Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplicial graviton from selfdual Ashtekar variables

In perturbative gravity, it is straight-forward to characterize the two local degrees of freedom of the gravitational field in terms of a mode expansion of the linearized perturbation. In the non-perturbative regime, we are in a more difficult position. It is not at all obvious how to construct Dirac observables that can separate the gauge orbits. Standard procedures rely on asymptotic boundary conditions or formal Taylor expansions of relational observables. In this paper, we lay out a new non-perturbative lattice approach to tackle the problem in terms of Ashtekar’s self-dual formulation. Starting from a simplicial decomposition of space, we introduce a local kinematical phase space at the lattice sites. At each lattice site, we introduce a set of constraints that replace the generators of the hypersurface deformation algebra in the continuum. We show that the discretized constraints close under the Poisson bracket. The resulting reduced phase space describes two complex physical degrees of freedom representing the two radiative modes at the discretized level. The paper concludes with a discussion of the key open problems ahead and the implications for quantum gravity.
Enhancing Efficiency of Quadrupedal Locomotion Over Challenging Terrains with Extensible Feet

Recent advancements in legged locomotion research have made legged robots a preferred choice for navigating challenging terrains when compared to their wheeled counterparts. This paper presents a novel locomotion policy, trained using Deep Reinforcement Learning, for a quadrupedal robot equipped with an additional prismatic joint between the knee and foot of each leg. The training is performed in NVIDIA Isaac Gym simulation environment. Our study investigates the impact of these joints on maintaining the quadruped's desired height and following commanded velocities while traversing challenging terrains. We provide comparison results, based on a Cost of Transport (CoT) metric, between quadrupeds with and without prismatic joints. The learned policy is evaluated on a set of challenging terrains using the CoT metric in simulation. Our results demonstrate that the added degrees of actuation offer the locomotion policy more flexibility to use the extra joints to traverse terrains that would be deemed infeasible or prohibitively expensive for the conventional quadrupedal design, resulting in significantly improved efficiency.
DynamicStereo: Consistent Dynamic Depth from Stereo Videos

We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing meth-ods for depth from stereo treat different stereo frames in-dependently, leading to temporally inconsistent depth pre-dictions. Temporal consistency is especially important for immersive AR or VR scenarios, where flickering greatly di-minishes the user experience. We propose DynamicStereo, a novel transformer-based architecture to estimate dispar-ity for stereo videos. The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions. Our architecture is designed to process stereo videos efficiently through divided attention layers. We also introduce Dynamic Replica, a new bench-mark dataset containing synthetic videos of people and ani-mals in scanned environments, which provides complemen-tary training and evaluation data for dynamic stereo closer to real applications than existing datasets. Training with this dataset further improves the quality of predictions of our proposed DynamicStereo as well as prior methods. Finally, it acts as a benchmark for consistent stereo methods. Project page: https://dynamic-stereo.github. io/
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging

In this paper, we examine computational approaches for measuring the "fairness" of image tagging systems, finding that they cluster into five distinct categories, each with its own analytic foundation. We also identify a range of normative concerns that are often collapsed under the terms "unfairness," "bias," or even "discrimination" when discussing problematic cases of image tagging. Specifically, we identify four types of representational harms that can be caused by image tagging systems, providing concrete examples of each. We then consider how different computational measurement approaches map to each of these types, demonstrating that there is not a one-to-one mapping. Our findings emphasize that no single measurement approach will be definitive and that it is not possible to infer from the use of a particular measurement approach which type of harm was intended to be measured. Lastly, equipped with this more granular understanding of the types of representational harms that can be caused by image tagging systems, we show that attempts to mitigate some of these types of harms may be in tension with one another.
Constraining the dark matter interpretation of the positron excess with $\gamma$-ray data

