Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-power event-based face detection with asynchronous neuromorphic hardware

The rise of mobility, IoT and wearables has shifted processing to the edge of the sensors, driven by the need to reduce latency, communication costs and overall energy consumption. While deep learning models have achieved remarkable results in various domains, their deployment at the edge for real-time applications remains computationally expensive. Neuromorphic computing emerges as a promising paradigm shift, characterized by co-localized memory and computing as well as event-driven asynchronous sensing and processing. In this work, we demonstrate the possibility of solving the ubiquitous computer vision task of object detection at the edge with low-power requirements, using the event-based N-Caltech101 dataset. We present the first instance of an on-chip spiking neural network for event-based face detection deployed on the SynSense Speck neuromorphic chip, which comprises both an event-based sensor and a spike-based asynchronous processor implementing Integrate-and-Fire neurons. We show how to reduce precision discrepancies between off-chip clock-driven simulation used for training and on-chip event-driven inference. This involves using a multi-spike version of the Integrate-and-Fire neuron on simulation, where spikes carry values that are proportional to the extent the membrane potential exceeds the firing threshold. We propose a robust strategy to train spiking neural networks with back-propagation through time using multi-spike activation and firing rate regularization and demonstrate how to decode output spikes into bounding boxes. We show that the power consumption of the chip is directly proportional to the number of synaptic operations in the spiking neural network, and we explore the trade-off between power consumption and detection precision with different firing rate regularization, achieving an on-chip face detection mAP[0.5] of ~0.6 while consuming only ~20 mW.
Asynchronous Composition of LTL Properties over Infinite and Finite Traces

The verification of asynchronous software components poses significant challenges due to the way components interleave and exchange input/output data concurrently. Compositional strategies aim to address this by separating the task of verifying individual components on local properties from the task of combining them to achieve global properties. This paper concentrates on employing symbolic model checking techniques to verify properties specified in Linear-time Temporal Logic (LTL) on asynchronous software components that interact through data ports. Unlike event-based composition, local properties can now impose constraints on input from other components, increasing the complexity of their composition. We consider both the standard semantics over infinite traces as well as the truncated semantics over finite traces to allow scheduling components only finitely many times. We propose a novel LTL rewriting approach, which converts a local property into a global one while considering the interleaving of infinite or finite execution traces of components. We prove the semantic equivalence of local properties and their rewritten version projected on the local symbols. The rewriting is also optimized to reduce formula size and to leave it unchanged when the temporal property is stutter invariant. These methods have been integrated into the OCRA tool, as part of the contract refinement verification suite. Finally, the different composition approaches were compared through an experimental evaluation that covers various types of specifications.
A strontium quantum-gas microscope

The development of quantum-gas microscopes has brought novel ways of probing quantum degenerate many-body systems at the single-atom level. Until now, most of these setups have focused on alkali atoms. Expanding quantum-gas microscopy to alkaline-earth elements will provide new tools, such as SU(N)-symmetric fermionic isotopes or ultranarrow optical transitions, to the field of quantum simulation. Here, we demonstrate the site-resolved imaging of a $^{84}$Sr bosonic quantum gas in a Hubbard-regime optical lattice. The quantum gas is confined by a two-dimensional in-plane lattice and a light-sheet potential, which operate at the strontium clock-magic wavelength of 813.4 nm. We realize fluorescence imaging using the broad 461 nm transition, which provides high spatial resolution. Simultaneously, we perform attractive Sisyphus cooling with the narrow 689 nm intercombination line. We reconstruct the atomic occupation from the fluorescence images, obtaining imaging fidelities above 94%. Finally, we realize a $^{84}$Sr superfluid in the Bose-Hubbard regime. We observe its interference pattern upon expansion, a probe of phase coherence, with single-atom resolution. Our strontium quantum-gas microscope provides a new platform to study dissipative Hubbard models, quantum optics in atomic arrays, and SU(N) fermions at the microscopic level.
Non-Denoising Forward-Time Diffusions

