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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)

Despite advancements in Natural Language Processing (NLP) and the growing availability of pretrained models, the English language remains the primary focus of model development. Continued pretraining on language-specific corpora provides a practical solution for adapting models to other languages. However, the impact of different pretraining settings on downstream tasks remains underexplored. This work introduces $\texttt{ptt5-v2}$, investigating the continued pretraining of T5 models for Portuguese. We first develop a baseline set of settings and pretrain models with sizes up to 3B parameters. Finetuning on three Portuguese downstream tasks (assin2 STS, assin2 RTE, and TweetSentBR) yields SOTA results on the latter two. We then explore the effects of different pretraining configurations, including quality filters, optimization strategies, and multi-epoch pretraining. Perhaps surprisingly, their impact remains subtle compared to our baseline. We release $\texttt{ptt5-v2}$ pretrained checkpoints and the finetuned MonoT5 rerankers on HuggingFace at https://huggingface.co/collections/unicamp-dl/ptt5-v2-666538a650188ba00aa8d2d0 and https://huggingface.co/collections/unicamp-dl/monoptt5-66653981877df3ea727f720d.

Traffic incidents involving vulnerable road users (VRUs) constitute a significant proportion of global road accidents. Advances in traffic communication ecosystems, coupled with sophisticated signal processing and machine learning techniques, have facilitated the utilization of data from diverse sensors. Despite these advancements and the availability of extensive datasets, substantial progress is required to mitigate traffic casualties. This paper provides a comprehensive survey of state-of-the-art technologies and methodologies to enhance the safety of VRUs. The study delves into the communication networks between vehicles and VRUs, emphasizing the integration of advanced sensors and the availability of relevant datasets. It explores preprocessing techniques and data fusion methods to enhance sensor data quality. Furthermore, our study assesses critical simulation environments essential for developing and testing VRU safety systems. Our research also highlights recent advances in VRU detection and classification algorithms, addressing challenges such as variable environmental conditions. Additionally, we cover cutting-edge research in predicting VRU intentions and behaviors, which is crucial for proactive collision avoidance strategies. Through this survey, we aim to provide a comprehensive understanding of the current landscape of VRU safety technologies, identifying areas of progress and areas needing further research and development., Comment: 46 pages, 8 figures, citing 337 (up-to-date) papers, preprint submitted to Expert Systems with Applications (Elsevier)

$\eta$ Tel is an 18 Myr system with a 2.09 M$_{\odot}$ A-type star and an M7-M8 brown dwarf companion, $\eta$ Tel B, separated by 4.2'' (208 au). High-contrast imaging campaigns over 20 years have enabled orbital and photometric characterization. $\eta$ Tel B, bright and on a wide orbit, is ideal for detailed examination. We analyzed three new SPHERE/IRDIS coronagraphic observations to explore $\eta$ Tel B's orbital parameters, contrast, and surroundings, aiming to detect a circumplanetary disk or close companion. Reduced IRDIS data achieved a contrast of 1.0$\times 10^{-5}$, enabling astrometric measurements with uncertainties of 4 mas in separation and 0.2 degrees in position angle, the smallest so far. With a contrast of 6.8 magnitudes in the H band, $\eta$ Tel B's separation and position angle were measured as 4.218'' and 167.3 degrees, respectively. Orbital analysis using Orvara code, considering Gaia-Hipparcos acceleration, revealed a low eccentric orbit (e $\sim$ 0.34), inclination of 81.9 degrees, and semi-major axis of 218 au. $\eta$ Tel B's mass was determined to be 48 \MJup, consistent with previous calculations. No significant residual indicating a satellite or disk around $\eta$ Tel B was detected. Detection limits ruled out massive objects around $\eta$ Tel B with masses down to 1.6 \MJup at a separation of 33 au., Comment: 13 pages, 10 figures, accepted for publication in A&A (01/05/2024)

A quantum system governed by a non-Hermitian Hamiltonian may exhibit zero temperature phase transitions that are driven by interactions, just as its Hermitian counterpart, raising the fundamental question how non-Hermiticity affects quantum criticality. In this context we consider a non-Hermitian system consisting of an $\rm XY$ model with a complex-valued four-state clock interaction that may or may not have parity-time-reversal ($\mathcal{PT}$) symmetry. When the $\mathcal{PT}$ symmetry is broken, and time-evolution becomes non-unitary, a scaling behavior similar to the Berezinskii-Kosterlitz-Thouless phase transition ensues, but in a highly unconventional way, as the line of fixed points is absent. From the analysis of the $d$-dimensional RG equations, we obtain that the unconventional behavior in the $\mathcal{PT}$ broken regime follows from the collision of two fixed points in the $d\to 2$ limit, leading to walking behavior or pseudocriticality. For $d=2+1$ the near critical behavior is characterized by a correlation length exponent $\nu=3/8$, a value smaller than the mean-field one. These results are in sharp contrast with the $\mathcal{PT}$-symmetric case where only one fixed point arises for $2

In this work we extend the Kugo-Ojima-Nakanishi covariant operator formalism to quantize two higher derivative systems, considering their extended phase space structures. More specifically, the one describing spin-$0$ particles by a vector field and the generalized electrodynamics. We investigate the commutator structure of these theories and present the definition of their physical Hilbert subspaces. Remarkably, the correspondence principle demands a higher derivative structure for the auxiliary field Lagrangian. The first model presents a reducible gauge symmetry implying the necessity of two sets of auxiliary fields. The massless limit is also carefully analyzed. For the case of the generalized QED$_4$, we derive a set of suitable definitions for the physical Hilbert subspace to eliminate contributions from spurious modes and the problematic negative norm transverse excitations. However, preliminary discussions on the interacting regime are also developed to show that, in this case, the states with transverse ghost modes can be produced from ghost-free initial ones, meaning that they cannot be decoupled. Additionally, we reveal the very source of renormalization improvements in these kinds of theories and also establish a suitable specific interacting model compatible with a time-invariant positive norm subspace.

Multilingual pretraining has been a successful solution to the challenges posed by the lack of resources for languages. These models can transfer knowledge to target languages with minimal or no examples. Recent research suggests that monolingual models also have a similar capability, but the mechanisms behind this transfer remain unclear. Some studies have explored factors like language contamination and syntactic similarity. An emerging line of research suggests that the representations learned by language models contain two components: a language-specific and a language-agnostic component. The latter is responsible for transferring a more universal knowledge. However, there is a lack of comprehensive exploration of these properties across diverse target languages. To investigate this hypothesis, we conducted an experiment inspired by the work on the Scaling Laws for Transfer. We measured the amount of data transferred from a source language to a target language and found that models initialized from diverse languages perform similarly to a target language in a cross-lingual setting. This was surprising because the amount of data transferred to 10 diverse target languages, such as Spanish, Korean, and Finnish, was quite similar. We also found evidence that this transfer is not related to language contamination or language proximity, which strengthens the hypothesis that the model also relies on language-agnostic knowledge. Our experiments have opened up new possibilities for measuring how much data represents the language-agnostic representations learned during pretraining., Comment: NAACL 2024

This paper analyzes the redistributive impacts of indirect taxation reforms in Brazil inspired by PEC 45/2019, particularly in the version that led to EC 132/2023. Comparisons are made between the current system and the simulated reforms, considering the distribution of the tax burden among families in different income classes, as well as the impact on poverty and inequality indicators. The simulations are conducted based on the combination of effective tax rates that apply to goods and services consumed by households, using nationally representative microdata from family budgets., Comment: in Portuguese language

Despite Portuguese being one of the most spoken languages in the world, there is a lack of high-quality information retrieval datasets in that language. We present Quati, a dataset specifically designed for the Brazilian Portuguese language. It comprises a collection of queries formulated by native speakers and a curated set of documents sourced from a selection of high-quality Brazilian Portuguese websites. These websites are frequented more likely by real users compared to those randomly scraped, ensuring a more representative and relevant corpus. To label the query-document pairs, we use a state-of-the-art LLM, which shows inter-annotator agreement levels comparable to human performance in our assessments. We provide a detailed description of our annotation methodology to enable others to create similar datasets for other languages, providing a cost-effective way of creating high-quality IR datasets with an arbitrary number of labeled documents per query. Finally, we evaluate a diverse range of open-source and commercial retrievers to serve as baseline systems. Quati is publicly available at https://huggingface.co/datasets/unicamp-dl/quati and all scripts at https://github.com/unicamp-dl/quati ., Comment: 22 pages

The high computational cost associated with pretraining large language models limits their research. Two strategies have emerged to address this issue: domain specialization and pretraining with high-quality data. To explore these strategies, we specialized the Sabi\'a-2 Small model with 1.9 billion unique tokens from reputable Brazilian legal sources and conducted few-shot evaluations on legal and general knowledge exams. Our model, Juru, demonstrates the benefits of domain specialization with a reduced amount of pretraining data. However, this specialization comes at the expense of degrading performance in other knowledge areas within the same language. This study contributes to the growing body of scientific evidence showing that pretraining data selection may enhance the performance of large language models, enabling the exploration of these models at a lower cost.

We introduce Sabi\'a-2, a family of large language models trained on Portuguese texts. The models are evaluated on a diverse range of exams, including entry-level tests for Brazilian universities, professional certification exams, and graduate-level exams for various disciplines such as accounting, economics, engineering, law and medicine. Our results reveal that our best model so far, Sabi\'a-2 Medium, matches or surpasses GPT-4's performance in 23 out of 64 exams and outperforms GPT-3.5 in 58 out of 64 exams. Notably, specialization has a significant impact on a model's performance without the need to increase its size, allowing us to offer Sabi\'a-2 Medium at a price per token that is 10 times cheaper than GPT-4. Finally, we identified that math and coding are key abilities that need improvement.

The Yang and Prentice (YP) regression models have garnered interest from the scientific community due to their ability to analyze data whose survival curves exhibit intersection. These models include proportional hazards (PH) and proportional odds (PO) models as specific cases. However, they encounter limitations when dealing with multivariate survival data due to potential dependencies between the times-to-event. A solution is introducing a frailty term into the hazard functions, making it possible for the times-to-event to be considered independent, given the frailty term. In this study, we propose a new class of YP models that incorporate frailty. We use the exponential distribution, the piecewise exponential distribution (PE), and Bernstein polynomials (BP) as baseline functions. Our approach adopts a Bayesian methodology. The proposed models are evaluated through a simulation study, which shows that the YP frailty models with BP and PE baselines perform similarly to the generator parametric model of the data. We apply the models in two real data sets., Comment: 12 pages, 1 figure

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Clinical dermatology necessitates precision and innovation for efficient diagnosis and treatment of various skin conditions. This paper introduces the development of a cutting-edge hyperspectral dermatoscope (the Hyperscope) tailored for human skin analysis. We detail the requirements to such a device and the design considerations, from optical configurations to sensor selection, necessary to capture a wide spectral range with high fidelity. Preliminary results from 15 individuals and 160 recorded skin images demonstrate the potential of the Hyperscope in identifying and characterizing various skin conditions, offering a promising avenue for non-invasive skin evaluation and a platform for future research in dermatology-related hyperspectral imaging., Comment: 12 pages, 11 Figures

This research introduces a Machine Learning-centric approach to replicate olfactory experiences, validated through experimental quantification of perfume perception. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. This model includes an AI-driven molecule generator (utilizing Graph and Generative Neural Networks), quantification and prediction of odor intensity, and refinery of optimal solvent and molecule combinations for desired fragrances. Additionally, a thermodynamic-based model establishes a link between olfactory perception and liquid-phase concentrations. The methodology employs Transfer Learning and selects the most suitable molecules based on vapor pressure and fragrance notes. Ultimately, a mathematical optimization problem is formulated to minimize discrepancies between new and target olfactory experiences. The methodology is validated by reproducing two distinct olfactory experiences using available experimental data.

Dynamical masses of young planets aged between 10 and 200 Myr detected in imaging play a crucial role in shaping models of giant planet formation. Regrettably, only a few such objects possess these characteristics. Furthermore, the evolutionary pattern of young sub-stellar companions in near-infrared colour-magnitude diagrams might diverge from free-floating objects, possibly due to differing formation processes. The recent identification of a giant planet around AF Lep, part of the beta Pic moving group (BPMG), encouraged us to re-examine these points. We considered updated dynamical masses and luminosities for the sub-stellar objects in the BPMG. In addition, we compared the properties of sub-stellar companions and free-floating objects in the BPMG and other young associations remapping the positions of the objects in the colour-magnitude diagram into a dustiness-temperature plane. We found that cold-start evolutionary models do not reproduce the mass-luminosity relation for sub-stellar companions in the BPMG. This aligns rather closely with predictions from 'hot start' scenarios and is consistent with recent planet formation models. We obtain rather good agreement with masses from photometry and the remapping approach compared to actual dynamical masses. We also found a strong suggestion that the near-infrared colour-magnitude diagram for young companions is different from that of free-floating objects belonging to the same young associations. If confirmed by further data, this last result would imply that cloud settling - which likely causes the transition between L and T spectral type - occurs at a lower effective temperature in young companions than in free-floating objects. This might tentatively be explained with a different chemical composition., Comment: Accepted on Astronomy and Astrophysics. 19 pages, 12 figures

Objective: In cochlear implant users with residual acoustic hearing, compound action potentials (CAPs) can be evoked by acoustic (aCAP) or electric (eCAP) stimulation and recorded through the electrodes of the implant. We propose a novel computational model to simulate aCAPs and eCAPs in humans, considering the interaction between combined electric-acoustic stimulation that occurs in the auditory nerve. Methods: The model consists of three components: a 3D finite element method model of an implanted cochlea, a phenomenological single-neuron spiking model for electric-acoustic stimulation, and a physiological multi-compartment neuron model to simulate the individual nerve fiber contributions to the CAP. Results: The CAP morphologies closely resembled those known from humans. The spread of excitation derived from eCAPs by varying the recording electrode along the cochlear implant electrode array was consistent with published human data. The predicted CAP amplitude growth functions largely resembled human data, with deviations in absolute CAP amplitudes for acoustic stimulation. The model reproduced the suppression of eCAPs by simultaneously presented acoustic tone bursts for different masker frequencies and probe stimulation electrodes. Conclusion: The proposed model can simulate CAP responses to electric, acoustic, or combined electric-acoustic stimulation. It considers the dependence on stimulation and recording sites in the cochlea, as well as the interaction between electric and acoustic stimulation in the auditory nerve. Significance: The model enhances comprehension of CAPs and peripheral electric-acoustic interaction. It can be used in the future to investigate objective methods, such as hearing threshold assessment or estimation of neural health through aCAPs or eCAPs., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

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 Lissard, a benchmark comprising seven tasks whose goal is to assess the ability of models to process and generate wide-range sequence lengths, requiring repetitive procedural execution. Our evaluation of open-source (Mistral-7B and Mixtral-8x7B) and proprietary models (GPT-3.5 and GPT-4) show a consistent decline in performance across all models as the complexity of the sequence increases. The datasets and code are available at https://github.com/unicamp-dl/Lissard

ExaRanker recently introduced an approach to training information retrieval (IR) models, incorporating natural language explanations as additional labels. The method addresses the challenge of limited labeled examples, leading to improvements in the effectiveness of IR models. However, the initial results were based on proprietary language models such as GPT-3.5, which posed constraints on dataset size due to its cost and data privacy. In this paper, we introduce ExaRanker-Open, where we adapt and explore the use of open-source language models to generate explanations. The method has been tested using different LLMs and datasets sizes to better comprehend the effective contribution of data augmentation. Our findings reveal that incorporating explanations consistently enhances neural rankers, with benefits escalating as the LLM size increases. Notably, the data augmentation method proves advantageous even with large datasets, as evidenced by ExaRanker surpassing the target baseline by 0.6 nDCG@10 points in our study. To encourage further advancements by the research community, we have open-sourced both the code and datasets at https://github.com/unicamp-dl/ExaRanker.

This report introduces LLaMandement, a state-of-the-art Large Language Model, fine-tuned by the French government and designed to enhance the efficiency and efficacy of processing parliamentary sessions (including the production of bench memoranda and documents required for interministerial meetings) by generating neutral summaries of legislative proposals. Addressing the administrative challenges of manually processing a growing volume of legislative amendments, LLaMandement stands as a significant legal technological milestone, providing a solution that exceeds the scalability of traditional human efforts while matching the robustness of a specialized legal drafter. We release all our fine-tuned models and training data to the community., Comment: 21 pages, 9 figures

Predictive modeling of CO2 storage sites requires a detailed understanding of physico-chemical processes and potential challenges for scale-up. Dramatic injectivity decline may occur due to salt precipitation pore clogging in high-salinity reservoirs, even over a short time frame. This study aims to elucidate the adverse impact of CO2-induced salt crystallization in porous media on the geomechanical properties of near-wellbore reservoir sandstones. As the impact of salt precipitation cannot be isolated from the precursor effects of interaction with CO2 and carbonic acid, we initiated our study by a comprehensive review of CO2 chemo-mechanical interactions with sandstones. We conducted laboratory geochemical CO2-brine-rock interactions at elevated pressures and temperatures on two sets of porous sandstone with contrasting petrophysical qualities. Two paths were followed: treatment with (a) CO2-acidified brine at 10 MPa fluid pressure and 60C for 7 days, and a second subset continuation with (b) supercritical injection until complete dry-out and salt precipitation. Afterward, the core samples were tested in a triaxial apparatus at varying stresses and temperatures. The elastic moduli of intact, CO2-reacted, and salt-damaged sandstones were juxtaposed to elucidate the extent of crystallization damages. The salt-affected specimens showed a maximum of 50 percent reduction in Young's and shear moduli and twice an increase in Poisson's ratio compared to intact condition. The deterioration was notably higher for the tighter rocks with higher initial stiffness.

This paper introduces INACIA (Instru\c{c}\~ao Assistida com Intelig\^encia Artificial), a groundbreaking system designed to integrate Large Language Models (LLMs) into the operational framework of Brazilian Federal Court of Accounts (TCU). The system automates various stages of case analysis, including basic information extraction, admissibility examination, Periculum in mora and Fumus boni iuris analyses, and recommendations generation. Through a series of experiments, we demonstrate INACIA's potential in extracting relevant information from case documents, evaluating its legal plausibility, and formulating propositions for judicial decision-making. Utilizing a validation dataset alongside LLMs, our evaluation methodology presents a novel approach to assessing system performance, correlating highly with human judgment. These results underscore INACIA's potential in complex legal task handling while also acknowledging the current limitations. This study discusses possible improvements and the broader implications of applying AI in legal contexts, suggesting that INACIA represents a significant step towards integrating AI in legal systems globally, albeit with cautious optimism grounded in the empirical findings.