Miracle-dz/UniFAR-FTUs huggingface dataset:
Patch Network for medical image Segmentation

Accurate and fast segmentation of medical images is clinically essential, yet current research methods include convolutional neural networks with fast inference speed but difficulty in learning image contextual features, and transformer with good performance but high hardware requirements. In this paper, we present a Patch Network (PNet) that incorporates the Swin Transformer notion into a convolutional neural network, allowing it to gather richer contextual information while achieving the balance of speed and accuracy. We test our PNet on Polyp(CVC-ClinicDB and ETIS- LaribPolypDB), Skin(ISIC-2018 Skin lesion segmentation challenge dataset) segmentation datasets. Our PNet achieves SOTA performance in both speed and accuracy.
The Adams differentials on the classes $h_j^3$

In filtration 1 of the Adams spectral sequence, using secondary cohomology operations, Adams computed the differentials on the classes $h_j$, resolving the Hopf invariant one problem. In Adams filtration 2, using equivariant and chromatic homotopy theory, Hill--Hopkins--Ravenel proved that the classes $h_j^2$ support non-trivial differentials for $j \geq 7$, resolving the celebrated Kervaire invariant one problem. The precise differentials on the classes $h_j^2$ for $j \geq 7$ and the fate of $h_6^2$ remains unknown. In this paper, in Adams filtration 3, we prove an infinite family of non-trivial $d_4$-differentials on the classes $h_j^3$ for $j \geq 6$, confirming a conjecture of Mahowald. Our proof uses two different deformations of stable homotopy theory -- $\mathbb{C}$-motivic stable homotopy theory and $\mathbb{F}_2$-synthetic homotopy theory -- both in an essential way. Along the way, we also show that $h_j^2$ survives to the Adams $E_5$-page and that $h_6^2$ survives to the Adams $E_9$-page.
Hilbert space fragmentation in a long-range system

We study the role of the interaction range on the Hilbert space fragmentation and many-body scar states considering a spin-1/2 many-body Hamiltonian describing a generalized Fredkin spin chain. We show that both scar states and weak fragmentation of the Hilbert space survive for almost any range of the coupling. Finally we investigate the effects of such structures of the Hilbert space on the out-of-equilibrium dynamics, triggered by certain initial states, characterized by either local persistent oscillations or non-uniform stationary profile of the magnetization.
Don’t Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science

Across academia, government, and industry, data stewards are facing increasing pressure to make datasets more openly accessible for researchers while also protecting the privacy of data subjects. Differential privacy (DP) is one promising way to offer privacy along with open access, but further inquiry is needed into the tensions between DP and data science. In this study, we conduct interviews with 19 data practitioners who are non-experts in DP as they use a DP data analysis prototype to release privacy-preserving statistics about sensitive data, in order to understand perceptions, challenges, and opportunities around using DP. We find that while DP is promising for providing wider access to sensitive datasets, it also introduces challenges into every stage of the data science workflow. We identify ethics and governance questions that arise when socializing data scientists around new privacy constraints and offer suggestions to better integrate DP and data science.
Optimal Flight-Gate Assignment on a Digital Quantum Computer

We investigate the performance of the variational quantum eigensolver (VQE) for the optimal flight gate assignment problem. This problem is a combinatorial optimization problem that aims at finding an optimal assignment of flights to the gates of an airport, in order to minimize the passenger travel time. To study the problem, we adopt a qubit-efficient binary encoding with a cyclic mapping, which is suitable for a digital quantum computer. Using this encoding in conjunction with the Conditional Value at Risk (CVaR) as an aggregation function, we systematically explore the performance of the approach by classically simulating the CVaR-VQE. Our results indicate that the method allows for finding a good solution with high probability, and the method significantly outperforms the naive VQE approach. We examine the role of entanglement for the performance, and find that ans\"atze with entangling gates allow for better results than pure product states. Studying the problem for various sizes, our numerical data show that the scaling of the number of cost function calls for obtaining a good solution is not exponential for the regimes we investigate in this work.
XL-MIMO Channel Modeling and Prediction for Wireless Power Transfer

Massive antenna arrays form physically large apertures with a beam-focusing capability, leading to outstanding wireless power transfer (WPT) efficiency paired with low radiation levels outside the focusing region. However, leveraging these features requires accurate knowledge of the multipath propagation channel and overcoming the (Rayleigh) fading channel present in typical application scenarios. For that, reciprocity-based beamforming is an optimal solution that estimates the actual channel gains from pilot transmissions on the uplink. But this solution is unsuitable for passive backscatter nodes that are not capable of sending any pilots in the initial access phase. Using measured channel data from an extremely large-scale MIMO (XL-MIMO) testbed, we compare geometry-based planar wavefront and spherical wavefront beamformers with a reciprocity-based beamformer, to address this initial access problem. We also show that we can predict specular multipath components (SMCs) based only on geometric environment information. We demonstrate that a transmit power of 1 W is sufficient to transfer more than 1 mW of power to a device located at a distance of 12.3 m when using a 40 × 25 array at 3.8 GHz. The geometry-based beamformer exploiting predicted SMCs suffers a loss of only 2 dB compared with perfect channel state information.
A Definition of Non-Stationary Bandits

