Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Canonical Nucleon Decays as Window into Light New Physics

Nucleon decays are generic predictions of motivated theories, including those based on the unification of forces and supersymmetry. We demonstrate that non-canonical nucleon decays offer a unique opportunity to broadly probe light new particles beyond the Standard Model with masses below $\sim$few GeV over decades in mass range, including axion-like particles, dark photons, sterile neutrinos, and scalar dark matter. Conventional searches can misinterpret and even completely miss such new physics. We propose a general strategy based on momenta of visible decay final states to probe these processes, offering a rich physics program for existing and upcoming experiments such as Super-Kamiokande, Hyper-Kamiokande, DUNE, and JUNO.
Advantage of multi-partite entanglement for quantum cryptography over long and short ranged networks

The increasing sophistication of available quantum networks has seen a corresponding growth in the pursuit of multi-partite cryptographic protocols. Whilst the use of multi-partite entanglement is known to offer an advantage in certain abstractly motivated contexts, the quest to find practical advantage scenarios is ongoing and substantial difficulties in generalising some bi-partite security proofs still remain. We present rigorous results that address both these challenges at the same time. First, we prove the security of a variant of the GHZ state based secret sharing protocol against general attacks, including participant attacks which break the security of the original GHZ state scheme. We then identify parameters for a performance advantage over realistic bottleneck networks. We show that whilst channel losses limit the advantage region to short distances over direct transmission networks, the addition of quantum repeaters unlocks the performance advantage of multi-partite entanglement over point-to-point approaches for long distance quantum cryptography.
Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns

Recently, the paradigm of pre-training and fine-tuning graph neural networks has been intensively studied and applied in a wide range of graph mining tasks. Its success is generally attributed to the structural consistency between pre-training and downstream datasets, which, however, does not hold in many real-world scenarios. Existing works have shown that the structural divergence between pre-training and downstream graphs significantly limits the transferability when using the vanilla fine-tuning strategy. This divergence leads to model overfitting on pre-training graphs and causes difficulties in capturing the structural properties of the downstream graphs. In this paper, we identify the fundamental cause of structural divergence as the discrepancy of generative patterns between the pre-training and downstream graphs. Furthermore, we propose G-Tuning to preserve the generative patterns of downstream graphs. Given a downstream graph G, the core idea is to tune the pre-trained GNN so that it can reconstruct the generative patterns of G, the graphon W. However, the exact reconstruction of a graphon is known to be computationally expensive. To overcome this challenge, we provide a theoretical analysis that establishes the existence of a set of alternative graphons called graphon bases for any given graphon. By utilizing a linear combination of these graphon bases, we can efficiently approximate W. This theoretical finding forms the basis of our model, as it enables effective learning of the graphon bases and their associated coefficients. Compared with existing algorithms, G-Tuning demonstrates consistent performance improvement in 7 in-domain and 7 out-of-domain transfer learning experiments.
Earthquakes and faults

Abstract The hypothesis that earthquakes are caused by faulting has been prominent in seismological theory for half a century, but continues to present many difficulties. Although the chief support comes from studies of large shallow earthquakes that have been accompanied by surface faulting, the evidence given by these infrequent events can be interpreted otherwise. No satisfactory explanation of deep-focus earthquakes has emerged; sudden faulting may be essentially a surface phenomenon. Nor does the hypothesis aid the understanding of such phenomena as sudden regional uplift, or slow fault creep. There is much to encourage the view that fracture of the ground is but a gross form of earthquake damage. On the other hand, the similarity between natural earthquakes and underground nuclear explosions, as radiators of seismic waves, suggests that sudden local phase transitions may provide a source mechanism for earthquakes at all depths.
The growth rate of multicolor Ramsey numbers of $3$-graphs

The $q$-color Ramsey number of a $k$-uniform hypergraph $G,$ denoted $r(G;q)$, is the minimum integer $N$ such that any coloring of the edges of the complete $k$-uniform hypergraph on $N$ vertices contains a monochromatic copy of $G$. The study of these numbers is one of the most central topics in combinatorics. One natural question, which for triangles goes back to the work of Schur in 1916, is to determine the behaviour of $r(G;q)$ for fixed $G$ and $q$ tending to infinity. In this paper we study this problem for $3$-uniform hypergraphs and determine the tower height of $r(G;q)$ as a function of $q$. More precisely, given a hypergraph $G$, we determine when $r(G; q)$ behaves polynomially, exponentially or double-exponentially in $q$. This answers a question of Axenovich, Gy\'{a}rf\'{a}s, Liu and Mubayi.
Area metric gravity revisited

