Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of AoI-Aware 5G Uplink Scheduler Using Reinforcement Learning

Age of Information (AoI) reflects the time that is elapsed from the generation of a packet by a 5G user equipment (UE) to the reception of the packet by a controller. A design of an AoI-aware radio resource scheduler for UEs via reinforcement learning is proposed in this paper. In this paper, we consider a remote control environment in which a number of UEs are transmitting time-sensitive measurements to a remote controller. We consider the AoI minimization problem and formulate the problem as a trade-off between minimizing the sum of the expected AoI of all UEs and maximizing the throughput of the network. Inspired by the success of machine learning in solving large networking problems at low complexity, we develop a reinforcement learning-based method to solve the formulated problem. We used the state-of-the-art proximal policy optimization algorithm to solve this problem. Our simulation results show that the proposed algorithm outperforms the considered baselines in terms of minimizing the expected AoI while maintaining the network throughput.
Advanced Statistical Learning on Short Term Load Process Forecasting

Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine learning methods are recalled nowadays to manage complicated structural big datasets, which are characterized by having a nonlinear temporal dependence structure. We propose different statistical nonlinear models to manage these challenges of hard type datasets and forecast 15-min frequency electricity load up to 2-days ahead. We show that the Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) models applied to the production line of a chemical production facility outperform several other predictive models in terms of out-of-sample forecasting accuracy by the Diebold-Mariano (DM) test with several metrics. The predictive information is fundamental for the risk and production management of electricity consumers.
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data

Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. Analysing agitation episodes will also help identify modifiable factors such as ambient temperature and sleep as possible components causing agitation in an individual. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data. The in-home monitoring data includes motion sensors, physiological measurements, and the use of kitchen appliances from 46 homes of PLWD between April 2019-June 2021. We apply a recurrent deep learning model to identify agitation episodes validated and recorded by a clinical monitoring team. We present the experiments to assess the efficacy of the proposed model. The proposed model achieves an average of 79.78% recall, 27.66% precision and 37.64% F1 scores when employing the optimal parameters, suggesting a good ability to recognise agitation events. We also discuss using machine learning models for analysing the behavioural patterns using continuous monitoring data and explore clinical applicability and the choices between sensitivity and specificity in-home monitoring applications.
EuCAPT White Paper: Opportunities and Challenges for Theoretical Astroparticle Physics in the Next Decade

Astroparticle physics is undergoing a profound transformation, due to a series of extraordinary new results, such as the discovery of high-energy cosmic neutrinos with IceCube, the direct detection of gravitational waves with LIGO and Virgo, and many others. This white paper is the result of a collaborative effort that involved hundreds of theoretical astroparticle physicists and cosmologists, under the coordination of the European Consortium for Astroparticle Theory (EuCAPT). Addressed to the whole astroparticle physics community, it explores upcoming theoretical opportunities and challenges for our field of research, with particular emphasis on the possible synergies among different subfields, and the prospects for solving the most fundamental open questions with multi-messenger observations.
Water jet space charge spectroscopy: route to direct measurement of electron dynamics for organic systems in their natural environment

The toolbox for time-resolved direct measurements of electron dynamics covers a variety of methods. Since the experimental effort is increasing rapidly with achievable time resolution, there is an urge for simple and robust measurement techniques. Within this paper prove-of-concept experiments and numerical simulations are utilized to investigate the applicability of a new setup for the generation of ultrashort electron pulses in the energy range of 300 eV up to 1.6 keV. The experimental approach combines an in-vacuum liquid microjet and a few-cycle femtosecond laser system, while the threshold for electron impact ionization serves as a gate for the effective electron pulse duration. The experiments prove that electrons in the keV regime are accessible and that the electron spectrum can be easily tuned by laser intensity and focal position alignment with respect to the water jet. Numerical simulations show that a sub-picosecond temporal resolution is achievable.
Who Calls The Shots? Rethinking Few-Shot Learning for Audio

Few-shot learning aims to train models that can recognize novel classes given just a handful of labeled examples, known as the support set. While the field has seen notable advances in recent years, they have often focused on multi-class image classification. Audio, in contrast, is often multi-label due to overlapping sounds, resulting in unique properties such as polyphony and signal-to-noise ratios (SNR). This leads to unanswered questions concerning the impact such audio properties may have on few-shot learning system design, performance, and human-computer interaction, as it is typically up to the user to collect and provide inference-time support set examples. We address these questions through a series of experiments designed to elucidate the answers to these questions. We introduce two novel datasets, FSD-MIX-CLIPS and FSD-MIX-SED, whose programmatic generation allows us to explore these questions systematically. Our experiments lead to audio-specific insights on few-shot learning, some of which are at odds with recent findings in the image domain: there is no best one-size- fits-all model, method, and support set selection criterion. Rather, it depends on the expected application scenario. Our code and data are available at https://github.com/wangyu/rethink-audio-fsl.
The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks

The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Recent efforts have mainly focused on separating speech from noise, speech from speech, musical instruments from each other, or sound events from each other. However, separating an audio mixture (e.g., movie soundtrack) into the three broad categories of speech, music, and sound effects (understood to include ambient noise and natural sound events) has been left largely unexplored, despite a wide range of potential applications. This paper formalizes this task as the cocktail fork problem, and presents the Divide and Remaster (DnR) dataset to foster research on this topic. DnR is built from three well-established audio datasets (LibriSpeech, FMA, FSD50k), taking care to reproduce conditions similar to professionally produced content in terms of source overlap and relative loudness, and made available at CD quality. We benchmark standard source separation algorithms on DnR, and further introduce a new multi-resolution model to better address the variety of acoustic characteristics of the three source types. Our best model produces SI-SDR improvements over the mixture of 11.0 dB for music, 11.2 dB for speech, and 10.8 dB for sound effects.
Using RDMA for Efficient Index Replication in LSM Key-Value Stores

Log-Structured Merge tree (LSM tree) Key-Value (KV) stores have become a foundational layer in the storage stacks of datacenter and cloud services. Current approaches for achieving reliability and availability avoid replication at the KV store level and instead perform these operations at higher layers, e.g., the DB layer that runs on top of the KV store. The main reason is that past designs for replicated KV stores favor reducing network traffic and increasing I/O size. Therefore, they perform costly compactions to reorganize data in both the primary and backup nodes, which hurts overall system performance. In this paper, we design and implement Talos, an efficient rack-scale LSM-based KV store that aims to significantly reduce the I/O amplification and CPU overhead in backup nodes and make replication in the KV store practical. We rely on two observations: (a) the increased use of RDMA in the datacenter, which reduces CPU overhead for communication, and (b) the use of KV separation that is becoming prevalent in modern KV stores. We use a primary-backup replication scheme that performs compactions only on the primary nodes and sends the pre-built index to the backup nodes of the region, avoiding all compactions in backups. Our approach includes an efficient mechanism to deal with pointer translation across nodes in the region index. Our results show that Talos reduces in the backup nodes, I/O amplification by up to $3\times$, CPU overhead by up to $1.6\times$, and memory size needed for the write path by up to $2\times$, without increasing network bandwidth excessively, and by up to $1.3\times$. Overall, we show that our approach has benefits even when small KV pairs dominate in a workload (80%-90%). Finally, it enables KV stores to operate with larger growth factors (from 10 to 16) to reduce space amplification without sacrificing precious CPU cycles.
Millimeter-sized Dust Grains Surviving the Water-sublimating Temperature in the Inner 10 au of the FU Ori Disk

Previous observations have shown that the ≲10 au, ≳400 K hot inner disk of the archetypal accretion outburst young stellar object, FU Ori, is dominated by viscous heating. To constrain dust properties in this region, we have performed radio observations toward this disk using the Karl G. Jansky Very Large Array in 2020 June–July, September, and November. We also performed complementary optical photometric monitoring observations. We found that the dust thermal emission from the hot inner disk mid-plane of FU Ori has been approximately stationary and the maximum dust grain size is ≳1.6 mm in this region. If the hot inner disk of FU Ori, which is inward of the 150–170 K water snowline, is turbulent (e.g., corresponding to a Sunyaev & Shakura viscous α t ≳ 0.1), or if the actual maximum grain size is still larger than the lower limit we presently constrain, then as suggested by the recent analytical calculations and the laboratory measurements, water-ice-free dust grains may be stickier than water-ice-coated dust grains in protoplanetary disks. Additionally, we find that the free–free emission and the Johnson B- and V-band magnitudes of these binary stars were brightening in 2016–2020. The optical and radio variability might be related to the dynamically evolving protostellar- or disk-accretion activities. Our results highlight that the hot inner disks of outbursting objects are important laboratories for testing models of dust grain growth. Given the active nature of such systems, to robustly diagnose the maximum dust grain sizes, it is important to carry out coordinated multiwavelength radio observations.
One Source, Two Source(s): Ribs and Tethers

