Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissemination of antimicrobial‐resistant isolates of Salmonella spp. in wild boars and its relationship with management practices

Abstract Antimicrobial resistance (AMR) is a global concern and controlling its spread is critical for the effectiveness of antibiotics. Members of the genus Salmonella are broadly distributed, and wild boar may play an important role in its circulation between peri‐urban areas and the environment, due to its frequent interactions both with livestock or human garbage. As the population of these animals is rising due to management on certain hunting estates or the absence of natural predators, the aim of the present work is to identify the mechanisms of AMR present and/or expressed in Salmonella spp. from wild boar populations and to determine the possible role of management‐related factors applied to different game estates located in central Spain. The detection of Salmonella spp. was carried out in 121 dead wild boar from 24 game estates, and antimicrobial resistance traits were determined by antibiotic susceptibility testing and screening for their genetic determinants. The effects of feeding supplementation, the proximity of livestock, the existence of a surrounding fence and the density of wild boar on the AMR of the isolates were evaluated. The predominant subspecies and serovar found were S. enterica subsp. enterica (n = 69) and S. choleraesuis (n = 33), respectively. The other subspecies found were S. enterica subsp. diarizonae, S. enterica subsp. salamae and S. enterica subsp. houtenae. AMR was common among isolates (75.2%) and 15.7% showed multi drug resistance (MDR). Resistance to sulphonamides was the most frequent (85.7%), as well as sul1 which was the AMR determinant most commonly found. Plasmids appeared in 38.8% of the isolates, with IncHI1 being the replicon detected with the highest prevalence. The AMR of the isolates increased when the animals were raised with feeding supplementation and enclosed by fences around the estates.
Optimal Coreset for Gaussian Kernel Density Estimation

Given a point set $P\subset \mathbb{R}^d$, the kernel density estimate of $P$ is defined as \[ \overline{\mathcal{G}}_P(x) = \frac{1}{\left|P\right|}\sum_{p\in P}e^{-\left\lVert x-p \right\rVert^2} \] for any $x\in\mathbb{R}^d$. We study how to construct a small subset $Q$ of $P$ such that the kernel density estimate of $P$ is approximated by the kernel density estimate of $Q$. This subset $Q$ is called a coreset. The main technique in this work is constructing a $\pm 1$ coloring on the point set $P$ by discrepancy theory and we leverage Banaszczyk's Theorem. When $d>1$ is a constant, our construction gives a coreset of size $O\left(\frac{1}{\varepsilon}\right)$ as opposed to the best-known result of $O\left(\frac{1}{\varepsilon}\sqrt{\log\frac{1}{\varepsilon}}\right)$. It is the first result to give a breakthrough on the barrier of $\sqrt{\log}$ factor even when $d=2$.
When Kinematics Dominates Mechanics: Locally Volume-Preserving Primitives for Model Reduction in Finite Elasticity

A new, and extremely fast, computational modeling paradigm is introduced here for specific finite elasticity problems that arise in the context of soft robotics. Whereas continuum mechanics is a very classical area of study, and significant effort has been devoted to the development of intricate constitutive models for finite elasticity, we show that in the kinds of large-strain mechanics problems arising in soft robotics, many of the parameters in constitutive models are irrelevant. For the most part, the isochoric (locally volume-preserving) constraint dominates behavior, and this can be built into closed-form kinematic deformation fields before even considering other aspects of constitutive modeling. We therefore focus on developing and applying primitive deformations that each observe this constraint. It is shown that by composing a wide enough variety of such deformations that the most common behaviors observed in soft robots can be replicated. Case studies include an inflatable rubber chamber, a slender rubber rod, and a rubber block subjected to different boundary conditions. We show that this method is at least 50 times faster than the ABAQUS implementation of the finite element method (FEM). Physical experiments and measurements show that both our method and ABAQUS have approximately 10% error relative to experimentally measured displacements, as well as to each other. Our method provides a real-time alternative to FEM, and captures essential degrees of freedom for use in feedback control systems.
Ensemble and Multimodal Approach for Forecasting Cryptocurrency Price