Area metrics are an intriguing generalization of length metrics which appears in several quantum-gravity approaches. We describe the space of diffeomorphism-invariant area-metric actions quadratic in fluctuations and derivatives. A general theory is found to be specified by four parameters, two of which are mass parameters for the non-length degrees of freedom. We find that a two-parameter subclass of theories exhibits an additional shift symmetry of the kinetic term, and leads to a ghost-free graviton propagator for the effective theory obtained after integrating out the non-length degrees of freedom. One of the two parameters determines the strength of parity violations, the other defines a mass parameter for the non-length degrees of freedom. The same type of action has been found to appear from modified Plebanski theory and in the continuum limit of (effective) spin foams. We moreover find that area-metric actions in Lorentzian (but not in Euclidean) signature feature wrong-sign kinetic and mass terms for the non-length degrees of freedom. Nevertheless, despite a coupling of these degrees of freedom to the length metric, the linearized dynamics turns out to be stable for the above subclass of actions.
Machine Learning Based Compton Suppression for Nuclear Fusion Plasma Diagnostics

Diagnostics are critical on the path to commercial fusion reactors, since measurements and characterisation of the plasma is important for sustaining fusion reactions. Gamma spectroscopy is commonly used to provide information about the neutron energy spectrum from activation analysis, which can be used to calculate the neutron flux and fusion power. The detection limits for measuring nuclear dosimetry reactions used in such diagnostics are fundamentally related to Compton scattering events making up a background continuum in measured spectra. This background lies in the same energy region as peaks from low-energy gamma rays, leading to detection and characterisation limitations. This paper presents a digital machine learning Compton suppression algorithm (MLCSA), that uses state-of-the-art machine learning techniques to perform pulse shape discrimination (PSD) for high purity germanium (HPGe) detectors. The MLCSA identifies key features of individual pulses to differentiate between those that are generated from photopeaks and Compton scatter events. Compton events are then rejected, reducing the low energy background. This novel suppression algorithm improves gamma spectroscopy results by lowering minimum detectable activity (MDA) limits and thus reducing the measurement time required to reach the desired detection limit. In this paper, the performance of the MLCSA is demonstrated using an HPGe detector, with a gamma spectrum containing americium-241 (Am-241) and cobalt-60 (Co-60). The MDA of Am-241 improved by 51% and the signal to noise ratio (SNR) improved by 49%, while the Co-60 peaks were partially preserved (reduced by 78%). The MLCSA requires no modelling of the specific detector and so has the potential to be detector agnostic, meaning the technique could be applied to a variety of detector types and applications.
Uncovering a graph

Uncover the vertices of a given graph, deterministic or random, in random order; we consider both a discrete-time and a continuous-time version. We study the evolution of the number of visible edges, and show convergence after normalization to a Gaussian process. This problem was studied by Hackl, Panholzer, and Wagner for the case when the graph is a random labelled tree; we generalize their result to more general graphs, including both other classes of random and non-random trees, and denser graphs. The results are similar in all cases, but some differences can be seen depending on the size of the average degree and of the variance of the vertex degrees.
Critical slowing down in thermal soft-sphere glasses via energy minimization

Using hybrid molecular dynamics/SWAP Monte Carlo (MD/SMC) simulations, we show that the terminal relaxation times $\tau$ for FIRE energy minimization of soft-sphere glasses exhibit thermal onset as samples become increasingly well-equilibrated. We show that although $\tau(\phi)$ can decrease by orders of magnitude as equilibration proceeds and the jamming density $\phi_{\rm J}$ increases via thermal onset, it always scales as $\tau(\phi) \sim (\phi_{\rm J} - \phi)^{-2} \sim [Z_{\rm iso} - Z_{\rm ms}(\tau)]^{-2}$, where $\phi_{\rm J}$ is the jamming density and $Z_{\rm ms}(\tau)$ is the average coordination number of particles satisfying a minimal local mechanical stability criterion ($Z \geq d+1$) \textit{at the top of} the final PEL sub-basin the system encounters. This scaling allows us to (nearly) collapse $\tau$ datasets that look very different when plotted as a function of $\phi$, and to address another closely related question: how should the vibrational spectra of thermal soft-sphere glasses evolve as they age at constant density?
Data Transformation to Construct a Dataset for Generating Entity-Relationship Model from Natural Language