We present the unique and challenging case of a radio galaxy in Abell 3266 observed as part of the MeerKAT Galaxy Cluster Legacy Survey. It has quasi-periodic bright patches along the tail which connect to never-before-seen thin transverse extensions, which we call “ribs”, reaching up to ∼50 kpc from the central axis of the tail. At a distance of ∼400 kpc from the host (assuming the z=0.0594 redshift of Abell 3266) we found what appears to be a triple source with its own apparent host at a photometric redshift of 0.78. Mysteriously, the part of the tail far from the host and the triple are connected by a series of thin filaments, which we call “tethers”. The far tail, tethers and triple also have similar spectra and Faraday rotation measures, suggesting that there is only one—quite complicated—source, with a serendipitous background AGN in the triple. We look at possible causes for the “rib” and “tether” structures, and the emerging phenomena of intracluster medium filaments associated with radio galaxies.
Dynamical scaling of correlations generated by short- and long-range dissipation

We study the spatio-temporal spreading of correlations in an ensemble of spins due to dissipation characterized by short- and long-range spatial profiles. We consider systems initially in an uncorrelated state, and find that correlations widen and contract in a novel pattern intimately related to both the dissipative nature of the dynamical channel and its spatial profile. Additionally, we make a methodological contribution by generalizing non-equilibrium spin-wave theory to the case of dissipative systems and derive equations of motion for any translationally invariant spin chain whose dynamics can be described by a combination of Hamiltonian interactions and dissipative Lindblad channels. Our work aims at extending the study of correlation dynamics to purely dissipative quantum simulators and compare them with the established paradigm of correlations spreading in hamiltonian systems.
Fast Sausage Oscillations in Coronal Loops with Fine Structures

Fast sausage modes (FSMs) in flare loops have long been invoked to account for rapid quasi-periodic pulsations (QPPs) with periods of order seconds in flare lightcurves. However, most theories of FSMs in solar coronal cylinders assume a perfectly axisymmetric equilibrium, an idealized configuration apparently far from reality. In particular, it remains to be examined whether FSMs exist in coronal cylinders with fine structures. Working in the framework of ideal magnetohydrodynamics (MHD), we numerically follow the response to an axisymmetric perturbation of a coronal cylinder for which a considerable number of randomly distributed fine structures are superposed on an axisymmetric background. The parameters for the background component are largely motivated by the recent IRIS identification of a candidate FSM in Fe xxi 1354 Å observations. We find that the composite cylinder rapidly settles to an oscillatory behavior largely compatible with a canonical trapped FSM. This happens despite that kink-like motions develop in the fine structures. We further synthesize the Fe xxi 1354 Å emissions, finding that the transverse Alfvén time characterizes the periodicities in the intensity, Doppler shift, and Doppler width signals. Distinct from the case without fine structuring, a nonvanishing Doppler shift is seen even at the apex. We conclude that density-enhanced equilibria need not be strictly axisymmetric to host FSM-like motions in general, and FSMs remain a candidate interpretation for rapid QPPs in solar flares.
Understanding Convolutional Neural Networks from Theoretical Perspective via Volterra Convolution

This study proposes a general and uniﬁed perspective of convolutional neural networks by exploring the relationship between (deep) convolutional neural networks and ﬁnite Volterra convolutions. It provides a novel approach to explain and study the overall characteristics of neural networks without being disturbed by the complex network architectures. Concretely, we examine the basic structures of ﬁnite term Volterra convolutions and convolutional neural networks. Our results show that convolutional neural network is an approximation of the ﬁnite term Volterra convolution, whose order increases exponentially with the number of layers and kernel size increases exponentially with the strides. With this perspective, the specialized perturbations are directly obtained from the approximated kernels rather than iterative generated adversarial examples. Extensive experiments on synthetic and real-world data sets show the correctness and eﬀectiveness of our results.
Relational Neural Markov Random Fields

Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty. However, most of these models have been limited to discrete domains due to their limited potential functions. We introduce Relational Neural Markov Random Fields (RN-MRFs) which allow for handling of complex relational hybrid domains. The key advantage of our model is that it makes minimal data distributional assumptions and can seamlessly allow for human knowledge through potentials or relational rules. We propose a maximum pseudolikelihood estimation-based learning algorithm with importance sampling for training the neural potential parameters. Our empirical evaluations across diverse domains such as image processing and relational object mapping, clearly demonstrate its effectiveness against non-neural counterparts.
Food Odor Recognition via Multi-step Classification

