Miracle-dz/UniFAR-FTUs huggingface dataset:
Shifting Climates: Climate Change Communication from YouTube to TikTok

Public discourse on critical issues such as climate change is progressively shifting to social media platforms that prioritize short-form video content. Content creators acting on those platforms play a pivotal role in shaping the discourse, yet the dynamics of communication and audience reactions across platforms remain underexplored. To improve our understanding of this transition, we studied the video content produced by 21 prominent YouTube creators who have expanded their influence to TikTok as information disseminators. Using dictionary-based tools and BERT-based embeddings, we analyzed the transcripts of nearly 7k climate-related videos across both platforms and the 574k comments they received. We found that, when publishing on TikTok, creators use a more emotionally resonant, self-referential, and action-oriented language compared to YouTube. We also observed a strong semantic alignment between videos and comments, with creators who excel at diversifying their TikTok content from YouTube typically receiving responses that more closely align with their produced content. This suggests that tailored communication strategies hold greater promise in directing public discussion toward desired topics, which bears implications for the design of effective climate communication campaigns.
Estimating Fréchet bounds for validating programmatic weak supervision

We develop methods for estimating Fr\'echet bounds on (possibly high-dimensional) distribution classes in which some variables are continuous-valued. We establish the statistical correctness of the computed bounds under uncertainty in the marginal constraints and demonstrate the usefulness of our algorithms by evaluating the performance of machine learning (ML) models trained with programmatic weak supervision (PWS). PWS is a framework for principled learning from weak supervision inputs (e.g., crowdsourced labels, knowledge bases, pre-trained models on related tasks, etc), and it has achieved remarkable success in many areas of science and engineering. Unfortunately, it is generally difficult to validate the performance of ML models trained with PWS due to the absence of labeled data. Our algorithms address this issue by estimating sharp lower and upper bounds for performance metrics such as accuracy/recall/precision/F1 score.
Impact of the Fizeau drag effect on Goos-Hänchen shifts in graphene

We investigate the Goos-H\"{a}nchen shifts in reflection for a light beam within a graphene structure, utilizing the Fizeau drag effect induced by its massless Dirac electrons in incident light. The magnitudes of spatial and angular shifts for a light beam propagating against the direction of drifting electrons are significantly enhanced, while shifts for a beam co-propagating with the drifting electrons are suppressed. The Goos-H\"{a}nchen shifts exhibit augmentation with increasing drift velocities of electrons in graphene. The impact of incident wavelength on the angular and spatial shifts in reflection is discussed. Furthermore, the study highlights the crucial roles of the density of charged particles in graphene, the particle relaxation time, and the thickness of the graphene in manipulating the drag-affected Goos-H\"{a}nchen shifts. This investigation offers valuable insights for efficiently guiding light in graphene structures under the influence of the Fizeau drag effect.
Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning

Despite their tremendous success in many applications, large language models often fall short of consistent reasoning and planning in various (language, embodied, and social) scenarios, due to inherent limitations in their inference, learning, and modeling capabilities. In this position paper, we present a new perspective of machine reasoning, LAW, that connects the concepts of Language models, Agent models, and World models, for more robust and versatile reasoning capabilities. In particular, we propose that world and agent models are a better abstraction of reasoning, that introduces the crucial elements of deliberate human-like reasoning, including beliefs about the world and other agents, anticipation of consequences, goals/rewards, and strategic planning. Crucially, language models in LAW serve as a backend to implement the system or its elements and hence provide the computational power and adaptability. We review the recent studies that have made relevant progress and discuss future research directions towards operationalizing the LAW framework.
Almost rectifiable Lie algebras and $k$-wave solutions of nonlinear hydrodynamic-type systems

The aim of this paper is to define and to study families of almost rectifiable vector fields and analyse their applications to the study of hydrodynamic-type equations involving Riemann invariants. Our results explain, in a brief and simple manner, previous findings in the theory of Riemann invariants for hydrodynamic-type equations. Facts concerning the structure of families of almost rectifiable vector fields, their relation to Hamiltonian integrable systems, and practical procedures for studying such vector fields are developed. We also introduce and analyse almost rectifiable Lie algebras, which are motivated by geometric, algebraic, and practical reasons. We classify one-, two-, and three-dimensional almost rectifiable Lie algebras, as well as four- and five-dimensional indecomposable almost rectifiable Lie algebras, and other higher-dimensional ones. New methods for solving systems of hydrodynamic-type equations are established to illustrate our theoretical results. In particular, we study several hydrodynamic-type systems of partial differential equations. Hydrodynamic-type systems admitting k-wave solutions with an associated almost rectifiable Lie algebra of vector fields are defined. We provide new methods for obtaining the submanifolds related to Riemann invariants through almost rectifiable Lie algebras of vector fields and partial differential equations admitting a (nonlinear) superposition rule: the so-called PDE Lie systems.
Distributed Autonomous Organizations as Public Services Supplying Platform

