Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Microplastic Classifiers using Linear Hyperspectral Mixture Images

Hyperspectral images of microplastic samples can, under certain conditions, contain mixed spectra, for example when the resolution of the imaging instrument is lower than the particle dimensions. This work presents a tool for the evaluation of the performance of a microplastic classifier when faced with spectral mixtures and noisy data. Linear hyperspectral mixture images comprising mixed spectra with varying amounts of added noise are created from two selected spectra and subsequently classified. The classified mixture images are translated into features that facilitate the analysis of the classifier under examination. Among these features are the parameters defining the logistic function, that fits the response values of a classified mixture image at a particular noise level. Three random forest classifiers with different training sets and features were evaluated. Two of them use manually selected spectral descriptors (SPDCs) as features, while the last classifier works with automatically selected SPDCs. One of the first two classifiers, named experts classifier, was trained on a significantly larger training set compared to the other two. The experiments conducted suggest that the features ”offset” and ”midpoint”, describing logistic function parameters, capture the behavior of a classifier applied to mixture images best. The results of the experiments covering the experts classifier revealed discrepancies in the performance of the individual binary classifiers. PAN showed much more rapid deterioration of the classifier responses with increasing noise level compared to ABS and PS. The classifiers using the manually chosen features showed comparable or better performance for mixtures of polymer and non-polymer spectra with the exception of EVOH and PE. Here the automatically selected features facilitate better separation despite similarities in the spectra of EVOH, PE and non-polymers.
A Distributional Model of Verb-Specific Semantic Roles Inferences

In a standard view, commonly adopted in psycholinguistics and computational linguistics, thematic roles are approached as primitive entities able to represent the roles played by the arguments of a predicate. In theoretical linguistics, however, the inability to reach a consensus on a primitive set of semantic roles led to the proposal of new approaches in which thematic roles are better described as a bundle of more primitive entities (e.g., Dowty, 1991; Van Valin, 1999) or as structural configurations (e.g., Jackendoff, 1987). In a complementary way, psycholinguistic evidence supports the idea that thematic roles and nominal concepts are represented in similar ways (McRae et al., 1997b; Ferretti et al., 2001), thus suggesting that the former can be accounted for as predicate-specific bundles of inferences activated by the semantics of the verb (e.g., the patient of kill is typically alive before the event and dead afterward). Such inferences can take the form of either presuppositions or entailment relations activated when a filler saturates a specific argument position for a given predicate. Our aim in this chapter is twofold. First, we report behavioral data collected to obtain a more fine-grained characterization of the thematic role properties activated by a subset of English verbs. To this end, we employed the modified version of the McRae et al. (1997b) elicitation paradigm proposed by Lebani et al. (2015) to describe which semantic properties of the participants are more relevant in each phase of the action described by the predicate. Next, we test the possibility to model such verb-specific inference patterns by exploiting corpus-based distributional data, thus proposing a novel approach to represent the same level of semantic knowledge that is currently described by means of a finite set of thematic roles.
Phylogenomic Analysis of 155 Helminth Species Reveals Widespread Absence of Oxygen Metabolic Capacity

Abstract The terminal electron acceptor of most aerobic respiratory chains, cytochrome c oxidase (COX), has been highly conserved throughout evolution, from aerobic prokaryotes to complex eukaryotes. Oxygen metabolism in parasitic helminths differs significantly from that of most aerobic eukaryotes, as these organisms can switch between aerobic and anaerobic metabolisms throughout their life cycles. Early studies suggested a lack of COX activity in certain parasitic helminths, and the role of COX in helminth mitochondria remains unclear. To determine whether a functional COX is widely present in helminths, we analyzed the phylogenetic distribution of oxygen metabolism systems across 155 helminth genomes, investigating three distinct sets of protein-coding genes involved in different aspects of oxygen metabolism: COX and its assembly factors, peroxisomes, and the most abundant reactive oxygen species (ROS)-metabolizing proteins. While glycolytic and citric acid cycle enzymes are highly conserved in helminthic species, we observed an apparent widespread absence of essential COX genes across 52% of helminth species investigated. While the most common proteins involved in the defense against ROS are highly maintained across virtually all lineages, we also observed an apparent absence of essential peroxisomal protein-coding genes in 42% of species investigated. Our results suggest that a subset of parasitic helminths utilize oxygen differently from related, nonparasitic species such as Caenorhabditis elegans, with significant differences in their mitochondrial electron transport chains and peroxisomes. The identification of substantive differences between parasite and host metabolism offers a new avenue for the development of anthelmintic agents that could target these divergent pathways.
Functional logistic regression: a comparison of three methods