Since the birth of Bitcoin in 2009, cryptocurrencies have emerged to become a global phenomenon and an important decentralized financial asset. Due to this decentralization, the value of these digital currencies against fiat currencies is highly volatile over time. Therefore, forecasting the crypto-fiat currency exchange rate is an extremely challenging task. For reliable forecasting, this paper proposes a multimodal AdaBoost-LSTM ensemble approach that employs all modalities which derive price fluctuation such as social media sentiments, search volumes, blockchain information, and trading data. To better support investment decision making, the approach forecasts also the fluctuation distribution. The conducted extensive experiments demonstrated the effectiveness of relying on multimodalities instead of only trading data. Further experiments demonstrate the outperformance of the proposed approach compared to existing tools and methods with a 19.29% improvement.
PISA: A Binary-Weight Processing-In-Sensor Accelerator for Edge Image Processing

This work proposes a Processing-In-Sensor Accelerator, namely PISA, as a flexible, energy-efficient, and high-performance solution for real-time and smart image processing in AI devices. PISA intrinsically implements a coarse-grained convolution operation in Binarized-Weight Neural Networks (BWNNs) leveraging a novel compute-pixel with non-volatile weight storage at the sensor side. This remarkably reduces the power consumption of data conversion and transmission to an off-chip processor. The design is completed with a bit-wise near-sensor processing-in-DRAM computing unit to process the remaining network layers. Once the object is detected, PISA switches to typical sensing mode to capture the image for a fine-grained convolution using only the near-sensor processing unit. Our circuit-to-application co-simulation results on a BWNN acceleration demonstrate acceptable accuracy on various image datasets in coarse-grained evaluation compared to baseline BWNN models, while PISA achieves a frame rate of 1000 and efficiency of ~1.74 TOp/s/W. Lastly, PISA substantially reduces data conversion and transmission energy by ~84% compared to a baseline CPU-sensor design.
How Do Magnetic Field Models Affect Astrophysical Limits on Light Axion-like Particles? An X-Ray Case Study with NGC 1275

Axion-like particles (ALPs) are a well-motivated extension to the standard model of particle physics, and X-ray observations of cluster-hosted AGN currently place the most stringent constraints on the ALP coupling to electromagnetism, g a γ , for very light ALPs (m a ≲ 10−11 eV). We revisit limits obtained by Reynolds et al. using Chandra X-ray grating spectroscopy of NGC 1275, the central AGN in the Perseus cluster, examining the impact of the X-ray spectral model and magnetic field model. We also present a new publicly available code, ALPro, which we use to solve the ALP propagation problem. We discuss evidence for turbulent magnetic fields in Perseus and show that it can be important to resolve the magnetic field structure on scales below the coherence length. We reanalyze the NGC 1275 X-ray spectra using an improved data reduction and baseline spectral model. We find the limits are insensitive to whether a partially covering absorber is used in the fits. At low m a (m a ≲ 10−13 eV), we find marginally weaker limits on g a γ (by 0.1–0.3 dex) with different magnetic field models, compared to Model B from Reynolds et al. (2020). A Gaussian random field (GRF) model designed to mimic ∼50 kpc scale coherent structures also results in only slightly weaker limits. We conclude that the existing Model B limits are robust assuming that β pl ≈ 100, and are insensitive to whether cell-based or GRF methods are used. However, astrophysical uncertainties regarding the strength and structure of cluster magnetic fields persist, motivating high-sensitivity RM observations and tighter constraints on the radial profile of β pl.
Generalizing Aggregation Functions in GNNs: High-Capacity GNNs via Nonlinear Neighborhood Aggregators