Servizi Elaborazioni Dati SpA is a public company owned by Municipality of L Aquila, it supplies the institution with network services and software applications for distributing services to citizens. The future policy of the company is to enlarge the offer of its services to nearby communities that are unable to set up and maintain their own network and software structures. This paper presents thus a possible architecture model to support small municipalities in supplying public services to citizens, with the aid of SED Spa. Through second level platforms based on Blockchain networks and Multi-agents Systems running on smart contracts, the system will focus on Waste Tax (Ta.Ri) management system in the Fascicolo del Cittadino environment.
Generating Explanations to Understand and Repair Embedding-based Entity Alignment

Entity alignment (EA) seeks identical entities in different knowledge graphs, which is a long-standing task in the database research. Recent work leverages deep learning to embed entities in vector space and align them via nearest neighbor search. Although embedding-based EA has gained marked success in recent years, it lacks explanations for alignment decisions. In this paper, we present the first framework that can generate explanations for understanding and repairing embedding-based EA results. Given an EA pair produced by an embedding model, we first compare its neighbor entities and relations to build a matching subgraph as a local explanation. We then construct an alignment dependency graph to understand the pair from an abstract perspective. Finally, we repair the pair by resolving three types of alignment conflicts based on dependency graphs. Experiments on a variety of EA datasets demonstrate the effectiveness, generalization, and robustness of our framework in explaining and repairing embedding-based EA results.
DARLEI: Deep Accelerated Reinforcement Learning with Evolutionary Intelligence

We present DARLEI, a framework that combines evolutionary algorithms with parallelized reinforcement learning for efficiently training and evolving populations of UNIMAL agents. Our approach utilizes Proximal Policy Optimization (PPO) for individual agent learning and pairs it with a tournament selection-based generational learning mechanism to foster morphological evolution. By building on Nvidia's Isaac Gym, DARLEI leverages GPU accelerated simulation to achieve over 20x speedup using just a single workstation, compared to previous work which required large distributed CPU clusters. We systematically characterize DARLEI's performance under various conditions, revealing factors impacting diversity of evolved morphologies. For example, by enabling inter-agent collisions within the simulator, we find that we can simulate some multi-agent interactions between the same morphology, and see how it influences individual agent capabilities and long-term evolutionary adaptation. While current results demonstrate limited diversity across generations, we hope to extend DARLEI in future work to include interactions between diverse morphologies in richer environments, and create a platform that allows for coevolving populations and investigating emergent behaviours in them. Our source code is also made publicly at https://saeejithnair.github. io/darlei.
Clearing Financial Networks with Derivatives: From Intractability to Algorithms

Financial networks raise a significant computational challenge in identifying insolvent firms and evaluating their exposure to systemic risk. This task, known as the clearing problem, is computationally tractable when dealing with simple debt contracts. However under the presence of certain derivatives called credit default swaps (CDSes) the clearing problem is $\textsf{FIXP}$-complete. Existing techniques only show $\textsf{PPAD}$-hardness for finding an $\epsilon$-solution for the clearing problem with CDSes within an unspecified small range for $\epsilon$. We present significant progress in both facets of the clearing problem: (i) intractability of approximate solutions; (ii) algorithms and heuristics for computable solutions. Leveraging $\textsf{Pure-Circuit}$ (FOCS'22), we provide the first explicit inapproximability bound for the clearing problem involving CDSes. Our primal contribution is a reduction from $\textsf{Pure-Circuit}$ which establishes that finding approximate solutions is $\textsf{PPAD}$-hard within a range of roughly 5%. To alleviate the complexity of the clearing problem, we identify two meaningful restrictions of the class of financial networks motivated by regulations: (i) the presence of a central clearing authority; and (ii) the restriction to covered CDSes. We provide the following results: (i.) The $\textsf{PPAD}$-hardness of approximation persists when central clearing authorities are introduced; (ii.) An optimisation-based method for solving the clearing problem with central clearing authorities; (iii.) A polynomial-time algorithm when the two restrictions hold simultaneously.
Optimal Multi-Pass Lower Bounds for MST in Dynamic Streams

