cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Enantioselective adsorption on magnetic surfaces
Creators: name: Safari, Mohammad Reza
name: Matthes, Frank
name: Caciuc, Vasile
name: Atodiresei, Nicolae
name: Schneider, Claus M.
name: Ernst, Karl-Heinz
name: Bürgler, Daniel E.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: From the beginning of molecular theory, the interplay of chirality and magnetism has intrigued scientists. There is still the question if enantiospecific adsorption of chiral molecules occurs on magnetic surfaces. Enantiomer discrimination was conjectured to arise from chirality-induced spin separation within the molecules and exchange interaction with the substrate's magnetization. Here we show that single helical aromatic hydrocarbons undergo enantioselective adsorption on ferromagnetic cobalt surfaces. Spin and chirality sensitive scanning tunneling microscopy reveals that molecules of opposite handedness prefer adsorption onto cobalt islands with opposite out-of-plane magnetization. As mobility ceases in the final chemisorbed state, it is concluded that enantioselection must occur in a physisorbed transient precursor state. State-of-the-art spin-resolved ab initio simulations support this scenario by refuting enantio-dependent chemisorption energies. These findings demonstrate that van der Waals interaction should also include spin-fluctuations which are crucial for molecular magnetochiral processes.
description: 19 pages, 4 figures, plus Supporting Information
How Does a Foucauldian Genealogical Approach Enhance the Study of Long-Term Care through a Critical Disability Lens?
Creators: name: Hewitt, Michelle
Publisher: name: Multidisciplinary Digital Publishing Institute
Year: 2022

description: Younger disabled adults in long-term care, particularly those with physical disabilities and chronic illnesses, receive care that does not fit their needs. This article looks at whether a Foucauldian genealogical approach would enhance a study that focuses on the societal values that have allowed this situation to persist. It looks at the historical and cultural contingencies of genealogy, and its ability to explore the complex power relations at play, in normalization and biopower. It concludes that there is a place for this approach–one that can be adapted from the 1970s approach of Foucault to fit power dynamics and positioning in care in the 2020s.
Recent Advances in Plasma-Engineered Polymers for Biomarker-Based Viral Detection and Highly Multiplexed Analysis
Creators: name: Mousavi, Seyyed Mojtaba
name: Hashemi, Seyyed Alireza
name: Kalashgrani, Masoomeh Yari
name: Gholami, Ahmad
name: Omidifar, Navid
name: Babapoor, Aziz
name: Vijayakameswara Rao, Neralla
name: Chiang, Wei-Hung
Publisher: name: Multidisciplinary Digital Publishing Institute
Year: 2022

description: Infectious diseases remain a pervasive threat to global and public health, especially in many countries and rural urban areas. The main causes of such severe diseases are the lack of appropriate analytical methods and subsequent treatment strategies due to limited access to centralized and equipped medical centers for detection. Rapid and accurate diagnosis in biomedicine and healthcare is essential for the effective treatment of pathogenic viruses as well as early detection. Plasma-engineered polymers are used worldwide for viral infections in conjunction with molecular detection of biomarkers. Plasma-engineered polymers for biomarker-based viral detection are generally inexpensive and offer great potential. For biomarker-based virus detection, plasma-based polymers appear to be potential biological probes and have been used directly with physiological components to perform highly multiplexed analyses simultaneously. The simultaneous measurement of multiple clinical parameters from the same sample volume is possible using highly multiplexed analysis to detect human viral infections, thereby reducing the time and cost required to collect each data point. This article reviews recent studies on the efficacy of plasma-engineered polymers as a detection method against human pandemic viruses. In this review study, we examine polymer biomarkers, plasma-engineered polymers, highly multiplexed analyses for viral infections, and recent applications of polymer-based biomarkers for virus detection. Finally, we provide an outlook on recent advances in the field of plasma-engineered polymers for biomarker-based virus detection and highly multiplexed analysis.
Closing the gap between SVRG and TD-SVRG with Gradient Splitting
Creators: name: Mustafin, Arsenii
name: Olshevsky, Alex
name: Paschalidis, Ioannis Ch.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Temporal difference (TD) learning is a policy evaluation in reinforcement learning whose performance can be enhanced by variance reduction methods. Recently, multiple works have sought to fuse TD learning with Stochastic Variance Reduced Gradient (SVRG) method to achieve a geometric rate of convergence. However, the resulting convergence rate is significantly weaker than what is achieved by SVRG in the setting of convex optimization. In this work we utilize a recent interpretation of TD-learning as the splitting of the gradient of an appropriately chosen function, thus simplifying the algorithm and fusing TD with SVRG. Our main result is a geometric convergence bound with predetermined learning rate of $1/8$, which is identical to the convergence bound available for SVRG in the convex setting. Our theoretical findings are supported by a set of experiments.
description: 42 pages, 8 figures, 6 tables
title: Evaluating the effects of agroforestry versus arable systems on functional biodiversity and associated ecosystem services
Creators: name: Staton, Tom
Publisher: name: University of Reading
Year: 2022