Graph neural networks (GNNs) have achieved great success in many graph learning tasks. The main aspect powering existing GNNs is the multi-layer network architecture to learn the nonlinear graph representations for the specific learning tasks. The core operation in GNNs is message propagation in which each node updates its representation by aggregating its neighbors' representations. Existing GNNs mainly adopt either linear neighborhood aggregation (mean,sum) or max aggregator in their message propagation. (1) For linear aggregators, the whole nonlinearity and network's capacity of GNNs are generally limited due to deeper GNNs usually suffer from over-smoothing issue. (2) For max aggregator, it usually fails to be aware of the detailed information of node representations within neighborhood. To overcome these issues, we re-think the message propagation mechanism in GNNs and aim to develop the general nonlinear aggregators for neighborhood information aggregation in GNNs. One main aspect of our proposed nonlinear aggregators is that they provide the optimally balanced aggregators between max and mean/sum aggregations. Thus, our aggregators can inherit both (i) high nonlinearity that increases network's capacity and (ii) detail-sensitivity that preserves the detailed information of representations together in GNNs' message propagation. Promising experiments on several datasets show the effectiveness of the proposed nonlinear aggregators.
Improving Intrinsic Exploration with Language Abstractions

Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One common solution is to use intrinsic rewards to encourage agents to explore their environment. However, recent intrinsic exploration methods often use state-based novelty measures which reward low-level exploration and may not scale to domains requiring more abstract skills. Instead, we explore natural language as a general medium for highlighting relevant abstractions in an environment. Unlike previous work, we evaluate whether language can improve over existing exploration methods by directly extending (and comparing to) competitive intrinsic exploration baselines: AMIGo (Campero et al., 2021) and NovelD (Zhang et al., 2021). These language-based variants outperform their non-linguistic forms by 47-85% across 13 challenging tasks from the MiniGrid and MiniHack environment suites.
An Analog Signal Processing EIC-PIC Solution for Coherent Data Center Interconnects

Data center interconnects (DCIs) will have to support throughputs of 400 Gbps or more per wavelength in the near future. To achieve such high data rates, coherent modulation and detection is used, which conventionally requires high-speed data conversion and signal processing in the digital domain. Alternatively, high-speed signal conditioning and processing could be carried out in co-designed photonic and electronic integrated circuits, in the optical and electrical analog domains, respectively, to achieve reduced power consumption, latency, form factor, and cost. A few demonstrations of analog domain processing electronic integrated circuits (EICs), including those of equalizer and carrier phase recovery (CPR) modules showcase progress in this direction in the literature. In this brief, for the first time, we present integration of a silicon photonic integrated coherent receiver (ICR) module with a CPR module, as a part of a complete coherent receiver solution. A phase shifter in the ICR (fabricated in a 220 nm silicon-on-insulator technology) receives feedback from a CPR EIC, and the combination compensates for the time varying phase offset between the modulated signal and the unmodulated carrier in the closed loop configuration. In this proof-of-concept demonstration, we present experimental results obtained from the stand-alone silicon photonic ICR along with its system level integration with CPR chip, for QPSK signals. The technique can be extended to a higher-order modulation format, such as 16-QAM, for data rate scaling. The proposed scheme is suitable for homodyne systems, such as polarization multiplexed carrier based self-homodyne links.
Dynamic Service Provisioning in the Edge-cloud Continuum with Provable Guarantees

We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing the corresponding chain properly and allocating a suitable amount of computing resources. Furthermore, chain migration may be necessary to meet the services' target delay, or convenient to keep the service provisioning cost low. We tackle such issues by formalizing the problem of optimal chain placement and resource allocation in the edge-cloud continuum, taking into account migration, bandwidth, and computation costs. Specifically, we first envision an algorithm that, leveraging resource augmentation, addresses the above problem and provides an upper bound to the amount of resources required to find a feasible solution. We use this algorithm as a building block to devise an efficient approach targeting the minimum-cost solution, while minimizing the required resource augmentation. Our results, obtained through trace-driven, large-scale simulations, show that our solution can provide a feasible solution by using half the amount of resources required by state-of-the-art alternatives.
Modeling overcontact binaries. I. The effect of tidal deformation