The seminal work of Ahn, Guha, and McGregor in 2012 introduced the graph sketching technique and used it to present the first streaming algorithms for various graph problems over dynamic streams with both insertions and deletions of edges. This includes algorithms for cut sparsification, spanners, matchings, and minimum spanning trees (MSTs). These results have since been improved or generalized in various directions, leading to a vastly rich host of efficient algorithms for processing dynamic graph streams. A curious omission from the list of improvements has been the MST problem. The best algorithm for this problem remains the original AGM algorithm that for every integer $p \geq 1$, uses $n^{1+O(1/p)}$ space in $p$ passes on $n$-vertex graphs, and thus achieves the desired semi-streaming space of $\tilde{O}(n)$ at a relatively high cost of $O(\frac{\log{n}}{\log\log{n}})$ passes. On the other hand, no lower bounds beyond a folklore one-pass lower bound is known for this problem. We provide a simple explanation for this lack of improvements: The AGM algorithm for MSTs is optimal for the entire range of its number of passes! We prove that even for the simplest decision version of the problem -- deciding whether the weight of MSTs is at least a given threshold or not -- any $p$-pass dynamic streaming algorithm requires $n^{1+\Omega(1/p)}$ space. This implies that semi-streaming algorithms do need $\Omega(\frac{\log{n}}{\log\log{n}})$ passes. Our result relies on proving new multi-round communication complexity lower bounds for a variant of the universal relation problem that has been instrumental in proving prior lower bounds for single-pass dynamic streaming algorithms. The proof also involves proving new composition theorems in communication complexity, including majority lemmas and multi-party XOR lemmas, via information complexity approaches.
HALO: An Ontology for Representing Hallucinations in Generative Models

Recent progress in generative AI, including large language models (LLMs) like ChatGPT, has opened up significant opportunities in fields ranging from natural language processing to knowledge discovery and data mining. However, there is also a growing awareness that the models can be prone to problems such as making information up or `hallucinations', and faulty reasoning on seemingly simple problems. Because of the popularity of models like ChatGPT, both academic scholars and citizen scientists have documented hallucinations of several different types and severity. Despite this body of work, a formal model for describing and representing these hallucinations (with relevant meta-data) at a fine-grained level, is still lacking. In this paper, we address this gap by presenting the Hallucination Ontology or HALO, a formal, extensible ontology written in OWL that currently offers support for six different types of hallucinations known to arise in LLMs, along with support for provenance and experimental metadata. We also collect and publish a dataset containing hallucinations that we inductively gathered across multiple independent Web sources, and show that HALO can be successfully used to model this dataset and answer competency questions.
Ranking-based neural network for ambiguity resolution in ACTS

The reconstruction of particle trajectories is a key challenge of particle physics experiments, as it directly impacts particle identification and physics performances while also representing one of the main CPU consumers of many high-energy physics experiments. As the luminosity of particle colliders increases, this reconstruction will become more challenging and resource-intensive. New algorithms are thus needed to address these challenges efficiently. One potential step of track reconstruction is ambiguity resolution. In this step, performed at the end of the tracking chain, we select which tracks candidates should be kept and which must be discarded. The speed of this algorithm is directly driven by the number of track candidates, which can be reduced at the cost of some physics performance. Since this problem is fundamentally an issue of comparison and classification, we propose to use a machine learning-based approach to the Ambiguity Resolution. Using a shared-hits-based clustering algorithm, we can efficiently determine which candidates belong to the same truth particle. Afterwards, we can apply a Neural Network (NN) to compare those tracks and decide which ones are duplicates and which ones should be kept. This approach is implemented within A Common Tracking Software (ACTS) framework and tested on the Open Data Detector (ODD), a realistic virtual detector similar to a future ATLAS one. This new approach was shown to be 15 times faster than the default ACTS algorithm while removing 32 times more duplicates down to less than one duplicated track per event.
Development and Assessment of Autonomous Vehicles in Both Fully Automated and Mixed Traffic Conditions

Autonomous Vehicle (AV) technology is advancing rapidly, promising a significant shift in road transportation safety and potentially resolving various complex transportation issues. With the increasing deployment of AVs by various companies, questions emerge about how AVs interact with each other and with human drivers, especially when AVs are prevalent on the roads. Ensuring cooperative interaction between AVs and between AVs and human drivers is critical, though there are concerns about possible negative competitive behaviors. This paper presents a multi-stage approach, starting with the development of a single AV and progressing to connected AVs, incorporating sharing and caring V2V communication strategy to enhance mutual coordination. A survey is conducted to validate the driving performance of the AV and will be utilized for a mixed traffic case study, which focuses on how the human drivers will react to the AV driving alongside them on the same road. Results show that using deep reinforcement learning, the AV acquired driving behavior that reached human driving performance. The adoption of sharing and caring based V2V communication within AV networks enhances their driving behavior, aids in more effective action planning, and promotes collaborative behavior amongst the AVs. The survey shows that safety in mixed traffic cannot be guaranteed, as we cannot control human ego-driven actions if they decide to compete with AV. Consequently, this paper advocates for enhanced research into the safe incorporation of AVs on public roads.
The TESS-Keck Survey XVII: Precise Mass Measurements in a Young, High Multiplicity Transiting Planet System using Radial Velocities and Transit Timing Variations

