Miracle-dz/UniFAR-FTUs huggingface dataset:
The Generative AI Paradox: "What It Can Create, It May Not Understand"

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models now take only seconds to produce outputs that would challenge or exceed the capabilities even of expert humans. At the same time, models still show basic errors in understanding that would not be expected even in non-expert humans. This presents us with an apparent paradox: how do we reconcile seemingly superhuman capabilities with the persistence of errors that few humans would make? In this work, we posit that this tension reflects a divergence in the configuration of intelligence in today's generative models relative to intelligence in humans. Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon -- and can therefore exceed -- their ability to understand those same types of outputs. This contrasts with humans, for whom basic understanding almost always precedes the ability to generate expert-level outputs. We test this hypothesis through controlled experiments analyzing generation vs. understanding in generative models, across both language and image modalities. Our results show that although models can outperform humans in generation, they consistently fall short of human capabilities in measures of understanding, as well as weaker correlation between generation and understanding performance, and more brittleness to adversarial inputs. Our findings support the hypothesis that models' generative capability may not be contingent upon understanding capability, and call for caution in interpreting artificial intelligence by analogy to human intelligence.
Assessing Mobile Application Privacy: A Quantitative Framework for Privacy Measurement

The proliferation of mobile applications and the subsequent sharing of personal data with service and application providers have given rise to substantial privacy concerns. Application marketplaces have introduced mechanisms to conform to regulations and provide individuals with control over their data. However, a notable absence persists regarding clear indications, labels or scores elucidating the privacy implications of these applications. In response to this challenge, this paper introduces a privacy quantification framework. The purpose of this framework is to systematically evaluate the level of privacy risk when using particular Android applications. The main goal is to provide individuals with qualitative labels to make informed decisions about their privacy. This work aims to contribute to a digital environment that prioritizes privacy, promotes informed decision-making, and endorses the privacy-preserving design principles incorporation.
Description of Nesting Habitat for Least Bell's Vireo in San Diego County

Least Bell's Vireo (Vireo bellii pusillus) nesting sites on three rivers in coastal southern California were characterized to provide data for a habitat restoration plan for this endangered species. In addition, riparian areas outside vireo territories were sampled to compare with nesting habitat. The parameters measured were percent cover, percent open ground, plant heights, plant density, height class frequency, species frequency, and species diversity. The vireo nest sites showed no significant differences from the non-nest riparian sites for any of the variables studied. The nest sites on the San Diego River had lower native plant density and diversity than nest sites on the Sweetwater and San Luis Rey rivers. There were no significant differences in nesting habitat between the Sweetwater and San Luis Rey. We suggest using the data from all three rivers in designing revegetation sites for the Least Bell's Vireo in coastal San Diego County if data on the specific watershed being revegetated can not be obtained.
LATIS: Constraints on the Galaxy–Halo Connection at z ∼ 2.5 from Galaxy–Galaxy and Galaxy–Lyα Clustering

The connection between galaxies and dark matter halos is often quantified using the stellar mass–halo mass (SMHM) relation. Optical and near-infrared imaging surveys have led to a broadly consistent picture of the evolving SMHM relation based on measurements of galaxy abundances and angular correlation functions. Spectroscopic surveys at z ≳ 2 can also constrain the SMHM relation via the galaxy autocorrelation function and through the cross-correlation between galaxies and Lyα absorption measured in transverse sight lines; however, such studies are very few and have produced some unexpected or inconclusive results. We use ∼3000 spectra of z ∼ 2.5 galaxies from the Lyα Tomography IMACS Survey (LATIS) to measure the galaxy–galaxy and galaxy–Lyα correlation functions in four bins of stellar mass spanning 109.2 ≲ M */M ⊙ ≲ 1010.5. Parallel analyses of the MultiDark N-body and ASTRID hydrodynamic cosmological simulations allow us to model the correlation functions, estimate covariance matrices, and infer halo masses. We find that results of the two methods are mutually consistent and broadly accord with standard SMHM relations. This consistency demonstrates that we are able to measure and model Lyα transmission fluctuations δ F in LATIS accurately. We also show that the galaxy–Lyα cross-correlation, a free by-product of optical spectroscopic galaxy surveys at these redshifts, can constrain halo masses with similar precision to galaxy–galaxy clustering.
Online Student-t Processes with an Overall-local Scale Structure for Modelling Non-stationary Data