ABSTRACT Functional logistic regression is becoming more popular as there are many situations where we are interested in the relation between functional covariates (as input) and a binary response (as output). Several approaches have been advocated, and this paper goes into detail about three of them: dimension reduction via functional principal component analysis, penalized functional regression, and wavelet expansions in combination with Least Absolute Shrinking and Selection Operator penalization. We discuss the performance of the three methods on simulated data and also apply the methods to data regarding lameness detection for horses. Emphasis is on classification performance, but we also discuss estimation of the unknown parameter function.
rSVDdpd: A Robust Scalable Video Surveillance Background Modelling Algorithm

A basic algorithmic task in automated video surveillance is to separate background and foreground objects. Camera tampering, noisy videos, low frame rate, etc., pose difficulties in solving the problem. A general approach that classifies the tampered frames, and performs subsequent analysis on the remaining frames after discarding the tampered ones, results in loss of information. Several robust methods based on robust principal component analysis (PCA) have been introduced to solve this problem. To date, considerable effort has been expended to develop robust PCA via Principal Component Pursuit (PCP) methods with reduced computational cost and visually appealing foreground detection. However, the convex optimizations used in these algorithms do not scale well to real-world large datasets due to large matrix inversion steps. Also, an integral component of these foreground detection algorithms is singular value decomposition which is nonrobust. In this paper, we present a new video surveillance background modelling algorithm based on a new robust singular value decomposition technique rSVDdpd which takes care of both these issues. We also demonstrate the superiority of our proposed algorithm on a benchmark dataset and a new real-life video surveillance dataset in the presence of camera tampering. Software codes and additional illustrations are made available at the accompanying website rSVDdpd Homepage (https://subroy13.github.io/rsvddpd-home/)
Evidence for Pseudogap Phase in Cerium Superhydrides: CeH$_{10}$ and CeH$_9$

Polyhydride superconductors have been shown to possess metallic properties with a Bardeen-Cooper-Schrieffer-type superconducting ground state. Here, we provide evidence for unconventional transport associated with a pseudogap phase in cubic cerium superhydride CeH$_{10}$ ($\textit{T}$$_C$ = 116 K) at pressure of 115-125 GPa. A large negative magnetoresistance in the non-superconducting state below 90 K, quasi $\textit{T}$-linear electrical resistance, and a sign-change of its temperature dependence mark the emergence of this phase. We studied the magnetic phase diagrams and the upper critical fields $\textit{B}$$_{C2}$(T) of CeH$_{10}$, CeH$_9$, and CeD$_9$ in pulsed fields up to 70 T. $\textit{B}$$_{C2}$(T) of CeH$_9$ and CeD$_9$ exhibits pronounced saturation at low temperatures in accordance with the Werthamer-Helfand-Hohenberg model, whereas CeH$_{10}$ stands out in particular, as it does not obey this model. Our observations, therefore, reveal the unconventional nature of non-superconducting state of cerium superhydride CeH$_{10}$.
Affective Affordance of Message Balloon Animations: An Early Exploration of AniBalloons

