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In the past years, many proposals have emerged in order to address online advertising use-cases without access to third-party cookies. All these proposals leverage some privacy-enhancing technologies such as aggregation or differential privacy. Yet, no public and rich-enough ground truth is currently available to assess the relevancy of aforementioned private advertising frameworks. We are releasing the largest, in terms of number of features, bidding dataset specifically built in alignment with the design of major browser vendors proposals such as Chrome Privacy Sandbox. This dataset, coined CriteoPrivateAd, stands for an anonymised version of Criteo production logs and provides sufficient data to learn bidding models commonly used in online advertising under many privacy constraints (delayed reports, display and user-level differential privacy, user signal quantisation or aggregated reports). We ensured that this dataset, while being anonymised, is able to provide offline results close to production performance of adtech companies including Criteo - making it a relevant ground truth to design private advertising systems. The dataset is available in Hugging Face: https://huggingface.co/datasets/criteo/CriteoPrivateAd., Comment: 11 pages

Perovskite solar cells (PSCs) have emerged as a promising photovoltaic technology, already achieving efficiencies surpassing 25%. However, effects such as hysteresis are commonly observed due to the interplay of ionic and electronic transport occurring over different timescales. Despite the widespread use of impedance spectroscopy (IS), physical interpretation in PSCs is specially challenging due to memory effects. In this study, we focus on integrating experimental data with an analytical device transport model. The PSCs under investigation were fabricated using a Cs$_{0.17}$FA$_{0.83}$Pb(I$_{0.83}$Br$_{0.17}$)$_3$ active layer between Nb$_2$O$_5$/TiO$_2$ (compact/mesoporous) and Spiro-OMeTAD. Our fully analytical charge transport model incorporates independent charge transport channels, enabling the correlation of experimental observations in both dark and under illumination. We employed IS together with various voltammetry techniques to reveal the dynamics of the transport processes, including voltage pulses and both small and large amplitude sinusoidal excitations. Although small perturbations are commonly used in IS, our findings demonstrate that large sinusoidal excitation provides new valuable insights in the transition from capacitive to inductive-like responses. The proposed model based purely on charge trapping and generation effectively captures device behavior under both small and large voltages without inductive elements, validated through accurate simulations of hysteresis and other electrical phenomena., Comment: Main text: 26 pages, 5 figures. Supplementary information: 4 figures, 6 pages

Cochlear implants (CIs) are devices that restore the sense of hearing in people with severe sensorineural hearing loss. An electrode array inserted in the cochlea bypasses the natural transducer mechanism that transforms mechanical sound waves into neural activity by artificially stimulating the auditory nerve fibers with electrical pulses. The perception of sounds is possible because the brain extracts features from this neural activity, and loudness is among the most fundamental perceptual features. A computational model that uses a three-dimensional (3D) representation of the peripheral auditory system of CI users was developed to predict categorical loudness from the simulated peripheral neural activity. In contrast, current state-of-the-art computational loudness models predict loudness from the electrical pulses with minimal parametrization of the electrode-nerve interface. In the proposed model, the spikes produced in a population of auditory nerve fibers were grouped by cochlear places, a physiological representation of the auditory filters in psychoacoustics, to be transformed into loudness contribution. Then, a loudness index was obtained with a spatiotemporal integration over this loudness contribution. This index served to define the simulated threshold of hearing (THL) and most comfortable loudness (MCL) levels resembling the growth function in CI users. The performance of real CI users in loudness summation experiments was also used to validate the computational model. These experiments studied the effect of stimulation rate, electrode separation and amplitude modulation. The proposed model provides a new set of perceptual features that can be used in computational frameworks for CIs and narrows the gap between simulations and the human peripheral neural activity., Comment: Preprint

Bimodal stimulation, combining cochlear implant (CI) and acoustic input from the opposite ear, typically enhances speech perception but varies due to factors like temporal mismatch. Previously, we used cortical auditory evoked potentials (CAEPs) to estimate this mismatch based on N1 latency differences. This study expands on that by assessing the impact of temporal mismatch compensation on speech perception. We tested bimodal CI users in three conditions: clinical, compensated temporal mismatch, and a 50 ms mismatch. Measures included speech understanding, pupillometry, CAEPs, selective attention decoding, and parietal alpha power. Despite stable speech understanding across conditions, neural measures showed stronger effects. CAEP N1P2 amplitudes were highest in the compensated condition. Phase-locking value (PLV) and selective attention decoding improved but lacked significance. Parietal alpha power increased under 50 ms mismatch, suggesting cognitive resource allocation. Pupillometry correlated with speech understanding but showed limited sensitivity. Findings highlight that neural metrics are more sensitive than behavioral tests for detecting interaural mismatch. While CAEP N1P2 amplitudes significantly improved with compensation, other neural measures showed limited effects, suggesting the need for combined temporal and spectral compensation strategies., Comment: 28 pages, 15 figures