Despite the subject of non-stationary bandit learning having attracted much recent attention, we have yet to identify a formal definition of non-stationarity that can consistently distinguish non-stationary bandits from stationary ones. Prior work has characterized non-stationary bandits as bandits for which the reward distribution changes over time. We demonstrate that this definition can ambiguously classify the same bandit as both stationary and non-stationary; this ambiguity arises in the existing definition's dependence on the latent sequence of reward distributions. Moreover, the definition has given rise to two widely used notions of regret: the dynamic regret and the weak regret. These notions are not indicative of qualitative agent performance in some bandits. Additionally, this definition of non-stationary bandits has led to the design of agents that explore excessively. We introduce a formal definition of non-stationary bandits that resolves these issues. Our new definition provides a unified approach, applicable seamlessly to both Bayesian and frequentist formulations of bandits. Furthermore, our definition ensures consistent classification of two bandits offering agents indistinguishable experiences, categorizing them as either both stationary or both non-stationary. This advancement provides a more robust framework for non-stationary bandit learning.
Analysis of nonlinear poroviscoelastic flows with discontinuous porosities

Existence and uniqueness of solutions is shown for a class of viscoelastic flows in porous media with particular attention to problems with nonsmooth porosities. The considered models are formulated in terms of the time-dependent nonlinear interaction between porosity and effective pressure, which in certain cases leads to porosity waves. In particular, conditions for well-posedness in the presence of initial porosities with jump discontinuities are identified.
On the Observability of Recurrent Nova Super-Remnants

The nova super-remnant (NSR) surrounding M31N 2008-12a (12a), the annually erupting recurrent nova (RN), is the only known example of this phenomenon. As this structure has grown as a result of frequent eruptions from 12a, we might expect to see NSRs around other RNe; this would confirm the RN--NSR association and strengthen the connection between novae and type Ia supernovae (SN Ia) as NSRs centered on SN Ia provide a lasting, unequivocal signpost to the single degenerate progenitor type of that explosion. The only previous NSR simulation used identical eruptions from a static white dwarf (WD). In this Paper, we simulate the growth of NSRs alongside the natural growth/erosion of the central WD, within a range of environments, accretion rates, WD temperatures, and initial WD masses. The subsequent evolving eruptions create dynamic NSRs tens of parsecs in radius comprising a low-density cavity, bordered by a hot ejecta pile-up region, and surrounded by a cool high-density, thin, shell. Higher density environments restrict NSR size, as do higher accretion rates, whereas the WD temperature and initial mass have less impact. NSRs form around growing or eroding WDs, indicating that NSRs also exist around old novae with low-mass WDs. Observables such as X-ray and H$\alpha$ emission from the modelled NSRs are derived to aid searches for more examples; only NSRs around high accretion rate novae will currently be observable. The observed properties of the 12a NSR can be reproduced when considering both the dynamically grown NSR and photoionisation by the nova system.
fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks

To design with AI models, user experience (UX) designers must assess the fit between the model and user needs. Based on user research, they need to contextualize the model’s behavior and potential failures within their product-specific data instances and user scenarios. However, our formative interviews with ten UX professionals revealed that such a proactive discovery of model limitations is challenging and time-intensive. Furthermore, designers often lack technical knowledge of AI and accessible exploration tools, which challenges their understanding of model capabilities and limitations. In this work, we introduced a failure-driven design approach to AI, a workflow that encourages designers to explore model behavior and failure patterns early in the design process. The implementation of fAIlureNotes, a designer-centered failure exploration and analysis tool, supports designers in evaluating models and identifying failures across diverse user groups and scenarios. Our evaluation with UX practitioners shows that fAIlureNotes outperforms today’s interactive model cards in assessing context-specific model performance.
Back‐End and Flexible Substrate Compatible Analog Ferroelectric Field‐Effect Transistors for Accurate Online Training in Deep Neural Network Accelerators