In order to reduce the manual cost of designing ER models, recent approaches have been proposed to address the task of NL2ERM, i.e., automatically generating entity-relationship (ER) models from natural language (NL) utterances such as software requirements. These approaches are typically rule-based ones, which rely on rigid heuristic rules; these approaches cannot generalize well to various linguistic ways of describing the same requirement. Despite having better generalization capability than rule-based approaches, deep-learning-based models are lacking for NL2ERM due to lacking a large-scale dataset. To address this issue, in this paper, we report our insight that there exists a high similarity between the task of NL2ERM and the increasingly popular task of text-to-SQL, and propose a data transformation algorithm that transforms the existing data of text-to-SQL into the data of NL2ERM. We apply our data transformation algorithm on Spider, one of the most popular text-to-SQL datasets, and we also collect some data entries with different NL types, to obtain a large-scale NL2ERM dataset. Because NL2ERM can be seen as a special information extraction (IE) task, we train two state-of-the-art IE models on our dataset. The experimental results show that both the two models achieve high performance and outperform existing baselines.
First measurement of the neutron-emission probability with a surrogate reaction in inverse kinematics at a heavy-ion storage ring

Neutron-induced reaction cross sections of short-lived nuclei are imperative to understand the origin of heavy elements in stellar nucleosynthesis and for societal applications, but their measurement is extremely complicated due to the radioactivity of the targets involved. One way of overcoming this issue is to combine surrogate reactions with the unique possibilities offered by heavy-ion storage rings. In this work, we describe the first surrogate-reaction experiment in inverse kinematics, which we successfully conducted at the Experimental Storage Ring (ESR) of the GSI/FAIR facility, using the $^{208}$Pb(p,p') reaction as a surrogate for neutron capture on $^{207}$Pb. Thanks to the outstanding detection efficiencies possible at the ESR, we were able to measure for the first time the neutron-emission probability as a function of the excitation energy of $^{208}$Pb. We demonstrate the strong connection between this probability and the neutron-induced radiative capture cross section of $^{207}$Pb, and provide reliable results for this cross section at neutron energies for which no experimental data exist.
Adaptation using spatially distributed Gaussian Processes

We consider the accuracy of an approximate posterior distribution in nonparametric regression problems by combining posterior distributions computed on subsets of the data defined by the locations of the independent variables. We show that this approximate posterior retains the rate of recovery of the full data posterior distribution, where the rate of recovery adapts to the smoothness of the true regression function. As particular examples we consider Gaussian process priors based on integrated Brownian motion and the Mat\'ern kernel augmented with a prior on the length scale. Besides theoretical guarantees we present a numerical study of the methods both on synthetic and real world data. We also propose a new aggregation technique, which numerically outperforms previous approaches.
Subject-Oriented Video Captioning

Describing video content according to users' needs is a long-held goal. Although existing video captioning methods have made significant progress, the generated captions may not focus on the entity that users are particularly interested in. To address this problem, we propose a new video captioning task, subject-oriented video captioning, which allows users to specify the describing target via a bounding box. To support this task, we construct two subject-oriented video captioning datasets based on two widely used video captioning datasets: MSVD and MSRVTT, by annotating subjects in each video for each caption. These datasets pave the way for future technique development. As the first attempt, we evaluate four state-of-the-art general video captioning models, and have observed a large performance drop. We then explore several strategies to enable them to describe the desired target. Experimental results show obvious improvement, but there is still a large room for further exploration in this field.
Theory of interlayer exciton dynamics in two-dimensional transition metal dichalcogenide heterolayers under the influence of strain reconstruction and disorder

Monolayers of transition metal dichalcogenides (TMDC) became one of the most studied nanostructures in the last decade. Combining two different TMDC monolayers results in a heterostructure whose properties can be individually tuned by the twist angle between the lattices of the two van-der-Waals layers and the relative placement of the layers, leading to Moir\'e cells. For small twist angles, lattice reconstruction leads to strong strain fields in the Moir\'e cells. In this paper, we combine an existing theory for lattice reconstruction with a quantum dynamic theory for interlayer excitons and their dynamics due to exciton phonon scattering using a polaron transformation. The exciton theory is formulated in real space instead of the commonly used quasi-momentum space to account for imperfections in the heterolayer breaking lattice translational symmetry. We can analyze the structure of the localized and delocalized exciton states and their exciton-phonon scattering rates for single phonon processes using Born-Markov approximation and multi-phonon processes using a polaron transformation. Furthermore, linear optical spectra and exciton relaxation Green functions are calculated and discussed. A P-stacked MoSe$_2$/WSe$_2$ heterolayer is used as an illustrative example. It shows excitons localized in the potential generated through the Moir\'e-pattern and strain and a delocalized continuum. The exciton-phonon relaxation times vary depending on the strain and range from sub-pico seconds up to nanoseconds.
SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization

Temporal Action Localization (TAL) is a complex task that poses relevant challenges, particularly when attempting to generalize on new -- unseen -- domains in real-world applications. These scenarios, despite realistic, are often neglected in the literature, exposing these solutions to important performance degradation. In this work, we tackle this issue by introducing, for the first time, an approach for Unsupervised Domain Adaptation (UDA) in sparse TAL, which we refer to as Semantic Adversarial unsupervised Domain Adaptation (SADA). Our contribution is threefold: (1) we pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks; (2) we tackle the limitations of global-distribution alignment techniques by introducing a novel adversarial loss that is sensitive to local class distributions, ensuring finer-grained adaptation; and (3) we present a novel experimental setup, based on EpicKitchens100, that evaluates multiple types of domain shifts in a comprehensive manner. Our experimental results indicate that SADA improves the adaptation across domains when compared to fully supervised state-of-the-art and alternative UDA methods, attaining a relative performance boost of up to 14%.
Computational Spectral Imaging with Unified Encoding Model: A Comparative Study and Beyond

Computational spectral imaging is drawing increasing attention owing to the snapshot advantage, and amplitude, phase, and wavelength encoding systems are three types of representative implementations. Fairly comparing and understanding the performance of these systems is essential, but challenging due to the heterogeneity in encoding design. To overcome this limitation, we propose the unified encoding model (UEM) that covers all physical systems using the three encoding types. Specifically, the UEM comprises physical amplitude, physical phase, and physical wavelength encoding models that can be combined with a digital decoding model in a joint encoder-decoder optimization framework to compare the three systems under a unified experimental setup fairly. Furthermore, we extend the UEMs to ideal versions, namely, ideal amplitude, ideal phase, and ideal wavelength encoding models, which are free from physical constraints, to explore the full potential of the three types of computational spectral imaging systems. Finally, we conduct a holistic comparison of the three types of computational spectral imaging systems and provide valuable insights for designing and exploiting these systems in the future.
The superradiant instability spectrum of the hydrodynamic vortex model

We study analytically the superradiant instability properties of the hydrodynamic vortex model, an asymptotically flat acoustic geometry which, like the spinning Kerr black-hole spacetime, possesses an effective ergoregion. In particular, we derive a compact analytical formula for the complex resonant frequencies that characterize the long-wavelength dynamics of sound modes in this physically interesting acoustic spacetime.
Quadratic and cubic Lagrange finite elements for mixed Laplace eigenvalue problems on criss-cross meshes

In [6], it was shown that the linear Lagrange element space on criss-cross meshes and its divergence exhibit spurious eigenvalues when applied in the mixed formulation of the Laplace eigenvalue problem, despite satisfying both the inf-sup condition and ellipticity on the discrete kernel. The lack of a Fortin interpolation is responsible for the spurious eigenvalues produced by the linear Lagrange space. In contrast, results in [8] confirm that quartic and higher-order Lagrange elements do not yield spurious eigenvalues on general meshes without nearly singular vertices, including criss-cross meshes as a special case. In this paper, we investigate quadratic and cubic Lagrange elements on criss-cross meshes. We prove the convergence of discrete eigenvalues by fitting the Lagrange elements on criss-cross meshes into a complex and constructing a Fortin interpolation. As a by-product, we construct bounded commuting projections for the finite element Stokes complex, which induces isomorphisms between cohomologies of the continuous and discrete complexes. We provide numerical examples to validate the theoretical results.
MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks

To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the ax- ioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the ex- isting attribution methods present challenges for effective in- terpretation and efficient computation. In this work, we in- troduce MFABA, an attribution algorithm that adheres to ax- ioms, as a novel method for interpreting DNN. Addition- ally, we provide the theoretical proof and in-depth analy- sis for MFABA algorithm, and conduct a large scale exper- iment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art at- tribution algorithms. The effectiveness of MFABA is thor- oughly evaluated through the statistical analysis in compar- ison to other methods, and the full implementation package is open-source at: https://github.com/LMBTough/MFABA.
Automated Clinical Coding for Outpatient Departments

Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records. While there is active research pushing the state of the art on clinical coding for hospitalized patients, the outpatient setting -- where doctors tend to non-hospitalised patients -- is overlooked. Although both settings can be formalised as a multi-label classification task, they present unique and distinct challenges, which raises the question of whether the success of inpatient clinical coding approaches translates to the outpatient setting. This paper is the first to investigate how well state-of-the-art deep learning-based clinical coding approaches work in the outpatient setting at hospital scale. To this end, we collect a large outpatient dataset comprising over 7 million notes documenting over half a million patients. We adapt four state-of-the-art clinical coding approaches to this setting and evaluate their potential to assist coders. We find evidence that clinical coding in outpatient settings can benefit from more innovations in popular inpatient coding benchmarks. A deeper analysis of the factors contributing to the success -- amount and form of data and choice of document representation -- reveals the presence of easy-to-solve examples, the coding of which can be completely automated with a low error rate.