description: The intensification of agricultural production in recent decades is widely recognised to have contributed substantially to global declines in biodiversity and associated ecosystem services, such as natural pest control and pollination. Agroforestry systems, where trees and/or shrubs are integrated into agricultural fields or landscapes, have the potential to increase biodiversity and associated ecosystem services. This thesis therefore aims to evaluate how agroforestry systems affect invertebrate pests, their natural enemies, and pollinators, in addition to productivity and farm income, relative to arable monocultures. A review and meta-analysis of the literature revealed that arthropod pests are significantly suppressed, and natural enemies significantly enhanced, in agroforestry systems relative to arable monocultures. However, the results were equivocal with high heterogeneity. Empirical data collected from three agroforestry sites with paired arable controls confirmed higher levels of plant and invertebrate biodiversity in agroforestry systems, and also revealed that the agroforestry systems led to a change in plant and invertebrate communities. These changes could be explained in terms of life-history traits, for example, plant communities in agroforestry systems were more perennial while invertebrates were less likely to be winged. Functional trait diversity of natural enemies was significantly higher in the agroforestry systems, indicating a higher level of biological control. Furthermore, species-level pollinator data from the same sites revealed that additional bee species in agroforestry contributed to functional trait diversity through niche complementarity. To further explore causes of heterogeneity, understorey management was manipulated at one agroforestry site, and was found to significantly affect natural enemy abundance and diversity, aphid suppression, and pollinator visitation. Although arable yields were up to 11% lower in agroforestry than arable systems, financial modelling predicted that agroforestry systems were capable of increasing farm income after at least seven years. Agroforestry systems therefore represent a viable option to restore farmland biodiversity and improve agricultural sustainability.
Social Tagging as interpretative production: liminality and information practice
Creators: name: Rafferty, Pauline
name: Foster, Allen
Publisher: name: ISKO UK
Year: 2015
Subjects: Social tagging
Crowdsources annotations
Tagging

description:  This paper examines recent approaches to tagging and crowdsourcing as communicative practice, viewing social tagging and crowdsourced annotationas liminal communicative practice that throws into relief many conventional assumptions regarding information roles, actions, performances and functions.In this paper, tags and annotations are viewed as paratextual elements, at one and the same time responses, interpretations and productive communicativegestures linked to other documents, themselves also productions and responses. Taggers operate as readers and writers, consumers and producers.yet currently in most systems the opportunities for tagging and communicative practice are limited to the inclusion of one word tags or masheduptags. There is space for new collaborative tagging models to be developed that draw on richer forms of user generated content, perhaps through storytelling, for example, and that develop the creativity inherent in tagging practice to design enhanced KO models and systems.It is in this context that this paper proposes to examine a range of current tagging and annotation practices, and to discuss novel approaches to the representation of knowledge that capitalise on tagging as liminal, productive and performative information practice.
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
Creators: name: Li, Shiwei
name: Guo, Huifeng
name: Hou, Lu
name: Zhang, Wei
name: Tang, Xing
name: Tang, Ruiming
name: Zhang, Rui
name: Li, Ruixuan
Publisher: name: arXiv
Year: 2022
Subjects: subject: Machine Learning (cs.LG)
subject: Information Retrieval (cs.IR)
subject: FOS: Computer and information sciences

description: Embedding tables are usually huge in click-through rate (CTR) prediction models. To train and deploy the CTR models efficiently and economically, it is necessary to compress their embedding tables at the training stage. To this end, we formulate a novel quantization training paradigm to compress the embeddings from the training stage, termed low-precision training (LPT). Also, we provide theoretical analysis on its convergence. The results show that stochastic weight quantization has a faster convergence rate and a smaller convergence error than deterministic weight quantization in LPT. Further, to reduce the accuracy degradation, we propose adaptive low-precision training (ALPT) that learns the step size (i.e., the quantization resolution) through gradient descent. Experiments on two real-world datasets confirm our analysis and show that ALPT can significantly improve the prediction accuracy, especially at extremely low bit widths. For the first time in CTR models, we successfully train 8-bit embeddings without sacrificing prediction accuracy. The code of ALPT is publicly available.
description: Accepted by AAAI2023
Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
Creators: name: Zhang, Borong
name: Zepeda-Núñez, Leonardo
name: Li, Qin
Publisher: name: arXiv
Year: 2022
Subjects: subject: Numerical Analysis (math.NA)
subject: Machine Learning (cs.LG)
subject: FOS: Mathematics
subject: FOS: Computer and information sciences

