Miracle-dz/UniFAR-FTUs huggingface dataset:
The rise of rationality in blockchain dynamics

Taking informed decisions, namely acting rationally, is an individual attitude of paramount relevance in nature and human societies. In this work, we study how rationality spreads in a community. To this end, through an agent-based model, we analyse the dynamics of a population whose individuals, endowed with a rational attitude controlled by a numerical parameter, play a simple game. The latter consists of multiple strategies, each associated with a given reward. The proposed model is then used as a benchmark for studying the behaviour of Bitcoin users, inferred by analysing transactions recorded in the Blockchain. Remarkably, a population undergoing a sharp transition from irrational to rational attitudes shows a behavioural pattern similar to that of Bitcoin users, whose rationality showed up as soon as their cryptocurrency became worth just a few cents (USD). To conclude, a behavioural analysis that relies on an entropy measure combined with a simple agent-based model allows us to detect the rise of rationality across a community. Although further investigations are essential to corroborate our results, we deem the proposed approach could also get used for studying other social phenomena and behaviours.
Smart Home Device Detection Algorithm Based on FSA-YOLOv5

Smart home device detection is a critical aspect of human-computer interaction. However, detecting targets in indoor environments can be challenging due to interference from ambient light and background noise. In this paper, we present a new model called FSA-YOLOv5, which addresses the limitations of traditional convolutional neural networks by introducing the Transformer to learn long-range dependencies. Additionally, we propose a new attention module, the full-separation attention module, which integrates spatial and channel dimensional information to learn contextual information. To improve tiny device detection, we include a prediction head for the indoor smart home device detection task. We also release the Southeast University Indoor Smart Speaker Dataset (SUSSD) to supplement existing data samples. Through a series of experiments on SUSSD, we demonstrate that our method outperforms other methods, highlighting the effectiveness of FSA-YOLOv5.
Well-being policy evaluation methodology based on WE pluralism

Methodologies for evaluating and selecting policies that contribute to the well-being of diverse populations need clarification. To bridge the gap between objective indicators and policies related to well-being, this study shifts from constitutive pluralism based on objective indicators to conceptual pluralism that emphasizes subjective context, develops from subject-object pluralism through individual-group pluralism to WE pluralism, and presents a new policy evaluation method that combines joint fact-finding based on policy plurality. First, to evaluate policies involving diverse stakeholders, I develop from individual subjectivity-objectivity to individual subjectivity and group intersubjectivity, and then move to a narrow-wide WE pluralism in the gradation of I-family-community-municipality-nation-world. Additionally, by referring to some functional forms of well-being, I formulate the dependence of well-being on narrow-wide WE. Finally, given that policies themselves have a plurality of social, ecological, and economic values, I define a set of policies for each of the narrow-wide WE and consider a mapping between the two to provide an evaluation basis. Furthermore, by combining well-being and joint fact-finding on the narrow-wide WE consensus, the policy evaluation method is formulated. The fact-value combined parameter system, combined policy-making approach, and combined impact evaluation are disclosed as examples of implementation. This paper contributes to the realization of a well-being society by bridging philosophical theory and policies based on WE pluralism and presenting a new method of policy evaluation based on subjective context and consensus building.
Updated Orbital Ephemeris and Detection of Superhump Modulation in X-Ray Band for the Ultra-compact Low Mass X-Ray Binary 4U 1820-30

The 4U 1820-30 is an ultra-compact low-mass X-ray binary (LMXB) near the center of the globular cluster NGC 6624. Its negative orbital period derivative, observed from the phase evolution of its sinusoidal-like orbital variation, contradicts the positive value that was obtained from the theoretical prediction. In this paper, we present the analysis of the 4U 1820-30 orbital modulation from light curves obtained from the Neutron star Interior Composition ExploreR (NICER) observations from 2017 to mid-2022. Combined with historical records, the orbital derivative is measured from the orbital phase evolution between 1976 and 2002 is Ṗ/P=(−5.21±0.13)×10−8 yr−1. No significant second order orbital period derivative is detected with a 2σ upper limit of ∣P̈∣<5.48×10−22 s s−2. We discuss the possible intrinsic orbital period derivative of 4U 1820-30 and suggest that this binary system may have a significant mass outflow similar to some other LMXBs. In addition, a periodic modulation with a period of 691.6 ± 0.7 s, which is consistent with the superhump period discovered in the far-ultraviolet band of the Hubble Space Telescope, was also detected in the X-ray light curves that were collected by NICER. We conclude that this modulation is probably caused by a period of 0.8 ± 0.1 day apsidal precession of the accretion disk, similar to the SU UMa type dwarf novae and some LMXBs. However we cannot exclude the possibility that it is induced by a hierarchical third star that orbits around the binary system.
Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!

Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space. However, there exist nonnegligible gaps between training and inference, owing to the absence of the forward process during inference. Thus, the model only predicts based on the previously generated reverse noise rather than the noise computed by the forward process. Besides, the widely-used downsampling strategy in speeding up the inference will cause the mismatch of diffusion trajectories between training and inference. To understand and mitigate the above two types of training-inference discrepancies, we launch a thorough preliminary study. Based on our observations, we propose two simple yet effective methods to bridge the gaps mentioned above, named Distance Penalty and Adaptive Decay Sampling. Extensive experiments on \textbf{6} generation tasks confirm the superiority of our methods, which can achieve $100\times \rightarrow 200\times$ speedup with better performance.
NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking

To enhance the ability to find credible evidence in news articles, we propose a novel task of expert recommendation, which aims to identify trustworthy experts on a specific news topic. To achieve the aim, we describe the construction of a novel NewsQuote dataset consisting of 24,031 quote-speaker pairs that appeared on a COVID-19 news corpus. We demonstrate an automatic pipeline for speaker and quote extraction via a BERT-based Question Answering model. Then, we formulate expert recommendations as document retrieval task by retrieving relevant quotes first as an intermediate step for expert identification, and expert retrieval by directly retrieving sources based on the probability of a query conditional on a candidate expert. Experimental results on NewsQuote show that document retrieval is more effective in identifying relevant experts for a given news topic compared to expert retrieval
Information Mutation and Spread of Misinformation in Timely Gossip Networks

We consider a network of $n$ user nodes that receives updates from a source and employs an age-based gossip protocol for faster dissemination of version updates to all nodes. When a node forwards its packet to another node, the packet information gets mutated with probability $p$ during transmission, creating misinformation. The receiver node does not know whether an incoming packet information is different from the packet information originally at the sender node. We assume that truth prevails over misinformation, and therefore, when a receiver encounters both accurate information and misinformation corresponding to the same version, the accurate information gets chosen for storage at the node. We study the expected fraction of nodes with correct information in the network and version age at the nodes in this setting using stochastic hybrid systems (SHS) modelling and study their properties. We observe that very high or very low gossiping rates help curb misinformation, and misinformation spread is higher with moderate gossiping rates. We support our theoretical findings with simulation results which shed further light on the behavior of above quantities.
Data-Driven Bee Identification for DNA Strands

We study a data-driven approach to the bee identification problem for DNA strands. The bee-identification problem, introduced by Tandon et al. (2019), requires one to identify M bees, each tagged by a unique barcode, via a set of M noisy measurements. Later, Chrisnata et al. (2022) extended the model to case where one observes N noisy measurements of each bee, and applied the model to address the unordered nature of DNA storage systems.In such systems, a unique address is typically prepended to each DNA data block to form a DNA strand, but the address may possibly be corrupted. While clustering is usually used to identify the address of a DNA strand, this requires ℳ2 data comparisons (when ℳ is the number of reads). In contrast, the approach of Chrisnata et al. (2022) avoids data comparisons completely. In this work, we study an intermediate, data-driven approach to this identification task.For the binary erasure channel, we first show that we can almost surely correctly identify all DNA strands under certain mild assumptions. Then we propose a data-driven pruning procedure and demonstrate that on average the procedure uses only a fraction of ℳ2 data comparisons. Specifically, for ℳ = 2n and erasure probability p, the expected number of data comparisons performed by the procedure is κℳ2, where ${\left( {\frac{{1 + 2p - {p^2}}}{2}} \right)^n} \leq \kappa \leq {\left( {\frac{{1 + p}}{2}} \right)^n}$.
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic Approach

In this paper we present a neurosymbolic architecture for coupling language-guided visual reasoning with robot manipulation. A non-expert human user can prompt the robot using unconstrained natural language, providing a referring expression (REF), a question (VQA), or a grasp action instruction. The system tackles all cases in a task-agnostic fashion through the utilization of a shared library of primitive skills. Each primitive handles an independent sub-task, such as reasoning about visual attributes, spatial relation comprehension, logic and enumeration, as well as arm control. A language parser maps the input query to an executable program composed of such primitives, depending on the context. While some primitives are purely symbolic operations (e.g. counting), others are trainable neural functions (e.g. visual grounding), therefore marrying the interpretability and systematic generalization benefits of discrete symbolic approaches with the scalability and representational power of deep networks. We generate a 3D vision-and-language synthetic dataset of tabletop scenes in a simulation environment to train our approach and perform extensive evaluations in both synthetic and real-world scenes. Results showcase the benefits of our approach in terms of accuracy, sample-efficiency, and robustness to the user's vocabulary, while being transferable to real-world scenes with few-shot visual fine-tuning. Finally, we integrate our method with a robot framework and demonstrate how it can serve as an interpretable solution for an interactive object-picking task, both in simulation and with a real robot. We make our datasets available in https://gtziafas.github. io/neurosymbolic-manipulation.
A wide field of view plasma spectrometer

We present a fundamentally new type of space plasma spectrometer, the wide field of view plasma spectrometer, whose field of view is > 1. 25π ster using fewer resources than traditional methods. The enabling component is analogous to a pinhole camera with an electrostatic energy‐angle filter at the image plane. Particle energy‐per‐charge is selected with a tunable bias voltage applied to the filter plate relative to the pinhole aperture plate. For a given bias voltage, charged particles from different directions are focused by different angles to different locations. Particles with appropriate locations and angles can transit the filter plate and are measured using a microchannel plate detector with a position‐sensitive anode. Full energy and angle coverage are obtained using a single high‐voltage power supply, resulting in considerable resource savings and allowing measurements at fast timescales. We present laboratory prototype measurements and simulations demonstrating the instrument concept and discuss optimizations of the instrument design for application to space measurements.
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing

Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion. Though such systems have achieved impressive results across a variety of datasets and domains, recent works have called into question whether they are ultimately limited in their ability to compositionally generalize. In this work, we approach semantic parsing from, quite literally, the opposite direction; that is, we introduce a neural semantic parsing generation method that constructs logical forms from the bottom up, beginning from the logical form’s leaves. The system we introduce is lazy in that it incrementally builds up a set of potential semantic parses, but only expands and processes the most promising candidate parses at each generation step. Such a parsimonious expansion scheme allows the system to maintain an arbitrarily large set of parse hypotheses that are never realized and thus incur minimal computational overhead. We evaluate our approach on compositional generalization; specifically, on the challenging CFQ dataset and two other Text-to-SQL datasets where we show that our novel, bottom-up semantic parsing technique outperforms general-purpose semantic parsers while also being competitive with semantic parsers that have been tailored to each task.
Latest Trends in Artificial Intelligence Technology: A Scoping Review

Artificial intelligence is more ubiquitous in multiple domains. Smartphones, social media platforms, search engines, and autonomous vehicles are just a few examples of applications that utilize artificial intelligence technologies to enhance their performance. This study carries out a scoping review of the current state-of-the-art artificial intelligence technologies following the PRISMA framework. The goal was to find the most advanced technologies used in different domains of artificial intelligence technology research. Three recognized journals were used from artificial intelligence and machine learning domain: Journal of Artificial Intelligence Research, Journal of Machine Learning Research, and Machine Learning, and articles published in 2022 were observed. Certain qualifications were laid for the technological solutions: the technology must be tested against comparable solutions, commonly approved or otherwise well justified datasets must be used while applying, and results must show improvements against comparable solutions. One of the most important parts of the technology development appeared to be how to process and exploit the data gathered from multiple sources. The data can be highly unstructured and the technological solution should be able to utilize the data with minimum manual work from humans. The results of this review indicate that creating labeled datasets is very laborious, and solutions exploiting unsupervised or semi-supervised learning technologies are more and more researched. The learning algorithms should be able to be updated efficiently, and predictions should be interpretable. Using artificial intelligence technologies in real-world applications, safety and explainable predictions are mandatory to consider before mass adoption can occur.
Learning from synthetic data generated with GRADE

Recently, synthetic data generation and realistic rendering has advanced tasks like target tracking and human pose estimation. Simulations for most robotics applications are obtained in (semi)static environments, with specific sensors and low visual fidelity. To solve this, we present a fully customizable framework for generating realistic animated dynamic environments (GRADE) for robotics research, first introduced in [1]. GRADE supports full simulation control, ROS integration, realistic physics, while being in an engine that produces high visual fidelity images and ground truth data. We use GRADE to generate a dataset focused on indoor dynamic scenes with people and flying objects. Using this, we evaluate the performance of YOLO and Mask R-CNN on the tasks of segmenting and detecting people. Our results provide evidence that using data generated with GRADE can improve the model performance when used for a pre-training step. We also show that, even training using only synthetic data, can generalize well to real-world images in the same application domain such as the ones from the TUM-RGBD dataset. The code, results, trained models, and the generated data are provided as open-source at https://eliabntt.github. io/grade-rr.
Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach

Matrix manifolds, such as manifolds of Symmetric Positive Definite (SPD) matrices and Grassmann manifolds, appear in many applications. Recently, by applying the theory of gyrogroups and gyrovector spaces that is a powerful framework for studying hyperbolic geometry, some works have attempted to build principled generalizations of Euclidean neural networks on matrix manifolds. However, due to the lack of many concepts in gyrovector spaces for the considered manifolds, e.g., the inner product and gyroangles, techniques and mathematical tools provided by these works are still limited compared to those developed for studying hyperbolic geometry. In this paper, we generalize some notions in gyrovector spaces for SPD and Grassmann manifolds, and propose new models and layers for building neural networks on these manifolds. We show the effectiveness of our approach in two applications, i.e., human action recognition and knowledge graph completion.
Gravitational wave memory of compact binary coalescence in the presence of matter effects

Binary neutron stars (BNSs) and neutron star--black hole (NSBH) binaries are two of the most promising gravitational wave (GW) sources to probe matter effects. Upcoming observing runs of LIGO-Virgo-KAGRA detectors and future third generation detectors like Einstein Telescope and Cosmic Explorer will allow the extraction of detailed information on these matter effects from the GW signature of BNS and NSBH systems. One subtle effect which may be helpful to extract more information from the detection of compact binary systems is the nonlinear memory. In this work, we investigate the observational consequences of gravitational wave nonlinear memory in the presence of matter effects. We start by quantifying the impact of nonlinear memory on distinguishing BNS mergers from binary black holes (BBHs) or NSBH mergers. We find that for the third generation detectors, the addition of nonlinear memory to the GW signal model expands the parameter space where BNS signals become distinguishable from the BBH and NSBH signals. Using numerical relativity simulations, we also study the nonlinear memory generated from the postmerger phase of BNS systems. We find that it does not show a strong dependence on the equation of state of the NS. However, the amplitude of nonlinear memory from the BNS postmerger phase is much lower than the one from BBH systems of the same masses. Furthermore, we compute the detection prospects of nonlinear memory from the postmerger phase of NS systems by accumulating signal strength from a population of BNS mergers for the current and future detectors. Finally, we discuss the impact of possible linear memory from the dynamical ejecta of BNS and NSBH systems and its signal strength relative to the nonlinear memory. We find that linear memory almost always has a much weaker effect than nonlinear memory.
Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data

Jointly extracting entity pairs and their relations is challenging when working on distantly-supervised data with ambiguous or noisy labels.To mitigate such impact, we propose uncertainty-aware bootstrap learning, which is motivated by the intuition that the higher uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths.Specifically, we first explore instance-level data uncertainty to create an initial high-confident examples. Such subset serves as filtering noisy instances and facilitating the model to converge fast at the early stage.During bootstrap learning, we propose self-ensembling as a regularizer to alleviate inter-model uncertainty produced by noisy labels. We further define probability variance of joint tagging probabilities to estimate inner-model parametric uncertainty, which is used to select and build up new reliable training instances for the next iteration.Experimental results on two large datasets reveal that our approach outperforms existing strong baselines and related methods.
On-shell supersymmetry breaking in the Abelian Chern-Simons-matter model in three dimensions of spacetime

This study examines on-shell supersymmetry breaking in the Abelian $\mathcal{N}=1$ Chern-Simons-matter model within a three-dimensional spacetime. The classical Lagrangian is scale-invariant, but two-loop radiative corrections to the effective potential break this symmetry, along with gauge and on-shell supersymmetry. To investigate this issue, the Renormalization Group Equation is used to calculate the two-loop effective potential.
On Contrastive Learning of Semantic Similarity forCode to Code Search

This paper introduces a novel code-to-code search technique that enhances the performance of Large Language Models (LLMs) by including both static and dynamic features as well as utilizing both similar and dissimilar examples during training. We present the first-ever code search method that encodes dynamic runtime information during training without the need to execute either the corpus under search or the search query at inference time and the first code search technique that trains on both positive and negative reference samples. To validate the efficacy of our approach, we perform a set of studies demonstrating the capability of enhanced LLMs to perform cross-language code-to-code search. Our evaluation demonstrates that the effectiveness of our approach is consistent across various model architectures and programming languages. We outperform the state-of-the-art cross-language search tool by up to 44.7\%. Moreover, our ablation studies reveal that even a single positive and negative reference sample in the training process results in substantial performance improvements demonstrating both similar and dissimilar references are important parts of code search. Importantly, we show that enhanced well-crafted, fine-tuned models consistently outperform enhanced larger modern LLMs without fine tuning, even when enhancing the largest available LLMs highlighting the importance for open-sourced models. To ensure the reproducibility and extensibility of our research, we present an open-sourced implementation of our tool and training procedures called Cosco.