Context. In the realm of massive stars, strong binary interactions are commonplace. One extreme case is that of over- contact systems, which are expected to be part of the evolution of all stars evolving towards a merger and hypothesized as playing a role in the formation of binary black holes. However, important simpliﬁcations are made to model the evolution of overcontact binaries. The deformation from tidal forces is almost always put aside, and even rotation is frequently ignored in such models. Yet, both observations and theory have shown that overcontact stars are tidally deformed to a great extent, leaving a potentially important eﬀect on the outer layers unaccounted for in models. Fur-thermore, in eclipsing binaries where radii can be determined to high precision, the question of how large the eﬀect of tidal deformation is on the inferred properties of stellar models is still uncertain. Aims. We aim to consistently model overcontact binary stars in a one-dimensional (1D) stellar evolution code. To that end, we developed the required methodology to represent tidally distorted stars in 1D evolution codes. Methods. Using numerical methods, we computed the structure correction factors to the 1D spherical stellar structure equations of hydrostatic equilibrium and radiative energy transfer due to the binary Roche potential. We then compared them to existing results and the structure corrections of single, rotating stars. We implemented the new structure correction factors in the stellar evolution code MESA and explored several case studies. We compared the diﬀerences between our simulations: when no rotation is included, when we treat rotation using single star corrections (only accounting for centrifugal deformation), or when we use tidal deformation. Results. We ﬁnd that ignoring rotation in deformed detached eclipsing binaries can produce a radius discrepancy of up to 5%. The diﬀerence between tidal and single star centrifugal distortion models is more benign at 1%, showing that single rotating star models are a suitable approximation of tidally deformed stars in a binary system. In overcontact conﬁgurations, we ﬁnd a similar 5% variation in surface properties as a result of tidal distortion with respect to non-rotating models, showing that it is inappropriate to model binary stars ﬁlling their Roche lobe signiﬁcantly as non-rotating.
Proofs, Circuits, and Communication

We survey lower-bound results in complexity theory that have been obtained via newfound interconnections between propositional proof complexity, boolean circuit complexity, and query/communication complexity. We advocate for the theory of total search problems (TFNP) as a unifying language for these connections and discuss how this perspective suggests a whole programme for further research.
Classification of ADHD Patients by Kernel Hierarchical Extreme Learning Machine

These days, the diagnosis of neuropsychiatric diseases through brain imaging technology has received more and more attention. The exploration of interactions in brain functional connectivity based on functional magnetic resonance imaging (fMRI) data is critical for the study of mental illness. Because attention-deficit/hyperactivity disorder (ADHD) is a chronic disease that affects millions of children, it is difficult to diagnose, so there is still much space for improvement in the accuracy of the diagnosis of the disease. In this paper, we consider the dynamics of brain functional connectivity, modeling a functional brain dynamics model from medical imaging, which helps to find differences in brain function interactions between normal control (NC) children and ADHD children. In more detail, our method is used by Bayesian Connectivity Change Point Model for dynamic detection, Local Binary Encoding Method for local feature extraction, and Kernel Hierarchical Extreme Learning Machine implementation classification. To validate our approach, experimental comparisons of fMRI imaging data on 23 ADHD and 45 NC children were performed, and our experimental methods achieved better classification results than existing methods.
tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices

Advances in low-power electronics and machine learning techniques lead to many novel wearable IoT devices. These devices have limited battery capacity and computational power. Thus, energy harvesting from ambient sources is a promising solution to power these low-energy wearable devices. They need to manage the harvested energy optimally to achieve energy-neutral operation, which eliminates recharging requirements. Optimal energy management is a challenging task due to the dynamic nature of the harvested energy and the battery energy constraints of the target device. To address this challenge, we present a reinforcement learning-based energy management framework, tinyMAN, for resource-constrained wearable IoT devices. The framework maximizes the utilization of the target device under dynamic energy harvesting patterns and battery constraints. Moreover, tinyMAN does not rely on forecasts of the harvested energy which makes it a prediction-free approach. We deployed tinyMAN on a wearable device prototype using TensorFlow Lite for Micro thanks to its small memory footprint of less than 100 KB. Our evaluations show that tinyMAN achieves less than 2.36 ms and 27.75 $\mu$J while maintaining up to 45% higher utility compared to prior approaches.
RemixIT: Continual Self-Training of Speech Enhancement Models via Bootstrapped Remixing

We present RemixIT, a simple yet effective self-supervised method for training speech enhancement without the need of a single isolated in-domain speech nor a noise waveform. Our approach overcomes limitations of previous methods which make them dependent on clean in-domain target signals and thus, sensitive to any domain mismatch between train and test samples. RemixIT is based on a continuous self-training scheme in which a pre-trained teacher model on out-of-domain data infers estimated pseudo-target signals for in-domain mixtures. Then, by permuting the estimated clean and noise signals and remixing them together, we generate a new set of bootstrapped mixtures and corresponding pseudo-targets which are used to train the student network. Vice-versa, the teacher periodically refines its estimates using the updated parameters of the latest student models. Experimental results on multiple speech enhancement datasets and tasks not only show the superiority of our method over prior approaches but also showcase that RemixIT can be combined with any separation model as well as be applied towards any semi-supervised and unsupervised domain adaptation task. Our analysis, paired with empirical evidence, sheds light on the inside functioning of our self-training scheme wherein the student model keeps obtaining better performance while observing severely degraded pseudo-targets.
Synergism between B and Nb Improves Fire Resistance in Microalloyed Steels

The long exposure of structural components to high temperatures (above 600 °C) negatively changes their mechanical properties, severely compromising the structural capacity of buildings and other structures in which safety is a primary concern. Developing new cheaper fire-resistant steels with better mechanical and thermal performances represents a challenging, cutting-edge materials science and engineering research topic. Alloying elements such as Nb and Mo are generally used to improve the strength at both room and high temperatures due to the formation of precipitates and harder microconstituents. This study shows that adding small amounts of boron in Nb-microalloyed fire-resistant steels may be crucial in maintaining mechanical properties at high temperatures. The widely used 66% yield-strength criteria for fire resistance was achieved at ≈574 °C for the B-added alloys. In contrast, for those without boron, this value reached ≈460 °C, representing a remarkable boron-induced mechanical strengthening enhancement. First-principles quantum mechanics calculations demonstrate that boron additions can lower 11.7% of the vacancy formation energy compared to pure ferrite. Furthermore, for Nb-added steels, the reduction in the vacancy formation energy may reach 33.2%, suggesting that the boron-niobium combination could act as an effective pinning-based steel-strengthening agent due to the formation of B-induced higher-density vacancy-related crystalline defects, as well as other well-known steel strengthening mechanisms reported in the literature. Adding boron and niobium may, therefore, be essential in designing better structural alloys.
Churn modeling of life insurance policies via statistical and machine learning methods - Analysis of important features

Life assurance companies typically possess a wealth of data covering multiple systems and databases. These data are often used for analyzing the past and for describing the present. Taking account of the past, the future is mostly forecasted by traditional statistical methods. So far, only a few attempts were undertaken to perform estimations by means of machine learning approaches. In this work, the individual contract cancellation behavior of customers within two partial stocks is modeled by the aid of various classification methods. Partial stocks of private pension and endowment policy are considered. We describe the data used for the modeling, their structured and in which way they are cleansed. The utilized models are calibrated on the basis of an extensive tuning process, then graphically evaluated regarding their goodness-of-fit and with the help of a variable relevance concept, we investigate which features notably affect the individual contract cancellation behavior.