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Highly Ordered Pyrolytic Graphite (HOPG) has been extensively researched due to its chemical and physical properties that make it suitable for applications in several technologies. Its high thermal conductivity makes HOPG an excellent heat sink, a crucial characteristic for manufacturing targets used in nuclear reactions, such as those proposed by the NUMEN project. However, when subjected to different radiation sources, this material undergoes changes in its crystalline structure, which alters its intended functionality. This study examined HOPG sheets before and after exposure to a 14 MeV neutron beam. Morphological and crystallographic analyses reveal that even minor disruptions in the high atomic ordering result in modifications to its thermal properties. The results of this study are essential to establish the survival time of the HOPG used as thermal interface material to improve heat dissipation of a nuclear target to be bombarded by an intense high-energy heavy-ion beam., Comment: Accepted for publication in "Diamond & Related Materials"

Over the past decade, the high-contrast observation of disks and gas giant planets around nearby stars has been made possible on ground-based instruments using extreme adaptive optics (XAO). While these facilities produce images with a Strehl ratio larger than 90% in H-band in median observing conditions and high-flux regime, the correction leaves AO residuals which impede the study of fainter or less massive exoplanets. Cascade AO systems with a fast second stage based on a Pyramid wavefront sensor have recently emerged as an appealing solution to reduce the atmospheric wavefront errors. Since these aberrations are expected to be small, they can also be accurately measured by a Zernike wavefront sensor (ZWFS), a well-known concept for its high sensitivity and moderate linear capture range. We propose an alternative second stage that relies on the ZWFS to correct for the AO residuals. We implemented the cascade AO with a ZWFS-based control loop on the ESO's GHOST testbed to validate the scheme in monochromatic light. In median wind speed and seeing, our second-stage AO with a ZWFS and a basic integrator reduce the atmospheric residuals by a factor of 6 and increase the wavefront error stability with a gain of 2 from open to closed loop. In the presence of non common path aberrations, we also reach a contrast gain by a factor of 2 in the images with a Lyot coronagraph at short separations from the source, proving the ability of our scheme to work in cascade with an XAO loop. In addition, it may prove useful for imaging fainter or lighter close-in companions. In more challenging conditions, contrast improvements are also achieved by adjusting the control loop features. Our study validates the ZWFS-based second-stage AO loop as an effective solution to address small residuals left from a single-stage XAO system for the coronagraphic observations of circumstellar environments., Comment: 15 pages, 15 figures, accepted for publication in A&A, version 2 after language edition

Social-media platforms are one of the most prevalent communication media today. In such systems, a large amount of content is generated and available to the platform. However, not all content can be transmitted to every possible user at all times. At the other end are the users, who have their own preferences about which content they enjoy, which is often unknown ex ante to the platform. We model the interaction between the platform and the users as a signaling game with asymmetric information, where each user optimizes its preference disclosure policy, and the platform optimizes its information disclosure policy. We provide structural as well as existence of policies that constitute Bayesian Nash Equilibria, and necessary optimality conditions used to explicitly compute the optimal policies., Comment: Submitted to American Control Conference 2025

This paper proposes a novel architecture to generate multi-hop answers to open domain questions that require information from texts and tables, using the Open Table-and-Text Question Answering dataset for validation and training. One of the most common ways to generate answers in this setting is to retrieve information sequentially, where a selected piece of data helps searching for the next piece. As different models can have distinct behaviors when called in this sequential information search, a challenge is how to select models at each step. Our architecture employs reinforcement learning to choose between different state-of-the-art tools sequentially until, in the end, a desired answer is generated. This system achieved an F1-score of 19.03, comparable to iterative systems in the literature.

Collecting the most informative data from a large dataset distributed over a network is a fundamental problem in many fields, including control, signal processing and machine learning. In this paper, we establish a connection between selecting the most informative data and finding the top-$k$ elements of a multiset. The top-$k$ selection in a network can be formulated as a distributed nonsmooth convex optimization problem known as quantile estimation. Unfortunately, the lack of smoothness in the local objective functions leads to extremely slow convergence and poor scalability with respect to the network size. To overcome the deficiency, we propose an accelerated method that employs smoothing techniques. Leveraging the piecewise linearity of the local objective functions in quantile estimation, we characterize the iteration complexity required to achieve top-$k$ selection, a challenging task due to the lack of strong convexity. Several numerical results are provided to validate the effectiveness of the algorithm and the correctness of the theory., Comment: Submitted to the IEEE Transactions on Automatic Control

Many modern distributed systems consist of devices that generate more data than what can be transmitted via a communication link in near real time with high-fidelity. We consider the scheduling problem in which a device has access to multiple data sources, but at any moment, only one of them is revealed in real-time to a remote receiver. Even when the sources are Gaussian, and the fidelity criterion is the mean squared error, the globally optimal data selection strategy is not known. We propose a data-driven methodology to search for the elusive optimal solution using linear function approximation approach called neuroscheduling and establish necessary and sufficient conditions for the optimal scheduler to not over fit training data. Additionally, we present several numerical results that show that the globally optimal scheduler and estimator pair to the Gaussian case are nonlinear., Comment: Submitted for presentation at the 2024 Asilomar Conference on Signals, Systems, and Computers

Unitarity sets upper limits on partial-wave elastic and inelastic cross-sections, which are often violated by perturbative computations. We discuss the dynamics underlying these limits in the non-relativistic regime, namely long-range interactions, and show how the resummation of the 2-particle-irreducible diagrams arising from squaring inelastic processes unitarizes both elastic and inelastic cross-sections. We provide a simple prescription to obtain the unitarized cross-sections from those that do not include resummation of the squared inelastic processes. Our results are model-independent, apply to all partial waves, and affect elastic and inelastic cross-sections, with extensive implications for new physics scenarios, such as dark-matter freeze-out, indirect detection and self-interactions., Comment: 12 pages, 4 figures; v2. discussion expanded, published version

We investigate the generation of the baryon asymmetry within the framework of cosmological gra\-vi\-ta\-tional particle production, employing the Bogoliubov approach. We examine two well-known baryogenesis scenarios, namely baryogenesis in Grand Unified Theories (GUT) and leptogenesis, while considering reheating temperatures sufficiently low for thermal processes to be negligible. Considering $\alpha-$attractor T-models for the inflaton potential, we demonstrate that GUT baryogenesis from scalar decays can be successful across a large range of conformal couplings with gravity, without necessitating substantial levels of CP violation. In the case of leptogenesis, we find that the reheating temperature should be $T_{\rm RH}\lesssim 10^{6}~{\rm GeV}$ for right-handed neutrino masses $M_1 \lesssim 6 \times 10^{12}~{\rm GeV}$ to generate the observed asymmetry., Comment: 10 pages, 4 figures

Recently, a number of novel scenarios for primordial black hole (PBH) formation have been discovered. Some of them require very minimal new physics, some others require no new ingredients besides those already present in commonly considered models, such as supersymmetry. At the same time, new strategies have emerged for detection of PBHs. For example, an observation of an orphan kilonova unaccompanied by the gravitational waves signal of merging neutron stars, but associated with a fast radio burst, could be a smoking gun of PBH dark matter. We review some new ideas for PBH formation and detection., Comment: 25 pages, 3 figures. To appear in the book "Primordial Black Holes", ed. Chris Byrnes, Gabriele Franciolini, Tomohiro Harada, Paolo Pani, Misao Sasaki; Springer (2024)

Saliency post-hoc explainability methods are important tools for understanding increasingly complex NLP models. While these methods can reflect the model's reasoning, they may not align with human intuition, making the explanations not plausible. In this work, we present a methodology for incorporating rationales, which are text annotations explaining human decisions, into text classification models. This incorporation enhances the plausibility of post-hoc explanations while preserving their faithfulness. Our approach is agnostic to model architectures and explainability methods. We introduce the rationales during model training by augmenting the standard cross-entropy loss with a novel loss function inspired by contrastive learning. By leveraging a multi-objective optimization algorithm, we explore the trade-off between the two loss functions and generate a Pareto-optimal frontier of models that balance performance and plausibility. Through extensive experiments involving diverse models, datasets, and explainability methods, we demonstrate that our approach significantly enhances the quality of model explanations without causing substantial (sometimes negligible) degradation in the original model's performance., Comment: 27 pages, 22 figures, 8 tables; to appear in NAACL Findings 2024; code and data available at https://github.com/visual-ds/plausible-nlp-explanations

Many socioeconomic phenomena, such as technology adoption, collaborative problem-solving, and content engagement, involve a collection of agents coordinating to take a common action, aligning their decisions to maximize their individual goals. We consider a model for networked interactions where agents learn to coordinate their binary actions under a strict bound on their rationality. We first prove that our model is a potential game and that the optimal action profile is always to achieve perfect alignment at one of the two possible actions, regardless of the network structure. Using a stochastic learning algorithm known as Log Linear Learning, where agents have the same finite rationality parameter, we show that the probability of agents successfully agreeing on the correct decision is monotonically increasing in the number of network links. Therefore, more connectivity improves the accuracy of collective decision-making, as predicted by the phenomenon known as Wisdom of Crowds. Finally, we show that for a fixed number of links, a regular network maximizes the probability of success. We conclude that when using a network of irrational agents, promoting more homogeneous connectivity improves the accuracy of collective decision-making., Comment: Submitted to 2024 IEEE Conference on Decision and Control

Magnification bias, an observational effect of gravitational lensing in the weak regime, allows testing the cosmological model through angular correlations of sources at different redshifts. This effect has been observed in various contexts, particularly with sub-millimeter galaxies (SMGs), offering astrophysical and cosmological insights. The study aims to investigate the magnification bias effect exerted by galaxy clusters on SMGs and its implications for astrophysical and cosmological parameters within the $\Lambda$CDM model. Magnification bias was explored through quantifying the cross-correlation function, utilised to derive constraints on cosmological and astrophysical parameters with a Markov Chain Monte Carlo algorithm. Two distinct galaxy cluster samples were used to assess result robustness and understand the influence of sample characteristics. Cluster samples show higher cross-correlation values than galaxies, with an excess at larger scales suggesting contributions from additional large-scale structures. The parameters obtained, while consistent with galaxies, are less constrained due to broader redshift distributions and limited cluster statistics. Results align with weak lensing studies, hinting at slightly lower $\sigma_8$ and $\Omega_m$ values than \emph{Planck}'s CMB data, emphasizing the need for enhanced precision and alternative low-redshift universe tests. While yielding constraints compatible with the $\Lambda$CDM model, limitations include broader redshift distributions and a limited number of lenses, resulting in less constrained parameters compared to previous galaxy studies. Nonetheless, the study underscores the potential of using galaxy clusters as lenses for magnification bias studies, capitalising on their elevated mass, providing a promising avenue to test current Cosmology theories. Further progress can be made by expanding the lens sample size.

We reexamine production of primordial black holes in a supercooled phase transition. While a mere overdensity associated with a surviving false-vacuum patch does not imply formation of a black hole, it is possible for such a patch to evolve and create a black hole, thanks to the gradient energy stored in the bubble wall., Comment: 12 pages, 3 figures

Logical reasoning is central to complex human activities, such as thinking, debating, and planning; it is also a central component of many AI systems as well. In this paper, we investigate the extent to which encoder-only transformer language models (LMs) can reason according to logical rules. We ask whether those LMs can deduce theorems in propositional calculus and first-order logic; if their relative success in these problems reflects general logical capabilities; and which layers contribute the most to the task. First, we show for several encoder-only LMs that they can be trained, to a reasonable degree, to determine logical validity on various datasets. Next, by cross-probing fine-tuned models on these datasets, we show that LMs have difficulty in transferring their putative logical reasoning ability, which suggests that they may have learned dataset-specific features, instead of a general capability. Finally, we conduct a layerwise probing experiment, which shows that the hypothesis classification task is mostly solved through higher layers.

This work proposes a novel multi-objective optimization approach that globally finds a representative non-inferior set of solutions, also known as Pareto-optimal solutions, by automatically formulating and solving a sequence of weighted sum method scalarization problems. The approach is called MONISE (Many-Objective NISE) because it represents an extension of the well-known non-inferior set estimation (NISE) algorithm, which was originally conceived to deal with two-dimensional objective spaces. The proposal is endowed with the following characteristics: (1) uses a mixed-integer linear programming formulation to operate in two or more dimensions, thus properly supporting many (i.e., three or more) objectives; (2) relies on an external algorithm to solve the weighted sum method scalarization problem to optimality; and (3) creates a faithful representation of the Pareto frontier in the case of convex problems, and a useful approximation of it in the non-convex case. Moreover, when dealing specifically with two objectives, some additional properties are portrayed for the estimated non-inferior set. Experimental results validate the proposal and indicate that MONISE is competitive, in convex and non-convex (combinatorial) problems, both in terms of computational cost and the overall quality of the non-inferior set, measured by the acquired hypervolume., Comment: This paper is identical to arXiv:1709.00797 but I created a new submission by mistake

A singular foliation $\mathcal{F}$ on a complete Riemannian manifold $M$ is called Singular Riemannian foliation (SRF for short) if its leaves are locally equidistant, e.g., the partition of $M$ into orbits of an isometric action. In this paper, we investigate variational problems in compact Riemannian manifolds equipped with SRF with special properties, e.g. isoparametric foliations, SRF on fibers bundles with Sasaki metric, and orbit-like foliations. More precisely, we prove two results analogous to Palais' Principle of Symmetric Criticality, one is a general principle for $\mathcal{F}$ symmetric operators on the Hilbert space $W^{1,2}(M)$, the other one is for $\mathcal{F}$ symmetric integral operators on the Banach spaces $W^{1,p}(M)$. These results together with a $\mathcal{F}$ version of Rellich Kondrachov Hebey Vaugon Embedding Theorem allow us to circumvent difficulties with Sobolev's critical exponents when considering applications of Calculus of Variations to find solutions to PDEs. To exemplify this we prove the existence of weak solutions to a class of variational problems which includes $p$-Kirschoff problems., Comment: 54 pages

[abridged] We propose a model of the Universe (dubbed $\eta$CDM) featuring a stochastic evolution of the cosmological quantities, that is meant to render small deviations from homogeneity/isotropy on scales of $30-50\, h^{-1}$ Mpc at late cosmic times, associated to the emergence of the cosmic web. Specifically, we prescribe that the behavior of the matter/radiation energy densities in different patches of the Universe with such a size can be effectively described by a stochastic version of the mass-energy evolution equation. The latter includes an appropriate noise term that statistically accounts for local fluctuations due to inhomogeneities, anisotropic stresses and matter flows. The evolution of the different patches as a function of cosmic time is rendered via the diverse realizations of the noise term; meanwhile, at any given cosmic time, sampling the ensemble of patches will originate a nontrivial spatial distribution of the cosmological quantities. The overall behavior of the Universe will be obtained by averaging over the patch ensemble. We assume a physically reasonable parameterization of the noise term, gauging it against a wealth of cosmological datasets. We find that, with respect to standard $\Lambda$CDM, the ensemble-averaged cosmic dynamics in the $\eta$CDM model is substantially altered in three main respects: (i) an accelerated expansion is enforced at late cosmic times without the need for any additional exotic component (e.g., dark energy); (ii) the spatial curvature can stay small even in a low-density Universe; (iii) matter can acquire an effective negative pressure at late times. We provide predictions for the variance of the cosmological quantities among different patches of the Universe at late cosmic times. Finally, we show that in $\eta$CDM the Hubble-tension is solved, and the cosmic coincidence problem is relieved without invoking the anthropic principle., Comment: 28 pages, 7 figures, typos corrected. Accepted by ApJ

Coordination is a desirable feature in many multi-agent systems such as robotic and socioeconomic networks. We consider a task allocation problem as a binary networked coordination game over an undirected regular graph. Each agent in the graph has bounded rationality, and uses a distributed stochastic learning algorithm to update its action choice conditioned on the actions currently played by its neighbors. After establishing that our framework leads to a potential game, we analyze the regime of bounded rationality, where the agents are allowed to make sub-optimal decisions with some probability. Our analysis shows that there is a relationship between the connectivity of the network, and the rationality of the agents. In particular, we show that in some scenarios, an agent can afford to be less rational and still converge to a near optimal collective strategy, provided that its connectivity degree increases. Such phenomenon is akin to the wisdom of crowds., Comment: Submitted to American Control Conference 2024

Pir\'a is a reading comprehension dataset focused on the ocean, the Brazilian coast, and climate change, built from a collection of scientific abstracts and reports on these topics. This dataset represents a versatile language resource, particularly useful for testing the ability of current machine learning models to acquire expert scientific knowledge. Despite its potential, a detailed set of baselines has not yet been developed for Pir\'a. By creating these baselines, researchers can more easily utilize Pir\'a as a resource for testing machine learning models across a wide range of question answering tasks. In this paper, we define six benchmarks over the Pir\'a dataset, covering closed generative question answering, machine reading comprehension, information retrieval, open question answering, answer triggering, and multiple choice question answering. As part of this effort, we have also produced a curated version of the original dataset, where we fixed a number of grammar issues, repetitions, and other shortcomings. Furthermore, the dataset has been extended in several new directions, so as to face the aforementioned benchmarks: translation of supporting texts from English into Portuguese, classification labels for answerability, automatic paraphrases of questions and answers, and multiple choice candidates. The results described in this paper provide several points of reference for researchers interested in exploring the challenges provided by the Pir\'a dataset., Comment: Accepted at Data Intelligence. Online ISSN 2641-435X

Many large-scale distributed multi-agent systems exchange information over low-power communication networks. In particular, agents intermittently communicate state and control signals in robotic network applications, often with limited power over an unlicensed spectrum, prone to eavesdropping and denial-of-service attacks. In this paper, we argue that a widely popular low-power communication protocol known as LoRa is vulnerable to denial-of-service attacks by an unauthenticated attacker if it can successfully identify a target signal's bandwidth and spreading factor. Leveraging a structural pattern in the LoRa signal's instantaneous frequency representation, we relate the problem of jointly inferring the two unknown parameters to a classification problem, which can be efficiently implemented using neural networks., Comment: Accepted for presentation at Asilomar Conference on Signals, Systems, and Computers, 2023

Supersymmetry predicts multiple flat directions, some of which carry a net baryon or lepton number. Condensates in such directions form during inflation and later fragment into Q-balls, which can become the building blocks of primordial black holes. Thus supersymmetry can create conditions for an intermediate matter-dominated era with black holes dominating the energy density of the universe. Unlike particle matter, black holes decay suddenly enough to result in an observable gravitational wave signal via the poltergeist mechanism. We investigate the gravitational waves signatures of supersymmetry realized at energy scales that might not be accessible to present-day colliders., Comment: 9 pages, 3 figures

In the presence of (relatively) long-range forces, structures can form even during the radiation dominated era, leading to compact objects, such as Fermi balls or primordial black holes (PBHs), which can account for all or part of dark matter. We present a detailed analysis of a model in which fermions are produced from the inflaton decay developing some particle-antiparticle asymmetry. These fermions undergo clustering and structure formation driven by a Yukawa interaction. The same interaction provides a cooling channel for the dark halos via scalar radiation, leading to rapid collapse and the formation of a compact object. We discuss the criteria for the formation of either PBHs and Fermi balls. In the PBH formation regime, supermassive PBHs can seed the active galactic nuclei or quasars found at high redshift. Alternatively, Fermi balls can account for all of the cold dark matter, while evading microlensing constraints., Comment: 9 pages, 6 figures

We suggest that "$G$ objects" recently discovered in the Galactic Center may be clouds of gas bound by the gravitational field of stellar-mass black holes produced in the interactions of sublunar primordial black holes with neutron stars. If dark matter is composed of primordial black holes with masses $(10^{-16} - 10^{-10}) M_\odot$, these black holes can be captured by neutron stars in the Galactic Center, where the dark matter density is high. After the capture, the neutron star is consumed by the black hole, resulting in a population of $(1-2) M_\odot $ black holes. These stellar-mass black holes, accompanied by gaseous atmospheres, can account for the observed properties of the $G$ objects, including their resilience to tidal disruption by the supermassive black hole in the Galactic Center while also producing emission consistent with inferred luminosities., Comment: 8 pages, 2 figures, Accepted into Phys. Rev. D

We show that the present-day dark matter abundance can be produced through a novel mechanism that involves a very rapid thermal freeze-out caused by inhomogeneous heating and successive fast cooling of small fireballs in the early Universe. The fireballs can be produced from energy deposited in small scale structure growth induced by Yukawa interactions in certain particle species. Yukawa interactions are known to cause growth of halos even during a radiation dominated era, and the same interactions facilitate cooling and collapse of the halos by the emission of scalars. Energy deposited in the Standard Model plasma at the locations of the halo collapse can heat the plasma, re-establishing thermal equilibrium. The subsequent expansion and cooling of plasma fireballs leads to freeze-out of dark matter on timescales much shorter than the Hubble time. This mechanism can produce the right abundance of dark matter for masses and annihilation cross sections previously thought to be ruled out., Comment: 6 pages, 3 figures

The main goal of this work, the second in a three-paper series, is to test the impact of a methodological improvement in measuring the magnification bias signal on a sample of submillimeter galaxies and its implications for constraining cosmological parameters. The analysis considers the angular cross-correlation function between a foreground sample of GAMA galaxies ($0.2

We study the problem of achieving decentralized coordination by a group of strategic decision makers choosing to engage or not in a task in a stochastic setting. First, we define a class of symmetric utility games that encompass a broad class of coordination games, including the popular framework known as \textit{global games}. With the goal of studying the extent to which agents engaging in a stochastic coordination game indeed coordinate, we propose a new probabilistic measure of coordination efficiency. Then, we provide an universal information theoretic upper bound on the coordination efficiency as a function of the amount of noise in the observation channels. Finally, we revisit a large class of global games, and we illustrate that their Nash equilibrium policies may be less coordination efficient then certainty equivalent policies, despite of them providing better expected utility. This counter-intuitive result, establishes the existence of a nontrivial trade-offs between coordination efficiency and expected utility in coordination games., Comment: Submitted to IEEE Conference on Decision and Control 2023

Multi-agent systems often communicate over low-power shared wireless networks in unlicensed spectrum, prone to denial-of-service attacks. We consider the following scenario: multiple pairs of agents communicating strategically over shared communication networks in the presence of a jammer who may launch a denial-of-service. We cast this problem as a game between a coordinator who optimizes the transmission and estimation policies jointly and a jammer who optimizes its probability of performing an attack. We consider two cases: point-to-point channels and large-scale networks with a countably infinite number of sensor-receiver pairs. When the jammer proactively attacks the channel, the game is nonconvex from the coordinator's perspective. However, despite the lack of convexity, we construct a saddle point equilibrium solution for any multi-variate Gaussian distribution for the observations. When the jammer is reactive, we obtain an algorithm based on sequential convex optimization, which converges swiftly to first-order Nash-equilibria. Interestingly, blocking the channel is often optimal when the jammer is reactive, even when it is idle, to create ambiguity at the receiver., Comment: Submitted to IEEE Transactions on Automatic Control

Coordination is a desirable feature in multi-agent systems, allowing the execution of tasks that would be impossible by individual agents. We study coordination by a team of strategic agents choosing to undertake one of the multiple tasks. We adopt a stochastic framework where the agents decide between two distinct tasks whose difficulty is randomly distributed and partially observed. We show that a Nash equilibrium with a simple and intuitive linear structure exists for diffuse prior distributions on the task difficulties. Additionally, we show that the best response of any agent to an affine strategy profile can be nonlinear when the prior distribution is not diffuse. Finally, we state an algorithm that allows us to efficiently compute a data-driven Nash equilibrium within the class of affine policies., Comment: Submitted to the 57th Conference on Information Science and Systems

The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for data-to-text generation have been proposed with an end-to-end deep learning flavor, which handles non-linguistic input in natural language without explicit intermediary representations. This study compares the most often employed methods for generating Brazilian Portuguese texts from structured data. Results suggest that explicit intermediate steps in the generation process produce better texts than the ones generated by neural end-to-end architectures, avoiding data hallucination while better generalizing to unseen inputs. Code and corpus are publicly available., Comment: Accepted at the 19th National Meeting on Artificial and Computational Intelligence (ENIAC 2022)

The existence of scalar fields can be probed by observations of stochastic gravitational waves. Scalar fields mediate attractive forces, usually stronger than gravity, on the length scales shorter than their Compton wavelengths, which can be non-negligible in the early Universe, when the horizon size is small. These attractive forces exhibit an instability similar to the gravitational instability, only stronger. They can, therefore, lead to the growth of structures in some species. We identify a gravitational waves signature of such processes and show that it can be detected by future gravitational waves experiments., Comment: 9 pages, 2 figure

We describe the first steps in the development of an artificial agent focused on the Brazilian maritime territory, a large region within the South Atlantic also known as the Blue Amazon. The "BLue Amazon Brain" (BLAB) integrates a number of services aimed at disseminating information about this region and its importance, functioning as a tool for environmental awareness. The main service provided by BLAB is a conversational facility that deals with complex questions about the Blue Amazon, called BLAB-Chat; its central component is a controller that manages several task-oriented natural language processing modules (e.g., question answering and summarizer systems). These modules have access to an internal data lake as well as to third-party databases. A news reporter (BLAB-Reporter) and a purposely-developed wiki (BLAB-Wiki) are also part of the BLAB service architecture. In this paper, we describe our current version of BLAB's architecture (interface, backend, web services, NLP modules, and resources) and comment on the challenges we have faced so far, such as the lack of training data and the scattered state of domain information. Solving these issues presents a considerable challenge in the development of artificial intelligence for technical domains.

We show that viable electroweak baryogenesis can be realized without a first-order phase transition if plasma is heated inhomogeneously by non-gravitational structure formation in some particle species. Yukawa interactions can mediate relatively long-range attractive forces in the early Universe. This creates an instability and leads to growth of structure in some species even during the radiation dominated era. At temperatures below the elecroweak scale, the collapsing and annihilating halos can heat up plasma in fireballs that expand and create the out-of-equilibrium high-temperature environment suitable for generating the baryon asymmetry. The plasma temperature at the time of baryogenesis can be as low as 10 MeV, making it consistent with both standard and low-reheat cosmologies., Comment: 6 pages, 2 figures

Consider a set of agents collaboratively solving a distributed convex optimization problem, asynchronously, under stringent communication constraints. In such situations, when an agent is activated and is allowed to communicate with only one of its neighbors, we would like to pick the one holding the most informative local estimate. We propose new algorithms where the agents with maximal dissent average their estimates, leading to an information mixing mechanism that often displays faster convergence to an optimal solution compared to randomized gossip. The core idea is that when two neighboring agents whose distance between local estimates is the largest among all neighboring agents in the network average their states, it leads to the largest possible immediate reduction of the quadratic Lyapunov function used to establish convergence to the set of optimal solutions. As a broader contribution, we prove the convergence of max-dissent subgradient methods using a unified framework that can be used for other state-dependent distributed optimization algorithms. Our proof technique bypasses the need of establishing the information flow between any two agents within a time interval of uniform length by intelligently studying convergence properties of the Lyapunov function used in our analysis.

In this paper, we discuss how to travel along horizontal broken geodesics of a homogenous Finsler submersion, i.e., we study, what in Riemannian geometry was called by Wilking, the dual leaves. More precisely, we investigate the attainable sets $\mathcal{A}_{q}(\mathcal{C})$ of the set of analytic vector fields $\mathcal{C}$ determined by the family of horizontal unit geodesic vector fields $\mathcal{C}$ to the fibers $\mathcal{F}=\{\rho^{-1}(c)\}$ of a homogenous analytic Finsler submersion $\rho: M\to B$. Since reverse of geodesics don't need to be geodesics in Finsler geometry, one can have examples on non compact Finsler manifolds $M$ where the attainable sets (the dual leaves) are no longer orbits or even submanifolds. Nevertheless we prove that, when $M$ is compact and the orbits of $\mathcal{C}$ are embedded, then the attainable sets coincide with the orbits. Furthermore, if the flag curvature is positive then $M$ coincides with the attainable set of each point. In other words, fixed two points of $M$, one can travel from one point to the other along horizontal broken geodesics. In addition, we show that each orbit $\mathcal{O}(q)$ of $\mathcal{C}$ associated to a singular Finsler foliation coincides with $M$, when the flag curvature is positive, i.e, we prove Wilking's result in Finsler context. In particular we review Wilking's transversal Jacobi fields in Finsler case.