Miracle-dz/UniFAR-FTUs huggingface dataset:
Older adults who resistance train improve physical function and adopt long-term exercise habits despite COVID-19 restrictions

ABSTRACT Exercise interventions targeting older adults often focus on acute changes, but lasting improvements require the adoption of long-term, independent exercise habits. This study aimed to assess the influence of eight-weeks of resistance training (SSSH) on clinically relevant fall-risk indicators in older adults and to evaluate if SSSH participation altered independent exercise engagement 12 months later. Sixty adults aged 50 yrs+ were randomised into SSSH, Walk, or Control groups and completed questionnaires and muscle strength and flexibility tests pre/post 8 weeks. SSSH and Walk met 2x/wk for 60 min. Twelve months later 24 participants also completed a follow-up survey amid COVID-19 restrictions. Eight-week group changes were analysed using one-way ANOVA with Bonferroni post hoc analyses, and survey responses were compared using paired t-tests with a Bonferroni correction. SSSH demonstrated greater absolute changes over 8 weeks in sleep quality, activity engagement, 30-second-sit-to-stand and upper-body flexibility than Walk or Controls (p < 0.05). Twelve months later, SSSH participants reported significantly increasing independent resistance (+68), aerobic (+125) and flexibility (+26) training minutes per week (all p < 0.01). In conclusion, SSSH reduced fall risk in 8 weeks and sparked older adults to begin and sustain positive exercise habits 12 months later, despite COVID-19 restrictions.
Dual-band operation of a circularly polarized radiator on a finite artificial magnetic conductor surface

This paper proposes a new way of designing a dual-band circularly polarized (CP) antenna by using a single-band CP radiator on a finite artificial magnetic conductor (AMC) surface. The AMC surface consists of a lattice of square metal plates on grounded dielectric substrate while the single-band radiator is a compact crossed dipole, which employs a 90° phase-delay line consisting of a vacant-quarter printed ring to generate broadband CP radiation. The dual-band operation is obtained by utilizing the original band generated by the radiator and the first additional band caused by surface waves propagating on the finite AMC surface. For verifying the design method, the proposed antenna is optimized for the L1 and L2 bands of the global positioning system with low-profile broadband characteristics and excellent CP radiation. The final design with an overall size of 85 × 85 × 11.493 mm3 (~0.3485λo × 0.3485λo × 0.0471λo at the 1.23 GHz) yields a measured impedance bandwidth of 1.202–1.706 GHz for the |S11| < –10 dB and 3-dB axial ratio bandwidths of 1.205–1.240 GHz and 1.530–1.625 GHz, respectively. Furthermore, the antenna yields right-hand circular polarization and high radiation efficiencies in both bands.
High-energy Neutrino Production from AGN Disk Transients Impacted by the Circum-disk Medium

Various supernovae, compact object coalescences, and tidal disruption events are widely believed to occur embedded in active galactic nucleus (AGN) accretion disks and generate detectable electromagnetic signals. We collectively refer to them as AGN disk transients. The inelastic hadronuclear (pp) interactions between shock-accelerated cosmic rays and AGN disk materials shortly after the ejecta shock breaks out of the disk can produce high-energy neutrinos. However, the expected efficiency of neutrino production would decay rapidly by adopting a pure Gaussian density atmosphere profile applicable for stable gas-dominated disks. On the other hand, AGN outflows and disk winds are commonly found around AGN accretion disks. In this paper, we show that the circum-disk medium would further consume the shock kinetic energy to more efficiently produce high-energy neutrinos, especially for ∼ TeV−PeV neutrinos that IceCube detects. Thanks to the existence of the circum-disk medium, we find that the neutrino production will be enhanced significantly and make a much higher contribution to the diffuse neutrino background. Optimistically, ∼20% of the diffuse neutrino background can be contributed by AGN disk transients.
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

The 1st Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Mar-itime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing method-ologies of over 130 submissions. The methods are sum-marized in the appendix. The datasets, evaluation code and the leaderboard are publicly available (https://seadronessee.cs.uni-tuebingen.de/macvi).
The intersection of machine learning with forecasting and optimisation: theory and applications

Forecasting and optimisation are two major fields of operations research that are widely used in practice. These methods have contributed to each other growth in several ways. However, the nature of the relationship between these two fields and integrating them have not been explored or understood enough. We advocate the integration of these two fields and explore several problems that require both forecasting and optimisation to deal with the uncertainties. We further investigate some of the methodologies that lie at the intersection of machine learning with prediction and optimisation to address real-world problems. Finally, we provide several research directions for those interested to work in this domain.
Kauzmann paradox: A possible crossover due to diminishing local excitations

