Miracle-dz/UniFAR-FTUs huggingface dataset:
WIMP constraints from black hole low-mass X-ray binaries

The abnormally fast orbital decay observed in the black hole (BH) Low-Mass X-ray binaries (BH-LMXB) A0620-00 and XTE J1118+480 can be explained by the dynamical friction between Dark Matter (DM) and the companion star orbiting around the low-mass BH (≃ a few M ⊙) of the system. In this case the value of the index γ sp of the DM spike surrounding the BH can be pinned down with an accuracy of ≃ a few percent, way better than that for much bigger systems such as the super massive BHs (SMBHs) in the Galactic Center or in M87. We have used data from XTE J1118+480 to put bounds on the WIMP annihilation cross section times velocity ⟨σv⟩, assuming that DM annihilation is driven by the χχ → bb̅ annihilation channel and that it proceeds in s-wave. The bounds are driven by the radio synchrotron signal produced by e ± final states propagating in the magnetic field in the vicinity of the BH. We find that for DM masses m χ up to the TeV scale XTE J1118+480 allows to constrain ⟨σv⟩ well below the standard value ⟨σv⟩thermal, corresponding to the observed DM relic density in the Universe for a thermal WIMP. On the other hand, for m χ ≳ 15 GeV, the bounds from the SMBHs in the GC or in M87 do not reach ⟨σv⟩thermal when the very large uncertainties on the corresponding spike indices are taken into account, in spite of potentially producing much larger DM densities compared to XTE J1118+480. Our bounds for XTE J1118+480 have a mild sensitivity on the effect of spatial diffusion (which implies at most a weakening of the bounds of a factor ≲ 6 at large m χ). However, diffusion is instrumental in enhancing the sensitivity of the results upon the intensity of the magnetic field. In particular, our bounds rest on the assumption that the magnetic field B reaches the equipartition value B eq. We find that a reduction factor of the magnetic field B eq/B larger than about 14 at low m χ, becoming progressively smaller at higher WIMP masses, would be sufficient to relax the XTE J1118+480 bound to the level of other existing bounds. Recent estimates, albeit not conclusive, may suggest values of B eq/B in BH-LMXB systems as large as 20. This implies that the intensity of the magnetic field in BH-LMXB systems represents the major uncertainty in using them as an alternative to heavier BHs to search for WIMPs.
Transfer Learning between Motor Imagery Datasets using Deep Learning - Validation of Framework and Comparison of Datasets

We present a simple deep learning-based framework commonly used in computer vision and demonstrate its effectiveness for cross-dataset transfer learning in mental imagery decoding tasks that are common in the field of Brain-Computer Interfaces (BCI). We investigate, on a large selection of 12 motor-imagery datasets, which ones are well suited for transfer, both as donors and as receivers. Challenges. Deep learning models typically require long training times and are data-hungry, which impedes their use for BCI systems that have to minimize the recording time for (training) examples and are subject to constraints induced by experiments involving human subjects. A solution to both issues is transfer learning, but it comes with its own challenge, i.e., substantial data distribution shifts between datasets, subjects and even between subsequent sessions of the same subject. Approach. For every pair of pre-training (donor) and test (receiver) dataset, we first train a model on the donor before training merely an additional new linear classification layer based on a few receiver trials. Performance of this transfer approach is then tested on other trials of the receiver dataset. Significance. First, we lower the threshold to use transfer learning between motor imagery datasets: the overall framework is extremely simple and nevertheless obtains decent classification scores. Second, we demonstrate that deep learning models are a good option for motor imagery cross-dataset transfer both for the reasons outlined in the first point and because the framework presented is viable in online scenarios. Finally, analysing which datasets are best suited for transfer learning can be used as a reference for future researchers to determine which to use for pre-training or benchmarking.
A Coarse-grained Model for Aqueous Two-phase Systems: Application to Ferrofluids