We consider a parametric family of orientation-preserving piecewise contractions defined for every $x\in[0,1)$ by $f(x)=\lambda x + \delta + d \theta_a(x) \pmod 1$, where $\lambda\in(0,1)$, $d\in(0,1-\lambda)$, $\delta\in[0,1]$, $a\in[0,1]$ and $\theta_a(x)=1$ if $x\geq a$ and $\theta_a(x)=0$ otherwise. In the special case where $a=1$, the family reduces to the well studied "contracted rotations" $x\mapsto \lambda x + \delta \pmod 1$, which are 2-interval piecewise contractions when $\delta\in(1-\lambda,1)$. In the present paper, we allow any $a\in[0,1]$ in order to also consider maps with an additional discontinuity, that is, $3$-interval piecewise contractions. Supposing $\lambda$ and $d$ fixed, for any $\rho\in(0,1)$, we provide necessary and sufficient conditions on the values of the parameters $\delta$ and $a$ for $\rho$ to be the rotation number of the corresponding map. Under these conditions, we relate the map $f$ to a rotation $R_\rho$ of angle $\rho$, by giving the expression of a function $\phi:[0,1)\to\Lambda$ such that $f \circ \phi = \phi \circ R_\rho$, where $\Lambda$ is the support of the asymptotic dynamics of $f$. This enables us to study the dynamics as a function of $\delta$ and $a$ and to construct concrete examples of 3-interval piecewise contractions with some prescribed rotation number, attractor and symbolic dynamics., Comment: 42 pages, 7 figures

The size of a protoplanetary disk is a fundamental property, yet most remain unresolved, even in nearby star-forming regions (d $\sim$ 140-200 pc). We present the complete continuum size distribution for the $105$ brightest protoplanetary disks (M$_{\text{dust}}$ $\gtrsim$ 2 M$_{\oplus}$) in the Ophiuchus cloud, obtained from ALMA Band 8 (410 GHz) observations at 0.05$^{\prime\prime}$ (7 au) to 0.15$^{\prime\prime}$ (21 au) resolution. This sample includes 54 Class II and 51 Class I and Flat Spectrum sources, providing a comprehensive distribution across evolutionary stages. We measure the Half Width at Half Maximum (HWHM) and the radius encircling $68\%$ of the flux ($R_{68\%}$) for most non-binary disks, yielding the largest flux-limited sample of resolved disks in any star-forming region. The distribution is log-normal with a median value of $\sim$14 au and a logarithmic standard deviation $\sigma_{\log} = 0.46$ (factor of 2.9 in linear scale). Disks in close binary systems ($<$ 200 au separation) have smaller radii, with median value of $\sim$5 au, indicating efficient radial drift as predicted by dust evolution models. The size distribution for young embedded objects (SED Class I and Flat Spectrum, age $\lesssim$ 1 Myr) is similar to that of Class II objects (age $\sim$ a few Myr), implying that pressure bumps must be common at early disk stages to prevent mm-sized particle migration at au scales., Comment: 12 pages, 6 figures

This study examines the impact of a remote laboratory experiment on Physics learning, using a case study approach. Societal advancements over the past century have spurred discussions regarding restructuring the current educational system, with active learning methodologies proposed as alternatives to conventional models. These active methods empower students to develop new skills and competencies beyond the academic knowledge imparted in lectures. To improve student learning outcomes in Physics, this research will develop an experimental model for application in engineering classes. This involves creating a laboratory experiment procedure for oblique projectile motion, constructing the necessary apparatus, recording the experiment on a film, presenting the video for a remote student's audience, and evaluating the model using questionnaires focusing on conceptual understanding and related questions., Comment: 20 pages, 26 figures

In a rapidly evolving knowledge landscape and the increasing adoption of large language models, a need has emerged to keep these models continuously updated with current events. While existing benchmarks evaluate general factual recall, they often overlook two critical aspects: the ability of models to integrate evolving knowledge through continual learning and the significant regional disparities in their performance. To address these gaps, we introduce the Timely Events Benchmark (TiEBe), a dataset containing over 11,000 question-answer pairs focused on globally and regionally significant events. TiEBe leverages structured retrospective data from Wikipedia, enabling continuous updates to assess LLMs' knowledge of evolving global affairs and their understanding of events across different regions. Our benchmark demonstrates that LLMs exhibit substantial geographic disparities in factual recall, emphasizing the need for more balanced global knowledge representation. Furthermore, TiEBe serves as a tool for evaluating continual learning strategies, providing insights into models' ability to acquire new information without forgetting past knowledge.