The configurational entropy of supercooled liquids extrapolates to zero at the Kauzmann temperature, causing a crisis called the Kauzmann paradox. Here, using a class of multicomponent lattice glass models, we study a resolution of the paradox characterized by a sudden but smooth turn in the entropy as temperature goes sufficiently low. A scalar variant of the models reproduces the Kauzmann paradox with thermodynamic properties at very low temperatures dominated by correlations. An exactly solvable vector variant without correlation illustrates that a sudden entropy turn occurs when discrete local excitations are largely suppressed. Despite being disordered and infinitely degenerate, the ground states have zero entropy per particle.
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles

Nonlinear model predictive control (MPC) is a flexible and increasingly popular framework used to synthesize feedback control strategies that can satisfy both state and control input constraints. In this framework, an optimization problem, subjected to a set of dynamics constraints characterized by a nonlinear dynamics model, is solved at each time step. Despite its versatility, the performance of nonlinear MPC often depends on the accuracy of the dynamics model. In this work, we leverage deep learning tools, namely knowledge-based neural ordinary differential equations (KNODE) and deep ensembles, to improve the prediction accuracy of this model. In particular, we learn an ensemble of KNODE models, which we refer to as the KNODE ensemble, to obtain an accurate prediction of the true system dynamics. This learned model is then integrated into a novel learning-enhanced nonlinear MPC framework. We provide sufficient conditions that guarantees asymptotic stability of the closed-loop system and show that these conditions can be implemented in practice. We show that the KNODE ensemble provides more accurate predictions and illustrate the efficacy and closed-loop performance of the proposed nonlinear MPC framework using two case studies.
GEFF: Improving Any Clothes-Changing Person ReID Model using Gallery Enrichment with Face Features

In the Clothes-Changing Re-Identification (CC-ReID) problem, given a query sample of a person, the goal is to determine the correct identity based on a labeled gallery in which the person appears in different clothes. Several models tackle this challenge by extracting clothes-independent features. However, the performance of these models is still lower for the clothes-changing setting compared to the same-clothes setting in which the person appears with the same clothes in the labeled gallery. As clothing-related features are often dominant features in the data, we propose a new process we call Gallery Enrichment, to utilize these features. In this process, we enrich the original gallery by adding to it query samples based on their face features, using an unsupervised algorithm. Additionally, we show that combining ReID and face feature extraction modules alongside an enriched gallery results in a more accurate ReID model, even for query samples with new outfits that do not include faces. Moreover, we claim that existing CC-ReID benchmarks do not fully represent real-world scenarios, and propose a new video CC-ReID dataset called 42Street, based on a theater play that includes crowded scenes and numerous clothes changes. When applied to multiple ReID models, our method (GEFF) achieves an average improvement of 33.5% and 6.7% in the Top-1 clothes-changing metric on the PRCC and LTCC benchmarks. Combined with the latest ReID models, our method achieves new SOTA results on the PRCC, LTCC, CCVID, LaST and VC-Clothes benchmarks and the proposed 42Street dataset.
Enhanced Inversion of Schema Evolution with Provenance

Long-term data-driven studies have become indispensable in many areas of science. Often, the data formats, structures and semantics of data change over time, the data sets evolve. Therefore, studies over several decades in particular have to consider changing database schemas. The evolution of these databases lead at some point to a large number of schemas, which have to be stored and managed, costly and time-consuming. However, in the sense of reproducibility of research data each database version must be reconstructable with little effort. So a previously published result can be validated and reproduced at any time. Nevertheless, in many cases, such an evolution can not be fully reconstructed. This article classifies the 15 most frequently used schema modification operators and defines the associated inverses for each operation. For avoiding an information loss, it furthermore defines which additional provenance information have to be stored. We define four classes dealing with dangling tuples, duplicates and provenance-invariant operators. Each class will be presented by one representative. By using and extending the theory of schema mappings and their inverses for queries, data analysis, why-provenance, and schema evolution, we are able to combine data analysis applications with provenance under evolving database structures, in order to enable the reproducibility of scientific results over longer periods of time. While most of the inverses of schema mappings used for analysis or evolution are not exact, but only quasi-inverses, adding provenance information enables us to reconstruct a sub-database of research data that is sufficient to guarantee reproducibility.
Video on Demand Streaming Using RL-based Edge Caching in 5G Networks