description: This paper introduces a novel deep neural network architecture for solving the inverse scattering problem in frequency domain with wide-band data, by directly approximating the inverse map, thus avoiding the expensive optimization loop of classical methods. The architecture is motivated by the filtered back-projection formula in the full aperture regime and with homogeneous background, and it leverages the underlying equivariance of the problem and compressibility of the integral operator. This drastically reduces the number of training parameters, and therefore the computational and sample complexity of the method. In particular, we obtain an architecture whose number of parameters scale sub-linearly with respect to the dimension of the inputs, while its inference complexity scales super-linearly but with very small constants. We provide several numerical tests that show that the current approach results in better reconstruction than optimization-based techniques such as full-waveform inversion, but at a fraction of the cost while being competitive with state-of-the-art machine learning methods.
description: 21 pages, 9 figures, and 4 tables
Competition of glass and crystal: phase-field model
Creators: name: Vasin, M. G.
name: Ankudinov, V.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: The phase-field model for the description of the solidification processes with the glass-crystal competition is suggested. The model combines the first-order phase transition model in the phase-field formalism and gauge-field theory of glass transition. We present a self-consistent system of stochastic motion equations for unconserved order parameters describing the crystal-like short-range ordering and vitrification. It is shown, that the model qualitatively describes the glass-crystal competition during quenching with finite cooling speed. The nucleation of the crystalline phase at slow cooling speeds and low undercoolings proceeds by a fluctuation mechanism. The model demonstrates the tendency to amorphization with the increase of its cooling rate.
ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware
Creators: name: Quintana, Fernando M.
name: Perez-Peña, Fernando
name: Galindo, Pedro L.
name: Neftci, Emre O.
name: Chicca, Elisabetta
name: Khacef, Lyes
Publisher: name: arXiv
Year: 2023
Subjects: subject: Neural and Evolutionary Computing (cs.NE)
subject: FOS: Computer and information sciences

description: Neuromorphic perception with event-based sensors, asynchronous hardware and spiking neurons is showing promising results for real-time and energy-efficient inference in embedded systems. The next promise of brain-inspired computing is to enable adaptation to changes at the edge with online learning. However, the parallel and distributed architectures of neuromorphic hardware based on co-localized compute and memory imposes locality constraints to the on-chip learning rules. We propose in this work the Event-based Three-factor Local Plasticity (ETLP) rule that uses (1) the pre-synaptic spike trace, (2) the post-synaptic membrane voltage and (3) a third factor in the form of projected labels with no error calculation, that also serve as update triggers. We apply ETLP with feedforward and recurrent spiking neural networks on visual and auditory event-based pattern recognition, and compare it to Back-Propagation Through Time (BPTT) and eProp. We show a competitive performance in accuracy with a clear advantage in the computational complexity for ETLP. We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learn spatio-temporal patterns with a rich temporal structure. Finally, we provide a proof of concept hardware implementation of ETLP on FPGA to highlight the simplicity of its computational primitives and how they can be mapped into neuromorphic hardware for online learning with low-energy consumption and real-time interaction.
Composizione corporea e forza nel fitness: analisi dei risultati ottenuti in 3 anni di allenamento muscolare, regole alimentari e integrazione
Creators: name: STECCHI, ALFREDO
Publisher: name: Università degli Studi di Roma "Tor Vergata"
Year: 2009
Subjects: subject: allenamento neuromuscolare; composizione corporea; forza massimale; regole alimentari; recupero; integrazione
subject: allenamento neuromuscolare
subject: composizione corporea
subject: forza massimale
subject: regole alimentari
subject: recupero
subject: integrazione

description: 21. ciclo
Nearly Gorenstein projective monomial curves of small codimension
Creators: name: Miyashita, Sora
Publisher: name: arXiv
Year: 2023
Subjects: subject: Commutative Algebra (math.AC)
subject: FOS: Mathematics
subject: 13H10 (Primary), 13M05 (Secondary)

description: In this paper, we characterize nearly Gorenstein projective monomial curves of codimension 2 and 3.
description: 11 pages, 1 figure
Positronium Physics and Biomedical Applications
Creators: name: Bass, Steven D.
name: Mariazzi, Sebastiano
name: Moskal, Pawel
name: Stepien, Ewa
Publisher: name: arXiv
Year: 2023
Subjects: subject: Medical Physics (physics.med-ph)
subject: High Energy Physics - Phenomenology (hep-ph)
subject: Instrumentation and Detectors (physics.ins-det)
subject: FOS: Physical sciences

description: Positronium is the simplest bound state, built of an electron and a positron. Studies of positronium in vacuum and its decays in medium tell us about Quantum Electrodynamics, QED, and about the structure of matter and biological processes of living organisms at the nanoscale, respectively. Spectroscopic measurements constrain our understanding of QED bound state theory. Searches for rare decays and measurements of the effect of gravitation on positronium are used to look for new physics phenomena. In biological materials positronium decays are sensitive to the inter- and intra-molecular structure and to the metabolism of living organisms ranging from single cells to human beings. This leads to new ideas of positronium imaging in medicine using the fact that during positron emission tomography (PET) as much as 40% of positron annihilation occurs through the production of positronium atoms inside the patient's body. A new generation of the high sensitivity and multi-photon total-body PET systems opens perspectives for clinical applications of positronium as a biomarker of tissue pathology and the degree of tissue oxidation.
description: Colloquium, 23 pages, preprint of article in Reviews of Modern Physics
PolarStar: Expanding the Scalability Horizon of Diameter-3 Networks
Creators: name: Lakhotia, Kartik
name: Monroe, Laura
name: Isham, Kelly
name: Besta, Maciej
name: Blach, Nils
name: Hoefler, Torsten
name: Petrini, Fabrizio
Publisher: name: arXiv
Year: 2023
Subjects: subject: Networking and Internet Architecture (cs.NI)
subject: Distributed, Parallel, and Cluster Computing (cs.DC)
subject: Combinatorics (math.CO)
subject: FOS: Computer and information sciences
subject: FOS: Mathematics
subject: B.4.3; B.4.4; G.2.2