Online training of deep neural networks (DNN) can be significantly accelerated by performing in situ vector‐matrix multiplication in a crossbar array of analog memories. However, training accuracies often suffer due to nonideal properties of synapses such as nonlinearity, asymmetry, limited bit precision, and dynamic weight update range within a constrained power budget. Herein, a fully scalable process is reported for digital and analog ferroelectric memory transistors with possibilities for both volatile and nonvolatile data retention and <4 V operation that would be suitable as programmable synaptic weight elements. Ferroelectric copolymer P(VDF‐TrFE) gate insulator and 2D semiconductor MoS2 as the n‐type semiconducting channel material make them suitable for flexible and wearable substrate integration. The ferroelectric‐only devices show excellent performance as digital nonvolatile memory operating at <±5 V while the hybrid ferroelectric–dielectric devices show quasi‐continuous resistive switching resulting from gradual ferroelectric domain rotation. Analog conductance states of the hybrid devices allow good linearity and symmetry of weight updates and produce a dynamic conductance range of 104 with >16 reproducible conducting states. Network training experiments with these ferroelectric field‐effect transistors show >96% classification accuracy with Modified National Institute of Standards and Technology (MNIST) handwritten datasets highlighting their potential for implementation in scaled DNN architectures.
Phonon Transport in Silicon Thin Film: Effect of Temperature Oscillation on Effective Thermal Conductivity

In this article, we consider phonon transport in thin silicon film and examine the influence of temporal oscillation of the temperature heat source on the phonon transport. We solve the frequency-dependent transient two-dimensional Boltzmann equation numerically to observe the quasi-ballistic effects of the phonons on the film effective thermal conductivity. Temperature oscillation at different periods is incorporated at the bottom face of the film as a heat source. The size of the temperature source is varied by introducing the spatial Gaussian distribution along this face. In order to assess the phonon distribution in the film, we introduce the equivalent equilibrium temperature. We determine the effective thermal conductivity of the film and analyze its variation due to the different periods of the temperature oscillation and the Gaussian parameters. It is found that reducing the period of the temperature oscillation lowers the effective thermal conductivity of the film, which is more pronounced for low Gaussian parameters. In this case, the quasi-ballistic behavior of phonons contributes adversely to the effective thermal conductivity of the film.
Demographics of Hierarchical Black Hole Mergers in Dense Star Clusters

With about one hundred mergers of binary black holes (BBHs) detected via gravitational waves by the LIGO-Virgo-KAGRA (LVK) Collaboration, our understanding of the darkest objects in the universe has taken unparalleled steps forward. While most of the events are expected to consist of black holes (BHs) directly formed from the collapse of massive stars, some may contain the remnants of previous BBH mergers. In the most massive globular clusters and in nuclear star clusters, successive mergers can produce second- (2G) or higher-generation BHs, and even form intermediate-mass BHs (IMBHs). Overall, we predict that up to ∼10%, ∼1%, or ∼0.1% of the BBH mergers have one component being a 2G, 3G, or 4G BH, respectively. Assuming that ∼500 BBH mergers will be detected in O4 by LVK, this means that ∼50, ∼5, or ∼0.5 events, respectively, will involve a 2G, 3G, or 4G BH, if most sources are produced dynamically in dense star clusters. With their distinctive signatures of higher masses and spins, such hierarchical mergers offer an unprecedented opportunity to learn about the BH populations in the densest stellar systems and to shed light on the elusive IMBHs that may form therein.
AGN STORM 2. II. Ultraviolet Observations of Mrk 817 with the Cosmic Origins Spectrograph on the Hubble Space Telescope

We present reverberation mapping measurements for the prominent ultraviolet broad emission lines of the active galactic nucleus Mrk 817 using 165 spectra obtained with the Cosmic Origins Spectrograph on the Hubble Space Telescope. Our ultraviolet observations are accompanied by X-ray, optical, and near-infrared observations as part of the AGN Space Telescope and Optical Reverberation Mapping Program 2 (AGN STORM 2). Using the cross-correlation lag analysis method, we find significant correlated variations in the continuum and emission-line light curves. We measure rest-frame delayed responses between the far-ultraviolet continuum at 1180 Å and Lyα λ1215 Å ( 10.4−1.4+1.6 days), N v λ1240 Å ( 15.5−4.8+1.0 days), Si iv + ]O iv λ1397 Å ( 8.2−1.4+1.4 days), C iv λ1549 Å ( 11.8−2.8+3.0 days), and He ii λ1640 Å ( 9.0−1.9+4.5 days) using segments of the emission-line profile that are unaffected by absorption and blending, which results in sampling different velocity ranges for each line. However, we find that the emission-line responses to continuum variations are more complex than a simple smoothed, shifted, and scaled version of the continuum light curve. We also measure velocity-resolved lags for the Lyα and C iv emission lines. The lag profile in the blue wing of Lyα is consistent with virial motion, with longer lags dominating at lower velocities, and shorter lags at higher velocities. The C iv lag profile shows the signature of a thick rotating disk, with the shortest lags in the wings, local peaks at ±1500 km s−1, and a local minimum at the line center. The other emission lines are dominated by broad absorption lines and blending with adjacent emission lines. These require detailed models, and will be presented in future work.
Backward orbits of critical points

