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We consider a population spreading across a finite number of sites. Individuals can move from one site to the other according to a network (oriented links between the sites) that vary periodically over time. On each site, the population experiences a growth rate which is also periodically time varying. Recently, this kind of models have been extensively studied, using various technical tools to derive precise necessary and sufficient conditions on the parameters of the system (ie the local growth rate on each site, the time period and the strength of migration between the sites) for the population to grow. In the present paper, we take a completely different approach: using elementary comparison results between linear systems, we give sufficient condition for the growth of the population This condition is easy to check and can be applied in a broad class of examples. In particular, in the case when all sites are sinks (ie, in the absence of migration, the population become extinct in each site), we prove that when our condition of growth if satisfied, the population grows when the time period is large and for values of the migration strength that are exponentially small with respect to the time period, which answers positively to a conjecture stated by Katriel., Comment: 43 pages

CTC compressor can be an effective approach to integrate audio encoders to decoder-only models, which has gained growing interest for different speech applications. In this work, we propose a novel CTC compressor based joint speech and text training (CJST) framework for decoder-only ASR. CJST matches speech and text modalities from both directions by exploring a simple modality adaptor and several features of the CTC compressor, including sequence compression, on-the-fly forced peaky alignment and CTC class embeddings. Experimental results on the Librispeech and TED-LIUM2 corpora show that the proposed CJST achieves an effective text injection without the need of duration handling, leading to the best performance for both in-domain and cross-domain scenarios. We also provide a comprehensive study on CTC compressor, covering various compression modes, edge case handling and behavior under both clean and noisy data conditions, which reveals the most robust setting to use CTC compressor for decoder-only models., Comment: submitted to ICASSP2025

YbCu4Au is a unique material exhibiting multiple quantum fluctuations simultaneously. In this study, we investigated the field-induced criticality in YbCu4Au, based on comprehensive micro and macro measurements, including powder X-ray diffraction (XRD), neutron powder diffraction (NPD), nuclear magnetic resonance, magnetization, resistivity, specific heat, muon spin rotation relaxation (muSR), and X-ray absorption spectroscopy (XAS). Single crystals of YbCu4Au were grown, and their crystal structure was determined using XRD, and NPD measurements. Magnetic successive transitions were observed below 1 T by specific heat, resistivity, NPD, and muSR measurements. XAS measurements further indicate that the valence of Yb ions (+2.93) remained unchanged above 2 T. Moreover, the change in quadrupole frequency observed in the previous study is attributable to the electric quadrupole, as the expected value of the electric quadrupole was finite under magnetic fields [S. Wada et al., Journal of Physics: Condensed Matter, 20, 175201 (2008).]. These experimental results suggest that YbCu4Au exhibited bicritical behavior near 1 T, arising from the competition between RKKY interaction, accounting for the magnetic phases, and the Zeeman effect., Comment: 9 pages, 8 figures

As speech becomes an increasingly common modality for interacting with large language models (LLMs), it is becoming desirable to develop systems where LLMs can take into account users' emotions or speaking styles when providing their responses. In this work, we study the potential of an LLM to understand these aspects of speech without fine-tuning its weights. To do this, we utilize an end-to-end system with a speech encoder; the encoder is trained to produce token embeddings such that the LLM's response to an expressive speech prompt is aligned with its response to a semantically matching text prompt where the speaker's emotion has also been specified. We find that this training framework allows the encoder to generate tokens that capture both semantic and paralinguistic information in speech and effectively convey it to the LLM, even when the LLM remains completely frozen. We also explore training on additional emotion and style-related response alignment tasks, finding that they further increase the amount of paralinguistic information explicitly captured in the speech tokens. Experiments demonstrate that our system is able to produce higher quality and more empathetic responses to expressive speech prompts compared to several baselines.

We introduce a new sphericalization mapping for metric spaces that is applicable in very general situations, including totally disconnected fractal type sets. For an unbounded complete metric space which is uniformly perfect at a base point for large radii and equipped with a doubling measure, we make a more specific construction based on the measure and equip it with a weighted measure. This mapping is then shown to preserve the doubling property of the measure and the Besov (fractional Sobolev) energy. The corresponding results for flattening of bounded complete metric spaces are also obtained. Finally, it is shown that for the composition of a sphericalization with a flattening, or vice versa, the obtained space is biLipschitz equivalent with the original space and the resulting measure is comparable to the original measure.