Scaling laws for language models so far focused on finding the compute-optimal model size and token count for training from scratch. However, achieving this optimal balance requires significant compute resources due to the extensive data demands when training models from randomly-initialized weights. Continual pre-training offers a cost-effective alternative, leveraging the compute investment from pre-trained models to incorporate new knowledge without requiring extensive new data. Recent findings suggest that data quality influences constants in scaling laws, thereby altering the optimal parameter-token allocation ratio. Building on this insight, we investigate the interplay between domain specialization and model size during continual pre-training under compute-constrained scenarios. Our goal is to identify a compute-efficient training regime for this scenario and, potentially, detect patterns in this interplay that can be generalized across different model sizes and domains. To compare general and specialized training, we filtered a web-based dataset to extract legal domain data. We pre-trained models with 1.5B, 3B, 7B and 14B parameters on both the unfiltered and filtered datasets, then evaluated their performance on legal exams. Results show that as model size increases, the compute-effectiveness gap between specialized and general models widens.

To reduce costs and delays related to developing new and effective drugs, there is a critical need for improved human liver tissue models. Here we describe an approach for 3D bioprinting functional human liver tissue models, in which we fabricate disc-shaped structures (discoids) 200 {\mu}m in thickness and 1-3 mm in diameter, embedded in a highly permeable support medium made from packed microgels. We demonstrate that the method is precise, accurate, and scalable; up to 100 tissues per hour can be manufactured with a variability and error in diameter of about 4%. Histologic and immunohistochemical evaluation of printed discs reveal self-organization, cell cohesion, and key liver marker expression. During the course of 3-4 weeks in culture, the tissues stably synthesize albumin and urea at high levels, outperforming spheroid tissue models. We find the tissues express more than 100 genes associated with molecular absorption, distribution, metabolism, and excretion (ADME) at levels within the range of human liver. The liver tissue models exhibit enzymatic formation of metabolites after exposure to multiple test compounds. Together, these results demonstrate the promise of 3D printed discoids for pharmacological and toxicological applications., Comment: 34 pages, 7 figures, 2 supplementary figures

Research in neutrino physics has been very active, both in experimental advances, with a new generation of detectors in operation and planning, and in theoretical discussions regarding the fundamental nature of the neutrino. This scientific dynamism has attracted many new students to the field. One of the first topics studied in neutrino physics by newcomers is the formalism of neutrino flavor oscillations and its associated phenomenology. We present this work as a compilation of this basic knowledge, through a step-by-step approach that facilitates an efficient understanding of this vast theoretical and experimental landscape., Comment: 14 pages, 4 figures

CuInS2 quantum dots have been studied in a broad range of applications, but despite this, the fine details of their charge carrier dynamics remain a subject of intense debate. Two of the most relevant points of discussion are the hole dynamics and the influence of Cu:In synthesis stoichiometry on them. It has been proposed that Cu-deficiency leads to the formation of Cu2+, affecting the localization of holes into Cu defects. Importantly, it is precisely these confined hole states which are used to explain the interesting photoluminescence properties of CuInS2 quantum dots. We use static X-ray spectroscopy to reveal no evidence for a measurable amount of native Cu2+ states in Cu-deficient samples. Instead, the improved properties of these samples are explained by an increase of crystallinity, reducing the concentration of mid gap states. Furthermore, to understand the charge carrier dynamics, herein we employ ultrafast optical transient absorption, and fluorescence up-conversion spectroscopies in combination with ultrafast X-ray absorption spectroscopy using a hard X-ray free electron laser. We demonstrate that in non-passivated samples, holes are transferred from Cu atoms in sub-picosecond timescales. We assign this transfer to occur towards the thiol-based ligands. Finally, we observe that Cu-deficient samples are more robust against the photothermal heating effects of using higher laser fluences. This is not the case for the stoichiometric sample, where heating effects on the structure are directly observed.