We introduce the preliminary exploration of AniBalloons, a novel form of chat balloon animations aimed at enriching nonverbal affective expression in text-based communications. AniBalloons were designed using extracted motion patterns from affective animations and mapped to six commonly communicated emotions. An evaluation study with 40 participants assessed their effectiveness in conveying intended emotions and their perceived emotional properties. The results showed that 80% of the animations effectively conveyed the intended emotions. AniBalloons covered a broad range of emotional parameters, comparable to frequently used emojis, offering potential for a wide array of affective expression in daily communication. The findings suggest AniBalloons’ promise for enhancing emotional expressiveness in text-based communication and provide early insights for future affective design.
Zooming in on the circumgalactic medium: resolving small-scale gas structure with the GIBLE cosmological simulations

We introduce Project GIBLE (Gas Is Better resoLved around galaxiEs), a suite of cosmological zoom-in simulations where gas in the circumgalactic medium (CGM) is preferentially simulated at ultra-high numerical resolution. Our initial sample consists of eight galaxies, all selected as Milky Way-like galaxies at $z=0$ from the TNG50 simulation. Using the same galaxy formation model as IllustrisTNG, and the moving-mesh code AREPO, we re-simulate each of these eight galaxies maintaining a resolution equivalent to TNG50-2 ($m_{\rm{gas}}$ $\sim$ $8 \times 10^5 {\rm M}_{\odot}$). However, we use our super-Lagrangian refinement scheme to more finely resolve gas in the CGM around these galaxies. Our highest resolution runs achieve 512 times better mass resolution ($\sim$ $10^3 {\rm M}_{\odot}$). This corresponds to a median spatial resolution of $\sim$ $75$ pc at $0.15~R_{\rm{200,c}}$, which coarsens with increasing distance to $\sim$ $700$ pc at the virial radius. We make predictions for the covering fractions of several observational tracers of multi-phase CGM gas: HI, MgII, CIV and OVII. We then study the impact of improved resolution on small scale structure. While the abundance of the smallest cold, dense gas clouds continues to increase with improving resolution, the number of massive clouds is well converged. We conclude by quantifying small scale structure with the velocity structure function and the auto-correlation function of the density field, assessing their resolution dependence. The GIBLE cosmological hydrodynamical simulations enable us to improve resolution in a computationally efficient manner, thereby achieving numerical convergence of a subset of key CGM gas properties and observables.
Plasmonic Enhancement of Second Harmonic Generation in Weyl Semimetal TaAs

In this work a hybrid nanoplasmonic‐Weyl Semimetal (WSM) structure is realized for the first time utilizing silver nanopatch antennas and WSM Tantalum Arsenide (TaAs). The studied hybrid WSM‐nanoplasmonic structure demonstrated a substantial, over x4.5 enhancement of the second harmonic generation (SHG) process compared to a bare TaAs film. To realize the hybrid structure while preserving TaAs properties, a scalable, non‐destructive manufacturing approach is developed that involves the fabrication of TaAs flakes from single crystalline TaAs, overgrowth of a silicon nitride overlayer, and drop‐casting of silver nanopatch antennas. The strong polarization response of both the bare flakes, along with the hybrid‐nanoplasmonic cavities demonstrates that this approach uniquely preserves the TaAs crystal structure and its optical response while providing significant enhancement of the nonlinear properties. The developed method allows leveraging the capabilities of plasmonics to control and enhance light‐matter interactions at the nanometer scale to access and engineer WSM response. This work is the first step towards high‐performance nanophotonic devices utilizing WSM topological properties.
Exact Community Recovery in the Geometric SBM

We study the problem of exact community recovery in the Geometric Stochastic Block Model (GSBM), where each vertex has an unknown community label as well as a known position, generated according to a Poisson point process in $\mathbb{R}^d$. Edges are formed independently conditioned on the community labels and positions, where vertices may only be connected by an edge if they are within a prescribed distance of each other. The GSBM thus favors the formation of dense local subgraphs, which commonly occur in real-world networks, a property that makes the GSBM qualitatively very different from the standard Stochastic Block Model (SBM). We propose a linear-time algorithm for exact community recovery, which succeeds down to the information-theoretic threshold, confirming a conjecture of Abbe, Baccelli, and Sankararaman. The algorithm involves two phases. The first phase exploits the density of local subgraphs to propagate estimated community labels among sufficiently occupied subregions, and produces an almost-exact vertex labeling. The second phase then refines the initial labels using a Poisson testing procedure. Thus, the GSBM enjoys local to global amplification just as the SBM, with the advantage of admitting an information-theoretically optimal, linear-time algorithm.
Surrogate model solver for impurity-induced superconducting subgap states