Time-dependent data often exhibit characteristics, such as non-stationarity and heavy-tailed errors, that would be inappropriate to model with the typical assumptions used in popular models. Thus, more flexible approaches are required to be able to accommodate such issues. To this end, we propose a Bayesian mixture of student-$t$ processes with an overall-local scale structure for the covariance. Moreover, we use a sequential Monte Carlo (SMC) sampler in order to perform online inference as data arrive in real-time. We demonstrate the superiority of our proposed approach compared to typical Gaussian process-based models on real-world data sets in order to prove the necessity of using mixtures of student-$t$ processes.
Universal symmetry of optimal control at the microscale

Optimal control is an important field in thermodynamics, mathematics and engineering which searches for strategies e.g. to move an object to a target position within a given time with minimal energetic effort. Especially for micro- and nanomachines, for which power supply is often limited, knowledge of optimal control protocols is crucial for their operation under realistic conditions. Here we investigate experimentally and theoretically the optimal protocol for transporting a microscopic colloidal particle with an optical trap in such a way that the required work is minimal. The experiments were conducted in viscous and viscoelastic media, which represent typical environments for synthetic and biological nanomachines. Despite marked differences between the protocols in both fluids, we find that the optimal transport protocol and the corresponding average particle trajectory always obeys time-reversal symmetry. This symmetry, which surprisingly appears here for a class of processes far away from thermal equilibrium, is moreover expected to hold universally for e.g. active, granular and long-range correlated media in their linear regimes. The uncovered symmetry provides a rigorous and versatile criterion for optimal control which greatly facilitates the search of energy-efficient transport strategies in a wide range of systems.
Emergence of Collective Open-Ended Exploration from Decentralized Meta-Reinforcement Learning

Recent works have proven that intricate cooperative behaviors can emerge in agents trained using meta reinforcement learning on open ended task distributions using self-play. While the results are impressive, we argue that self-play and other centralized training techniques do not accurately reflect how general collective exploration strategies emerge in the natural world: through decentralized training and over an open-ended distribution of tasks. In this work we therefore investigate the emergence of collective exploration strategies, where several agents meta-learn independent recurrent policies on an open ended distribution of tasks. To this end we introduce a novel environment with an open ended procedurally generated task space which dynamically combines multiple subtasks sampled from five diverse task types to form a vast distribution of task trees. We show that decentralized agents trained in our environment exhibit strong generalization abilities when confronted with novel objects at test time. Additionally, despite never being forced to cooperate during training the agents learn collective exploration strategies which allow them to solve novel tasks never encountered during training. We further find that the agents learned collective exploration strategies extend to an open ended task setting, allowing them to solve task trees of twice the depth compared to the ones seen during training. Our open source code as well as videos of the agents can be found on our companion website.
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities

The multimodal task of Visual Question Answering (VQA) encompassing elements of Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers to questions on any visual input. Over time, the scope of VQA has expanded from datasets focusing on an extensive collection of natural images to datasets featuring synthetic images, video, 3D environments, and various other visual inputs. The emergence of large pre-trained networks has shifted the early VQA approaches relying on feature extraction and fusion schemes to vision language pre-training (VLP) techniques. However, there is a lack of comprehensive surveys that encompass both traditional VQA architectures and contemporary VLP-based methods. Furthermore, the VLP challenges in the lens of VQA haven't been thoroughly explored, leaving room for potential open problems to emerge. Our work presents a survey in the domain of VQA that delves into the intricacies of VQA datasets and methods over the field's history, introduces a detailed taxonomy to categorize the facets of VQA, and highlights the recent trends, challenges, and scopes for improvement. We further generalize VQA to multimodal question answering, explore tasks related to VQA, and present a set of open problems for future investigation. The work aims to navigate both beginners and experts by shedding light on the potential avenues of research and expanding the boundaries of the field.
Geophysical Gravity Survey using an Atomic Gravimeter as an Absolute Reference