The particle origin of dark matter (DM) is still one of the main puzzles in modern physics. One of the most promising search strategy to detect DM at laboratories is through the indirect search of cosmic particles that are produced from DM annihilation in space. In particular, the flux of cosmic positrons has been measured with high precision by the AMS-02 experiment demonstrating that an excess above 10 GeV, with respect to the secondary production, is present. We study in this paper the possible DM origin of the positron excess finding the values of the DM mass $M$ and annihilation cross section $\langle \sigma v \rangle$ that are needed to fit high-energy positron data. In particular, we find that for DM annihilating into $b\bar{b}$ it is required to have $M=43$ TeV and $\langle \sigma v \rangle = 10^{-21}$ cm$^3$/s while for $\tau^+\tau^-$ $M=2$ TeV and $\langle \sigma v \rangle = 3\times 10^{-23}$ cm$^3$/s. If DM produce positrons, they are expected to generate gamma rays from the center of the Milky Way and around dwarf galaxy satellites of the Galaxy. We thus combine the values for the DM mass and annihilation cross section obtained with the fit to AMS-02 positron data with the upper limits derived with the non-detection of $\gamma$ rays with HESS in the direction of the Galactic center and Fermi-LAT for the combined analysis of dwarf galaxies. The main result of the paper is that only DM annihilating into $\mu^+ \mu^-$ with a mass around 500 GeV and $\langle \sigma v \rangle = 4\times 10^{-24}$ cm$^3$/s can fit AMS-02 data and be compatible with the upper limits found with $\gamma$ rays. As for the $\tau^+ \tau^-$ ($b\bar{b}$) channel, DM can contribute at most at a few tens $\%$ (a few \%) level.
GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions

In recent years, the increasing availability of personal data has raised concerns regarding privacy and security. One of the critical processes to address these concerns is data anonymization, which aims to protect individual privacy and prevent the release of sensitive information. This research focuses on the importance of face anonymization. Therefore, we introduce GANonymization, a novel face anonymization framework with facial expression-preserving abilities. Our approach is based on a high-level representation of a face, which is synthesized into an anonymized version based on a generative adversarial network (GAN). The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face. Additionally, the performance of preserving facial expressions was evaluated on several affect recognition datasets and outperformed the state-of-the-art methods in most categories. Finally, our approach was analyzed for its ability to remove various facial traits, such as jewelry, hair color, and multiple others. Here, it demonstrated reliable performance in removing these attributes. Our results suggest that GANonymization is a promising approach for anonymizing faces while preserving facial expressions.
LearnDefend: Learning to Defend against Targeted Model-Poisoning Attacks on Federated Learning

Targeted model poisoning attacks pose a significant threat to federated learning systems. Recent studies show that edge-case targeted attacks, which target a small fraction of the input space are nearly impossible to counter using existing fixed defense strategies. In this paper, we strive to design a learned-defense strategy against such attacks, using a small defense dataset. The defense dataset can be collected by the central authority of the federated learning task, and should contain a mix of poisoned and clean examples. The proposed framework, LearnDefend, estimates the probability of a client update being malicious. The examples in defense dataset need not be pre-marked as poisoned or clean. We also learn a poisoned data detector model which can be used to mark each example in the defense dataset as clean or poisoned. We estimate the poisoned data detector and the client importance models in a coupled optimization approach. Our experiments demonstrate that LearnDefend is capable of defending against state-of-the-art attacks where existing fixed defense strategies fail. We also show that LearnDefend is robust to size and noise in the marking of clean examples in the defense dataset.
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture

In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. In this study, we developed a novel and efficient digital pathology classifier called DPSeq, to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizon and vertical bidirectional long short-term memory (BiLSTM) networks. Using hematoxylin and eosin (H&E)-stained histopathological images of colorectal cancer (CRC) from two international datasets: The Cancer Genome Atlas (TCGA) and Molecular and Cellular Oncology (MCO), the predictive performance of DPSeq was evaluated in series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in CRC (MSI status, Hypermutation, CIMP status, BRAF mutation, TP53 mutation and chromosomal instability [CING]), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. Additionally, under the same experimental conditions using the same set of training and testing datasets, DPSeq surpassed 4 CNN (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and 2 transformer (ViT and Swin-T) models, achieving the highest AUROC and AUPRC values in predicting MSI status, BRAF mutation, and CIMP status. Furthermore, DPSeq required less time for both training and prediction due to its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.
Attention Based Feature Fusion For Multi-Agent Collaborative Perception

In the domain of intelligent transportation systems (ITS), collaborative perception has emerged as a promising approach to overcome the limitations of individual perception by enabling multiple agents to exchange information, thus enhancing their situational awareness. Collaborative perception overcomes the limitations of individual sensors, allowing connected agents to perceive environments beyond their line-of-sight and field of view. However, the reliability of collaborative perception heavily depends on the data aggregation strategy and communication bandwidth, which must overcome the challenges posed by limited network resources. To improve the precision of object detection and alleviate limited network resources, we propose an intermediate collaborative perception solution in the form of a graph attention network (GAT). The proposed approach develops an attention-based aggregation strategy to fuse intermediate representations exchanged among multiple connected agents. This approach adaptively highlights important regions in the intermediate feature maps at both the channel and spatial levels, resulting in improved object detection precision. We propose a feature fusion scheme using attention-based architectures and evaluate the results quantitatively in comparison to other state-of-the-art collaborative perception approaches. Our proposed approach is validated using the V2XSim dataset. The results of this work demonstrate the efficacy of the proposed approach for intermediate collaborative perception in improving object detection average precision while reducing network resource usage.
Binary Formation in a 100 μm Dark Massive Core