Edge caching can significantly improve the 5G networks' performance both in terms of delay and backhaul traffic. We use a reinforcement learning-based (RL-based) caching technique that can adapt to time-location-dependent popularity patterns for on-demand video contents. In a private 5G, we implement the proposed caching scheme as two virtual network functions (VNFs), edge and remote servers, and measure the cache hit ratio as a KPI. Combined with the HLS protocol, the proposed video-on-demand (VoD) streaming is a reliable and scalable service that can adapt to content popularity.
Using Selective Masking as a Bridge between Pre-training and Fine-tuning

Pre-training a language model and then fine-tuning it for downstream tasks has demonstrated state-of-the-art results for various NLP tasks. Pre-training is usually independent of the downstream task, and previous works have shown that this pre-training alone might not be sufficient to capture the task-specific nuances. We propose a way to tailor a pre-trained BERT model for the downstream task via task-specific masking before the standard supervised fine-tuning. For this, a word list is first collected specific to the task. For example, if the task is sentiment classification, we collect a small sample of words representing both positive and negative sentiments. Next, a word's importance for the task, called the word's task score, is measured using the word list. Each word is then assigned a probability of masking based on its task score. We experiment with different masking functions that assign the probability of masking based on the word's task score. The BERT model is further trained on MLM objective, where masking is done using the above strategy. Following this standard supervised fine-tuning is done for different downstream tasks. Results on these tasks show that the selective masking strategy outperforms random masking, indicating its effectiveness.
Rank-One Editing of Encoder-Decoder Models

Large sequence to sequence models for tasks such as Neural Machine Translation (NMT) are usually trained over hundreds of millions of samples. However, training is just the origin of a model’s life-cycle. Real-world deployments of models require further behavioral adaptations as new requirements emerge or shortcomings become known. Typically, in the space of model behaviors, behavior deletion requests are addressed through model retrainings whereas model finetuning is done to address behavior addition requests, both procedures being instances of data-based model intervention. In this work, we present a preliminary study investigating rank-one editing as a direct intervention method for behavior deletion requests in encoder-decoder transformer models. We propose four editing tasks for NMT and show that the proposed editing algorithm achieves high efficacy, while requiring only a single instance of positive example to fix an erroneous (negative) model behavior.
Network Security Modelling with Distributional Data

We investigate the detection of botnet command and control (C2) hosts in massive IP traffic using machine learning methods. To this end, we use NetFlow data -- the industry standard for monitoring of IP traffic -- and ML models using two sets of features: conventional NetFlow variables and distributional features based on NetFlow variables. In addition to using static summaries of NetFlow features, we use quantiles of their IP-level distributions as input features in predictive models to predict whether an IP belongs to known botnet families. These models are used to develop intrusion detection systems to predict traffic traces identified with malicious attacks. The results are validated by matching predictions to existing denylists of published malicious IP addresses and deep packet inspection. The usage of our proposed novel distributional features, combined with techniques that enable modelling complex input feature spaces result in highly accurate predictions by our trained models.
Scale and rotation invariant texture analysis based on structural property

Structural properties of texture elements in a surface have successfully been applied to characterize textures. For this purpose, histograms of a texture element's properties are used. The compactness property of an element has been used for characterization. The compactness depends upon it's perimeter. A novel method based on perimeter contribution, to compute the value of the perimeter is presented. The perimeter value computed using the proposed method is closer to the actual, than the perimeter value computed using the conventional methods. The value of perimeter using the proposed method is also more consistent than the conventional methods. The value of compactness using the proposed method is not only closer to the actual value but also more consistent under affine transformations. The invariant histogram of compactness using the invariant value of compactness characterizes the texture invariant of scaling and rotation.
On Krylov methods for large-scale CBCT reconstruction

