Miracle-dz/UniFAR-FTUs huggingface dataset:
The rise and fall of WallStreetBets: social roles and opinion leaders across the GameStop saga

Nowadays human interactions largely take place on social networks, with online users' behavior often falling into a few general typologies or"social roles". Among these, opinion leaders are of crucial importance as they have the ability to spread an idea or opinion on a large scale across the network, with possible tangible consequences in the real world. In this work we extract and characterize the different social roles of users within the Reddit WallStreetBets community, around the time of the GameStop short squeeze of January 2021 -- when a handful of committed users led the whole community to engage in a large and risky financial operation. We identify the profiles of both average users and of relevant outliers, including opinion leaders, using an iterative, semi-supervised classification algorithm, which allows us to discern the characteristics needed to play a particular social role. The key features of opinion leaders are large risky investments and constant updates on a single stock, which allowed them to attract a large following and, in the case of GameStop, ignite the interest of the community. Finally, we observe a substantial change in the behavior and attitude of users after the short squeeze event: no new opinion leaders are found and the community becomes less focused on investments. Overall, this work sheds light on the users' roles and dynamics that led to the GameStop short squeeze, while also suggesting why WallStreetBets no longer wielded such large influence on financial markets, in the aftermath of this event.
Synchronization-based lossless non-reciprocal scattering

Breaking the reciprocity of wave propagation is a problem of fundamental interest, and a mucht-sought functionality in practical applications, both in photonics and phononics. Although it has been achieved using resonant linear scattering from cavities with broken time-reversal symmetry, such realizations have remained inescapably plagued by inherent passivity constraints, which make absorption losses unavoidable, leading to stringent limitations in transmitted power. In this work, we solve this problem by converting the cavity resonance into a limit cycle, exploiting the uncharted interplay between non-linearity, gain, and non-reciprocity. Remarkably, strong enough incident waves can synchronize with these self-sustained oscillations and use their energy for amplification. We theoretically and experimentally demonstrate that this mechanism can simultaneously enhance non-reciprocity and compensate absorption. Real-world acoustic scattering experiments allow us to observe perfect non-reciprocal transmission of audible sound in a synchronisation-based 3-port circulator with full immunity against losses.
Pursuit Winning Strategies for Reach-Avoid Games with Polygonal Obstacles

This paper studies a multiplayer reach-avoid differential game in the presence of general polygonal obstacles that block the players' motions. The pursuers cooperate to protect a convex region from the evaders who try to reach the region. We propose a multiplayer onsite and close-to-goal (MOCG) pursuit strategy that can tell and achieve an increasing lower bound on the number of guaranteed defeated evaders. This pursuit strategy fuses the subgame outcomes for multiple pursuers against one evader with hierarchical optimal task allocation in the receding-horizon manner. To determine the qualitative subgame outcomes that who is the game winner, we construct three pursuit winning regions and strategies under which the pursuers guarantee to win against the evader, regardless of the unknown evader strategy. First, we utilize the expanded Apollonius circles and propose the onsite pursuit winning that achieves the capture in finite time. Second, we introduce convex goal-covering polygons (GCPs) and propose the close-to-goal pursuit winning for the pursuers whose visibility region contains the whole protected region, and the goal-visible property will be preserved afterwards. Third, we employ Euclidean shortest paths (ESPs) and construct a pursuit winning region and strategy for the non-goal-visible pursuers, where the pursuers are firstly steered to positions with goal visibility along ESPs. In each horizon, the hierarchical optimal task allocation maximizes the number of defeated evaders and consists of four sequential matchings: capture, enhanced, non-dominated and closest matchings. Numerical examples are presented to illustrate the results.
When do drugs trigger criminal behavior? a machine learning analysis of offenders and non-offenders with schizophrenia and comorbid substance use disorder

Introduction Comorbid substance use disorder (SUD) is linked to a higher risk of violence in patients with schizophrenia spectrum disorder (SSD). The objective of this study is to explore the most distinguishing factors between offending and non-offending patients diagnosed with SSD and comorbid SUD using supervised machine learning. Methods A total of 269 offender patients and 184 non-offender patients, all diagnosed with SSD and SUD, were assessed using supervised machine learning algorithms. Results Failures during opening, referring to rule violations during a permitted temporary leave from an inpatient ward or during the opening of an otherwise closed ward, was found to be the most influential distinguishing factor, closely followed by non-compliance with medication (in the psychiatric history). Following in succession were social isolation in the past, no antipsychotics prescribed (in the psychiatric history), and no outpatient psychiatric treatments before the current hospitalization. Discussion This research identifies critical factors distinguishing offending patients from non-offending patients with SSD and SUD. Among various risk factors considered in prior research, this study emphasizes treatment-related differences between the groups, indicating the potential for improvement regarding access and maintenance of treatment in this particular population. Further research is warranted to explore the relationship between social isolation and delinquency in this patient population.
Experimental and Numerical Study of Geothermal Rainwater Tanks for Buildings Passive Cooling