We examine the Galois groups of the extensions $K((f'\circ f^n)^{-1}(0))/K$ where $K$ is a number field for polynomials $f(x)\in K[x]$. We use our understanding of this group to study the proportion of primes for which $f$ has a $\mathfrak p$-adic attracting periodic point for a"typical"$f$ and apply the result to the split case of the Dynamical Mordell-Lang Conjecture.
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.
Smaller Is Bigger: Rethinking the Embedding Rate of Deep Hiding

Deep hiding, concealing secret information using Deep Neural Networks (DNNs), can significantly increase the embedding rate and improve the efficiency of secret sharing. Existing works mainly force on designing DNNs with higher embedding rates or fancy functionalities. In this paper, we want to answer some fundamental questions: how to increase and what determines the embedding rate of deep hiding. To this end, we first propose a novel Local Deep Hiding (LDH) scheme that significantly increases the embedding rate by hiding large secret images into small local regions of cover images. Our scheme consists of three DNNs: hiding, locating, and revealing. We use the hiding network to convert a secret image in a small imperceptible compact secret code that is embedded into a random local region of a cover image. The locating network assists the revealing process by identifying the position of secret codes in the stego image, while the revealing network recovers all full-size secret images from these identified local regions. Our LDH achieves an extremely high embedding rate, i.e., $16\times24$ bpp and exhibits superior robustness to common image distortions. We also conduct comprehensive experiments to evaluate our scheme under various system settings. We further quantitatively analyze the trade-off between the embedding rate and image quality with different image restoration algorithms.
Automatic Detecting Unethical Behavior in Open-source Software Projects

Given the rapid growth of Open-Source Software (OSS) projects, ethical considerations are becoming more important. Past studies focused on specific ethical issues (e.g., gender bias and fairness in OSS). There is little to no study on the different types of unethical behavior in OSS projects. We present the first study of unethical behavior in OSS projects from the stakeholders' perspective. Our study of 316 GitHub issues provides a taxonomy of 15 types of unethical behavior guided by six ethical principles (e.g., autonomy). Examples of new unethical behavior include soft forking (copying a repository without forking) and self-promotion (promoting a repository without self-identifying as contributor to the repository). We also identify 18 types of software artifacts affected by the unethical behavior. The diverse types of unethical behavior identified in our study (1) call for attentions of developers and researchers when making contributions in GitHub, and (2) point to future research on automated detection of unethical behavior in OSS projects. Based on our study, we propose Etor, an approach that can automatically detect six types of unethical behavior by using ontological engineering and Semantic Web Rule Language (SWRL) rules to model GitHub attributes and software artifacts. Our evaluation on 195,621 GitHub issues (1,765 GitHub repositories) shows that Etor can automatically detect 548 unethical behavior with 74.8% average true positive rate. This shows the feasibility of automated detection of unethical behavior in OSS projects.
The superconducting phases of UPt3 under pressure

The upper critical field Hc2(T) of the heavy fermion superconductor UPt3 for a magnetic field applied in the basal plane has been studied down to 150 mK and under hydrostatic pressure up to 10 kbar. The kink already observed in Hc2(T) at ambient pressure disappears by applying a relatively low pressure. This suggests that the high‐temperature low‐field superconducting phase reported recently is rapidly suppressed by pressure. A possible interpretation of these results is discussed in connection with recent theories.
Extension of General Relativity with MOND limit predicts novel orbital structure in and around galaxies

Detailed knowledge of the different classes of stellar orbits that can be accommodated in a given galactic potential is a prerequisite when building self-consistent models using for instance the Schwazschild technique. Furthermore, observational properties of galaxies depend on what these classes of orbits are and on the presence of chaos in the systems. In the realistic case in which the starting point for modeling is not a gravitational potential, but an observed density distribution, we will require a gravitational theory to make the connection between the stars that we see and the movement these stars may be having. The argument can be turned upside down: understanding what orbits may be allowed by each gravitational theory may give us a greater insight on what these theories are and on how we can test them. Our aim is thus to understand novel properties of orbits that are predicted by the latest extension of the MOND phenomenology into the relativistic world. We integrated orbits numerically in a fixed density distribution. The potential required for such integration was obtained also numerically by assuming different gravitational models. We find that thanks to the presence of a mass term in the field equations, the theory can allocate new classes of orbits that do not exist in Newtonian gravity nor standard MOND. We discuss consequences that these new families of orbits can have in non-linear cosmological structure formation as well as explore a possible alternative model for galactic structure based on them.