Predicting food labels and freshness from its odor remains a decades-old task that requires a complicated algorithm combined with high sensitivity sensors. In this paper, we initiate a multi-step classifier, which firstly clusters food into four categories, then classifies the food label concerning the predicted category, and finally identifies the freshness. We use BME688 gas sensors packed with BME AI studio for data collection and feature extraction. The normalized dataset was preprocessed with PCA and LDA. We evaluated the effectiveness of algorithms such as tree methods, MLP, and CNN through assessment indexes at each stage. We also carried out an ablation experiment to show the necessity and feasibility of the multi-step classifier. The results demonstrated the robustness and adaptability of the multi-step classifier.
Multimodal Analysis of Gravitational Wave Signals and Gamma-Ray Bursts from Binary Neutron Star Mergers

A major boost in the understanding of the universe was given by the revelation of the first coalescence event of two neutron stars (GW170817) and the observation of the same event across the entire electromagnetic spectrum. With third-generation gravitational wave detectors and the new astronomical facilities, we expect many multi-messenger events of the same type. We anticipate the need to analyse the data provided to us by such events not only to fulfil the requirements of real-time analysis, but also in order to decipher the event in its entirety through the information emitted in the different messengers using machine learning. We propose a change in the paradigm in the way we do multi-messenger astronomy, simultaneously using the complete information generated by violent phenomena in the Universe. What we propose is the application of a multimodal machine learning approach to characterize these events.
PR-CIM: a Variation-Aware Binary-Neural-Network Framework for Process-Resilient Computation-in-memory

Binary neural networks (BNNs) that use 1-bit weights and activations have garnered interest as extreme quantization provides low power dissipation. By implementing BNNs as computing-in-memory (CIM), which computes multiplication and accumulations on memory arrays in an analog fashion, namely analog CIM, we can further improve the energy efficiency to process neural networks. However, analog CIMs suffer from the potential problem that process variation degrades the accuracy of BNNs. Our Monte-Carlo simulations show that in an SRAM-based analog CIM of VGG-9, the classification accuracy of CIFAR-10 is degraded even below 20% under process variations of 65nm CMOS. To overcome this problem, we present a variation-aware BNN framework. The proposed framework is developed for SRAM-based BNN CIMs since SRAM is most widely used as on-chip memory, however easily extensible to BNN CIMs based on other memories. Our extensive experimental results show that under process variation of 65nm CMOS, our framework significantly improves the CIFAR-10 accuracies of SRAM-based BNN CIMs, from 10% and 10.1% to 87.76% and 77.74% for VGG-9 and RESNET-18 respectively.
Generating Symbolic Reasoning Problems with Transformer GANs

We study the capabilities of GANs and Wasserstein GANs equipped with Transformer encoders to generate sensible and challenging training data for symbolic reasoning domains. We conduct experiments on two problem domains where Transformers have been successfully applied recently: symbolic mathematics and temporal specifications in verification. Even without autoregression, our GAN models produce syntactically correct instances. We show that the generated data can be used as a substitute for real training data when training a classifier, and, especially, that training data can be generated from a dataset that is too small to be trained on directly. Using a GAN setting also allows us to alter the target distribution: We show that by adding a classifier uncertainty part to the generator objective, we obtain a dataset that is even harder to solve for a temporal logic classifier than our original dataset.
Some of Entity Resolution

Whether the goal is to estimate the number of people that live in a congressional district, to estimate the number of individuals that have died in an armed conflict, or to disambiguate individual authors using bibliographic data, all these applications have a common theme — integrating information from multiple sources. Before such questions can be answered, databases must be cleaned and integrated in a systematic and accurate way, commonly known as structured entity resolution (record linkage or de-duplication). In this article, we review motivational applications and seminal papers that have led to the growth of this area. We review modern probabilistic and Bayesian methods in statistics, computer science, machine learning, database management, economics, political science, and other disciplines that are used throughout industry and academia in applications such as human rights, official statistics, medicine, citation networks, among others. Finally, we discuss current research topics of practical importance.
Tropical Complementarity Problems and Nash Equilibria

Linear complementarity programming is a generalization of linear programming which encompasses the computation of Nash equilibria for bimatrix games. While the latter problem is PPAD-complete, we show that the tropical analogue of the complementarity problem associated with Nash equilibria can be solved in polynomial time. Moreover, we prove that the Lemke--Howson algorithm carries over the tropical setting and performs a linear number of pivots in the worst case. A consequence of this result is a new class of (classical) bimatrix games for which Nash equilibria computation can be done in polynomial time.