Most of the tracking detectors for high energy particle experiments are filled with silicon detectors since they are radiation hard, they can give very small spatial resolution and they can take advantage of the silicon electronics foundries developments and production lines. Strip detectors are very useful to cover large areas for tracking purposes, while consuming less power per area compared to pixel sensors. The majority of particle physics experiments use conventional silicon strip detectors fabricated in foundries that do not use stitching, relying on a very small number of foundries worldwide that can provide large amounts of strip detectors. Fabricating strip detectors in a CMOS foundry opens the possibility to use more foundries and to include active elements in the strips for future productions. For the passive CMOS strip detectors project we fabricated strip detectors in a CMOS foundry using two 1 cm2 reticles that are stitched together along the wafer. The fabricated strips stitched the reticles three and five times, and it was shown that the performance of those strips is not affected by the stitching. This paper shows 3D TCAD simulations of the stitching area to investigate the possible effects stitching can have on the performance of the strip detectors, considering different stitching mismatches. We will show that the mismatch of stitched structures up to 1 um does not impact the performance with TCAD simulations which agrees with the results obtained from the measurements., Comment: 8 pages, 7 figures, IWORID2024 workshop

As the share of Distributed energy resources (DER) in the low voltage distribution network (DN) is expected to rise, a higher and more variable electric load and generation could stress the DNs, leading to increased congestion and power losses. To address these challenges, DSOs will have to invest in strengthening the network infrastructure in the coming decade. This paper looks to minimize the need for flexibility through dynamic DN reconfiguration. Typically, European DNs predominantly use manual switches. Hence, the network configuration is set for longer periods of time. Therefore, an opportunity is missed to benefit from more short-term dynamic switching. In this paper, a method is proposed which identifies the best manual switches to replace with remotely controlled switches based on their performance in terms of avoided voltage congestion incidents and DN power losses. The developed method is an exhaustive search algorithm which divides the problem into 3 subsequent parts, i.e. radial configuration identification, multi-period power flow and impact assessment for reconfigurable switch replacement on DN operation. A numerical evaluation shows that replacing the two top-ranked switches in the test case reduced the power losses by 4.51% and the voltage constraint violations by 38.17%. Thus, investing in only a few reconfigurable switches can substantially improve the operational efficiency of DNs.

In contrast to human vision, artificial neural networks (ANNs) remain relatively susceptible to adversarial attacks. To address this vulnerability, efforts have been made to transfer inductive bias from human brains to ANNs, often by training the ANN representations to match their biological counterparts. Previous works relied on brain data acquired in rodents or primates using invasive techniques, from specific regions of the brain, under non-natural conditions (anesthetized animals), and with stimulus datasets lacking diversity and naturalness. In this work, we explored whether aligning model representations to human EEG responses to a rich set of real-world images increases robustness to ANNs. Specifically, we trained ResNet50-backbone models on a dual task of classification and EEG prediction; and evaluated their EEG prediction accuracy and robustness to adversarial attacks. We observed significant correlation between the networks' EEG prediction accuracy, often highest around 100 ms post stimulus onset, and their gains in adversarial robustness. Although effect size was limited, effects were consistent across different random initializations and robust for architectural variants. We further teased apart the data from individual EEG channels and observed strongest contribution from electrodes in the parieto-occipital regions. The demonstrated utility of human EEG for such tasks opens up avenues for future efforts that scale to larger datasets under diverse stimuli conditions with the promise of stronger effects.

We present X-ray to radio frequency observations of the bright long gamma-ray burst GRB 210702A. Our ALMA 97.5 GHz observations show a significant rebrightening by a factor of ~2 beginning at 8.2 days post-burst and rising to peak brightness at 18.1 days before declining again. This is the first such rebrightening seen in a millimeter afterglow light curve. A standard forward shock model in a stellar wind circumburst medium can explain most of our X-ray, optical and millimeter observations prior to the rebrightening, but significantly over-predicts the self-absorbed radio emission, and cannot explain the millimeter rebrightening. We investigate possible explanations for the millimeter rebrightening and find that energy injection or a reverse shock from a late-time shell collision are plausible causes. Similar to other bursts, our radio data may require alternative scenarios such as a thermal electron population or a structured jet to explain the data. Our observations demonstrate that millimeter light curves can exhibit some of the rich features more commonly seen in optical and X-ray afterglow light curves, motivating further millimeter wavelength studies of GRB afterglows., Comment: 22 pages, 10 figures, accepted for publication in ApJ

AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfakes, producing highly realistic yet fabricated content. While these technologies open up new creative possibilities, they also bring substantial ethical and security risks due to their potential misuse. The rise of such advanced media has led to the development of a cognitive bias known as Impostor Bias, where individuals doubt the authenticity of multimedia due to the awareness of AI's capabilities. As a result, Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques, especially Convolutional Neural Networks (CNNs). Research in forensic Deepfake technology encompasses five main areas: detection, attribution and recognition, passive authentication, detection in realistic scenarios, and active authentication. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.

Urban traffic management faces significant challenges due to the dynamic environments, and traditional algorithms fail to quickly adapt to this environment in real-time and predict possible conflicts. This study explores the ability of a Large Language Model (LLM), specifically, GPT-4o-mini to improve traffic management at urban intersections. We recruited GPT-4o-mini to analyze, predict position, detect and resolve the conflicts at an intersection in real-time for various basic scenarios. The key findings of this study to investigate whether LLMs can logically reason and understand the scenarios to enhance the traffic efficiency and safety by providing real-time analysis. The study highlights the potential of LLMs in urban traffic management creating more intelligent and more adaptive systems. Results showed the GPT-4o-mini was effectively able to detect and resolve conflicts in heavy traffic, congestion, and mixed-speed conditions. The complex scenario of multiple intersections with obstacles and pedestrians saw successful conflict management as well. Results show that the integration of LLMs promises to improve the effectiveness of traffic control for safer and more efficient urban intersection management.

The AMoRE collaboration searches for neutrinoless double beta decay of $^{100}$Mo using molybdate scintillating crystals via low temperature thermal calorimetric detection. The early phases of the experiment, AMoRE-pilot and AMoRE-I, have demonstrated competitive discovery potential. Presently, the AMoRE-II experiment, featuring a large detector array with about 90 kg of $^{100}$Mo isotope, is under construction.This paper discusses the baseline design and characterization of the lithium molybdate cryogenic calorimeters to be used in the AMoRE-II detector modules. The results from prototype setups that incorporate new housing structures and two different crystal masses (316 g and 517 - 521 g), operated at 10 mK temperature, show energy resolutions (FWHM) of 7.55 - 8.82 keV at the 2.615 MeV $^{208}$Tl $\gamma$ line, and effective light detection of 0.79 - 0.96 keV/MeV. The simultaneous heat and light detection enables clear separation of alpha particles with a discrimination power of 12.37 - 19.50 at the energy region around $^6$Li(n, $\alpha$)$^3$H with Q-value = 4.785 MeV. Promising detector performances were demonstrated at temperatures as high as 30 mK, which relaxes the temperature constraints for operating the large AMoRE-II array.

This paper is a follow-up to a previous work where we considered populations with time-varying growth rates living in patches and irreducible migration matrix between the patches. Each population, when isolated, would become extinct. Dispersal-induced growth (DIG) occurs when the populations are able to persist and grow exponentially when dispersal among the populations is present. We provide a mathematical analysis of this phenomenon, in the context of a deterministic model with periodic variation of growth rates and migration. The migration matrix can be reducible, so that the results apply in the case, important for applications, where there is migration in one direction in one season and in the other direction in another season. We also consider dispersal-induced decay (DID), where each population, when isolated, grows exponentially, while populations die out when dispersal between populations is present., Comment: 29 pages, 17 figures

AMoRE searches for the signature of neutrinoless double beta decay of $^{100}$Mo with a 100 kg sample of enriched $^{100}$Mo. Scintillating molybdate crystals coupled with a metallic magnetic calorimeter operate at milli-Kelvin temperatures to measure the energy of electrons emitted in the decay. As a demonstration of the full-scale AMoRE, we conducted AMoRE-I, a pre-experiment with 18 molybdate crystals, at the Yangyang Underground Laboratory for over two years. The exposure was 8.02 kg$\cdot$year (or 3.89 kg$_{\mathrm{^{100}Mo}}\cdot$year) and the total background rate near the Q-value was 0.025 $\pm$ 0.002 counts/keV/kg/year. We observed no indication of $0\nu\beta\beta$ decay and report a new lower limit of the half-life of $^{100}$Mo $0\nu\beta\beta$ decay as $ T^{0\nu}_{1/2}>3.0\times10^{24}~\mathrm{years}$ at 90\% confidence level. The effective Majorana mass limit range is $m_{\beta\beta}<$(210--610) meV using nuclear matrix elements estimated in the framework of different models, including the recent shell model calculations., Comment: 8 pages, 5 figures