This communication presents the study of a new hybrid system composed of a buried rainwater tank thermally activated through a water-to-water heat exchanger. This low-tech solution, scarcely studied in the literature (variable level of atmospheric water volume), performs the passive cooling of buildings and reduces domestic water network consumption (for non-potable uses). Experimental results retrieved from two at-scale prototypes are presented. Then, the discussion focusses on the numerical model built and its validation thanks to the experimental results. Sensitivity analysis used for model improvement will also be discussed.
MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs

Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets. To address this issue, we propose Multi-Passage to Dialogue (MP2D), a data generation framework that automatically creates conversational question-answering datasets with natural topic transitions. By leveraging the relationships between entities in a knowledge graph, MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation. It retrieves relevant passages corresponding to the topics and transforms them into dialogues through the passage-to-dialogue method. Through quantitative and qualitative experiments, we demonstrate MP2D's efficacy in generating dialogue with natural topic shifts. Furthermore, this study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog. Utilizing the dataset, we demonstrate that even Large Language Models (LLMs) struggle to handle topic shifts in dialogue effectively, and we showcase the performance improvements of models trained on datasets generated by MP2D across diverse topic shift dialogue tasks.
X-ray and molecular dynamics study of the temperature-dependent structure of molten NaF-ZrF4

The local atomic structure of NaF-ZrF$_4$ (53-47 mol%) molten system and its evolution with temperature are examined with x-ray scattering measurements and compared with $ab-initio$ and Neural Network-based molecular dynamics (NNMD) simulations in the temperature range 515-700 {\deg}C. The machine-learning enhanced NNMD calculations offer improved efficiency while maintaining accuracy at higher distances compared to ab-initio calculations. Looking at the evolution of the Pair Distribution Function with increasing temperature, a fundamental change in the liquid structure within the selected temperature range, accompanied by a slight decrease in overall correlation is revealed. NNMD calculations indicate the co-existence of three different fluorozirconate complexes: [ZrF$_6$]$^{2-}$, [ZrF$_7$]$^{3-}$, and [ZrF$_8$]$^{4-}$, with a temperature-dependent shift in the dominant coordination state towards a 6-coordinated Zr ion at 700{\deg}C. The study also highlights the metastability of different coordination structures, with frequent interconversions between 6 and 7 coordinate states for the fluorozirconate complex from 525 {\deg}C to 700 {\deg}C. Analysis of the Zr-F-Zr angular distribution function reveals the presence of both $"$edge-sharing$"$ and $"$corner-sharing$"$ fluorozirconate complexes with specific bond angles and distances in accord with previous studies, while the next-nearest neighbor cation-cation correlations demonstrate a clear preference for unlike cations as nearest-neighbor pairs, emphasizing non-random arrangement. These findings contribute to a comprehensive understanding of the complex local structure of the molten salt, providing insights into temperature-dependent preferences and correlations within the molten system.
Dissecting Deep RL with High Update Ratios: Combatting Value Overestimation and Divergence

We show that deep reinforcement learning can maintain its ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples. Under such large update-to-data ratios, a recent study by Nikishin et al. (2022) suggested the emergence of a primacy bias, in which agents overfit early interactions and downplay later experience, impairing their ability to learn. In this work, we dissect the phenomena underlying the primacy bias. We inspect the early stages of training that ought to cause the failure to learn and find that a fundamental challenge is a long-standing acquaintance: value overestimation. Overinflated Q-values are found not only on out-of-distribution but also in-distribution data and can be traced to unseen action prediction propelled by optimizer momentum. We employ a simple unit-ball normalization that enables learning under large update ratios, show its efficacy on the widely used dm_control suite, and obtain strong performance on the challenging dog tasks, competitive with model-based approaches. Our results question, in parts, the prior explanation for sub-optimal learning due to overfitting on early data.
The role of landscape preferences in the travel decisions of railway passengers: Evidence from Hungary