description: We present PolarStar, a novel family of diameter-3 network topologies derived from the star product of low-diameter factor graphs. PolarStar gives the largest known diameter-3 network topologies for almost all radixes, thus providing the best known scalable diameter-$3$ network. Compared to current state-of-the-art diameter-$3$ networks, PolarStar achieves $1.3\times$ geometric mean increase in scale over Bundlefly, $1.9\times$ over Dragonfly, and $6.7\times$ over {3-D} HyperX. PolarStar has many other desirable properties, including a modular layout, large bisection, high resilience to link failures and a large number of feasible configurations for every radix. We give a detailed evaluation with simulations of synthetic and real-world traffic patterns and show that PolarStar exhibits comparable or better performance than current diameter-3 networks.
description: 13 pages, published in SPAA 2024
Spatiotemporal characteristics of agricultural food import shocks
Creators: name: Zhang, Yin-Ting
name: Nguyen, Duc Khuong
name: Zhou, Wei-Xing
Publisher: name: arXiv
Year: 2023
Subjects: subject: Physics and Society (physics.soc-ph)
subject: FOS: Physical sciences

description: Ensuring food supply stability is key to food security for economies, and food imports become increasingly important to safeguard food supplies in economies with inadequate food production. Food import shocks have significant impacts on targeted economies. Using import trade data of four staple crops (maize, rice, soybean, and wheat) from 1986 to 2018, this paper identifies food import trade shocks that occurred to economies during the period of 1995--2018. We compare the temporal evolution and spatial distribution of import shocks occurring to different crops and analyze the shock intensity and shock recovery in various continents based on locally weighted polynomial regression and Cook's distance. The results reveal higher frequencies during the 2007/2008 food crisis and relatively higher shock frequencies in North America, Africa, and Asia. Meanwhile, there are regional differences in shock recovery, with the majority of shocks in Asia recovering in the short term. We also find that high import diversity and a low import dependency ratio buffer economies against import shocks, resulting in a low shock rate and a high recovery rate. These results contribute to our understanding of the external supply risks of food, placing emphasis on accessibility issues in food security.
Phonon-Assisted Tunneling through Quantum Dot Systems Connected to Majorana Bound States
Creators: name: Máthé, Levente
name: Kovács-Krausz, Zoltán
name: Botiz, Ioan
name: Grosu, Ioan
name: Anouz, Khadija El
name: Allati, Abderrahim El
name: Zârbo, Liviu P.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: FOS: Physical sciences

description: We theoretically analyze phonon-assisted tunneling transport in a quantum dot side connected to a Majorana bound state in a topological superconducting nanowire. We investigate the behavior of the current through the dot, for a range of experimentally relevant parameters, in the presence of one long-wave optical phonon mode. We consider the current-gate voltage, the current-bias voltage and the current-dot-Majorana coupling characteristics under the influence of the electron-phonon coupling. In the absence of electron-phonon interaction, the Majorana bound states suppress the current when the gate voltage matches the Fermi level, but the increase in the bias voltage counteracts this effect. In the presence of electron-phonon coupling, the current behaves similarly as a function of the renormalized gate voltage. As an added feature at large bias voltages, it presents a dip or a plateau, depending on the size of the dot-Majorana coupling. Lastly, we show that the currents are most sensitive to, and depend non-trivially on the parameters of the Majorana circuit element, in the regime of low temperatures combined with low voltages. Our results provide insights into the complex physics of quantum dot devices used to probe Majorana bound states.
description: 10 pages, 6 figures
Controlled Lagrangians and Stabilization of Euler--Poincaré Equations with Symmetry Breaking Nonholonomic Constraints
Creators: name: Garcia, Jorge S.
name: Ohsawa, Tomoki
Publisher: name: arXiv
Year: 2023
Subjects: subject: Optimization and Control (math.OC)
subject: FOS: Mathematics
subject: 34H15, 37J60, 70E17, 70G45, 70F25, 70G65, 93D05, 93D15