Supermassive black holes and their surrounding dense stellar environments nourish a variety of astrophysical phenomena. We focus on the distribution of stellar-mass black holes around the supermassive black hole and the consequent formation of extreme mass-ratio inspirals (EMRIs). We derive a steady-state distribution, considering the effects of two-body scatterings and gravitational wave emission, and calculate the EMRI formation rate, eccentricity distribution and EMRI-to-plunge ratio. Our model predicts: (I) A stronger segregation than previously estimated at the outskirts of the sphere of influence (at $\sim0.01\rm pc$ to $2\rm pc$ for a Milky Way-like galaxy). (II) An increased EMRI-to-plunge ratio, favoring EMRIs at galaxies where stellar-mass black holes are scarce. (III) A detection of about $2\times10^3$ resolvable EMRIs, with a signal-to-noise ratio above $20$, along a $4$ year LISA mission time. (IV) A confusion noise, induced by a cosmological population of unresolved EMRIs, reducing LISA sensitivity in the $1-5\ \rm mHz$ frequency range by up to a factor of $\approx2$, relative to the instrumental noise., Comment: 15 pages, 6 figures. Submitted to ApJ

AMoRE-II aims to search for neutrinoless double beta decay with an array of 423 Li$_2$$^{100}$MoO$_4$ crystals operating in the cryogenic system as the main phase of the Advanced Molybdenum-based Rare process Experiment (AMoRE). AMoRE has been planned to operate in three phases: AMoRE-pilot, AMoRE-I, and AMoRE-II. AMoRE-II is currently being installed at the Yemi Underground Laboratory, located approximately 1000 meters deep in Jeongseon, Korea. The goal of AMoRE-II is to reach up to $T^{0\nu\beta\beta}_{1/2}$ $\sim$ 6 $\times$ 10$^{26}$ years, corresponding to an effective Majorana mass of 15 - 29 meV, covering all the inverted mass hierarchy regions. To achieve this, the background level of the experimental configurations and possible background sources of gamma and beta events should be well understood. We have intensively performed Monte Carlo simulations using the GEANT4 toolkit in all the experimental configurations with potential sources. We report the estimated background level that meets the 10$^{-4}$counts/(keV$\cdot$kg$\cdot$yr) requirement for AMoRE-II in the region of interest (ROI) and show the projected half-life sensitivity based on the simulation study.

Recently, with the rapid development in the fields of technology and the increasing amount of text t available on the internet, it has become urgent to develop effective tools for processing and understanding texts in a way that summaries the content without losing the fundamental essence of the information. Given this challenge, we have developed an advanced text summarization system targeting Arabic textbooks. Relying on modern natu-ral language processing models such as MT5, AraBART, AraT5, and mBART50, this system evaluates and extracts the most important sentences found in biology textbooks for the 11th and 12th grades in the Palestinian curriculum, which enables students and teachers to obtain accurate and useful summaries that help them easily understand the content. We utilized the Rouge metric to evaluate the performance of the trained models. Moreover, experts in education Edu textbook authoring assess the output of the trained models. This approach aims to identify the best solutions and clarify areas needing improvement. This research provides a solution for summarizing Arabic text. It enriches the field by offering results that can open new horizons for research and development in the technologies for understanding and generating the Arabic language. Additionally, it contributes to the field with Arabic texts through creating and compiling schoolbook texts and building a dataset.