Aqueous two-phase systems (ATPSs), that is, phase-separating solutions of water soluble but mutually immiscible molecular species, offer fascinating prospects for selective partitioning, purification, and extraction. Here, we formulate a general Brownian dynamics based coarse-grained simulation model for a polymeric ATPS comprising two water soluble but mutually immiscible polymer species. A third solute species, representing, e.g., nanoparticles (NPs), additional macromolecular species, or impurities can readily be incorporated into the model. We demonstrate that the model captures satisfactorily the phase separation, partitioning, and interfacial properties of a model ATPS composed of a polymer mixture of dextran and polyethylene glycol (PEG) in which magnetic NPs selectively partition into one of the two polymeric phases. The NP partitioning is characterized both via the computational model and experimentally under different conditions. The simulation model captures the trends observed in the experiments and quantitatively links the partitioning behavior to the component species interactions. Finally, the response of the simulation model to external magnetic field, with the magnetic NPs as the additional partitioned component, shows that the ATPS interface fluctuations can be controlled by the magnetic field at length scales much smaller than those probed experimentally to date.
RelVAE: Generative Pretraining for few-shot Visual Relationship Detection

Visual relations are complex, multimodal concepts that play an important role in the way humans perceive the world. As a result of their complexity, high-quality, diverse and large scale datasets for visual relations are still absent. In an attempt to overcome this data barrier, we choose to focus on the problem of few-shot Visual Relationship Detection (VRD), a setting that has been so far neglected by the community. In this work we present the first pretraining method for few-shot predicate classification that does not require any annotated relations. We achieve this by introducing a generative model that is able to capture the variation of semantic, visual and spatial information of relations inside a latent space and later exploiting its representations in order to achieve efficient few-shot classification. We construct few-shot training splits and show quantitative experiments on VG200 and VRD datasets where our model outperforms the baselines. Lastly we attempt to interpret the decisions of the model by conducting various qualitative experiments.
"Do Users fall for Real Adversarial Phishing?" Investigating the Human response to Evasive Webpages

Phishing websites are everywhere, and countermeasures based on static blocklists cannot cope with such a threat. To address this problem, state-of-the-art solutions entail the application of machine learning (ML) to detect phishing websites by checking if they visually resemble webpages of well-known brands. These techniques have achieved promising results in research and, consequently, some security companies began to deploy them also in their phishing detection systems (PDS). However, ML methods are not perfect and some samples are bound to bypass even production-grade PDS. In this paper, we scrutinize whether 'genuine phishing websites' that evade 'commercial ML-based PDS' represent a problem"in reality". Although nobody likes landing on a phishing webpage, a false negative may not lead to serious consequences if the users (i.e., the actual target of phishing) can recognize that"something is phishy". Practically, we carry out the first user-study (N=126) wherein we assess whether unsuspecting users (having diverse backgrounds) are deceived by 'adversarial' phishing webpages that evaded a real PDS. We found that some well-crafted adversarial webpages can trick most participants (even IT experts), albeit others are easily recognized by most users. Our study is relevant for practitioners, since it allows prioritizing phishing webpages that simultaneously fool (i) machines and (ii) humans -- i.e., their intended targets.
As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priors

We present As-Plausible-as-Possible (APAP) mesh deformation technique that leverages 2D diffusion priors to preserve the plausibility of a mesh under user-controlled deformation. Our framework uses per-face Jacobians to represent mesh deformations, where mesh vertex coordinates are computed via a differentiable Poisson Solve. The deformed mesh is rendered, and the resulting 2D image is used in the Score Distillation Sampling (SDS) process, which enables extracting meaningful plausibility priors from a pretrained 2D diffusion model. To better preserve the identity of the edited mesh, we fine-tune our 2D diffusion model with LoRA. Gradients extracted by SDS and a user-prescribed handle displacement are then backpropagated to the per-face Jacobians, and we use iterative gradient descent to compute the final deformation that balances between the user edit and the output plausibility. We evaluate our method with 2D and 3D meshes and demonstrate qualitative and quantitative improvements when using plausibility priors over geometry-preservation or distortion-minimization priors used by previous techniques.
Graph Prompt Learning: A Comprehensive Survey and Beyond

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by \url{https://github.com/WxxShirley/Awesome-Graph-Prompt}, and \url{https://github.com/sheldonresearch/ProG}, respectively.
Edge AI for Internet of Energy: Challenges and Perspectives