description: We extend the method of Controlled Lagrangians to nonholonomic Euler--Poincaré equations with advected parameters, specifically to those mechanical systems on Lie groups whose symmetry is broken not only by a potential force but also by nonholonomic constraints. We introduce advected-parameter-dependent quasivelocities in order to systematically eliminate the Lagrange multipliers in the nonholonomic Euler--Poincaré equations. The quasivelocities facilitate the method of Controlled Lagrangians for these systems, and lead to matching conditions that are similar to those by Bloch, Leonard, and Marsden for the standard holonomic Euler--Poincaré equation. Our motivating example is what we call the pendulum skate, a simple model of a figure skater developed by Gzenda and Putkaradze. We show that the upright spinning of the pendulum skate is stable under certain conditions, whereas the upright sliding equilibrium is always unstable. Using the matching condition, we derive a control law to stabilize the sliding equilibrium.
description: 29 pages, 5 figures
Generalization of Quantum Machine Learning Models Using Quantum Fisher Information Metric
Creators: name: Haug, Tobias
name: Kim, M. S.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Quantum Physics (quant-ph)
subject: Machine Learning (cs.LG)
subject: Machine Learning (stat.ML)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences

description: Generalization is the ability of machine learning models to make accurate predictions on new data by learning from training data. However, understanding generalization of quantum machine learning models has been a major challenge. Here, we introduce the data quantum Fisher information metric (DQFIM). It describes the capacity of variational quantum algorithms depending on variational ansatz, training data and their symmetries. We apply the DQFIM to quantify circuit parameters and training data needed to successfully train and generalize. Using the dynamical Lie algebra, we explain how to generalize using a low number of training states. Counter-intuitively, breaking symmetries of the training data can help to improve generalization. Finally, we find that out-of-distribution generalization, where training and testing data are drawn from different data distributions, can be better than using the same distribution. Our work provides a useful framework to explore the power of quantum machine learning models.
description: 26 pages, 18 figures
Articoli di Fede. Articles of Faith
Creators: name: Unorganized Italian Christian Churches
name: Alves, Leonardo Marcondes
Publisher: name: Zenodo
Year: 2022
Subjects: Articles of faith, creed, confession

description: Placard with the articles of faith approved by the first convention of the Unorganized Italian Christian Churches in North America, a pentecostal body, convened at Niagara Falls, NY.
CROCODILE: Cross-correlation retrievals of directly-imaged self-luminous exoplanets
Creators: name: Hayoz, Jean
Publisher: name: Zenodo
Year: 2022
Subjects: High-Contrast Imaging
Atmospheric Retrieval

description: Obtaining medium or high-resolution spectroscopy of directly-imaged exoplanets is challenging. Cross-correlation spectroscopy, a technique where well-known molecular spectral signatures originating from the atmosphere of the planet are used to differentiate the signal coming from the planet to that of the PSF of the host star and from noise, has gotten traction in the recent years as a reliable method to detect molecular species in the atmospheres of exoplanets. My work has been focused on developing and testing the Python package CROCODILE (CROss-COrrelation retrievals of Directly-Imaged self-Luminous Exoplanets), which integrates this format of data into the Bayesian statistical framework of atmospheric retrievals. It can combine medium-resolution cross-correlation spectroscopy with low-resolution spectroscopy and photometry to obtain strong constraints on atmospheric properties of directly-imaged exoplanets such as molecular abundance ratios and thermal structure. I demonstrate the capabilities of CROCODILE using simulations of exoplanetary emission spectra from different configurations of the parameter space of exoplanets. Furthermore, I identify two cases, namely high C/O ratio and low metallicity, where free chemistry fails to reproduce the properties of the input atmosphere although the synthetic spectra are well fit, hinting at a modelling degeneracy. In summary, CROCODILE provides the statistical framework to interpret the three main observables of directly-imaged exoplanetary atmospheres, namely photometry, low-resolution spectroscopy, and medium (and higher) resolution cross-correlation spectroscopy, all of which will be measured by the next generation of instruments such as ERIS at the Very Large Telescope, MIRI aboard the James Webb Space Telescope, and METIS at the future Extremely Large Telescope.
description: {"references": ["\u00d6berg, K. I., Murray-Clay, R., and Bergin, E. A. (2011)", "Nowak, M., Lacour, S., Molli\u00e8re, P., et al. (2020)", "Molli\u00e8re, P., Wardenier, J. P., Van Boekel, R., et al. (2019)", "Cugno, G., Patapis, P., Stolker, T., et al. (2021)", "Brogi, M. and Line, M. R. (2019)"]}
Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
Creators: name: Lefringhausen, Robert
name: Srithasan, Supitsana
name: Lederer, Armin
name: Hirche, Sandra
Publisher: name: arXiv
Year: 2023
Subjects: subject: Systems and Control (eess.SY)
subject: Machine Learning (cs.LG)
subject: Optimization and Control (math.OC)
subject: Machine Learning (stat.ML)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences
subject: FOS: Mathematics