We report high-resolution ALMA observations toward a massive protostellar core C1-Sa (∼30 M ⊙) in the Dragon infrared dark cloud. At the resolution of 140 au, the core fragments into two kernels (C1-Sa1 and C1-Sa2) with a projected separation of ∼1400 au along the elongation of C1-Sa, consistent with a Jeans length scale of ∼1100 au. Radiative transfer modeling using RADEX indicates that the protostellar kernel C1-Sa1 has a temperature of ∼75 K and a mass of 0.55 M ⊙. C1-Sa1 also likely drives two bipolar outflows, one being parallel to the plane of the sky. C1-Sa2 is not detected in line emission and does not show any outflow activity but exhibits ortho-H2D+ and N2D+ emission in its vicinity; thus it is likely still starless. Assuming a 20 K temperature, C1-Sa2 has a mass of 1.6 M ⊙. At a higher resolution of 96 au, C1-Sa1 begins to show an irregular shape at the periphery, but no clear sign of multiple objects or disks. We suspect that C1-Sa1 hosts a tight binary with inclined disks and outflows. Currently, one member of the binary is actively accreting while the accretion in the other is significantly reduced. C1-Sa2 shows hints of fragmentation into two subkernels with similar masses, which requires further confirmation with higher sensitivity.
On a Unified and Simplified Proof for the Ergodic Convergence Rates of PPM, PDHG and ADMM

We present a unified viewpoint of proximal point method (PPM), primal-dual hybrid gradient (PDHG) and alternating direction method of multipliers (ADMM) for solving convex-concave primal-dual problems. This viewpoint shows the equivalence of these three algorithms upto a norm change, and it leads to a four-line simple proof of their $\mathcal O(1/k)$ ergodic rates. The simple proof technique is not limited to these three algorithms, but can also be utilized to analyze related algorithms, such as gradient descent, linearized PDHG, inexact algorithms, just to name a few.
L is unequal NL under the Strong Exponential Time Hypothesis

Due to Savitch's theorem we know $NL\subseteq DSPACE(\log^2(n))$. To show this upper bound, Savitch constructed an algorithm with $O(\log^2(n))$ space on the working tape. We will show that Savitch's algorithm also described a lower bound under the Strong Exponential Time Hypothesis. Every algorithm for the Connectivity Problem needs $O(\log^2(n))$ space in this case.
Probabilistic Formal Modelling to Uncover and Interpret Interaction Styles

We present a study using new computational methods, based on a novel combination of machine learning for inferring admixture hidden Markov models and probabilistic model checking, to uncover interaction styles in a mobile app. These styles are then used to inform a redesign, which is implemented, deployed, and then analysed using the same methods. The data sets are logged user traces, collected over two six-month deployments of each version, involving thousands of users and segmented into different time intervals. The methods do not assume tasks or absolute metrics such as measures of engagement, but uncover the styles through unsupervised inference of clusters and analysis with probabilistic temporal logic. For both versions there was a clear distinction between the styles adopted by users during the first day/week/month of usage, and during the second and third months, a result we had not anticipated.
A Nonlinear Approach for Determining Camera Intrinsic Parameters

This paper introduces a nonlinear approach for determining the camera intrinsic parameters. Firstly, by the image of a single rectangle in space, we obtain the images of two circular points which contains a parameter r, where r is the ratio of the length of two adjacent sides of the rectangle; and then establish some nonlinear constraints on the intrinsic parameters and the parameter r via the obtained images of the circular points. By turning the problem of solving the nonlinear equations into an optimization problem, the camera intrinsic parameters are determined. Experimental result shows that the approach has a high accuracy.
Special Session: Neuromorphic hardware design and reliability from traditional CMOS to emerging technologies

The field of neuromorphic computing has been rapidly evolving in recent years, with an increasing focus on hardware design and reliability. This special session paper provides an overview of the recent developments in neuromorphic computing, focusing on hardware design and reliability. We first review the traditional CMOS-based approaches to neuromorphic hardware design and identify the challenges related to scalability, latency, and power consumption. We then investigate alternative approaches based on emerging technologies, specifically integrated photonics approaches within the NEUROPULS project. Finally, we examine the impact of device variability and aging on the reliability of neuromorphic hardware and present techniques for mitigating these effects. This review is intended to serve as a valuable resource for researchers and practitioners in neuromorphic computing.
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems

Graph neural networks are often used to model interacting dynamical systems since they gracefully scale to systems with a varying and high number of agents. While there has been much progress made for deterministic interacting systems, modeling is much more challenging for stochastic systems in which one is interested in obtaining a predictive distribution over future trajectories. Existing methods are either computationally slow since they rely on Monte Carlo sampling or make simplifying assumptions such that the predictive distribution is unimodal. In this work, we present a deep state-space model which employs graph neural networks in order to model the underlying interacting dynamical system. The predictive distribution is multimodal and has the form of a Gaussian mixture model, where the moments of the Gaussian components can be computed via deterministic moment matching rules. Our moment matching scheme can be exploited for sample-free inference, leading to more efficient and stable training compared to Monte Carlo alternatives. Furthermore, we propose structured approximations to the covariance matrices of the Gaussian components in order to scale up to systems with many agents. We benchmark our novel framework on two challenging autonomous driving datasets. Both confirm the benefits of our method compared to state-of-the-art methods. We further demonstrate the usefulness of our individual contributions in a carefully designed ablation study and provide a detailed runtime analysis of our proposed covariance approximations. Finally, we empirically demonstrate the generalization ability of our method by evaluating its performance on unseen scenarios.
Fate of entanglement in magnetism under Lindbladian or non-Markovian dynamics and conditions for their transition to Landau-Lifshitz-Gilbert classical dynamics

It is commonly assumed in spintronics and magnonics that localized spins within antiferromagnets are in the N\'{e}el ground state (GS), as well as that such state evolves, when pushed out of equilibrium by current or external fields, according to the Landau-Lifshitz-Gilbert (LLG) equation viewing localized spins as classical vectors of fixed length. On the other hand, the true GS of antiferromagnets is highly entangled, as confirmed by very recent neutron scattering experiments witnessing their entanglement. Although GS of ferromagnets is always unentangled, their magnonic low-energy excitation are superpositions of many-body spin states and, therefore, entangled. In this study, we initialize quantum Heisenberg ferro- or antiferromagnetic chains hosing localized spins $S=1/2$, $S=1$ or $S=5/2$ into unentangled pure state and then evolve them by quantum master equations (QMEs) of Lindblad or non-Markovian type, derived by coupling localized spins to a bosonic bath (such as due to phonons) or by using additional ``reaction coordinate'' in the latter case. The time evolution is initiated by applying an external magnetic field, and entanglement of time-evolving {\em mixed} quantum states is monitored by computing its logarithmic negativity. We find that non-Markovian dynamics maintains some degree of entanglement, which shrinks the length of the vector of spin expectation values, thereby making the LLG equation inapplicable. Conversely, Lindbladian (i.e., Markovian) dynamics ensures that entanglement goes to zero, thereby enabling quantum-to-classical (i.e., to LLG) transition in all cases -- $S=1/2$, $S=1$ and $S=5/2$ ferromagnet or $S=5/2$ antiferromagnet -- {\em except} for $S=1/2$ and $S=1$ antiferromagnet. We also investigate the stability of entangled antiferromagnetic GS upon suddenly coupling it to the bosonic bath.
Quantum Encoding and Analysis on Continuous Time Stochastic Process with Financial Applications

Modeling stochastic phenomena in continuous time is an essential yet challenging problem. Analytic solutions are often unavailable, and numerical methods can be prohibitively time-consuming and computationally expensive. To address this issue, we propose an algorithmic framework tailored for quantum continuous time stochastic processes. This framework consists of two key procedures: data preparation and information extraction. The data preparation procedure is specifically designed to encode and compress information, resulting in a significant reduction in both space and time complexities. This reduction is exponential with respect to a crucial feature parameter of the stochastic process. Additionally, it can serve as a submodule for other quantum algorithms, mitigating the common data input bottleneck. The information extraction procedure is designed to decode and process compressed information with quadratic acceleration, extending the quantum-enhanced Monte Carlo method. The framework demonstrates versatility and flexibility, finding applications in statistics, physics, time series analysis and finance. Illustrative examples include option pricing in the Merton Jump Diffusion Model and ruin probability computing in the Collective Risk Model, showcasing the framework’s ability to capture extreme market events and incorporate history-dependent information. Overall, this quantum algorithmic framework provides a powerful tool for accurate analysis and enhanced understanding of stochastic phenomena.