The concern about global warming increased the interest in the energy efficiency of computer applications. Assuming power is constant, the general trend is that faster programs consume less energy, thus optimizing a program for speed would also improve its energy efficiency. We investigate this tendency in a set of C++ and Java solutions mined from Code Submission Evaluation System (CSES), a popular programming competition site, where each solution must give the correct answer under a given time limit. In such context, we can consider that all correct solutions for a problem were written with a speed concern, but not with energy efficiency in mind. We selected 15 problems from CSES and for each of them we mined at least 30 C++ and Java solutions, evaluating time and energy efficiency of each solution in at least two different machines. In our scenario, where there is a great diversity of programming styles, execution speed, and memory usage, we could confirm the general trend: faster programs consume less energy. Moreover, we were able to use ordinary least squares to fit a linear function, with good precision, that relates energy consumption of a program based on its execution time, as well as to automatically identify programs with abnormal energy consumption. A manual analysis of these programs revealed that often they perform a different amount of allocation and deallocation operations when compared to programs with similar execution times. We also calculated the energy consumption profile of sets of random C++ solutions for these 15 CSES problems, and we tried to associate each set with its corresponding CSES problem by using the energy consumption profiles previously computed for each one of them. With this approach, we restricted, for each set of random C++ solutions, the classification task to a subset of 7 CSES problems, a reduction of more than 50% in the search space.

Two-dimensional quantum antiferromagnets are believed to host phases of matter whose excitations are more fundamental than those of the ordered phases. When combining two such spin systems in a bilayer, strong interaction between the emergent excitations can produce phases not realized in either of its subsystems. We show that the critical fluctuations of a two-dimensional spin liquid state can induce a deconfined quantum critical point in a proximate antiferromagnet. The most relevant coupling between the associated effective field theories is given by a mixed Chern-Simons term of the emergent gauge fields in each layer. This describes a topological current-current interaction. In contrast to the local spin-spin interaction, it strongly modifies the renormalization group flow of the theory describing the N\'eel-valence-bond-solid transition of the antiferromagnet. In particular, the protected coupling constant associated with it implies non-trivial quantum critical scaling characterized by a non-universal power-law divergence of the correlation length in the critical domain and Berezinskii-Kosterlitz-Thouless divergence approaching it., Comment: 15 pages, 5 figures, Published in Physical review B

The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use neural networks to detect these actions. However, occlusion is still a major issue in HRI, especially when using uncrewed aerial vehicles (UAVs), since, during the robot's movement, the human operator is often out of the robot's field of view. Furthermore, in multi-robot scenarios, distributed training is also an open problem. In this sense, this work proposes an action recognition and control approach based on Long Short-Term Memory (LSTM) Deep Neural Networks with two layers in association with three densely connected layers and Federated Learning (FL) embedded in multiple drones. The FL enabled our approach to be trained in a distributed fashion, i.e., access to data without the need for cloud or other repositories, which facilitates the multi-robot system's learning. Furthermore, our multi-robot approach results also prevented occlusion situations, with experiments with real robots achieving an accuracy greater than 96%.

We report on the spin dynamics of two Terbium-based molecular nanomagnets, Tb-SQ and Tb-Trp, investigated by means of longitudinal muon spin relaxation ($\mu$SR) measurements as a function of applied field, flanked by AC susceptibility characterization. In the two molecules Tb(III) magnetic ion has an isotructural coordination sphere, but in the former the Tb(III) is coordinated by an organic paramagnetic ligand (SQ), while the latter is coordinated by a diamagnetic one (Trp). Thus Tb-SQ presents an exchange interaction between the Tb(III) ion and a radical while Tb-Trp does not. Both the samples exhibit a muon spin-lattice relaxation rate $\lambda_1(T, B_L)$ peak in the temperature range 10-25 K at all applied longitudinal magnetic fields $B_L = 50, 150, 300$ mT. In Tb-SQ, $\lambda_1(T, B_L)$ displays a BPP-like behavior led by three different correlations times: the first, dominating for $T\geq15K$, follows a thermally activated law $\tau_c =\tau_0 \exp(\sigma_A / k_B T)$ with energy barrier $\sigma_A/k_B$, while the second and third ones, dominating respectively for $8

The sharing of mobile network infrastructure has become a key topic with the introduction of 5G due to the high costs of deploying such infrastructures, with neutral host models coupled with features such as network function virtualization (NFV) and network slicing emerging as viable solutions for the challenges in this area. With this in mind, this work presents the design, implementation, and test of a flexible infrastructure-sharing 5G network architecture capable of providing services to any type of client, whether an operator or not. The proposed architecture leverages 5G's network slicing for traffic isolation and compliance with the policies of different clients, with roaming employed for the authentication of users of operator clients. The proposed architecture was implemented and tested in a simulation environment using the UERANSIM and Open5GS open-source tools. Qualitative tests successfully validated the authentication and the traffic isolation features provided by the slices for the two types of clients. Results also demonstrate that the proposed architecture has a positive impact on the performance of the neutral host network infrastructure, achieving 61.8% higher throughput and 96.8% lower packet loss ratio (PLR) in a scenario sharing the infrastructure among four clients and eight users when compared to a single client with all the network resources.