description: As control engineering methods are applied to increasingly complex systems, data-driven approaches for system identification appear as a promising alternative to physics-based modeling. While the Bayesian approaches prevalent for safety-critical applications usually rely on the availability of state measurements, the states of a complex system are often not directly measurable. It may then be necessary to jointly estimate the dynamics and the latent state, making the quantification of uncertainties and the design of controllers with formal performance guarantees considerably more challenging. This paper proposes a novel method for the computation of an optimal input trajectory for unknown nonlinear systems with latent states based on a combination of particle Markov chain Monte Carlo methods and scenario theory. Probabilistic performance guarantees are derived for the resulting input trajectory, and an approach to validate the performance of arbitrary control laws is presented. The effectiveness of the proposed method is demonstrated in a numerical simulation.
description: Accepted version submitted to the 2024 European Control Conference (ECC)
Metodi di analisi basati sull’inibizione delle colinestrerasi per applicazioni nel campo ambientale e alimentare
Creators: name: ARDUINI, FABIANA
Publisher: name: Università degli Studi di Roma "Tor Vergata"
Year: 2008
Subjects: subject: cholinesterase inhibition; aflatoxins; pesticides; nerve agents
subject: cholinesterase inhibition
subject: aflatoxins
subject: pesticides
subject: nerve agents

description: 19. ciclo
Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images
Creators: name: Nakarmi, Suprim
name: Pudasaini, Sanam
name: Thapaliya, Safal
name: Upretee, Pratima
name: Shrestha, Retina
name: Giri, Basant
name: Neupane, Bhanu Bhakta
name: Khanal, Bishesh
Publisher: name: arXiv
Year: 2023
Subjects: subject: Image and Video Processing (eess.IV)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Machine Learning (cs.LG)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences

description: The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, and more accessible alternatives for the detection of Giardia and Cryptosporidium than traditional brightfield microscopes. However, the images from smartphone microscopes are noisier and require manual cyst identification by trained technicians, usually unavailable in resource-limited settings. Automatic detection of (oo)cysts using deep-learning-based object detection could offer a solution for this limitation. We evaluate the performance of four state-of-the-art object detectors to detect (oo)cysts of Giardia and Cryptosporidium on a custom dataset that includes both smartphone and brightfield microscopic images from vegetable samples. Faster RCNN, RetinaNet, You Only Look Once (YOLOv8s), and Deformable Detection Transformer (Deformable DETR) deep-learning models were employed to explore their efficacy and limitations. Our results show that while the deep-learning models perform better with the brightfield microscopy image dataset than the smartphone microscopy image dataset, the smartphone microscopy predictions are still comparable to the prediction performance of non-experts. Also, we publicly release brightfield and smartphone microscopy datasets with the benchmark results for the detection of Giardia and Cryptosporidium, independently captured on reference (or standard lab setting) and vegetable samples. Our code and dataset are available at https://github.com/naamiinepal/smartphone_microscopy and https://doi.org/10.5281/zenodo.7813183, respectively.
description: 21 pages (including supplementary information), 5 figures, 7 tables, Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:014
Visibility graph analysis of the grains and oilseeds indices
Creators: name: Liu, Hao-Ran
name: Li, Ming-Xia
name: Zhou, Wei-Xing
Publisher: name: arXiv
Year: 2023
Subjects: subject: General Economics (econ.GN)
subject: Physics and Society (physics.soc-ph)
subject: FOS: Economics and business
subject: FOS: Physical sciences

description: The Grains and Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley are daily price indexes reflect the price changes of the global spot markets of staple agro-food crops. In this paper, we carry out a visibility graph (VG) analysis of the GOI and its five sub-indices. Our findings reveal that the degree distributions of the VGs, except for rice, exhibit exponentially truncated power-law tails, while the rice VG conforms to a power-law tail. The average clustering coefficients of the six VGs are quite large ($>0.5$) and exhibit a nice power-law relation with respect to the average degrees of the VGs. For each VG, the clustering coefficients of nodes are inversely proportional to their degrees for large degrees and are correlated to their degrees as a power law for small degrees. All the six VGs exhibit small-world characteristics. The degree-degree correlation coefficients shows that the VGs for maize and soyabeans indices exhibit weak assortative mixing patterns, while the other four VGs are weakly disassortative. The average nearest neighbor degree functions have similar patterns, and each function shows a more complex mixing pattern which decreases for small degrees, increases for mediate degrees, and decreases again for large degrees.
description: 16 pages, 9 figures
The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation
Creators: name: Nguyen, Thanh-Danh
name: Vu, Anh-Khoa Nguyen
name: Nguyen, Nhat-Duy
name: Nguyen, Vinh-Tiep
name: Ngo, Thanh Duc
name: Do, Thanh-Toan
name: Tran, Minh-Triet
name: Nguyen, Tam V.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data on concealed objects such as camouflaged animals in natural scenes. In this paper, we address the problem of few-shot learning for camouflaged object detection and segmentation. To this end, we first collect a new dataset, CAMO-FS, for the benchmark. As camouflaged instances are challenging to recognize due to their similarity compared to the surroundings, we guide our models to obtain camouflaged features that highly distinguish the instances from the background. In this work, we propose FS-CDIS, a framework to efficiently detect and segment camouflaged instances via two loss functions contributing to the training process. Firstly, the instance triplet loss with the characteristic of differentiating the anchor, which is the mean of all camouflaged foreground points, and the background points are employed to work at the instance level. Secondly, to consolidate the generalization at the class level, we present instance memory storage with the scope of storing camouflaged features of the same category, allowing the model to capture further class-level information during the learning process. The extensive experiments demonstrated that our proposed method achieves state-of-the-art performance on the newly collected dataset. Code is available at https://github.com/danhntd/FS-CDIS.
description: IEEE Access 2024
On the vanishing of (co)homology for modules admitting certain filtrations
Creators: name: Celikbas, Olgur
name: Yao, Yongwei
Publisher: name: arXiv
Year: 2023
Subjects: subject: Commutative Algebra (math.AC)
subject: FOS: Mathematics
subject: 13D07 (Primary), 13A35, 13D05, 13C12 (Secondary)

