposing a method for making imitation learning deployable onto resource-constrained robotic platforms. Here we cast the imitation learning problem as a conditional sequence modeling task and we train a decision transformer using expert demonstrations augmented with a custom reward. Then, we compress the resulting generative model using software optimization schemes, including quantization and pruning. We test our method in simulation using Isaac Gym, a realistic physics simulation environment designed for reinforcement learning. We empirically demonstrate that our method achieves natural looking gaits for Bittle, a resource-constrained quadruped robot. We also run multiple simulations to show the effects of pruning and quantization on the performance of the model. Our results show that quantization (down to 4 bits) and pruning reduce model size by around 30\% while maintaining a competitive reward, making the model deployable in a resource-constrained system., Comment: 10 pages, 4 figures

The dynamics of a rain forest is extremely complex involving births, deaths and growth of trees with complex interactions between trees, animals, climate, and environment. We consider the patterns of recruits (new trees) and dead trees between rain forest censuses. For a current census we specify regression models for the conditional intensity of recruits and the conditional probabilities of death given the current trees and spatial covariates. We estimate regression parameters using conditional composite likelihood functions that only involve the conditional first order properties of the data. When constructing assumption lean estimators of covariance matrices of parameter estimates we only need mild assumptions of decaying conditional correlations in space while assumptions regarding correlations over time are avoided by exploiting conditional centering of composite likelihood score functions. Time series of point patterns from rain forest censuses are quite short while each point pattern covers a fairly big spatial region. To obtain asymptotic results we therefore use a central limit theorem for the fixed timespan - increasing spatial domain asymptotic setting. This also allows us to handle the challenge of using stochastic covariates constructed from past point patterns. Conveniently, it suffices to impose weak dependence assumptions on the innovations of the space-time process. We investigate the proposed methodology by simulation studies and applications to rain forest data., Comment: This paper is still under revision

Determining when people are struggling from video enables a finer-grained understanding of actions and opens opportunities for building intelligent support visual interfaces. In this paper, we present a new dataset with three assembly activities and corresponding performance baselines for the determination of struggle from video. Three real-world problem-solving activities including assembling plumbing pipes (Pipes-Struggle), pitching camping tents (Tent-Struggle) and solving the Tower of Hanoi puzzle (Tower-Struggle) are introduced. Video segments were scored w.r.t. the level of struggle as perceived by annotators using a forced choice 4-point scale. Each video segment was annotated by a single expert annotator in addition to crowd-sourced annotations. The dataset is the first struggle annotation dataset and contains 5.1 hours of video and 725,100 frames from 73 participants in total. We evaluate three decision-making tasks: struggle classification, struggle level regression, and struggle label distribution learning. We provide baseline results for each of the tasks utilising several mainstream deep neural networks, along with an ablation study and visualisation of results. Our work is motivated toward assistive systems that analyze struggle, support users during manual activities and encourage learning, as well as other video understanding competencies.

Inspired by animals that co-adapt their brain and body to interact with the environment, we present a tendon-driven and over-actuated (i.e., n joint, n+1 actuators) bipedal robot that (i) exploits its backdrivable mechanical properties to manage body-environment interactions without explicit control, and (ii) uses a simple 3-layer neural network to learn to walk after only 2 minutes of 'natural' motor babbling (i.e., an exploration strategy that is compatible with leg and task dynamics; akin to childsplay). This brain-body collaboration first learns to produce feet cyclical movements 'in air' and, without further tuning, can produce locomotion when the biped is lowered to be in slight contact with the ground. In contrast, training with 2 minutes of 'naive' motor babbling (i.e., an exploration strategy that ignores leg task dynamics), does not produce consistent cyclical movements 'in air', and produces erratic movements and no locomotion when in slight contact with the ground. When further lowering the biped and making the desired leg trajectories reach 1cm below ground (causing the desired-vs-obtained trajectories error to be unavoidable), cyclical movements based on either natural or naive babbling presented almost equally persistent trends, and locomotion emerged with naive babbling. Therefore, we show how continual learning of walking in unforeseen circumstances can be driven by continual physical adaptation rooted in the backdrivable properties of the plant and enhanced by exploration strategies that exploit plant dynamics. Our studies also demonstrate that the bio-inspired codesign and co-adaptations of limbs and control strategies can produce locomotion without explicit control of trajectory errors.

This paper introduces adF, a novel system for analyzing the vulnerability of different devices, Operating Systems (OSes), and browsers to web fingerprinting. adF performs its measurements from code inserted in ads. We have used our system in several ad campaigns that delivered 5.40 million ad impressions. The collected data allow us to assess the vulnerability of current desktop and mobile devices to web fingerprinting. Based on our results, we estimate that 66% of desktop devices and 40% of mobile devices can be uniquely fingerprinted with our web fingerprinting system. However, the resilience to web fingerprinting varies significantly across browsers and device types, with Chrome on desktops being the most vulnerable configuration. To counter web fingerprinting, we propose ShieldF, a simple solution which blocks the reporting by browsers of those attributes that we found in the analysis of our dataset that present the most significant discrimination power. Our experiments reveal that ShieldF outperforms all anti-fingerprinting solutions proposed by major browsers (Chrome, Safari and Firefox) offering an increase in the resilience offered to web fingerprinting up to 62% for some device configurations. ShieldF is available as an add-on for any chromium-based browser. Moreover, it is readily adoptable by browser and mobile app developers. Its widespread use would lead to a significant improvement in the protection offered by browsers and mobile apps to web fingerprinting., Comment: 20 pages, 5 figures, 4 tables; added keywords

Jobs for the Future's (JFF) vision for the Big Blur calls for entirely new educational institutions and systems to better prepare 16-to-20-year-olds for college and careers. This paper examines the new type of governance needed to help states create more effective grade 11-14 schools and systems by erasing the arbitrary dividing line between high school and college to open opportunities for the learners the current systems leave behind. JFF makes the case for an entirely new type of public institution, neither high school nor college, designed to better meet the needs of 16-to-20-year-olds by enabling them to earn a postsecondary credential and prepare for a career--free of charge.