A simple impurity solver is shown to capture the impurity-induced superconducting subgap states in quantitative agreement with the numerical renormalization group and quantum Monte-Carlo simulations. The solver is based on the exact diagonalization of a single-impurity Anderson model with discretized superconducting reservoirs including only a small number of effective levels. Their energies and couplings to the impurity $d$-level are chosen so as to best reproduce the Matsubara frequency dependence of the hybridization function. We provide a number of critical benchmarks and demonstrate the solvers efficiency in combination with the reduced basis method [Phys. Rev. B 107, 144503 (2023)] by calculating the phase diagram for an interacting three-terminal junction.
The Completely Hackable Amateur Radio Telescope (CHART) project

We present the Completely Hackable Amateur Radio Telescope (CHART), a project that provides hands-on radio instrumentation and design experience to undergraduates while bringing accessible radio astronomy experiments to high school students and teachers. Here we describe a system which can detect 21 cm emission from the Milky Way which is optimised for cost and simplicity of construction. Software, documentation, and tutorials are all completely open source to improve the user experience and facilitate community involvement. We demonstrate the design with several observations which we compare with state-of-the-art surveys. The system is shown to detect galactic 21 cm emission in both rural and urban settings.
Extreme Level Crossing Rate: A New Performance Indicator for URLLC Systems

Level crossing rate (LCR) is a well-known statistical tool that is related to the duration of a random stationary fading process on average. In doing so, LCR cannot capture the behavior of extremely rare random events. Nonetheless, the latter events play a key role in the performance of ultra-reliable and low-latency communication systems rather than their average (expectation) counterparts. In this article, for the first time, we extend the notion of LCR to address this issue and sufficiently characterize the statistical behavior of extreme maxima or minima. This new indicator, entitled as extreme LCR (ELCR), is analytically introduced and evaluated by resorting to the extreme value theory and risk assessment. Capitalizing on ELCR, some key performance metrics emerge, i.e., the maximum outage duration, minimum effective duration, maximum packet error rate, and maximum transmission delay. They are all derived in simple closed-form expressions. The theoretical results are cross-compared and verified via extensive simulations whereas some useful engineering insights are manifested.
Exploring Technical Debt in Security Questions on Stack Overflow

Background: Software security is crucial to ensure that the users are protected from undesirable consequences such as malware attacks which can result in loss of data and, subsequently, financial loss. Technical Debt (TD) is a metaphor incurred by suboptimal decisions resulting in long-term consequences such as increased defects and vulnerabilities if not managed. Although previous studies have studied the relationship between security and TD, examining their intersection in developers' discussion on Stack Overflow (SO) is still unexplored. Aims: This study investigates the characteristics of security-related TD questions on SO. More specifically, we explore the prevalence of TD in security-related queries, identify the security tags most prone to TD, and investigate which user groups are more aware of TD. Method: We mined 117,233 security-related questions on SO and used a deep-learning approach to identify 45,078 security-related TD questions. Subsequently, we conducted quantitative and qualitative analyses of the collected security-related TD questions, including sentiment analysis. Results: Our analysis revealed that 38% of the security questions on SO are security-related TD questions. The most recurrent tags among the security-related TD questions emerged as “security” and “encryption. ” The latter typically have a neutral sentiment, are lengthier, and are posed by users with higher reputation scores. Conclusions: Our findings reveal that developers implicitly discuss TD, suggesting developers have a potential knowledge gap regarding the TD metaphor in the security domain. Moreover, we identified the most common security topics mentioned in TD-related posts, providing valuable insights for developers and researchers to assist developers in prioritizing security concerns in order to minimize TD and enhance software security.
Mechanical and adsorption properties of greenhouse gases filled carbon nanotubes