description: We study the vanishing of (co)homology along ring homomorphisms for modules that admit certain filtrations, and generalize a theorem of O. Celikbas-Takahashi. Our work produces new classes of rigid and test modules, in particular over local rings of prime characteristic. It also gives applications in the study of torsion in tensor products of modules, for example, concerning the Huneke-Wiegand conjecture.
Strong-randomness renormalization groups
Creators: name: Huse, David A.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Disordered Systems and Neural Networks (cond-mat.dis-nn)
subject: FOS: Physical sciences

description: This is a very brief review article, written for a book (in preparation) in memory of Michael E. Fisher and to celebrate 50+ years since the Wilson-Fisher renormalization group. Strong-randomness renormalization groups were first developed to treat various quantum critical ground states, especially in one-dimensional systems. After briefly reviewing some of the earlier work with these methods, the recent application of this approach to the many-body localization (MBL) phase transition is reviewed.
description: Brief review
Temporal rich club phenomenon and its formation mechanisms
Creators: name: Li, Mu-Yao
name: Zhang, Ying-Ting
name: Zhou, Wei-Xing
Publisher: name: arXiv
Year: 2023
Subjects: subject: Physics and Society (physics.soc-ph)
subject: FOS: Physical sciences

description: The temporal rich club (TRC) phenomenon is widespread in real systems, forming a tight and continuous collection of the prominent nodes that control the system. However, there is still a lack of sufficient understanding of the mechanisms of TRC formation. Here we use the international N-nutrient trade network as an example of an in-depth identification, analysis, and modeling of its TRC phenomenon. The system exhibits a statistically significant TRC phenomenon, with eight economies forming the cornerstone club. Our analysis reveals that node degree is the most influential factor in TRC formation compared to other variables. The mathematical evolution models we constructed propose that the TRC in the N-nutrient trade network arises from the coexistence of degree-homophily and path-dependence mechanisms. By comprehending these mechanisms, we introduce a novel perspective on TRC formation. Although our analysis is limited to the international trade system, the methodology can be extended to analyze the mechanisms underlying TRC emergence in other systems.
Molecular simulation from modern statistics: Continuous-time, continuous-space, exact
Creators: name: Hoellmer, Philipp
name: Maggs, A. C.
name: Krauth, Werner
Publisher: name: arXiv
Year: 2023
Subjects: subject: Chemical Physics (physics.chem-ph)
subject: Computational Physics (physics.comp-ph)
subject: FOS: Physical sciences

description: In a world made of atoms, the computer simulation of molecular systems, such as proteins in water, plays an enormous role in science. Software packages that perform these computations have been developed for decades. In molecular simulation, Newton's equations of motion are discretized and long-range potentials are treated through cutoffs or spacial discretization, which all introduce approximations and artifacts that must be controlled algorithmically. Here, we introduce a paradigm for molecular simulation that is based on modern concepts in statistics and is rigorously free of discretizations, approximations, and cutoffs. Our demonstration software reaches a break-even point with traditional molecular simulation at high precision. We stress the promise of our paradigm as a gold standard for critical applications and as a future competitive approach to molecular simulation.
description: 19 pages, 4 figures; 18 pages supplementary materials, 1 supplementary figure
Understanding the impacts of emission trading systems and carbon border adjustment mechanisms on Scottish business
Creators: name: Lavric, Lucia
name: Woods, Duncan
name: Johnson, Mark
name: Budaragina, Marianna
name: Jones-Morris, Catherine
name: Colonna, Francesco
Publisher: name: Ricardo Energy and Environment
Year: 2022
Subjects: Emission trading system
carbon border adjustment mechanism
ETS
CBAM
Scottish business
economic assessment
EU CBAM
impacts

