Northeastern University London

Non-dyadic higher-order interactions affect collective behavior in various networked dynamical systems. Here, we discuss the properties of a novel Ising model with higher-order interactions and characterize its phase transitions between the ordered and the disordered phase. By a mean-field treatment, we show that the transition is continuous when only three-body interactions are considered, but becomes abrupt when interactions of higher orders are introduced. Using a Georges-Yedidia expansion to go beyond a naïve mean-field approximation, we reveal a quantitative shift in the critical point of the phase transition, which does not affect the universality class of the model. Finally, we compare our results with traditional p-spin models with many-body interactions. Our work unveils new collective phenomena on complex interacting systems, revealing the importance of investigating higher-order systems beyond three-body interactions.

A photoresponsive hexa-arylazopyrazole-substitutedtris­(β-diketonato)Co­(III) complex was synthesized, and the Λ- and Δ-enantiomerswere isolated. The complex shows reversible E/Z isomerization using UV (λmax = 365 nm)and visible light (λmax = 520 nm), respectively.Using the Co­(III) complex as a dopant in the commercially availableliquid crystal N-(4-methoxybenzylidene)-4-butylaniline(MBBA), the chiral nematic phase can be induced. Upon photoisomerization,the helical pitch of the doped liquid crystal can be reversibly modulated,leading to very high changes in terms of helical twisting power (HTPor βM). Corresponding to this change,the HTP value of the doped liquid crystals varied between 615 and346 μm–1 or between −563 and −352μm–1 at 30 °C, where the positive andnegative signs correspond to the P and M helix, respectively. Thus, the Co­(III) complex effects both highHTP values of the liquid crystal and large changes upon photoisomerization,enabling induction of the chiral nematic phase at very low dopantconcentrations, which can be useful for the development of opticalapplications.

Goal-directed learning arises from distributed neural circuits including the prefrontal, posterior parietal and temporal cortices. However, the role of cortico-cortical functional interactions remains unclear. To address this question, we integrated information dynamics analysis with magnetoencephalography to investigate the encoding of learning signals through neural interactions. Our findings revealed that information gain (the reduction in uncertainty about the causal relationship between actions and outcomes) is represented over the visual, parietal, lateral prefrontal and ventromedial/orbital prefrontal cortices. Cortico-cortical interactions encoded information gain synergistically at the level of pairwise and higher-order relations, such as triplets and quadruplets. Higher-order synergistic interactions were characterized by long-range relationships centered in the ventromedial and orbitofrontal cortices, which served as key receivers in the broadcast of information gain across cortical circuits. Overall, this study provides evidence that information gain is encoded through synergistic and higher-order functional interactions and is broadcast to prefrontal reward circuits.

Mobility as a Service (MaaS) is anticipated to revolutionize transport by integrating conventional public transport with on-demand and shared services. This innovation promises enhanced convenience, flexibility, and sustainability in urban mobility, drawing interest from both researchers and industry. However, those systems heavily rely on the collection and sharing of personal data among various stakeholders, introducing security and privacy risks. To understand the scale and scope of cyber security and privacy concerns and risks associated with MaaS, we conducted a systematic literature review (SLR) covering 87 relevant research papers published between 2017 and April 2025. Our review represents the most comprehensive examination focusing on cyber security and privacy issues of MaaS systems. Our findings reveal three themes discussed within the MaaS literature: (i) cyber security and privacy risks inherent to MaaS systems, alongside proposed solutions to mitigate such risks; (ii) users’ concerns about these risks and how they affect MaaS adoption; and (iii) laws and policies that govern cyber security and privacy aspects of MaaS systems and solutions. As such, our research serves to not only inform MaaS service providers and users but also advise policymakers and legislators on the potential risks involved and the regulatory measures required to address them.