We investigate the mechanical and adsorption properties of single-walled carbon nanotubes (SWCNTs) filled with greenhouse gases through Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) simulations using a recently developed parameterization for the cross-terms of the Lenard-Jones (LJ) potential. Carbon nanotubes interact strongly with CO$_2$ compared to CH$_4$, resulting in a CO$_2$-rich composition inside the nanotubes, with the proportion of CO$_2$ decreasing as the diameter of the nanotubes increases. Contrarily, the smallest nanotubes showed a more even balance between CO$_2$ and CH$_4$ due to gas solidification. The gas does not affect the mechanical response of the nanotubes under tension, but under compression, it presents a complex relationship with the loading direction, nanotube's diameters, chirality, and to a minor extent, the gas composition. Filled zigzag nanotubes showed to be more stable in the presence of fillers, giving the best mechanical performance compared to the filled armchairs. The study confirms carbon nanotubes as effective means of separating CO$_2$ from CH$_4$, presenting good mechanical stability.
Batching for Green AI - An Exploratory Study on Inference

The batch size is an essential parameter to tune during the development of new neural networks. Amongst other quality indicators, it has a large degree of influence on the model’s accuracy, generalisability, training times and parallelisability. This fact is generally known and commonly studied. However, during the application phase of a deep learning model, when the model is utilised by an end-user for inference, we find that there is a disregard for the potential benefits of introducing a batch size. In this study, we examine the effect of input batching on the energy consumption and response times of five fully-trained neural networks for computer vision that were considered state-of-the-art at the time of their publication. The results suggest that batching has a significant effect on both of these metrics. Furthermore, we present a timeline of the energy efficiency and accuracy of neural networks over the past decade. We find that in general, energy consumption rises at a much steeper pace than accuracy and question the necessity of this evolution. Additionally, we highlight one particular network, ShuffleNetV2 (2018), that achieved a competitive performance for its time while maintaining a much lower energy consumption. Nevertheless, we highlight that the results are model dependent.
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space

Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation. However, standard DA produces synthetic data for augmentation with limited diversity. Generative Adversarial Networks (GANs) may unlock additional information in a dataset by generating synthetic samples having the appearance of real images. However, these models struggle to simultaneously address three key requirements: fidelity and high-quality samples; diversity and mode coverage; and fast sampling. Indeed, GANs generate high-quality samples rapidly, but have poor mode coverage, limiting their adoption in DA applications. We propose LatentAugment, a DA strategy that overcomes the low diversity of GANs, opening up for use in DA applications. Without external supervision, LatentAugment modifies latent vectors and moves them into latent space regions to maximise the synthetic images' diversity and fidelity. It is also agnostic to the dataset and the downstream task. A wide set of experiments shows that LatentAugment improves the generalisation of a deep model translating from MRI-to-CT beating both standard DA as well GAN-based sampling. Moreover, still in comparison with GAN-based sampling, LatentAugment synthetic samples show superior mode coverage and diversity. Code is available at: https://github.com/ltronchin/LatentAugment.
Diffusion Models for Probabilistic Deconvolution of Galaxy Images

Telescopes capture images with a particular point spread function (PSF). Inferring what an image would have looked like with a much sharper PSF, a problem known as PSF deconvolution, is ill-posed because PSF convolution is not an invertible transformation. Deep generative models are appealing for PSF deconvolution because they can infer a posterior distribution over candidate images that, if convolved with the PSF, could have generated the observation. However, classical deep generative models such as VAEs and GANs often provide inadequate sample diversity. As an alternative, we propose a classifier-free conditional diffusion model for PSF deconvolution of galaxy images. We demonstrate that this diffusion model captures a greater diversity of possible deconvolutions compared to a conditional VAE.