Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-particle Bose-Einstein correlations and their Lévy parameters in PbPb collisions at 
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Two-particle Bose-Einstein momentum correlation functions are studied for charged-hadron pairs in lead-lead collisions at a center-of-mass energy per nucleon pair of $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. The data sample, containing 4.27 $\times$ $10^{9}$ minimum bias events corresponding to an integrated luminosity of 0.607 nb$^{-1}$, was collected by the CMS experiment in 2018. The experimental results are discussed in terms of a L\'evy-type source distribution. The parameters of this distribution are extracted as functions of particle pair average transverse mass and collision centrality. These parameters include the L\'evy index or shape parameter ($\alpha$), the L\'evy scale parameter ($R$), and the correlation strength parameter ($\lambda$). The source shape, characterized by $\alpha$, is found to be neither Cauchy nor Gaussian, implying the need for a full L\'evy analysis. Similarly to what was previously found for systems characterized by Gaussian source radii, a hydrodynamical scaling is observed for the L\'evy $R$ parameter. The $\lambda$ parameter is studied in terms of the core-halo model.
Learning Space-Time Semantic Correspondences

We propose a new task of space-time semantic correspondence prediction in videos. Given a source video, a target video, and a set of space-time key-points in the source video, the task requires predicting a set of keypoints in the target video that are the semantic correspondences of the provided source keypoints. We believe that this task is important for fine-grain video understanding, potentially enabling applications such as activity coaching, sports analysis, robot imitation learning, and more. Our contributions in this paper are: (i) proposing a new task and providing annotations for space-time semantic correspondences on two existing benchmarks: Penn Action and Pouring; and (ii) presenting a comprehensive set of baselines and experiments to gain insights about the new problem. Our main finding is that the space-time semantic correspondence prediction problem is best approached jointly in space and time rather than in their decomposed sub-problems: time alignment and spatial correspondences.
On the Global Convergence of Natural Actor-Critic with Two-layer Neural Network Parametrization

Actor-critic algorithms have shown remarkable success in solving state-of-the-art decision-making problems. However, despite their empirical effectiveness, their theoretical underpinnings remain relatively unexplored, especially with neural network parametrization. In this paper, we delve into the study of a natural actor-critic algorithm that utilizes neural networks to represent the critic. Our aim is to establish sample complexity guarantees for this algorithm, achieving a deeper understanding of its performance characteristics. To achieve that, we propose a Natural Actor-Critic algorithm with 2-Layer critic parametrization (NAC2L). Our approach involves estimating the $Q$-function in each iteration through a convex optimization problem. We establish that our proposed approach attains a sample complexity of $\tilde{\mathcal{O}}\left(\frac{1}{\epsilon^{4}(1-\gamma)^{4}}\right)$. In contrast, the existing sample complexity results in the literature only hold for a tabular or linear MDP. Our result, on the other hand, holds for countable state spaces and does not require a linear or low-rank structure on the MDP.
Process-Voltage-Temperature Variability Estimation of Tunneling Current for Band-to-Band-Tunneling-Based Neuron

Compact and energy-efficient synapse and neurons are essential to realize the full potential of neuromorphic computing. In addition, a low variability is indeed needed for neurons in deep neural networks for higher accuracy. Further, process (P), voltage (V), and temperature (T) (PVT) variation are essential considerations for low-power circuits as performance impact and compensation complexities are added costs. Recently, band-to-band tunneling (BTBT) neuron has been demonstrated to operate successfully in a network to enable a liquid state machine (LSM). A comparison of the PVT with competing modes of operation (e.g., BTBT versus subthreshold and above threshold) of the same transistor is a critical factor in assessing performance. In this work, we demonstrate the PVT variation impact on the BTBT regime and benchmark the operation against the subthreshold regime (SS) and ON-regime (<inline-formula> <tex-math notation="LaTeX">$\text{I}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula>) of partially depleted silicon-on-insulator MOSFET. It is shown that the ON-state regime offers the lowest variability but dissipates higher power, hence not usable for low-power sources. Among the BTBT and SS regimes, which can enable the low-power neuron, the BTBT regime has shown <inline-formula> <tex-math notation="LaTeX">$\sim 3\,\,{\times }$ </tex-math></inline-formula> variability reduction (<inline-formula> <tex-math notation="LaTeX">${ \boldsymbol {\sigma }_{I{_{D}}}{/} \boldsymbol {\mu }_{I{_{D}}}}$ </tex-math></inline-formula>) compared to the SS regime, considering the cumulative PVT variability. The improvement is due to the well-known weaker P, V, and T dependence of BTBT versus SS. We show that the BTBT variation is uncorrelated with mutually correlated SS and <inline-formula> <tex-math notation="LaTeX">$\text{I}_{{ \mathrm{\scriptscriptstyle ON}}}$ </tex-math></inline-formula> operation—indicating its different origin from the mechanism and location perspectives. Hence, the BTBT regime is promising for low-current, low-power, and low device-to-device (D2D) variability neuron operation.
Forward kinematics of a general Stewart parallel manipulator using the genetic algorithm