We study the dynamics of nuclear star clusters, the dense stellar environments surrounding massive black holes in the centers of galaxies. We consider angular momentum diffusion due to two-body scatterings among stellar objects and energy advection due to gravitational wave emission upon interaction with the central massive black hole. Such dynamics is described by a two-dimensional Fokker-Planck equation in energy-angular momentum space. Focusing on the transition between the diffusion-dominated region and the advection-dominated one, we utilize self-similarity to obtain a full solution for the Fokker-Planck equation. This solution provides the density and flux of the stellar objects in nuclear star clusters. This improves the rate estimates for extreme mass-ratio inspirals, and has interesting implications for a new class of galactic center transients called quasi-periodic eruptions., Comment: Submitted to ApJ; comments are welcome

In high-energy physics, there is a need to investigate alternative silicon sensor concepts that offer cost-efficient, large-area coverage. Sensors based on CMOS imaging technology present such a silicon sensor concept for tracking detectors. The CMOS Strips project investigates passive CMOS strip sensors fabricated by LFoundry in a 150nm technology. By employing the technique of stitching, two different strip sensor formats have been realised. The sensor performance is characterised based on measurements at the DESY II Test Beam Facility. The sensor response was simulated utilising Monte Carlo methods and electric fields provided by TCAD device simulations. This study shows that employing the stitching technique does not affect the hit detection efficiency. A first look at the electric field within the sensor and its impact on generated charge carriers is being discussed., Comment: 5 pages, 6 figures, submitted to Nuclear Instruments and Methods in Physics Research, Section A as a conference proceeding for the 13th International "Hiroshima" Symposium on the Development and Application of Semiconductor Tracking Detectors

The vanishing inertia of synchronous generators in transmission systems requires the utilization of renewables for inertial support. These are often connected to the distribution system and their support should be coordinated to avoid violation of grid limits. To this end, this paper presents the Physics-informed Actor-Critic (PI-AC) algorithm for coordination of Virtual Inertia (VI) from renewable Inverter-based Resources (IBRs) in power distribution systems. Acquiring a model of the distribution grid can be difficult, since certain parts are often unknown or the parameters are highly uncertain. To favor model-free coordination, Reinforcement Learning (RL) methods can be employed, necessitating a substantial level of training beforehand. The PI-AC is a RL algorithm that integrates the physical behavior of the power system into the Actor-Critic (AC) approach in order to achieve faster learning. To this end, we regularize the loss function with an aggregated power system dynamics model based on the swing equation. Throughout this paper, we explore the PI-AC functionality in a case study with the CIGRE 14-bus and IEEE 37-bus power distribution system in various grid settings. The PI-AC is able to achieve better rewards and faster learning than the exclusively data-driven AC algorithm and the metaheuristic Genetic Algorithm (GA)., Comment: Submitted to IET GTD

This paper presents the methodology and key results which enabled to establish the so-called Ancillary Service Matrix (ASM) presenting the ability to deliver the different ancillary services of each of the 6 demonstrators of the XFLEX HYDRO research project combined with the applicable technologies studied in this analysis. These technologies include i) the variable speed technology with Doubly Fed Induction Machine (DFIM) or Full Size Frequency Converters (FSFC), ii) the Smart Power Plant Supervisor (SPPS) enabling to extend the operating range of the hydraulic units in turbine mode based on a better knowledge of the hydro unit wear and tear and associated costs over the full unit operating range, iii) the hydraulic short circuit (HSC) operation leading to simultaneous operation of pump and turbines of Pumped Storage Power Plants (PSPP) and iv) the Hydro-Battery-Hybrid (HBH) applied at Run-of-River demonstrator. The demonstrators considered for this study includes 4 pumped storage power plants, 1 conventional hydro storage plant and 1 run-of-the-river plant. For each demonstrator a 1D simulation model was developed and validated and was further enhanced to include the model of control system enabling to address the various ancillary services. The systematic 1D numerical simulation of ancillary service contribution of each demonstrator and related technologies enabled to quantify the magnitude of active power response to contribute to the different grid services. The results have been scored between 0 and 5 for each ancillary service, allowing to populate the Ancillary Service Matrix which is summarizing the results in a graphical and synthetic way. The analysis of the score of the Ancillary Services Matrix enables the reader to draw several key conclusions about the benefits unlocked by the implementation of these technologies which are summarized in the paper., Comment: 25 pages, Proceedings of HYDRO 2023, International Conference, October 16-18, 2023, Edinburgh, Scotland

We present two practical improvement techniques for unsupervised segmentation learning. These techniques address limitations in the resolution and accuracy of predicted segmentation maps of recent state-of-the-art methods. Firstly, we leverage image post-processing techniques such as guided filtering to refine the output masks, improving accuracy while avoiding substantial computational costs. Secondly, we introduce a multi-scale consistency criterion, based on a teacher-student training scheme. This criterion matches segmentation masks predicted from regions of the input image extracted at different resolutions to each other. Experimental results on several benchmarks used in unsupervised segmentation learning demonstrate the effectiveness of our proposed techniques.