The scope of this paper is generative modeling through diffusion processes. An approach falling within this paradigm is the work of Song et al. (2021), which relies on a time-reversal argument to construct a diffusion process targeting the desired data distribution. We show that the time-reversal argument, common to all denoising diffusion probabilistic modeling proposals, is not necessary. We obtain diffusion processes targeting the desired data distribution by taking appropriate mixtures of diffusion bridges. The resulting transport is exact by construction, allows for greater flexibility in choosing the dynamics of the underlying diffusion, and can be approximated by means of a neural network via novel training objectives. We develop a unifying view of the drift adjustments corresponding to our and to time-reversal approaches and make use of this representation to inspect the inner workings of diffusion-based generative models. Finally, we leverage on scalable simulation and inference techniques common in spatial statistics to move beyond fully factorial distributions in the underlying diffusion dynamics. The methodological advances contained in this work contribute toward establishing a general framework for generative modeling based on diffusion processes.
The Effect of Sparsity on k-Dominating Set and Related First-Order Graph Properties

We revisit $k$-Dominating Set, one of the first problems for which a tight $n^k-o(1)$ conditional lower bound (for $k\ge 3$), based on SETH, was shown (P\u{a}tra\c{s}cu and Williams, SODA 2007). However, the underlying reduction creates dense graphs, raising the question: how much does the sparsity of the graph affect its fine-grained complexity? We first settle the fine-grained complexity of $k$-Dominating Set in terms of both the number of nodes $n$ and number of edges $m$. Specifically, we show an $mn^{k-2-o(1)}$ lower bound based on SETH, for any dependence of $m$ on $n$. This is complemented by an $mn^{k-2+o(1)}$-time algorithm for all $k\ge 3$. For the $k=2$ case, we give a randomized algorithm that employs a Bloom-filter inspired hashing to improve the state of the art of $n^{\omega+o(1)}$ to $m^{\omega/2+o(1)}$. If $\omega=2$, this yields a conditionally tight bound for all $k\ge 2$. To study if $k$-Dominating Set is special in its sensitivity to sparsity, we consider a class of very related problems. The $k$-Dominating Set problem belongs to a type of first-order definable graph properties that we call monochromatic basic problems. These problems are the natural monochromatic variants of the basic problems that were proven complete for the class FOP of first-order definable properties (Gao, Impagliazzo, Kolokolova, and Williams, TALG 2019). We show that among these problems, $k$-Dominating Set is the only one whose fine-grained complexity decreases in sparse graphs. Only for the special case of reflexive properties, is there an additional basic problem that can be solved faster than $n^{k\pm o(1)}$ on sparse graphs. For the natural variant of distance-$r$ $k$-dominating set, we obtain a hardness of $n^{k-o(1)}$ under SETH for every $r\ge 2$ already on sparse graphs, which is tight for sufficiently large $k$.
Convolutional neural network for retrieval of the time-dependent bond length in a molecule from photoelectron momentum distributions

We apply deep learning for retrieval of the time-dependent bond length in the dissociating two-dimensional H2 + molecule using photoelectron momentum distributions. We consider a pump-probe scheme and calculate electron momentum distributions from strong-ﬁeld ionization by treating the motion of the nuclei classically, semiclassically or quantum mechanically. A convolutional neural network trained on momentum distributions obtained at ﬁxed internuclear distances retrieves the time-varying bond length with an absolute error of 0.2-0.3 a.u.
ZodiacEdge: a Datalog Engine With Incremental Rule Set Maintenance

In this paper, we tackle the incremental maintenance of Datalog inference materialisation when the rule set can be updated. This is particularly relevant in the context of the Internet of Things and Edge computing where smart devices may need to reason over newly acquired knowledge represented as Datalog rules. Our solution is based on an adaptation of a stratification strategy applied to a dependency hypergraph whose nodes correspond to rule sets in a Datalog program. Our implementation supports recursive rules containing both negation and aggregation. We demonstrate the effectiveness of our system on real and synthetic data.
An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms

Compliance with the European Union's Platform-to-Business (P2B) Regulation is challenging for online platforms, and assessing their compliance can be difficult for public authorities. This is partly due to the lack of automated tools for assessing the information (e.g., software documentation) platforms provide concerning ranking transparency. Our study tackles this issue in two ways. First, we empirically evaluate the compliance of six major platforms (Amazon, Bing, Booking, Google, Tripadvisor, and Yahoo), revealing substantial differences in their documentation. Second, we introduce and test automated compliance assessment tools based on ChatGPT and information retrieval technology. These tools are evaluated against human judgments, showing promising results as reliable proxies for compliance assessments. Our findings could help enhance regulatory compliance and align with the United Nations Sustainable Development Goal 10.3, which seeks to reduce inequality, including business disparities, on these platforms.
Strategic Bidding Wars in On-chain Auctions

The Ethereum block-building process has changed significantly since the emergence of Proposer-Builder Separation. Validators access blocks through a marketplace, where block builders bid for the right to construct the block and earn MEV (Maximal Extractable Value) rewards in an on-chain competition, known as the MEV-boost auction. While more than 90% of blocks are currently built via MEV-Boost, trade-offs between builders' strategic behaviors and auction design remain poorly understood. In this paper we address this gap. We introduce a game-theoretic model for MEV-Boost auctions and use simulations to study different builders' bidding strategies observed in practice. We study various strategic interactions and auction setups and evaluate how the interplay between critical elements such as access to MEV opportunities and improved connectivity to relays impact bidding performance. Our results demonstrate the importance of latency on the effectiveness of builders' strategies and the overall auction outcome from the proposer's perspective.
Simulating a two component Bose-Hubbard model with imbalanced hopping in a Rydberg tweezer array

Optical tweezer arrays of neutral atoms provide a versatile platform for quantum simulation due to the range of interactions and Hamiltonians that can be realized and explored. We propose to simulate a two-component Bose-Hubbard model with power-law hopping using arrays of multilevel Rydberg atoms featuring resonant dipolar interactions. The diversity of states that can be used to encode the local Hilbert space of the Bose-Hubbard model enables control of the relative hopping rate of each component and even the realization of spin-flip hopping. We use numerical simulations to show how multilevel Rydberg atoms provide an opportunity to explore the diverse non-equilibrium quench dynamics of the model. For example, we demonstrate a separation of the relaxation timescales of effective spin and charge degrees of freedom, and observe regimes of slow relaxation when the effective hopping rates of the two components are vastly different due to dynamical constraints arising from hardcore boson interactions. We discuss the technical details of realizing our proposal in state-of-the-art Rydberg tweezer arrays.
Neural Spline Fields for Burst Image Fusion and Layer Separation

Each photo in an image burst can be considered a sample of a complex 3D scene: the product of parallax, diffuse and specular materials, scene motion, and illuminant variation. While decomposing all of these effects from a stack of misaligned images is a highly ill-conditioned task, the conventional align-and-merge burst pipeline takes the other extreme: blending them into a single image. In this work, we propose a versatile intermediate representation: a two-layer alpha-composited image plus flow model constructed with neural spline fields -- networks trained to map input coordinates to spline control points. Our method is able to, during test-time optimization, jointly fuse a burst image capture into one high-resolution reconstruction and decompose it into transmission and obstruction layers. Then, by discarding the obstruction layer, we can perform a range of tasks including seeing through occlusions, reflection suppression, and shadow removal. Validated on complex synthetic and in-the-wild captures we find that, with no post-processing steps or learned priors, our generalizable model is able to outperform existing dedicated single-image and multi-view obstruction removal approaches.
Large Language Model (LLM) Bias Index - LLMBI