description: This report develops an approach to assess the impacts of Emission Trading Systems and Carbon Border Adjustment Mechanisms on Scottish business.
Autonomia privata ed emissione di obbligazioni nella società per azioni
Creators: name: CIOCCA, NICOLETTA
Publisher: name: Università degli Studi di Roma "Tor Vergata"
Year: 2008
Subjects: subject: società per azioni; rimborso condizionato all’andamento economico della società; obbligazioni; strumenti finanziari partecipativi; bonds; investitori professoniali soggetti a vigilanza prudenziale; art. 2411 c.c.; art. 2412 c.c.; reverse convertible bonds; obbligazioni subordinate
subject: società per azioni
subject: rimborso condizionato all’andamento economico della società
subject: obbligazioni
subject: strumenti finanziari partecipativi
subject: bonds
subject: investitori professoniali soggetti a vigilanza prudenziale
subject: art. 2411 c.c.
subject: art. 2412 c.c.
subject: reverse convertible bonds
subject: obbligazioni subordinate

description: 18. ciclo
The Whole Is Greater than the Sum of Its Parts: Improving Music Source Separation by Bridging Network
Creators: name: Sawata, Ryosuke
name: Takahashi, Naoya
name: Uhlich, Stefan
name: Takahashi, Shusuke
name: Mitsufuji, Yuki
Publisher: name: arXiv
Year: 2023
Subjects: subject: Audio and Speech Processing (eess.AS)
subject: Sound (cs.SD)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences

description: This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) with almost no increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation, which couples the individual instrument networks, and (iii) combination loss (CL). MDL enables the taking advantage of the frequency- and time-domain representations of audio signals. We modify the target network, i.e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information. MDL is then applied to the combinations of the output sources as well as each independent source; hence, we called it CL. MDL and CL can easily be applied to many DNN-based separation methods as they are merely loss functions that are only used during training and do not affect the inference step. Bridging operation does not increase the number of learnable parameters in the network. Experimental results showed that the validity of Open-Unmix (UMX), densely connected dilated DenseNet (D3Net) and convolutional time-domain audio separation network (Conv-TasNet) extended with our X-scheme, respectively called X-UMX, X-D3Net and X-Conv-TasNet, by comparing them with their original versions. We also verified the effectiveness of X-scheme in a large-scale data regime, showing its generality with respect to data size. X-UMX Large (X-UMXL), which was trained on large-scale internal data and used in our experiments, is newly available at https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX.
description: Acceptedt by EURASIP Journal on Audio, Speech, and Music Processing (under CC BY)
Pancakification and negative Hawking temperatures
Creators: name: McMaken, Tyler
Publisher: name: arXiv
Year: 2023
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: Vacuum models of charged or spinning black holes possess two horizons, the inner of which has the oft-overlooked property that gravitational tidal forces initially spaghettifying a freely falling observer will eventually change signs and flatten the observer like a pancake. Inner horizons also induce a classical blueshift instability known as mass inflation, and a number of recent studies have found that inner horizons exhibit even stronger quantum singular behavior. In this essay we explore the quantum effect of Hawking radiation, which in the presence of compressive tidal forces seems to predict negative temperatures. By analyzing the interaction of quantum fields with black hole geometries, we can come to a closer semiclassical understanding of what really happens near a black hole's inner horizon.
description: Essay written for the Gravity Research Foundation 2023 Awards for Essays on Gravitation (received Honorable Mention); 11 pages, 1 figure
Laser Beam Propagation Features via Atmospheric Turbulence-Induced Beam Wander: Testbed Conceptual Design
Creators: name: Al-Juboori, Haider M.
name: Garland, James
Publisher: name: Proceedings of the COAT-2023 Workshop (Communications and Observations through Atmospheric Turbulence). Durham, UK, 29-31 March 2024
Year: 2023
Subjects: MODELLING AND SIMULATION FOR OFSC
EMULATION GEO SATELLITE LINKS
ATMOSPHERIC TURBULENCE IMPACT AND MITIGATION

description: One of the promising technologies for sending high data rates over long distances with compact antennas and low power usage is optical free-space communications (OFSC). The high-speed links between satellites, up-and down-links from and to ground stations, and other links between aeronautical vehicles or in deep space communications (DSC) are all potential scenarios for future applications. Various effects severely degrade the signal and capabilities of OFSC, such as laser beam spreading, wandering and scintillation. The first part of our study discusses the emulation concepts of wandering impact. Then, we use the PILab numerical simulation platform to visualize the effective parameters and simulate atmospheric optical propagation especially for wandering effect. Noteworthy, machine learning (ML) algorithms have recently been considered promising approaches for solving highorder nonlinear equations and comparing the activation function for high-order equations. We propose a laboratory testbed with a novel wavefront aberration compensation technique using Machine Learning, especially ML have a good chance to support developing and testing processes related to adaptive nonlinear characterization for OFSC systems. The turbulence generator\'s development, the testbed\'s concept, and its optical design are illustrated. The conceptual principles of the testbed make it possible to reproduce test scenarios with realistic ratios of aperture size over the Fried parameter.