The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a meticulously curated research methodology, the article delves into the myriad of edge AI techniques specifically tailored for IoE. The myriad benefits, spanning from reduced latency and real-time analytics to the pivotal aspects of information security, scalability, and cost-efficiency, underscore the indispensability of edge AI in modern IoE frameworks. As the narrative progresses, readers are acquainted with pragmatic applications and techniques, highlighting on-device computation, secure private inference methods, and the avant-garde paradigms of AI training on the edge. A critical analysis follows, offering a deep dive into the present challenges including security concerns, computational hurdles, and standardization issues. However, as the horizon of technology ever expands, the review culminates in a forward-looking perspective, envisaging the future symbiosis of 5G networks, federated edge AI, deep reinforcement learning, and more, painting a vibrant panorama of what the future beholds. For anyone vested in the domains of IoE and AI, this review offers both a foundation and a visionary lens, bridging the present realities with future possibilities.
Cryogenic Thermal Shock Effects on Optical Properties of Quantum Emitters in Hexagonal Boron Nitride

Solid-state quantum emitters are vital building blocks for quantum information science and quantum technology. Among various types of solid-state emitters discovered to date, color centers in hexagonal boron nitride have garnered tremendous traction in recent years thanks to their environmental robustness, high brightness and room-temperature operation. Most recently, these quantum emitters have been employed for satellite-based quantum key distribution. One of the most important requirements to qualify these emitters for space-based applications is their optical stability against cryogenic thermal shock. Such understanding has, however, remained elusive to date. Here, we report on the effects caused by such thermal shock which induces random, irreversible changes in the spectral characteristics of the quantum emitters. By employing a combination of structural characterizations and density functional calculations, we attribute the observed changes to lattice strains caused by the cryogenic temperature shock. Our study shed light on the stability of the quantum emitters under extreme conditions, similar to those countered in outer space.
Control of the Power Flows of a Stochastic Power System

How to determine the power supply of a power system to guarantee that the state remains during a short horizon in a critical subset of the state set? The critical subset is related to the power flows of all power lines of a power system and to transient stability. The control objective is to minimize a cost function. That function is defined as the maximal power flow over all power lines, including a multiple of its standard deviation, as a function of the power supply vector. That the controlled system has an improved performance is shown by numerical results of three academic examples including an eight-node academic network, a twelve-node ring network, and a Manhattan-grid network.
Dendrogram distance: an evaluation metric for generative networks using hierarchical clustering

We present a novel metric for generative modeling evaluation, focusing primarily on generative networks. The method uses dendrograms to represent real and fake data, allowing for the divergence between training and generated samples to be computed. This metric focus on mode collapse, targeting generators that are not able to capture all modes in the training set. To evaluate the proposed method it is introduced a validation scheme based on sampling from real datasets, therefore the metric is evaluated in a controlled environment and proves to be competitive with other state-of-the-art approaches.
Rheology of granular particles immersed in a molecular gas under uniform shear flow

Non-Newtonian transport properties of a dilute gas of inelastic hard spheres immersed in a molecular gas are determined. We assume that the granular gas is sufficiently rarefied and hence, the state of the molecular gas is not disturbed by the presence of the solid particles. In this situation, one can treat the molecular gas as a bath (or thermostat) of elastic hard spheres at a given temperature. Moreover, in spite of the fact that the number density of grains is quite small, we take into account their inelastic collisions among themselves in its kinetic equation. The system (granular gas plus a bath of elastic hard spheres) is subjected to a simple (or uniform) shear flow (USF). In the low-density regime, the rheological properties of the granular gas are determined by solving the Boltzmann kinetic equation by means of Grad's moment method. These properties turn out to be highly nonlinear functions of the shear rate and the remaining parameters of the system. Our results show that the kinetic granular temperature and the non-Newtonian viscosity present a discontinuous shear thickening (DST) effect for sufficiently high values of the mass ratio $m/m_g$ ($m$ and $m_g$ being the mass of grains and gas particles, respectively). This effect becomes more pronounced as the mass ratio $m/m_g$ increases. In particular, in the Brownian limit ($m_g/m\to 0$) the expressions of the non-Newtonian transport properties derived here are consistent with those previously obtained by considering a coarse-grained approach where the effect of gas phase on grains is through an effective force. Theoretical results are compared against computer simulations in the Brownian limit showing an excellent agreement.
Concurrent Stochastic Lossy Channel Games