Neural Networks (NNs) are increasingly used in the last decade in several demanding applications, such as object detection and classification, autonomous driving, etc. Among different computing platforms for implementing NNs, FPGAs have multiple advantages due to design flexibility and high performance-to-watt ratio. Moreover, approximation techniques, such as quantization, have been introduced, which reduce the computational and storage requirements, thus enabling the integration of larger NNs into FPGA devices. On the other hand, FPGAs are sensitive to radiation-induced Single Event Upsets (SEUs). In this work, we perform an in-depth reliability analysis in an FPGA-based Binarized Fully Connected Neural Network (BNN) accelerator running a statistical fault injection campaign. The BNN benchmark has been produced by FINN, an open-source framework that provides an end-to-end flow from abstract level to design, making it easy to design customized FPGA NN accelerators, while it also supports various approximation techniques. The campaign includes the injection of faults in the configuration memory of a state-of-the-art Xilinx Ultrascale+ FPGA running the BNN, as well an exhaustive fault injection in the user flip flops. We have analyzed the fault injection results characterizing the SEU vulnerability of the circuit per network layer, per clock cycle, and register. In general, the results show that the BNNs are inherently resilient to soft errors, since a low portion of SEUs in the configuration memory and the flip flops, cause system crashes or misclassification errors., Comment: 7 pages, 5 figures, 4 tables. 2021 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT). IEEE, 2021

We construct a reflexive Banach space $X_\mathcal{D}$ with an unconditional basis such that all spreading models admitted by normalized block sequences in $X_\mathcal{D}$ are uniformly equivalent to the unit vector basis of $\ell_1$, yet every infinite-dimensional closed subspace of $X_\mathcal{D}$ fails the Lebesgue property. This is a new result in a program initiated by Odell in 2002 concerning the strong separation of asymptotic properties in Banach spaces., Comment: 23 pages

A new type of self-similarity is found in the problem of a plane-parallel, ultra-relativistic blast wave, propagating in a powerlaw density profile of the form $\rho \propto z^{-k}$. Self-similar solutions of the first kind can be found for $k<7/4$ using dimensional considerations. For steeper density gradients with $k>2$, second type solutions are obtained by eliminating a singularity from the equations. However, for intermediate powerlaw indices $7/4

Machine learning is a rapidly evolving field with a wide range of applications, including biological signal analysis, where novel algorithms often improve the state-of-the-art. However, robustness to algorithmic variability - measured by different algorithms, consistently uncovering similar findings - is seldom explored. In this paper we investigate whether established hypotheses in brain-age prediction from EEG research validate across algorithms. First, we surveyed literature and identified various features known to be informative for brain-age prediction. We employed diverse feature extraction techniques, processing steps, and models, and utilized the interpretative power of SHapley Additive exPlanations (SHAP) values to align our findings with the existing research in the field. Few of our models achieved state-of-the-art performance on the specific data-set we utilized. Moreover, analysis demonstrated that while most models do uncover similar patterns in the EEG signals, some variability could still be observed. Finally, a few prominent findings could only be validated using specific models. We conclude by suggesting remedies to the potential implications of this lack of robustness to model variability.

We report a study on the background of the Advanced Molybdenum-Based Rare process Experiment (AMoRE), a search for neutrinoless double beta decay (\znbb) of $^{100}$Mo. The pilot stage of the experiment was conducted using $\sim$1.9 kg of \CAMOO~ crystals at the Yangyang Underground Laboratory, South Korea, from 2015 to 2018. We compared the measured $\beta/\gamma$ energy spectra in three experimental configurations with the results of Monte Carlo simulations and identified the background sources in each configuration. We replaced several detector components and enhanced the neutron shielding to lower the background level between configurations. A limit on the half-life of $0\nu\beta\beta$ decay of $^{100}$Mo was found at $T_{1/2}^{0\nu} \ge 3.0\times 10^{23}$ years at 90\% confidence level, based on the measured background and its modeling. Further reduction of the background rate in the AMoRE-I and AMoRE-II are discussed.