The aim of this study is to estimate the implications for the reference rate and for the distribution of the tax burden among households from the consumption tax reform as outlined in the version of the Projeto de Lei Complementar (PLP) 68 approved by the Brazilian Chamber of Deputies in July 2024., Comment: in Portuguese language

This work explores the implications of the Exclusivity Principle (EP) in the context of quantum and post-quantum correlations. We first establish a key technical result demonstrating that given the set of correlations for a complementary experiment, the EP restricts the maximum set of correlations for the original experiment to the anti-blocking set. Based on it, we can prove our central result: if all quantum behaviors are accessible in Nature, the EP guarantees that no post-quantum behaviors can be realized. This can be seen as a generalization of the result of [Phys. Rev. A 89, 030101(R)], to a wider range of scenarios. It also provides novel insights into the structure of quantum correlations and their limitations., Comment: 5 pages

The initial value problem (IVP) for the non-isotropic Schr\"odinger equation posed on the two-dimensional cylinders and $\mathbb{T}^2$ is considered. The IVP is shown to be locally well-posed for small initial data in $H^s(\mathbb{T}\times\mathbb{R})$ if $s\geq0$. For the IVP posed on $\mathbb{R}\times\mathbb{T}$, given data are considered in the anisotropic Sobolev spaces thereby obtaining the local well-posedness result in $H^{s_1, s_2}(\mathbb{R}\times\mathbb{T})$, if $s_1\geq0$ and $s_2>\frac12$. In the purely periodic case, a particular case of the IVP is shown to be locally well-posed for any given initial data in $H^s(\mathbb{T}^2)$ if $s>\frac18$. In some cases, ill-posedness issues are also considered showing that the IVP posed on $\mathbb{T}\times \mathbb{R}$, in the focusing case, is ill-posed in the sense that the application data-solution fails to be uniformly continuous for data in $H^s(\mathbb{T}\times\mathbb{R})$ if $-\frac12\leq s<0$., Comment: 23 pages

This report presents Sabi\'a-3, our new flagship language model, and Sabiazinho-3, a more cost-effective sibling. The models were trained on a large brazilian-centric corpus. Evaluations across diverse professional and academic benchmarks show a strong performance on Portuguese and Brazil-related tasks. Sabi\'a-3 shows large improvements in comparison to our previous best of model, Sabia-2 Medium, especially in reasoning-intensive tasks. Notably, Sabi\'a-3's average performance matches frontier LLMs, while it is offered at a three to four times lower cost per token, reinforcing the benefits of domain specialization.

Language models are now capable of solving tasks that require dealing with long sequences consisting of hundreds of thousands of tokens. However, they often fail on tasks that require repetitive use of simple rules, even on sequences that are much shorter than those seen during training. For example, state-of-the-art LLMs can find common items in two lists with up to 20 items but fail when lists have 80 items. In this paper, we introduce MLissard, a multilingual benchmark designed to evaluate models' abilities to process and generate texts of varied lengths and offers a mechanism for controlling sequence complexity. Our evaluation of open-source and proprietary models show a consistent decline in performance across all models and languages as the complexity of the sequence increases. Surprisingly, the use of in-context examples in languages other than English helps increase extrapolation performance significantly. The datasets and code are available at https://github.com/unicamp-dl/Lissard, Comment: GenBench Workshop by EMNLP 2024: Camera-ready version

We investigate the effects of the presence of conserved charges on the momentum-space entanglement of Quantum Field Theories (QFTs). We show that if a given model has superselection sectors, then it allows for different notions of momentum modes, each associated with a complete set of commuting observables. Applying this idea to investigate the entanglement of momentum degrees of freedom on both sides of the abelian Bosonization duality, we find that different tensor product partitions are mapped into each other and give explicit examples to sustain our findings. The conditions on which our conclusions may be generalized to other duality transformations, which require the introduction of a notion stricter than the most general possible, are laid and directions for further work are given., Comment: 12 pages