We present a radial velocity (RV) analysis of TOI-1136, a bright TESS system with six confirmed transiting planets, and a seventh single-transiting planet candidate. All planets in the system are amenable to transmission spectroscopy, making TOI-1136 one of the best targets for intra-system comparison of exoplanet atmospheres. TOI-1136 is young ($\sim$ 700 Myr), and the system exhibits transit timing variations (TTVs). The youth of the system contributes to high stellar variability on the order of 50 m s$^{-1}$, much larger than the likely RV amplitude of any of the transiting exoplanets. Utilizing 359 HIRES and APF RVs collected as a part of the TESS-Keck Survey (TKS), and 51 HARPS-N RVs, we experiment with a joint TTV-RV fit. With seven possible transiting planets, TTVs, more than 400 RVs, and a stellar activity model, we posit that we may be presenting the most complex mass recovery of an exoplanet system in the literature to date. By combining TTVs and RVs, we minimized GP overfitting and retrieved new masses for this system: (m$_{b-g}$ = 3.50$^{+0.8}_{-0.7}$, 6.32$^{+1.1}_{-1.3}$, 8.35$^{+1.8}_{-1.6}$, 6.07$^{+1.09}_{-1.01}$, 9.7$^{+3.9}_{-3.7}$, 5.6$^{+4.1}_{-3.2}$ M$_{\oplus}$). We are unable to significantly detect the mass of the seventh planet candidate in the RVs, but we are able to loosely constrain a possible orbital period near 80 days. Future TESS observations might confirm the existence of a seventh planet in the system, better constrain the masses and orbital properties of the known exoplanets, and generally shine light on this scientifically interesting system.
Distributionally robust optimization techniques in batch bayesian optimisation

This report is concerned with performing batch bayesian optimization of an unknown function f . Using a Gaussian process (GP) framework to estimate the function, we are searching for the best batch of k points where the function will be evaluated. According to [3], the expected loss of a specific choice of k points involves the expensive integration over multidimensional regions. We reformulate the problem using worst case expectation techniques with second-order moment information [6]. This reformulation is a conservative approximation of the original problem that considers all the distributions with a given mean and covariance, including the Gaussian distribution which is assumed in the original problem. This reformulation is free from the expensive integrations of the original problem. We show that this formulation is overly conservative returning trivial solutions. A solution to avoid this problem is introduced by bounding the support of the distributions that are considered in the worst case expectation. However, this leads to a semi-infinite optimization problem. Sum-of-square techniques are suggested as a possible relaxation.
Interferometric determination of intrinsic nonlinear Kerr index in lead-halide perovskites

Lead halide perovskites have recently been reported to demonstrate an exceptionally high nonlinear (Kerr) refractive index n$_2$ of up to 10$^{-8}$ cm$^2$/W in CH$_3$NH$_3$PbBr$_3$. Other researchers however observe different, substantially more conservative numbers. In order to resolve this disagreement the nonlinear Kerr index of a bulk sample of lead halide perovskite was measured directly by means of an interferometer. This approach has many advantages as compared to the more standard z-scan technique. In particular this method allows studying the induced changes to the refractive index in a time-resolved manner, thus enabling to separate the different contributions to $n_2$. The extracted n$_2$ values for CsPbBr$_3$ and MAPbBr$_3$ at $\lambda \approx 1\mu m$ are $n_2=+2.1\times$ 10$^{-14}$ cm$^2$/W and $n_2=+6\times$ 10$^{-15}$ cm$^2$/W respectively hence substantially lower than what has been indicated in most of the previous reports implying the latter should be regarded with a great care.
A Review of Cooperation in Multi-agent Learning

Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous disciplines, including game theory, economics, social sciences, and evolutionary biology. Research in this area aims to understand both how agents can coordinate effectively when goals are aligned and how they may cooperate in settings where gains from working together are possible but possibilities for conflict abound. In this paper we provide an overview of the fundamental concepts, problem settings and algorithms of multi-agent learning. This encompasses reinforcement learning, multi-agent sequential decision-making, challenges associated with multi-agent cooperation, and a comprehensive review of recent progress, along with an evaluation of relevant metrics. Finally we discuss open challenges in the field with the aim of inspiring new avenues for research.
Analyticity and supershift with irregular sampling

The notion of supershift generalizes that one of superoscillation and expresses the fact that the sampling of a function in an interval allows to compute the values of the function outside the interval. In a previous paper we discussed the case in which the sampling of the function is regular and we are considering supershift in a bounded set, while here we investigate how irregularity in the sampling may affect the answer to the question of whether there is any relation between supershift and real analyticity on the whole real line. We show that the restriction to $\mathbb{R}$ of any entire function displays supershift, whereas the converse is, in general, not true. We conjecture that the converse is true as long as the sampling is regular, we discuss examples in support and we prove that the conjecture is indeed true for periodic functions.