Concurrent stochastic games are an important formalism for the rational verification of probabilistic multi-agent systems, which involves verifying whether a temporal logic property is satisfied in some or all game-theoretic equilibria of such systems. In this work, we study the rational verification of probabilistic multi-agent systems where agents can cooperate by communicating over unbounded lossy channels. To model such systems, we present concurrent stochastic lossy channel games (CSLCG) and employ an equilibrium concept from cooperative game theory known as the core, which is the most fundamental and widely studied cooperative equilibrium concept. Our main contribution is twofold. First, we show that the rational verification problem is undecidable for systems whose agents have almost-sure LTL objectives. Second, we provide a decidable fragment of such a class of objectives that subsumes almost-sure reachability and safety. Our techniques involve reductions to solving infinite-state zero-sum games with conjunctions of qualitative objectives. To the best of our knowledge, our result represents the first decidability result on the rational verification of stochastic multi-agent systems on infinite arenas.
Riemannian Self-Attention Mechanism for SPD Networks

Symmetric positive definite (SPD) matrix has been demonstrated to be an effective feature descriptor in many scientific areas, as it can encode spatiotemporal statistics of the data adequately on a curved Riemannian manifold, i.e., SPD manifold. Although there are many different ways to design network architectures for SPD matrix nonlinear learning, very few solutions explicitly mine the geometrical dependencies of features at different layers. Motivated by the great success of self-attention mechanism in capturing long-range relationships, an SPD manifold self-attention mechanism (SMSA) is proposed in this paper using some manifold-valued geometric operations, mainly the Riemannian metric, Riemannian mean, and Riemannian optimization. Then, an SMSA-based geometric learning module (SMSA-GLM) is designed for the sake of improving the discrimination of the generated deep structured representations. Extensive experimental results achieved on three benchmarking datasets show that our modification against the baseline network further alleviates the information degradation problem and leads to improved accuracy.
Gaussian Processes for Monitoring Air-Quality in Kampala

Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in low and middle-income countries have to rely on a sparse allocation of them. In this paper, we investigate the use of Gaussian Processes for both nowcasting the current air-pollution in places where there are no sensors and forecasting the air-pollution in the future at the sensor locations. In particular, we focus on the city of Kampala in Uganda, using data from AirQo's network of sensors. We demonstrate the advantage of removing outliers, compare different kernel functions and additional inputs. We also compare two sparse approximations to allow for the large amounts of temporal data in the dataset.
On the Impact of Sampling on Deep Sequential State Estimation

State inference and parameter learning in sequential models can be successfully performed with approximation techniques that maximize the evidence lower bound to the marginal log-likelihood of the data distribution. These methods may be referred to as Dynamical Variational Autoencoders, and our specific focus lies on the deep Kalman filter. It has been shown that the ELBO objective can oversimplify data representations, potentially compromising estimation quality. Tighter Monte Carlo objectives have been proposed in the literature to enhance generative modeling performance. For instance, the IWAE objective uses importance weights to reduce the variance of marginal log-likelihood estimates. In this paper, importance sampling is applied to the DKF framework for learning deep Markov models, resulting in the IW-DKF, which shows an improvement in terms of log-likelihood estimates and KL divergence between the variational distribution and the transition model. The framework using the sampled DKF update rule is also accommodated to address sequential state and parameter estimation when working with highly non-linear physics-based models. An experiment with the 3-space Lorenz attractor shows an enhanced generative modeling performance and also a decrease in RMSE when estimating the model parameters and latent states, indicating that tighter MCOs lead to improved state inference performance.
System-Environment Entanglement Phase Transitions

Entanglement in quantum many-body systems can exhibit universal phenomena governed by long-distance properties. We study universality and phase transitions of the entanglement inherent to open many-body systems, namely, the entanglement between a system of interest and its environment. Specifically, we consider the Tomonaga-Luttinger liquid (TLL) under a local measurement and analyze its unconditioned nonunitary evolution, where the measurement outcomes are averaged over. We quantify the system-environment entanglement by the R\'enyi entropy of the post-measurement density matrix, whose size-independent term encodes the universal low-energy physics. We develop a field-theoretical description to relate the universal term to the $g$ function in a boundary conformal field theory (CFT), and use the renormalization group (RG) method and the boundary CFT techniques to determine its value. We show that the universal contribution is determined by the TLL parameter $K$ and can exhibit singularity signifying an entanglement phase transition. Surprisingly, in certain cases the size-independent contribution can increase as a function of the measurement strength in contrast to what is na\"ively expected from the $g$-theorem. We argue that this unconventional behavior could be attributed to the dangerously irrelevant term which has been found in studies of the resistively shunted Josephson junction. We also check these results by numerical calculations in the spin-$\frac{1}{2}$ XXZ chain subject to a site-resolved measurement. Possible experimental realization in ultracold gases, which requires no postselections, is discussed.