For a perfect fluid, pressure $p$ and energy density $\rho$ are related via the equation of state (EOS) $\omega = p/\rho$, where $\omega$ is the EOS parameter, being its interpretation usually constrained to a numerical value for each universe era. Here, based on the Mie-Gr\"uneisen EOS, we show that $\omega$ is recognized as the effective Gr\"uneisen parameter $\Gamma_{eff}$, whose singular contribution, the so-called Gr\"uneisen ratio $\Gamma$, quantifies the barocaloric effect. Our analysis suggests that the negative $p$ associated with dark-energy implies a metastable state and that in the dark-energy-dominated era $\omega$ is time-dependent, which reinforces recent proposals of a time-dependent cosmological constant. Furthermore, we demonstrate that $\Gamma_{eff}$ is embodied in the energy-momentum stress tensor in the Einstein field equations, enabling us to analyse, in the frame of an imperfect fluid picture, anisotropic effects of the universe expansion. We propose that upon going from decelerated- to accelerated-expansion, a phase transition-like behavior can be inferred. Yet, our analysis in terms of entropy, $\Gamma$, and a by us adapted version of Avramov/Casalini's model to Cosmology unveil hidden aspects related to the expansion of the universe. Our findings pave the way to interpret cosmological phenomena in connection with concepts of condensed matter Physics via $\Gamma_{eff}$., Comment: 11 pages, 1 figure, comments are welcome

Neural health refers to the condition and functionality of the auditory nerve fibers (ANFs),which are crucial for transmitting sound signals from the cochlea to the brain.However, neural health cannot be directly measured due to current technological limitations. We utilize a computational model to evaluate different indirect methods for estimating ANF neural health.Two distinct measures for estimating neural health,(i)the threshold levels for focused quadrupolar stimulation and (ii) the change in the electrically evoked compound action potential (eCAP) amplitude growth function for different inter-phase gaps (IPGs), were evaluated in a computational model of an electrically stimulated implanted cochlea. The model combined a 3D finite element method model, an ANF geometry with a realistic spatial distribution, and a neuron model, including an existing phenomenological single-ANF model and an eCAP model. Our experiments simulated different neural health conditions (healthy, shrinked, and degenerated) to model dead regions in different locations of the cochlea.The results from the computational experiments demonstrated that the threshold levels with focused quadrupolar stimulation were more sensitive to neural health deficits than thresholds with monopolar stimulation. From our data, the difference in threshold levels between quadrupolar and monopolar stimulation seems to be a promising measure of neural health status. However, the results from the eCAP IPG slope and offset effects were not consistently associated with neural health conditions.Our results suggest that the difference in threshold levels with quadrupolar and monopolar stimulation is a possible method for estimating neural health. Significance: This study enhances the understanding of neural health and dead regions through a novel computational model, contributing to new approaches for neural health estimation.

Many organizations rely on Threat Intelligence (TI) feeds to assess the risk associated with security threats. Due to the volume and heterogeneity of data, it is prohibitive to manually analyze the threat information available in different loosely structured TI feeds. Thus, there is a need to develop automated methods to vet and extract actionable information from TI feeds. To this end, we present a machine learning pipeline to automatically detect vulnerability exploitation from TI feeds. We first model threat vocabulary in loosely structured TI feeds using state-of-the-art embedding techniques (Doc2Vec and BERT) and then use it to train a supervised machine learning classifier to detect exploitation of security vulnerabilities. We use our approach to identify exploitation events in 191 different TI feeds. Our longitudinal evaluation shows that it is able to accurately identify exploitation events from TI feeds only using past data for training and even on TI feeds withheld from training. Our proposed approach is useful for a variety of downstream tasks such as data-driven vulnerability risk assessment., Comment: This paper appears at IEEE International Conference on Cyber Security and Resilience (IEEE CSR 2024)

Document summarization is a task to shorten texts into concise and informative summaries. This paper introduces a novel dataset designed for summarizing multiple scientific articles into a section of a survey. Our contributions are: (1) SurveySum, a new dataset addressing the gap in domain-specific summarization tools; (2) two specific pipelines to summarize scientific articles into a section of a survey; and (3) the evaluation of these pipelines using multiple metrics to compare their performance. Our results highlight the importance of high-quality retrieval stages and the impact of different configurations on the quality of generated summaries., Comment: 15 pages, 6 figures, 1 table. Submitted to BRACIS 2024

In this work we consider some problems about a reflected graph map germ $f$ from $(\mathbb{C}^n,0)$ to $(\mathbb{C}^{n+1},0)$. A reflected graph map is a particular case of a reflection map, which is defined using an embedding of $\mathbb{C}^n$ in $\mathbb{C}^{p}$ and then applying the action of a reflection group $G$ on $\mathbb{C}^{p}$. In this work, we present a description of the presentation matrix of $f_*{\cal O}_n$ as an ${\cal O}_{n+1}$-module via $f$ in terms of the action of the associated reflection group $G$. We also give a description for a defining equation of the image of $f$ in terms of the action of $G$. Finally, we present an upper (and also a lower) bound for the multiplicity of the image of $f$ and some applications.