Advancing an affective-discursive approach, this article examines how distant witnessing bolsters digital activist organizing that challenges a Southern authoritarian regime’s nationalist politics on international social media. The research foregrounds an initiative coordinated by a group of overseas dissidents who sought to expose China’s foreign policy regarding the Russian invasion of Ukraine on X (Twitter). Amid ongoing battlefield stalemates that have prolonged the war, this initiative has evolved into a broader form of resistance, staging distant witnessing through X postings to circumvent the party-state’s internet censorship. As this process unfolds, activists capitalize on social media affordances to curate witness testimonies that counter China’s distorted geopolitical narratives and beyond. Resorting to both pre-discursive and discursive gestures, this repertoire contributes to the formation of affective publics, thereby stimulating public contention over the party-state’s authoritarian rule from afar. The findings highlight how such digital platforms as X enable an informal model of organization, allowing Southern activists to act as mediators who coordinate distant witnessing in a transnational context.

People nowadays express their opinions in online spaces, using different forms of interactions such as posting, sharing, and discussing with one another. How do these digital traces change in response to events happening in the real world? We leverage Reddit conversation data, exploiting its community-based structure, to elucidate how offline events influence online user interactions and behavior. Online conversations, as posts and comments, are analyzed along their temporal and semantic dimensions. Conversations tend to become repetitive with a more limited vocabulary, develop at a faster pace, and feature heightened emotions. As the event approaches, the shifts occurring in conversations are reflected in the users' dynamics. Users become more active, and they exchange information with a growing audience, despite using a less rich vocabulary and repetitive messages. The recurring patterns we discovered are persistent across a wide range of events and several contexts, representing a fingerprint of how online dynamics change in response to real-world occurrences.

Studying how we explore the world in search of novelties is key to understand the mechanisms that can lead to new discoveries. Previous studies analyzed novelties in various exploration processes, defining them as the first appearance of an element. However, novelties can also be generated by combining what is already known. We hence define higher-order novelties as the first time two or more elements appear together, and we introduce higher-order Heaps’ exponents as a way to characterize their pace of discovery. Through extensive analysis of real-world data, we find that processes with the same pace of discovery, as measured by the standard Heaps’ exponent, can instead differ at higher orders. We then propose to model an exploration process as a random walk on a network in which the possible connections between elements evolve in time. The model reproduces the empirical properties of higher-order novelties, revealing how the network we explore changes over time along with the exploration process.

Question-answering systems, characterized by their fundamental functions of question classification, information retrieval, and answer selection, demand refinement to enhance precision in retrieving exact answers. Question classification, a cornerstone task, anticipates the probable answer to a posed query. However, the performance of question classification algorithms is hampered, particularly in agglutinative languages with complex morphology like Persian, where linguistic resources are limited. In this study, we propose a novel multi-layer Long-short-term memory (LSTM) Attention Convolutional Neural Network (CNN) (LACNN) classifier, tailored to extract pertinent information from Persian language contexts. Notably, this model operates autonomously, obviating the need for prior knowledge or external features. Moreover, we introduce UIMQC, the first medical question dataset in Persian, derived from the English GARD dataset. The inquiries within UIMQC are inherently intricate, often pertaining to rare diseases necessitating specialized diagnosis. Our experimental findings demonstrate a notable enhancement over baseline methods, with a 9% performance increase on the UTQC dataset, and achieving 67.08% accuracy on the UIMQC dataset. Consequently, we advocate for the adoption of the LACNN model in various morphological analysis tasks across low-resource languages, as in Question Answering systems it improves the performance for retrieving accurate answers to the users’ queries.

Abstract This study considers the relative benefits for vocabulary learning of exposure to two types of texts—literary or nonliterary—used with two teaching approaches. These approaches were termed functional and creative, respectively. In the former, learners’ attention was drawn to factual information and linguistic features in order to develop their linguistic knowledge. In the latter, the aim was to stimulate learners’ personal and emotional response, by drawing their attention to the text's emotional content and how language was used to express meaning. We analyzed data from 160 learners of French in eight schools in England. Learners in four schools studied French poems and those in another four studied French factual texts. Teachers in each text condition employed functional and creative methods of exploitation within a counterbalanced design. We assessed two types of vocabulary knowledge at pre‐ and posttest: meaning recall of vocabulary contained in the texts, and learners’ general vocabulary size. Our results indicated learning gains across both text types. There were, however, important interactions between text type and teaching approach and between text type and the order in which the teaching approaches were used. Finally, we consider the implications of these findings for understanding of vocabulary learning through literature and for classroom practice.