Gravimetry is a versatile metrological approach in geophysics to accurately map subterranean mass and density anomalies. There is a broad diversification regarding the working principle of gravimeters, wherein atomic gravimeters are one of the most technologically progressive class of gravimeters which can monitor gravity at an absolute scale with a high-repetition without exhibiting drift. Despite the apparent utility for geophysical surveys, atomic gravimeters are (currently) laboratory-bound devices due to the vexatious task of transportation. Here, we demonstrated the utility of an atomic gravimeter on-site during a geophysical gravity survey, where the issue of immobility was circumvented with a relative spring gravimeter. The atomic gravimeter served as a means to map the relative data from the spring gravimeter to an absolute measurement with an effective resolution of 7.7 µ Gal. Absolute measurements provide a robust and feasible method method to define and control gravitational data taken at different sites, or a later date, which is critical to analyze underground geological units, in particular when it is combined with other geophysical approaches.
Emergent equilibrium and quantum criticality in a two-photon dissipative oscillator

We study the dissipative phase transition in a quantum oscillator with two-photon drive and two-photon dissipation. Using the semi-classical Langevin equation and the Fokker-Plank approach, we construct a theory of non-perturbative quantum fluctuations and go beyond the semi-classical approximation. We demonstrate the mapping of a two-photon quantum dissipative oscillator onto a classical equilibrium model of a nonlinear classical oscillator in a colored-noise environment. Then, we justify the applicability of the Landau theory for a given dissipative phase transition. To do that, we explicitly demonstrate the Boltzmann-like form of stationary distribution function depending on the effective temperature, which is determined by the frequency detuning and the rates of two-photon drive and dissipation. In addition, we provide a description of the quantum critical region and obtain critical exponents that appear to be in very good agreement with numerical simulations.
Improving MIMO channel estimation via receive power feedback

Estimating the channel state is known to be an important problem in wireless networks. To this end, it matters to exploit all the available information to improve channel estimation accuracy as much as possible. It turns out that the problem of exploiting the information associated with the receive power feedback (e.g., the received signal strength indicator -RSSI-) has not been identified and solved; in this setup, the transmitter is assumed to receive feedback from all the receivers in presence. As shown in this paper, to solve this problem, classical estimation tools can be used. Using the corresponding MMSE is shown to be always beneficial, whereas the relevance of using the MAP estimator would depend on the operating SNR.
Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits

Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learning is not directly applicable in this setting due to the collapse of quantum states after measurements. Thus, gradient estimations constitute a significant overhead in a gradient-based optimization of such quantum circuits. This paper introduces a random coordinate descent algorithm as a practical and easy-to-implement alternative to the full gradient descent algorithm. This algorithm only requires one partial derivative at each iteration. Motivated by the behavior of measurement noise in the practical optimization of parameterized quantum circuits, this paper presents an optimization problem setting that is amenable to analysis. Under this setting, the random coordinate descent algorithm exhibits the same level of stochastic stability as the full gradient approach, making it as resilient to noise. The complexity of the random coordinate descent method is generally no worse than that of the gradient descent and can be much better for various quantum optimization problems with anisotropic Lipschitz constants. Theoretical analysis and extensive numerical experiments validate our findings.
Near-Optimal k-Clustering in the Sliding Window Model