The adoption of microservice architecture has seen a considerable upswing in recent years, mainly driven by the need to modernize legacy systems and address their limitations. Legacy systems, typically designed as monolithic applications, often struggle with maintenance, scalability, and deployment inefficiencies. This study investigates the motivations, activities, and challenges associated with migrating from monolithic legacy systems to microservices, aiming to shed light on common practices and challenges from a practitioner's point of view. We conducted a comprehensive study with 53 software practitioners who use microservices, expanding upon previous research by incorporating diverse international perspectives. Our mixed-methods approach includes quantitative and qualitative analyses, focusing on four main aspects: (i) the driving forces behind migration, (ii) the activities to conduct the migration, (iii) strategies for managing data consistency, and (iv) the prevalent challenges. Thus, our results reveal diverse practices and challenges practitioners face when migrating to microservices. Companies are interested in technical benefits, enhancing maintenance, scalability, and deployment processes. Testing in microservice environments remains complex, and extensive monitoring is crucial to managing the dynamic nature of microservices. Database management remains challenging. While most participants prefer decentralized databases for autonomy and scalability, challenges persist in ensuring data consistency. Additionally, many companies leverage modern cloud technologies to mitigate network overhead, showcasing the importance of cloud infrastructure in facilitating efficient microservice communication.

Two-dimensional (2D) organic-inorganic hybrid perovskites emerged as a versatile platform for light-emitting and photovol-taic applications due to their unique structural design and chemical flexibility. Their properties depend heavily on both the choice of the inorganic lead halide framework and the surrounding organic layers. Recently, the introduction of chiral cations into 2D perovskites has attracted major interest due to their potential for introducing chirality and tuning the chiro-optical response. Importantly, the optical properties in these materials are dominated by tightly bound excitons that also serve as primary carriers for the energy transport. The mobility of photoinjected excitons is thus important from the perspectives of fundamental material properties and optoelectronic applications, yet remains an open question. Here, we demonstrate exciton propagation in a 2D chiral perovskite methylbenzylammonium lead iodide (MBA2PbI4) using transient photoluminescence microscopy and reveal density-dependent transport over more than 100 nanometers at room temperature with diffusion coeffi-cients as high as 2 cm2/s. We observe two distinct regimes of initially rapid diffusive propagation and subsequent localiza-tion. Moreover, perovskites with enantiomer pure cations are found to exhibit faster exciton diffusion compared to the race-mic mixture, correlated with the impact of the material composition on disorder. Altogether, the observations of efficient exciton diffusion at room temperature highlight the potential of 2D chiral perovskites to merge chiro-optical properties with strong light-matter interaction and efficient energy transport.

In this manuscript, we put forth a general scheme for defining initial value problems from Einstein's equations of General Relativity constrained by homogeneous and isotropic expansion. The cosmological models arising as solutions are naturally interpreted as spatially homogeneous and isotropic on ``large scales". In order to show the well-posedness and applicability of such a scheme, we specialize in a class of spacetimes filled with the general homogeneous perfect fluid and inhomogeneous viscoelastic matter. We prove the existence, uniqueness, and relative stability of solutions, and an additional inequality for the energy density. As a consequence of our theorems, a new mechanism of energy transfer appears involving the different components of matter. A class of exact solutions is also obtained to exemplify the general results., Comment: 18 pages; 3 figures