By making full use of the property that it is easy to obtain the inverse kinematics of the Stewart parallel manipulator, the forward kinematics of the Stewart parallel manipulator is transformed into an optimal problem, and a real coded geneticbased forward kinematics method for the general Stewart parallel manipulator is proposed in this paper. The feasibility of the genetic based forward kinematics method is illustrated with the Stewart fine tuning platform for the next generation large radio telescopes. The high accuracy of the forward kinematics is obtained, which has overcome the shortcoming that the accuracy of the forward kinematics is susceptible to the initial guess values with the conventional numerical methods. 
Spatial Secrecy Spectral Efficiency Optimization Enabled by Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surfaces (RISs) constitute a strong candidate physical-layer technology for the 6th Generation (6G) of wireless networks, offering new design degrees of freedom for efficiently addressing demanding performance objectives. In this paper, we consider a Multiple-Input Single-Output (MISO) physical-layer security system incorporating a reflective RIS to safeguard wireless communications between a legitimate transmitter and receiver under the presence of an eavesdropper. In contrast to current studies optimizing RISs for given positions of the legitimate and eavesdropping nodes, in this paper, we focus on devising RIS-enabled secrecy for given geographical areas of potential nodes' placement. We propose a novel secrecy metric, capturing the spatially averaged secrecy spectral efficiency, and present a joint design of the transmit digital beamforming and the RIS analog phase profile, which is realized via a combination of alternating optimization and minorization-maximization. The proposed framework bypasses the need for instantaneous knowledge of the eavesdropper's channel or position, and targets providing an RIS-boosted secure area of legitimate communications with a single configuration of the free parameters. Our simulation results showcase significant performance gains with the proposed secrecy scheme, even for cases where the eavesdropper shares similar pathloss attenuation with the legitimate receiver.
Adaptive Federated Learning with Auto-Tuned Clients

Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients without sharing their data. While being a flexible framework, where the distribution of local data, participation rate, and computing power of each client can greatly vary, such flexibility gives rise to many new challenges, especially in the hyperparameter tuning on the client side. We propose $\Delta$-SGD, a simple step size rule for SGD that enables each client to use its own step size by adapting to the local smoothness of the function each client is optimizing. We provide theoretical and empirical results where the benefit of the client adaptivity is shown in various FL scenarios.
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks

Graph structure learning is a well-established problem that aims at optimizing graph structures adaptive to specific graph datasets to help message passing neural networks (i.e., GNNs) to yield effective and robust node embeddings. However, the common limitation of existing models lies in the underlying closed-world assumption: the testing graph is the same as the training graph. This premise requires independently training the structure learning model from scratch for each graph dataset, which leads to prohibitive computation costs and potential risks for serious over-fitting. To mitigate these issues, this paper explores a new direction that moves forward to learn a universal structure learning model that can generalize across graph datasets in an open world. We first introduce the mathematical definition of this novel problem setting, and describe the model formulation from a probabilistic data-generative aspect. Then we devise a general framework that coordinates a single graph-shared structure learner and multiple graph-specific GNNs to capture the generalizable patterns of optimal message-passing topology across datasets. The well-trained structure learner can directly produce adaptive structures for unseen target graphs without any fine-tuning. Across diverse datasets and various challenging cross-graph generalization protocols, our experiments show that even without training on target graphs, the proposed model i) significantly outperforms expressive GNNs trained on input (non-optimized) topology, and ii) surprisingly performs on par with state-of-the-art models that independently optimizes adaptive structures for specific target graphs, with notably orders-of-magnitude acceleration for training on the target graph.
Pseudo session-based recommendation with hierarchical embedding and session attributes