The Large Language Model Bias Index (LLMBI) is a pioneering approach designed to quantify and address biases inherent in large language models (LLMs), such as GPT-4. We recognise the increasing prevalence and impact of LLMs across diverse sectors. This research introduces a novel metric, LLMBI, to systematically measure and mitigate biases potentially skewing model responses. We formulated LLMBI using a composite scoring system incorporating multiple dimensions of bias, including but not limited to age, gender, and racial biases. To operationalise this metric, we engaged in a multi-step process involving collecting and annotating LLM responses, applying sophisticated Natural Language Processing (NLP) techniques for bias detection, and computing the LLMBI score through a specially crafted mathematical formula. The formula integrates weighted averages of various bias dimensions, a penalty for dataset diversity deficiencies, and a correction for sentiment biases. Our empirical analysis, conducted using responses from OpenAI's API, employs advanced sentiment analysis as a representative method for bias detection. The research reveals LLMs, whilst demonstrating impressive capabilities in text generation, exhibit varying degrees of bias across different dimensions. LLMBI provides a quantifiable measure to compare biases across models and over time, offering a vital tool for systems engineers, researchers and regulators in enhancing the fairness and reliability of LLMs. It highlights the potential of LLMs in mimicking unbiased human-like responses. Additionally, it underscores the necessity of continuously monitoring and recalibrating such models to align with evolving societal norms and ethical standards.
A Multi-Stage Adaptive Feature Fusion Neural Network for Multimodal Gait Recognition

Gait recognition is a biometric technology that has received extensive attention. Most existing gait recognition algorithms are unimodal, and a few multimodal gait recognition algorithms perform multimodal fusion only once. None of these algorithms may fully exploit the complementary advantages of the multiple modalities. In this paper, by considering the temporal and spatial characteristics of gait data, we propose a multi-stage feature fusion strategy (MSFFS), which performs multimodal fusions at different stages in the feature extraction process. Also, we propose an adaptive feature fusion module (AFFM) that considers the semantic association between silhouettes and skeletons. The fusion process fuses different silhouette areas with their more related skeleton joints. Since visual appearance changes and time passage co-occur in a gait period, we propose a multiscale spatial-temporal feature extractor (MSSTFE) to learn the spatial-temporal linkage features thoroughly. Specifically, MSSTFE extracts and aggregates spatial-temporal linkages information at different spatial scales. Combining the strategy and modules mentioned above, we propose a multi-stage adaptive feature fusion (MSAFF) neural network, which shows state-of-the-art performance in many experiments on three datasets. Besides, MSAFF is equipped with feature dimensional pooling (FD Pooling), which can significantly reduce the dimension of the gait representations without hindering the accuracy.
MACS: Mass Conditioned 3D Hand and Object Motion Synthesis

The physical properties of an object, such as mass, significantly affect how we manipulate it with our hands. Surprisingly, this aspect has so far been neglected in prior work on 3D motion synthesis. To improve the naturalness of the synthesized 3D hand object motions, this work proposes MACS the first MAss Conditioned 3D hand and object motion Synthesis approach. Our approach is based on cascaded diffusion models and generates interactions that plausibly adjust based on the object mass and interaction type. MACS also accepts a manually drawn 3D object trajectory as input and synthesizes the natural 3D hand motions conditioned by the object mass. This flexibility enables MACS to be used for various downstream applications, such as generating synthetic training data for ML tasks, fast animation of hands for graphics workflows, and generating character interactions for computer games. We show experimentally that a small-scale dataset is sufficient for MACS to reasonably generalize across interpolated and extrapolated object masses unseen during the training. Furthermore, MACS shows moderate generalization to unseen objects, thanks to the mass-conditioned contact labels generated by our surface contact synthesis model ConNet. Our comprehensive user study confirms that the synthesized 3D hand-object interactions are highly plausible and realistic.
Generalised $T\bar{T}$-deformations of classical free particles