Abstract When surveying the motivation side of travel and tourism, we can state that tourists consider in their travel decisions, certain landscape preferences – to a certain extent. It also seems to be evident, however, that the objective evaluation of a landscape is a hard task for researchers for numerous reasons. In recent decades, several attempts have been made to create such methods but it seems that, in Hungary at least, this topic is rather neglected. The aim of this study is to provide an evaluation method for the landscape preferences of passengers travelling on Hungarian railway lines, demonstrating how the landscape around the railways could become an attraction during the travel. We survey what types of landscape appearance would be needed in order to generate travel decisions for tourists and also how the travel experience itself could become a tourism product.
A Bibliometric View of AI Ethics Development

Artificial Intelligence (AI) Ethics is a nascent yet critical research field. Recent developments in generative AI and foundational models necessitate a renewed look at the problem of AI Ethics. In this study, we perform a bibliometric analysis of AI Ethics literature for the last 20 years based on keyword search. Our study reveals a three-phase development in AI Ethics, namely an incubation phase, making AI human-like machines phase, and making AI human-centric machines phase. We conjecture that the next phase of AI ethics is likely to focus on making AI more machine-like as AI matches or surpasses humans intellectually, a term we coin as"machine-like human".
RA-ICM: A Novel Independent Cascade Model Incorporating User Relationships and Attitudes

The rapid development of social networks has a wide range of social effects, which facilitates the study of social issues. Accurately forecasting the information propagation process within social networks is crucial for promptly understanding the event direction and effectively addressing social problems in a scientific manner. The relationships between non-adjacent users and the attitudes of users significantly influence the information propagation process within social networks. However, existing research has ignored these two elements, which poses challenges for accurately predicting the information propagation process. This limitation significantly hinders the study of emotional contagion and influence maximization in social networks. To address these issues, by considering the relationships between non-adjacent users and the influence of user attitudes, we propose a new information propagation model based on the independent cascade model. Experimental results obtained from six real Weibo datasets validate the effectiveness of the proposed model, which is reflected in increased prediction accuracy and reduced time complexity. Furthermore, the information dissemination trend in social networks predicted by the proposed model closely resembles the actual information propagation process, which demonstrates the superiority of the proposed model.
A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets

Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting. The rise of Vision-Language models (VLMs) has unlocked numerous applications, leveraging their existing knowledge to fine-tune on custom data. However, training the whole model is computationally prohibitive, and VLMs while being versatile in general domains still struggle with fine-grained datasets crucial for many applications. We tackle these challenges with two proposed simple modules. The first, Session-Specific Prompts (SSP), enhances the separability of image-text embeddings across sessions. The second, Hyperbolic distance, compresses representations of image-text pairs within the same class while expanding those from different classes, leading to better representations. Experimental results demonstrate an average 10-point increase compared to baselines while requiring at least 8 times fewer trainable parameters. This improvement is further underscored on our three newly introduced fine-grained datasets.
Experimental realization of universal quantum gates and six-qubit state using photonic quantum walk

Controlled quantum walk forms the basis for various quantum algorithm and quantum simulation schemes. Though theoretical proposals are also available to realize universal quantum computation using quantum walks, no experimental demonstration of universal set of gates has been reported. Here we report the experimental realize of universal set of quantum gates using photonic quantum walk. Taking cue from the discrete-time quantum walk formalism, we encode multiple qubits using polarization and paths degree of freedom for photon and demonstrate realization of universal set of gates with 100\% success probability and high fidelity, as characterised by quantum state tomography. For a 3-qubit system we encode first qubit with $H$ and $V-$polarization of photon and path information for the second and third qubit, closely resembling a Mach-Zehnder interference setup. To generate a 6-qubit system and demonstrate 6-qubit GHZ state, entangled photon pairs are used as source to two 3-qubit systems. We also provide insights into the mapping of quantum circuits to quantum walk operations on photons and way to resourcefully scale. This work marks a significant progress towards using photonic quantum walk for quantum computing. It also provides a framework for photonic quantum computing using lesser number of photons in combination with path degree of freedom to increase the success rate of multi-qubit gate operations.
On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization

Text summarization and simplification are among the most widely used applications of AI. However, such models are often prone to hallucination, which can result from training models on unaligned data. One efficient approach to address this issue is Loss Truncation (Kang and Hashimoto, 2020), an approach to modify the standard log loss to adaptively remove noisy examples during training. However, we find that LT alone yields a considerable number of hallucinated entities on various datasets. We study the behavior of the underlying losses between factual and non-factual examples, to understand and refine the performance of LT. We demonstrate that LT’s performance is limited when the underlying assumption that noisy targets have higher NLL loss is not satisfied, and find that word-level NLL among entities provides better signal for distinguishing factuality. We then leverage this to propose a fine-grained NLL loss and fine-grained data cleaning strategies, and observe improvements in hallucination reduction across some datasets. Our work is available at https://github.com/yale-nlp/Simplification-Projects.
Socio-spatial segregation and human mobility: A review of empirical evidence

Social segregation, the spatial and social separation between individuals from different backgrounds, can affect sustainable urban development and social cohesion. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on income, ethnicity, and education. However, this approach overlooks the complexity of spatial segregation because daily activities often extend far beyond residential areas. Over the past one to two decades, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that day-to-day movements can impact segregation by reducing or amplifying segregation levels obtained when considering residential aspects alone. This literature review focuses on three critical questions: (a) to what extent do individual mobility patterns contribute to segregation? (b) Which factors explain the role played by mobility in segregation? and (c) What insights are gained by incorporating extensive mobility data into segregation research? Our literature review contributes to an improved understanding of socio-spatial segregation at the individual level and offers actionable insights into reducing segregation and addressing research gaps in the field.
LiDAR Point Cloud-based Multiple Vehicle Tracking with Probabilistic Measurement-Region Association

Multiple extended target tracking (ETT) has gained increasing attention due to the development of high-precision LiDAR and radar sensors in automotive applications. For LiDAR point cloud-based vehicle tracking, this paper presents a probabilistic measurement-region association (PMRA) ETT model, which can describe the complex measurement distribution by partitioning the target extent into different regions. The PMRA model overcomes the drawbacks of previous data-region association (DRA) models by eliminating the approximation error of constrained estimation and using continuous integrals to more reliably calculate the association probabilities. Furthermore, the PMRA model is integrated with the Poisson multi-Bernoulli mixture (PMBM) filter for tracking multiple vehicles. Simulation results illustrate the superior estimation accuracy of the proposed PMRA-PMBM filter in terms of both positions and extents of the vehicles comparing with PMBM filters using the gamma Gaussian inverse Wishart and DRA implementations.
The impact of Group‐Based Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants Matter?

This paper estimates the impact of participation, by gender, in the Grameen Bank and two other group‐based micro credit programs in Bangladesh on labor supply, schooling, household expenditure, and assets. The empirical method uses a quasi‐experimental survey design to correct for the bias from unobserved individual and village‐level heterogencity. We find that program credit has a larger effect on the behavior of poor households in Bangladesh when women are the program participants. For Example, annual household consumption expenditure increases 18 taka for every 100 additional taka borrowed by women from these credit programs, compared with 11 taka for men.
DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods

Social scientists increasingly use demographically stratified social media data to study the attitudes, beliefs, and behavior of the general public. To facilitate such analyses, we construct, validate, and release publicly the representative DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures. We enrich the user data with high-quality labels for gender, age, and location. DADIT enables us to train and compare the performance of various state-of-the-art models for the prediction of the gender and age of social media users. In particular, we investigate if tweets contain valuable information for the task, since popular classifiers like M3 don't leverage them. Our best XLM-based classifier improves upon the commonly used competitor M3 by up to 53% F1. Especially for age prediction, classifiers profit from including tweets as features. We also confirm these findings on a German test set.
One-Bit Target Detection in Collocated MIMO Radar with Colored Background Noise

One-bit sampling has emerged as a promising technique in multiple-input multiple-output (MIMO) radar systems due to its ability to significantly reduce data volume and processing requirements. Nevertheless, current detection methods have not adequately addressed the impact of colored noise, which is frequently encountered in real scenarios. In this paper, we present a novel detection method that accounts for colored noise in MIMO radar systems. Specifically, we derive Rao's test by computing the derivative of the likelihood function with respect to the target reflectivity parameter and the Fisher information matrix, resulting in a detector that takes the form of a weighted matched filter. To ensure the constant false alarm rate (CFAR) property, we also consider noise covariance uncertainty and examine its effect on the probability of false alarm. The detection probability is also studied analytically. Simulation results demonstrate that the proposed detector provides considerable performance gains in the presence of colored noise.