We are in a golden era observing Young Stellar Objects (YSOs), protoplanetary disks, and substellar objects, crucial for understanding their formation and evolution. This Ph.D. thesis explores two binary systems. Firstly, we study an eruptive YSO, HBC 494, using ALMA band 6 (1.3 mm) observations. It's a FUor system in Orion Molecular Cloud with a resolved binary system: HBC 494 N (primary) and HBC 494 S (secondary) separated by 75 au. The disks show hints of aligned formation scenarios, with HBC 494 N being brighter and larger. Molecular line observations reveal bipolar outflows and rotating envelopes. Cavity features within the continuum disks' area suggest continuum over-subtraction or slow-moving jets and chemical destruction along the line-of-sight. Secondly, we examine the young binary system $\eta$ Tel using VLT/SPHERE H band imaging. It consists of an A-type star and a brown dwarf companion $\eta$ Tel B, separated by 208 au. Astrometric measurements over 19 years yield a low eccentric orbit with an inclination of 81.9 degrees. The mass of $\eta$ Tel B is determined to be 48 M$_{Jup}$, consistent with previous literature. No significant residual indicative of a satellite or disk surrounding the companion is detected, with limits ruling out massive objects around $\eta$ Tel B at separations down to 33 au with masses as low as 1.6M$_{Jup}$. These studies employ sub-mm to near-IR observations, highlighting the complexity of (sub)stellar formation/evolution. This thesis contributes diverse analyses, providing insights into these intriguing processes., Comment: Ph.D. thesis in Astronomy approved at Universidad Diego Portales, spanning 176 pages with references across four chapters: Introduction, Nogueira et al. (2023), Nogueira et al. (2024), and Final Remarks and Conclusions

We unravel the Rodeo Algorithm to determine the eigenstates and eigenvalues spectrum for a general Hamiltonian considering arbitrary initial states. By presenting a novel methodology, we detail the original method and show how to define all properties without having prior knowledge regarding the eigenstates. To this end, we exploit Pennylane and Qiskit platforms resources to analyze scenarios where the Hamiltonians are described by the Zeeman model for one and two spins. We also introduce strategies and techniques to improve the algorithm's performance by adjusting its intrinsic parameters and reducing the fluctuations inherent to data distribution. First, we explore the dynamics of a single qubit on Xanadu simulators to set the parameters that optimize the method performance and select the best strategies to execute the algorithm. On the sequence, we extend the methodology for bipartite systems to discuss how the algorithm works when degeneracy and entanglement are taken into account. Finally, we compare the predictions with the results obtained on a real superconducting device provided by the IBM Q Experience program, establishing the conditions to increase the protocol efficiency for multi-qubit systems.

Despite the remarkable progress in emerging solar cell technologies such as hybrid organic-inorganic perovskites, there are still significant limitations related to the stability of the devices and their non-ideal electrical behavior under certain external stimuli. We present a conceptual framework for characterizing photovoltaic devices by integrating cyclic voltammetry (CV) and impedance spectroscopy (IS). This framework is constructed from a microscopic, multi-mode perspective that explicitly accounts for drift, diffusion, displacement, and memory contributions. We derive comprehensive analytical expressions for current-voltage relationships and complex admittance. Our model reveals the inseparable connection between hysteresis behaviors in current-voltage characteristics observed in CV and the apparent capacitive and inductive behaviors seen in IS spectral analysis. We demonstrate how CV and IS naturally complement each other, providing a deeper microscopic understanding of device performance and limitations. Additionally, we establish the relationship between intrinsic material parameters and experimentally accessible extrinsic parameters such as light intensity, temperature, DC bias, voltage amplitude, and frequency. This framework enables unprecedented optimization of solar cell performance, marking a significant advancement towards sustainability., Comment: 6 figures

3D human pose estimation aims to reconstruct the human skeleton of all the individuals in a scene by detecting several body joints. The creation of accurate and efficient methods is required for several real-world applications including animation, human-robot interaction, surveillance systems or sports, among many others. However, several obstacles such as occlusions, random camera perspectives, or the scarcity of 3D labelled data, have been hampering the models' performance and limiting their deployment in real-world scenarios. The higher availability of cameras has led researchers to explore multi-view solutions due to the advantage of being able to exploit different perspectives to reconstruct the pose. Thus, the goal of this survey is to present an overview of the methodologies used to estimate the 3D pose in multi-view settings, understand what were the strategies found to address the various challenges and also, identify their limitations. Based on the reviewed articles, it was possible to find that no method is yet capable of solving all the challenges associated with the reconstruction of the 3D pose. Due to the existing trade-off between complexity and performance, the best method depends on the application scenario. Therefore, further research is still required to develop an approach capable of quickly inferring a highly accurate 3D pose with bearable computation cost. To this goal, techniques such as active learning, methods that learn with a low level of supervision, the incorporation of temporal consistency, view selection, estimation of depth information and multi-modal approaches might be interesting strategies to keep in mind when developing a new methodology to solve this task., Comment: 22 pages, 5 figures, submitted to Image and Vision Computing (IMAVIS)