Recently, electronic commerce (EC) websites have been unable to provide an identification number (user ID) for each transaction data entry because of privacy issues. Because most recommendation methods assume that all data are assigned a user ID, they cannot be applied to the data without user IDs. Recently, session-based recommendation (SBR) based on session information, which is short-term behavioral information of users, has been studied. A general SBR uses only information about the item of interest to make a recommendation (e.g., item ID for an EC site). Particularly in the case of EC sites, the data recorded include the name of the item being purchased, the price of the item, the category hierarchy, and the gender and region of the user. In this study, we define a pseudo--session for the purchase history data of an EC site without user IDs and session IDs. Finally, we propose an SBR with a co-guided heterogeneous hypergraph and globalgraph network plus, called CoHHGN+. The results show that our CoHHGN+ can recommend items with higher performance than other methods.
AdaSelection: Accelerating Deep Learning Training through Data Subsampling

In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance. Our method is able to flexibly combines an arbitrary number of baseline sub-sampling methods incorporating the method-level importance and intra-method sample-level importance at each iteration. The standard practice of ad-hoc sampling often leads to continuous training with vast amounts of data from production environments. To improve the selection of data instances during forward and backward passes, we propose recording a constant amount of information per instance from these passes. We demonstrate the effectiveness of our method by testing it across various types of inputs and tasks, including the classification tasks on both image and language datasets, as well as regression tasks. Compared with industry-standard baselines, AdaSelection consistently displays superior performance.
Meerkat Behaviour Recognition Dataset

Recording animal behaviour is an important step in evaluating the well-being of animals and further understanding the natural world. Current methods for documenting animal behaviour within a zoo setting, such as scan sampling, require excessive human effort, are unfit for around-the-clock monitoring, and may produce human-biased results. Several animal datasets already exist that focus predominantly on wildlife interactions, with some extending to action or behaviour recognition. However, there is limited data in a zoo setting or data focusing on the group behaviours of social animals. We introduce a large meerkat (Suricata Suricatta) behaviour recognition video dataset with diverse annotated behaviours, including group social interactions, tracking of individuals within the camera view, skewed class distribution, and varying illumination conditions. This dataset includes videos from two positions within the meerkat enclosure at the Wellington Zoo (Wellington, New Zealand), with 848,400 annotated frames across 20 videos and 15 unannotated videos.
FDINet: Protecting against DNN Model Extraction via Feature Distortion Index

Machine Learning as a Service (MLaaS) platforms have gained popularity due to their accessibility, cost-efficiency, scalability, and rapid development capabilities. However, recent research has highlighted the vulnerability of cloud-based models in MLaaS to model extraction attacks. In this paper, we introduce FDINET, a novel defense mechanism that leverages the feature distribution of deep neural network (DNN) models. Concretely, by analyzing the feature distribution from the adversary's queries, we reveal that the feature distribution of these queries deviates from that of the model's training set. Based on this key observation, we propose Feature Distortion Index (FDI), a metric designed to quantitatively measure the feature distribution deviation of received queries. The proposed FDINET utilizes FDI to train a binary detector and exploits FDI similarity to identify colluding adversaries from distributed extraction attacks. We conduct extensive experiments to evaluate FDINET against six state-of-the-art extraction attacks on four benchmark datasets and four popular model architectures. Empirical results demonstrate the following findings FDINET proves to be highly effective in detecting model extraction, achieving a 100% detection accuracy on DFME and DaST. FDINET is highly efficient, using just 50 queries to raise an extraction alarm with an average confidence of 96.08% for GTSRB. FDINET exhibits the capability to identify colluding adversaries with an accuracy exceeding 91%. Additionally, it demonstrates the ability to detect two types of adaptive attacks.
Fractional Brownian motion with deterministic drift: How critical is drift regularity in hitting probabilities