Existence of 1D spin diffusion in the electrochemical sugar recognition system consisting of a nano-sized gold particle (GNP), a ruthenium complex and a phenylboronic acid was investigated by NMR and muSR. When sugar molecules are recognized by the phenylboronic site, the response of electrochemical voltammetry of the Ru site changes, enabling the system to work as a sensitive sugar-sensor. In this recognition process, the change in the electronic state at the boron site caused by sugar must be transferred to the Ru site via alkyl chains. We have utilized the muon-labelled electrons method and the proton NMR to find out a channel of the electron transfer from the phenylboronic acid site to the gold nano particle via the one dimensional alkyl chain. If this transfer is driven by diffusive spin channel, characteristic field dependence is expected in the longitudinal spin relaxation rate of muSR and 1H-NMR. We have observed significant decrease in the spin relaxation rates with increasing applied field. The result is discussed in terms of low dimensional spin diffusion.

We report on the quantum criticality of YbCu$_4$Ni as revealed by our combined micro- and macro-measurements. We determine the crystal structure of YbCu$_4$Ni with site mixing by neutron diffraction measurements, which suggests the possible presence of Kondo disorder. However, decreasing the local spin susceptibility distribution and the development of spin fluctuations below 10 K at ambient pressure by muon spin rotation and relaxation measurements suggests that YbCu4Ni exhibits quantum criticality. Therefore, our experimental results indicate that YbCu4Ni is a new material that exhibits quantum criticality under a zero magnetic field and ambient pressure., Comment: 8 pages, 5 figures

In this paper, we consider a flocculation model in a chemostat where one species is present in two forms: planktonic and aggregated bacteria with the presence of a single resource. The removal rates of isolated and attached bacteria are distinct and include the specific death rates. Considering distinct yield coefficients with a large class of growth rates, we present a mathematical analysis of the model by establishing the necessary and sufficient conditions of the existence and local asymptotic stability of all steady states according to the two operating parameters which are the dilution rate and the inflowing concentration of the substrate. Using these conditions, we determine first theoretically the operating diagram of the flocculation process describing the asymptotic behavior of the system with respect to two control parameters. The bifurcations analysis shows a rich set of possible types of bifurcations: transcritical bifurcation or branch points of steady states, saddle-node bifurcation or limit points of steady states, Hopf, and homoclinic bifurcations. Using the numerical method with MATCONT software based on a continuation and correction algorithm, we find the same operating diagram obtained theoretically. However, MATCONT detects other types of two-parameter bifurcations such as Bogdanov-Takens and Cusp bifurcations.

A network of chaotic systems can be designed in such a way that the cluster patterns formed by synchronous nodes can be controlled through the coupling parameters. We present a novel approach to exploit such a network for covert communication purposes, where controlled clusters encode the symbols spatio-temporally. The cluster synchronization network is divided into two subnetworks as transmitter and receiver. First, we specifically design the network whose controlled parameters reside in the transmitter. Second, we ensure that the nodes of the links connecting the transmitter and receiver are not in the same clusters for all the control parameters. The former condition ensures that the control parameters changed at the transmitter change the whole clustering scheme. The second condition enforces the transmitted signals are always continuous and chaotic. Hence, the transmitted signals are not modulated by the information directly, but distributed over the links connecting the subnetworks. The information cannot be deciphered by eavesdropping on the channel links without knowing the network topology. The performance has been assessed by extensive simulations of bit error rates under noisy channel conditions.

Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441)., Comment: Accepted by NAACL 2024

We consider populations with time-varying growth rates living in sinks. Each population, when isolated, would become extinct. Dispersal-induced growth (DIG) occurs when the populations are able to persist and grow exponentially when dispersal among the populations is present. We provide a mathematical analysis of this surprising phenomenon, in the context of a deterministic model with periodic variation of growth rates and non-symmetric migration which are assumed to be piecewise continuous. We also consider a stochastic model with random variation of growth rates and migration. This work extends existing results of the literature on the DIG effects obtained for periodic continuous growth rates and time independent symmetric migration., Comment: 61 pages, 15 figures