Krylov subspace methods are a powerful family of iterative solvers for linear systems of equations, which are commonly used for inverse problems due to their intrinsic regularization properties. Moreover, these methods are naturally suited to solve large-scale problems, as they only require matrix-vector products with the system matrix (and its adjoint) to compute approximate solutions, and they display a very fast convergence. Even if this class of methods has been widely researched and studied in the numerical linear algebra community, its use in applied medical physics and applied engineering is still very limited. e.g. in realistic large-scale computed tomography (CT) problems, and more specifically in cone beam CT (CBCT). This work attempts to breach this gap by providing a general framework for the most relevant Krylov subspace methods applied to 3D CT problems, including the most well-known Krylov solvers for non-square systems (CGLS, LSQR, LSMR), possibly in combination with Tikhonov regularization, and methods that incorporate total variation regularization. This is provided within an open source framework: the tomographic iterative GPU-based reconstruction toolbox, with the idea of promoting accessibility and reproducibility of the results for the algorithms presented. Finally, numerical results in synthetic and real-world 3D CT applications (medical CBCT and μ-CT datasets) are provided to showcase and compare the different Krylov subspace methods presented in the paper, as well as their suitability for different kinds of problems.
Distributed Symmetric Key Exchange: A scalable, quantum-proof key distribution system

We propose and implement a protocol for a scalable, cost-effective, information-theoretically secure key distribution and management system. The system, called Distributed Symmetric Key Exchange (DSKE) 1 , relies on pre-shared random numbers between DSKE clients and a group of Security Hubs. Any group of DSKE clients can use the DSKE protocol to distill from the pre-shared numbers a secret key. The clients are protected from Security Hub compromise via a secret sharing scheme that allows the creation of the final key without the need to trust individual Security Hubs. Precisely, if the number of compromised Security Hubs does not exceed a certain threshold, confidentiality is guaranteed to DSKE clients and, at the same time, robustness against denial-of-service (DoS) attacks. The DSKE system can be used for quantum-secure communication, can be easily integrated into existing network infrastructures, and can support arbitrary groups of communication parties that have access to a key. We discuss the high-level protocol, analyze its security, including its robustness against disruption. A proof-of-principle demonstration of secure communication between two distant clients with a DSKE-based VPN using Security Hubs on Amazon Web Server (AWS) nodes thousands of kilometres away from them was performed, demonstrating the feasibility of DSKE-enabled secret sharing one-time-pad encryption with a data rate above 50 Mbit/s and a latency below 70 ms.
Can Knowledge of End-to-End Text-to-Speech Models Improve Neural Midi-to-Audio Synthesis Systems?

With the similarity between music and speech synthesis from symbolic input and the rapid development of text-to-speech (TTS) techniques, it is worthwhile to explore ways to improve the MIDI-to-audio performance by borrowing from TTS techniques. In this study, we analyze the shortcomings of a TTS-based MIDI-to-audio system and improve it in terms of feature computation, model selection, and training strategy, aiming to synthesize highly natural-sounding audio. Moreover, we conducted an extensive model evaluation through listening tests, pitch measurement, and spectrogram analysis. This work demonstrates not only synthesis of highly natural music but offers a thorough analytical approach and useful outcomes for the community. Our code, pre-trained models, supplementary materials, and audio samples are open sourced at https://github.com/nii-yamagishilab/midi-to-audio.
Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties

Electric vehicles (EVs) are being rapidly adopted due to their economic and societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend. However, the long charging time and high recharging frequency of EVs pose challenges to efficiently managing EV AMoD systems. The complicated dynamic charging and mobility process of EV AMoD systems makes the demand and supply uncertainties significant when designing vehicle balancing algorithms. In this work, we design a data-driven distributionally robust optimization (DRO) approach to balance EVs for both the mobility service and the charging process. The optimization goal is to minimize the worst-case expected cost under both passenger mobility demand uncertainties and EV supply uncertainties. We then propose a novel distributional uncertainty sets construction algorithm that guarantees the produced parameters are contained in desired confidence regions with a given probability. To solve the proposed DRO AMoD EV balancing problem, we derive an equivalent computationally tractable convex optimization problem. Based on real-world EV data of a taxi system, we show that with our solution the average total balancing cost is reduced by 14.49%, and the average mobility fairness and charging fairness are improved by 15.78% and 34.51%, respectively, compared to solutions that do not consider uncertainties.
Multipartite-entanglement generation in coupled microcavity arrays

We consider photonic arrays made from quantum emitters in optically coupled microcavities as a platform for entanglement generation. These offer a large degree of tunability with the possibility of site-selective optical excitation. Coherent pumping is considered to drive transitions between vacuum and entangled target states both in a time-dependent manner, and in a quantum bath engineering approach to create entanglement in the steady-state. We demonstrate a numerical scheme that allows to generalize the determination of excitation parameters to larger array sizes and different classes of entangled states. This study is a step towards using coupled cavity arrays as a hardware platform in novel quantum-photonic applications in quantum computing and quantum machine learning.