Let $B^{H}$ be a $d$-dimensional fractional Brownian motion with Hurst index $H\in(0,1)$, $f:[0,1]\longrightarrow\mathbb{R}^{d}$ a Borel function, and $E\subset[0,1]$, $F\subset\mathbb{R}^{d}$ are given Borel sets. The focus of this paper is on hitting probabilities of the non-centered Gaussian process $B^{H}+f$. It aims to highlight how each component $f$, $E$ and $F$ is involved in determining the upper and lower bounds of $\mathbb{P}\{(B^H+f)(E)\cap F\neq \emptyset \}$. When $F$ is a singleton and $f$ is a general measurable drift, some new estimates are obtained for the last probability by means of suitables Hausdorff measure and capacity of the graph $Gr_E(f)$. As application we deal with the issue of polarity of points for $(B^H+f)\vert_E$ (the restriction of $B^H+f$ to the subset $E\subset (0,\infty)$).
Timestamped Embedding-Matching Acoustic-to-Word CTC ASR

In this work, we describe a novel method of training an embedding-matching word-level connectionist temporal classification (CTC) automatic speech recognizer (ASR) such that it directly produces word start times and durations, required by many real-world applications, in addition to the transcription. The word timestamps enable the ASR to output word segmentations and word confusion networks without relying on a secondary model or forced alignment process when testing. Our proposed system has similar word segmentation accuracy as a hybrid DNN-HMM (Deep Neural Network-Hidden Markov Model) system, with less than 3ms difference in mean absolute error in word start times on TIMIT data. At the same time, we observed less than 5% relative increase in the word error rate compared to the non-timestamped system when using the same audio training data and nearly identical model size. We also contribute more rigorous analysis of multiple-hypothesis embedding-matching ASR in general.
Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate the effectiveness of \textit{DeepClean}, a convolutional neural network architecture that uses witness sensors to estimate and subtract non-linear and non-stationary noise from gravitational-wave strain data. Our study uses LIGO data from the third observing run with injected compact binary signals. As a demonstration, we use \textit{DeepClean} to subtract the noise at 60 Hz due to the power mains and their sidebands arising from non-linear coupling with other instrumental noise sources. Our parameter estimation study on the injected signals shows that \textit{DeepClean} does not do any harm to the underlying astrophysical signals in the data while it can enhances the signal-to-noise ratio of potential signals. We show that \textit{DeepClean} can be used for low-latency noise regression to produce cleaned output data at latencies $\sim 1-2$\, s. We also discuss various considerations that may be made while training \textit{DeepClean} for low latency applications.
Reciprocal hydrodynamic response estimation in a random spreading sea

Direct estimation of the hydrodynamic response of an offshore structure in a random spreading sea can lead to large computational costs. In this paper the actual spreading sea is replaced by an idealised diffuse wave field and the diffuse field reciprocity (DFR) relationship is derived analytically and verified against diffraction analysis for offshore application. The DFR approach provides an analytical expression for the estimation of the wave loading spectrum in a spreading sea. It is very efficient because only the added damping coefficients are required. Furthermore, if normalised to the peak amplitude of a spreading sea, an upper bound response can be obtained using the reciprocal approach. And this is demonstrated using a spar type floating wind turbine. Given that the hydrodynamic coefficients are routine outputs for offshore structural design, engineers would obtain the upper bound response without additional computational cost using this new approach.
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models

Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models. Two different bimodal fusion approaches are examined: concatenation of probability vectors output from unimodal fMRI and EEG machine learning models, and data fusion with feature engineering. Same task inner speech data are recorded from four participants, and different processing strategies are compared and contrasted to previously-employed hybridisation methods. Data across participants are discovered to encode different underlying structures, which results in varying decoding performances between subject-dependent fusion models. Decoding performance is demonstrated as improved when pursuing bimodal fMRI-EEG fusion strategies, if the data show underlying structure.
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification

Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time Series Ordinal Classification (TSOC) is the field covering this gap, yet unexplored in the literature. There are a wide range of time series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this paper presents a first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art. Both convolutional- and deep learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 18 ordinal problems from two well-known archives has been made. In this way, this paper contributes to the establishment of the state-of-the-art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems.