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The Zero-touch Network & Service Management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this paper, taking advantage of recent advances in generative Artificial Intelligence, we introduce the Network ConFiguration Generator (LLM-NetCFG) that employs Large Language Model and architects ZSM configuration agents by Large Language Models. LLM-NetCFG can automatically generate configurations, verify them, and configure network devices based on intents expressed in natural language. We also show the automation and verification of network configurations with minimum human intervention. Moreover, we explore the opportunities and challenges of integrating LLM in functional areas of network management to fully achieve ZSM.

Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data. To address these limitations, we introduce \textit{Open-FinLLMs}, a series of Financial LLMs. We begin with FinLLaMA, pre-trained on a 52 billion token financial corpus, incorporating text, tables, and time-series data to embed comprehensive financial knowledge. FinLLaMA is then instruction fine-tuned with 573K financial instructions, resulting in FinLLaMA-instruct, which enhances task performance. Finally, we present FinLLaVA, a multimodal LLM trained with 1.43M image-text instructions to handle complex financial data types. Extensive evaluations demonstrate FinLLaMA's superior performance over LLaMA3-8B, LLaMA3.1-8B, and BloombergGPT in both zero-shot and few-shot settings across 19 and 4 datasets, respectively. FinLLaMA-instruct outperforms GPT-4 and other Financial LLMs on 15 datasets. FinLLaVA excels in understanding tables and charts across 4 multimodal tasks. Additionally, FinLLaMA achieves impressive Sharpe Ratios in trading simulations, highlighting its robust financial application capabilities. We will continually maintain and improve our models and benchmarks to support ongoing innovation in academia and industry., Comment: 33 pages, 13 figures

Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies. In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them. To accomplish this task, we adopt a bottom-up network construction approach that combines three years' worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports. Through such representation, we use machine learning to measure the relative significance of different port features. Our model examined such features and revealed that geographical characteristics and the depth of the port are indicators of a port's significance to the Ports Network. Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to ports' importance. The outcomes of our work are aimed to inform decision-making processes related to port development, resource allocation, and infrastructure planning in the industry.

We discuss the generation of a Schr\"odinger cat in a nanocavity created by the coupling of an electromagnetic mode with an exciton in a quantum dot considering the dispersive limit of the Jaynes-Cummings model. More than the generation itself, we focus on the effects of the environment over the bosonic state in the nanocavity, which has losses simulated by coupling with two different kind of reservoirs. In the first case, the interaction between the system with a finite reservoir shows that fragments of different sizes of the reservoir deliver the same amount of information about the physical system in the dynamics of the birth and death of the Schr\"odinger cat. The second case considers a structured reservoir, whose spectral density varies significantly with frequency. This situation becomes relevant in solid-state devices where quantum channels are embedded, as memory effects generally cannot be neglected. Under these circumstances, it is observed that the dynamics can differ substantially from the Markovian, presenting oscillations related to the average number of photons. These oscillations influence the information flow between the system and the environment, evidenced here by the measurement of non-Markovianity.

In this work, we investigate the effects of a controlled conical geometry on the electric charge transport through a two-dimensional quantum ring weakly coupled to both the emitter and the collector. These mesoscopic systems are known for being able to confine highly mobile electrons in a defined region of matter. Particularly, we consider a GaAs device having an average radius of $800\hspace{0.05cm}\text{nm}$ in different regimes of subband occupation at non-zero temperature and under the influence of a weak and uniform background magnetic field. Using the adapted Landauer formula for the resonant tunneling and the energy eigenvalues, we explore how the modified surface affects the Van-Hoove conductance singularities and the magnetoresistance interference patterns resulting from the Aharonov-Bohm oscillations of different frequencies. Magnetoresistance oscillations depending only on the curvature intensity are reported, providing a new feature that represents an alternative way to optimize the transport through the device by tuning its geometry., Comment: 12 pages, 9 figures

The galaxy SDSS1335+0728, which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). Its behaviour suggests that SDSS1335+0728 hosts a $\sim 10^6 M_{\odot}$ black hole (BH) that is currently in the process of `turning on'. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. We used archival photometry and spectroscopic data to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We find that: (a) since 2021, the UV flux is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1-W2 WISE colour has become redder; (c) since February 2024, the source has begun showing X-ray emission; (d) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (e) broad emission lines are not detected; and (f) the [OIII] line increased its flux $\sim 3.6$ years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. We conclude that the variations observed in SDSS1335+0728 could be either explained by an AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGN observed in the process of activating. If the latter, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour., Comment: Accepted for publication in Astronomy & Astrophysics. ESO press release available at https://www.eso.org/public/news/eso2409/. Abstract shortened for arXiv

To investigate the short-term variability and determine the size of the optical continuum emitting size of intermediate-mass black holes (IMBHs), we carried out high-cadence, multi-band photometric monitoring of a Seyfert 1 galaxy J0249-0815 across two nights, together with a one-night single-band preliminary test. The presence of the broad Ha component in our target was confirmed by recent Palomar/P200 spectroscopic observations, 23 years after Sloan Digital Sky Survey, ruling out the supernovae origin of the broad Ha line. The photometric experiment was primarily conducted utilizing four-channel imagers MuSCAT 3 & 4 mounted on 2-meter telescopes within the Las Cumbres Observatory Global Telescope Network. Despite the expectation of variability, we observed no significant variation (<1.4%) on timescales of 6-10 hours. This non-detection is likely due to substantial host galaxy light diluting the subtle AGN variability. Dual-band preliminary tests and tailored simulations may enhance the possibility of detecting variability and lag in future IMBH reverberation campaigns., Comment: 14 pages, 6 figures, submitted to ApJ, comments welcome

We present the second and final release of optical spectroscopy of Type Ia Supernovae (SNe Ia) obtained during the first and second phases of the Carnegie Supernova Project (CSP-I and CSP-II). The newly released data consist of 148 spectra of 30 SNe Ia observed in the course of the CSP-I, and 234 spectra of 127 SNe Ia obtained during the CSP-II. We also present 216 optical spectra of 46 historical SNe Ia, including 53 spectra of 30 SNe Ia observed by the Cal\'an/Tololo Supernova Survey. We combine these observations with previously published CSP data and publicly-available spectra to compile a large sample of measurements of spectroscopic parameters at maximum light, consisting of pseudo-equivalent widths and expansion velocities of selected features, for 232 CSP and historical SNe Ia (including more than 1000 spectra). Finally, we review some of the strongest correlations between spectroscopic and photometric properties of SNe Ia. Specifically, we define two samples: one consisting of SNe Ia discovered by targeted searches (most of them CSP-I objects) and the other composed of SNe Ia discovered by untargeted searches, which includes most of the CSP-II objects. The analysed correlations are similar for both samples. We find a larger incidence of SNe Ia belonging to the Cool (CL)and Broad Line (BL) Branch subtypes among the events discovered by targeted searches, Shallow Silicon (SS) SNe Ia are present with similar frequencies in both samples, while Core Normal (CN) SNe Ia are more frequent in untargeted searches., Comment: 59 pages, 16 figures, accepted for publication in The Astrophysical Journal. May 7, 2024: LaTex file updated: corrected one missing comma and an extraneous space in Table 2

How the human brain processes information during different cognitive tasks is one of the greatest questions in contemporary neuroscience. Understanding the statistical properties of brain signals during specific activities is one promising way to address this question. Here we analyze freely available data from implanted electrocorticography (ECoG) in five human subjects during two different cognitive tasks in the light of information theory quantifiers ideas. We employ a symbolic information approach to determine the probability distribution function associated with the time series from different cortical areas. Then we utilize these probabilities to calculate the associated Shannon entropy and a statistical complexity measure based on the disequilibrium between the actual time series and one with a uniform probability distribution function. We show that an Euclidian distance in the complexity-entropy plane and an asymmetry index for complexity are useful for comparing the two conditions. We show that our method can distinguish visual search epochs from blank screen intervals in different electrodes and patients. By using a multi-scale approach and embedding time delays to downsample the data, we find important time scales in which the relevant information is being processed. We also determine cortical regions and time intervals along the 2-second-long trials that present more pronounced differences between the two cognitive tasks. Finally, we show that the method is useful to distinguish cognitive processes using brain activity on a trial-by-trial basis.

We present the rest-frame ultraviolet-optical spectral properties of 65 broad absorption line (BAL) quasars from the Gemini Near Infrared Spectrograph-Distant Quasar Survey (GNIRS-DQS). These properties are compared with those of 195 non-BAL quasars from GNIRS-DQS in order to identify the drivers for the appearance of BALs in quasar spectra. In particular, we compare equivalent widths and velocity widths, as well as velocity offsets from systemic redshifts, of principal emission lines. In spite of the differences between their rest-frame ultraviolet spectra, we find that luminous BAL quasars are generally indistinguishable from their non-BAL counterparts in the rest-frame optical band at redshifts $1.55 \lesssim z \lesssim 3.50$. We do not find any correlation between BAL trough properties and the H$\beta$-based supermassive black hole masses and normalized accretion rates in our sample. Considering the Sloan Digital Sky Survey quasar sample, which includes the GNIRS-DQS sample, we find that a monochromatic luminosity at rest-frame 2500 A of $\gtrsim 10^{45}$ erg s$^{-1}$ is a necessary condition for launching BAL outflows in quasars. We compare our findings with other BAL quasar samples and discuss the roles that accretion rate and orientation play in the appearance of BAL troughs in quasar spectra., Comment: 18 pages (AASTeX 6.3.1), 5 figures, accepted for publication in ApJ

We present ALMA band-7 observations of J2057-0030, a multi-component merger system at $z$ $\sim$ 4.68 spanning at least 50 kpc in size, using the [CII] $\lambda$157.74 $\mu$m line and underlying far-infrared (FIR) continuum. We find two main components, the quasar (QSO) and a dusty star-forming galaxy (DSFG), both detected in [CII] and continuum emission as well as multiple neighboring clumps detected only in [CII]. Three of these clumps form a (tidal) tail that extends from the QSO in a straight direction to the west, covering a projected distance of $\sim$ 10 kpc. This perturbed morphology, added to a spatial distance of $\sim$ 20 kpc and a velocity offset of $\Delta{v}$ = 68 km s$^{-1}$ between the QSO and the DSFG, strongly supports a merging scenario. By fitting a spectral energy distribution model to the continuum data, we estimate star formation rates of $\approx$ 402 $M_{\odot}$ yr$^{-1}$ for the QSO host and $\approx$ 244 $M_{\odot}$ yr$^{-1}$ for the DSFG, which locate them on or close to the main sequence of star-forming galaxies. The J2057-0030 QSO was selected for being one of the brightest unobscured quasars at its redshift while presenting a rather modest star formation rate. Based on a commonly accepted paradigm regarding the formation of quasars, this result is expected for a quasar that has already passed an obscured phase of rapid star formation during a major merger. However, we see that the merger event in this system is far from being finished, and it is rather likely somewhere between the first pericenter and subsequent close passages. This is presumably another case of a high-$z$ quasar residing in a high-density environment with a companion obscured galaxy., Comment: 16 pages, 11 figures, 3 tables, accepted for publication in Astronomy and Astrophysics

While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity. To bridge this chasm, we introduce ICE-PIXIU, seamlessly amalgamating the ICE-INTENT model and ICE-FLARE benchmark for bilingual financial analysis. ICE-PIXIU uniquely integrates a spectrum of Chinese tasks, alongside translated and original English datasets, enriching the breadth and depth of bilingual financial modeling. It provides unrestricted access to diverse model variants, a substantial compilation of diverse cross-lingual and multi-modal instruction data, and an evaluation benchmark with expert annotations, comprising 10 NLP tasks, 20 bilingual specific tasks, totaling 95k datasets. Our thorough evaluation emphasizes the advantages of incorporating these bilingual datasets, especially in translation tasks and utilizing original English data, enhancing both linguistic flexibility and analytical acuity in financial contexts. Notably, ICE-INTENT distinguishes itself by showcasing significant enhancements over conventional LLMs and existing financial LLMs in bilingual milieus, underscoring the profound impact of robust bilingual data on the accuracy and efficacy of financial NLP., Comment: 19 pages, 3 figures, 12 tables, including Appendix

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

We report on the finding of a newborn AGN, i.e. current AGN activity in a galaxy previously classified as non-active, and characterize its evolution. Black hole ignition event candidates were selected from a parent sample of spectrally classified non-active galaxies (2.394.312 objects), that currently show optical flux variability indicative of a type I AGN, according to the ALeRCE light curve classifier. A second epoch spectrum for a sample of candidate newborn AGN were obtained with the SOAR telescope to search for new AGN features. We present spectral results for the most convincing case of new AGN activity, for a galaxy with a previous star-forming optical classification, where the second epoch spectrum shows the appearance of prominent, broad Balmer lines without significant changes in the narrow line flux ratios. Long term optical lightcurves show a steady increase in luminosity starting 1.5 years after the SDSS spectrum was taken and continuing for at least 7 years. MIR colors from the WISE catalog have also evolved from typical non active galaxy colors to AGN-like colors and recent X-ray flux detections confirm its AGN nature., Comment: Accepted for publication in A&A Letters

LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of comprehensive evaluation benchmarks, the rapid development of LLMs, and the complexity of financial tasks. In this paper, we introduce FinBen, the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks, covering seven critical aspects: information extraction (IE), textual analysis, question answering (QA), text generation, risk management, forecasting, and decision-making. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading. Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting. GPT-4 excels in IE and stock trading, while Gemini is better at text generation and forecasting. Instruction-tuned LLMs improve textual analysis but offer limited benefits for complex tasks such as QA. FinBen has been used to host the first financial LLMs shared task at the FinNLP-AgentScen workshop during IJCAI-2024, attracting 12 teams. Their novel solutions outperformed GPT-4, showcasing FinBen's potential to drive innovation in financial LLMs. All datasets, results, and codes are released for the research community: https://github.com/The-FinAI/PIXIU., Comment: 26 pages, 11 figures

Despite Spanish's pivotal role in the global finance industry, a pronounced gap exists in Spanish financial natural language processing (NLP) and application studies compared to English, especially in the era of large language models (LLMs). To bridge this gap, we unveil Tois\'on de Oro, the first bilingual framework that establishes instruction datasets, finetuned LLMs, and evaluation benchmark for financial LLMs in Spanish joint with English. We construct a rigorously curated bilingual instruction dataset including over 144K Spanish and English samples from 15 datasets covering 7 tasks. Harnessing this, we introduce FinMA-ES, an LLM designed for bilingual financial applications. We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks. The FLARE-ES benchmark results reveal a significant multilingual performance gap and bias in existing LLMs. FinMA-ES models surpass SOTA LLMs such as GPT-4 in Spanish financial tasks, due to strategic instruction tuning and leveraging data from diverse linguistic resources, highlighting the positive impact of cross-linguistic transfer. All our datasets, models, and benchmarks have been released., Comment: 10 pages, 2 figures

This work focuses on the main challenges and problems in developing a virtual oceanic environment reproducing real experiments using Unmanned Surface Vehicles (USV) digital twins. We introduce the key features for building virtual worlds, considering using Reinforcement Learning (RL) agents for autonomous navigation and control. With this in mind, the main problems concern the definition of the simulation equations (physics and mathematics), their effective implementation, and how to include strategies for simulated control and perception (sensors) to be used with RL. We present the modeling, implementation steps, and challenges required to create a functional digital twin based on a real robotic sailing vessel. The application is immediate for developing navigation algorithms based on RL to be applied on real boats.