Deformations of many-body Hamiltonians by certain products of conserved currents, referred to as $T\bar{T}$-deformations, are known to preserve integrability. Generalised $T\bar{T}$-deformations, based on the complete space of pseudolocal currents, were suggested [B. Doyon, J, Durnin, T. Yoshimura, Scipost Physics 13, 072 (2022)] to give rise to integrable systems with arbitrary two-body scattering shifts, going beyond those from known models or standard CDD factors. However, locality properties were not clear. We construct explicit generalised $T\bar{T}$-deformations of the system of classical free particles. We show rigorously that they are Liouville integrable Hamiltonian systems with finite-range interactions. We show elastic, factorised scattering, with a two-particle scattering shift that can be any continuously differentiable non-negative even function of momentum differences, fixed by the $T\bar{T}$-deformation function. We show that the scattering map (or wave operator) has a finite-range property allowing us to trace carriers of asymptotic momenta even at finite times - an important characteristics of many-body integrability. We evaluate the free energy and prove the thermodynamic Bethe ansatz with Maxwell-Boltzmann statistics, including with space-varying potentials and in finite and infinite volumes. We give equations for the particles' trajectories where time appears explicitly, generalising the contraction map of hard rod systems: the effect of generalised $T\bar{T}$-deformations is to modify the local metric perceived by each particle, adding extra space in a way that depends on their neighbours. The systems generalise the gas of interacting Bethe ansatz wave packets recently introduced in the Lieb-Liniger model. They form a new class of models that, we believe, most clearly make manifest the structures of many-body integrability.
Exploiting decoupled discretization in meshless lattice Boltzmann method for high-Reynolds number flows

One of the most severe limitations of the Lattice Boltzmann Method in the context of simulating inertial flows is the coupling of the discretization of space to the velocity discretization. It causes the need to increase the size of computational meshes whenever one wants to increase the Reynolds number in the system for a fixed velocity and viscosity. This work aims at adopting the recently proposed meshless LBM formulation to the problem of high Reynolds number flows by using its internal property of decoupled space and velocity discretizations. In meshless formulation, one can change the reference length of the problem by scaling the distances between points that discretize the domain while the streaming distance remains fixed. By doing that, we increase the Reynolds number at no cost: additional nodes need not be inserted in the discretization. We measure the accuracy and efficiency of this approach in the flow around a circular obstacle at $Re=80$-$160$, the lid-driven cavity flow at $Re=100$-$5000$, and the flow through a porous sample at $Re=0.8$-$1500$. Additionally, we apply the meshless streaming step to the recently proposed fixed relaxation time $\tau=1$ LBM to extend its applicability to model inertial flows.
Lifetime Reduction of Single Germanium-Vacancy Defects in Diamond via a Tunable Open Microcavity

Coupling between a single quantum emitter and an optical cavity presents a key capability for future quantum networking applications. Here, we explore interactions between individual germanium-vacancy (GeV) defects in diamond and an open microcavity at cryogenic temperatures. Exploiting the tunability of our microcavity system to characterize and select emitters, we observe a Purcell-effect-induced lifetime reduction of up to $4.5\pm0.3$, and extract coherent coupling rates up to $350\pm20$ MHz. Our results indicate that the GeV defect has favorable optical properties for cavity coupling, with a quantum efficiency of at least $0.32\pm0.05$ and likely much higher.
Extrapolating semileptonic form factors using Bayesian-inference fits regulated by unitarity and analyticity

We discuss our recently proposed model-independent framework for fitting hadronic form-factor data, which are often only available at discrete kinematical points, using parameterisations based on unitarity and analyticity. The accompanying dispersive bound on the form factors (unitarity constraint) is used to regulate the ill-posed fitting problem and allow model-independent predictions over the entire physical range. Kinematical constraints, for example for the vector and scalar form factors in semileptonic meson decays, can be imposed exactly. The core formulae are straight-forward to implement with standard math libraries. We demonstrate the method for the exclusive semileptonic decay $B_s\to K\ell\nu$, an example requiring one to use a generalisation of the original Boyd Grinstein Lebed (BGL) unitarity constraint. We further present a first application of the method to $B \to D^*\ell \nu$ decays.