Clustering is an important technique for identifying structural information in large-scale data analysis, where the underlying dataset may be too large to store. In many applications, recent data can provide more accurate information and thus older data past a certain time is expired. The sliding window model captures these desired properties and thus there has been substantial interest in clustering in the sliding window model. In this paper, we give the first algorithm that achieves near-optimal $(1+\varepsilon)$-approximation to $(k,z)$-clustering in the sliding window model, where $z$ is the exponent of the distance function in the cost. Our algorithm uses $\frac{k}{\min(\varepsilon^4,\varepsilon^{2+z})}\,\text{polylog}\frac{n\Delta}{\varepsilon}$ words of space when the points are from $[\Delta]^d$, thus significantly improving on works by Braverman et. al. (SODA 2016), Borassi et. al. (NeurIPS 2021), and Epasto et. al. (SODA 2022). Along the way, we develop a data structure for clustering called an online coreset, which outputs a coreset not only for the end of a stream, but also for all prefixes of the stream. Our online coreset samples $\frac{k}{\min(\varepsilon^4,\varepsilon^{2+z})}\,\text{polylog}\frac{n\Delta}{\varepsilon}$ points from the stream. We then show that any online coreset requires $\Omega\left(\frac{k}{\varepsilon^2}\log n\right)$ samples, which shows a separation from the problem of constructing an offline coreset, i.e., constructing online coresets is strictly harder. Our results also extend to general metrics on $[\Delta]^d$ and are near-optimal in light of a $\Omega\left(\frac{k}{\varepsilon^{2+z}}\right)$ lower bound for the size of an offline coreset.
EXTRACT: Explainable Transparent Control of Bias in Embeddings

Knowledge Graphs are a widely used method to represent relations between entities in various AI applications, and Graph Embedding has rapidly become a standard technique to represent Knowledge Graphs in such a way as to facilitate inferences and decisions. As this representation is obtained from behavioural data, and is not in a form readable by humans, there is a concern that it might incorporate unintended information that could lead to biases. We propose EXTRACT: a suite of Explainable and Transparent methods to ConTrol bias in knowledge graph embeddings, so as to assess and decrease the implicit presence of protected information. Our method uses Canonical Correlation Analysis (CCA) to investigate the presence, extent and origins of information leaks during training, then decomposes embeddings into a sum of their private attributes by solving a linear system. Our experiments, performed on the MovieLens1M dataset, show that a range of personal attributes can be inferred from a user's viewing behaviour and preferences, including gender, age, and occupation. Further experiments, performed on the KG20C citation dataset, show that the information about the conference in which a paper was published can be inferred from the citation network of that article. We propose four transparent methods to maintain the capability of the embedding to make the intended predictions without retaining unwanted information. A trade-off between these two goals is observed.
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows

High-fidelity computational simulations and physical experiments of hypersonic flows are resource intensive. Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situations that have not been seen before. However, high-fidelity data is itself in limited quantity to validate all outputs of the SciML model in unexplored input space. As such, an uncertainty-aware SciML model is desired. The SciML model's output uncertainties could then be used to assess the reliability and confidence of the model's predictions. In this study, we extend a DeepONet using three different uncertainty quantification mechanisms: mean-variance estimation, evidential uncertainty, and ensembling. The uncertainty aware DeepONet models are trained and evaluated on the hypersonic flow around a blunt cone object with data generated via computational fluid dynamics over a wide range of Mach numbers and altitudes. We find that ensembling outperforms the other two uncertainty models in terms of minimizing error and calibrating uncertainty in both interpolative and extrapolative regimes.
Evaluation of Induced Transmembrane potential on Membrane Poration through Molecular Dynamics Simulation and Analytical Calculation

A molecular dynamics (MD) simulation is used to quantitatively analyze the induced membrane potential for an applied external field varied between 0.4 V/nm to 2.0 V/nm. The change in the electrostatic potential in the DPPC is directly correlated to the membrane permeability. The effect of the decrease in external conductivity on the DPPC is also evaluated and the analytical results are compared with the simulation. The correlation between the electrostatic potential of the DPPC and the total dipole are compared, and a positive correlation is identified until saturation. This is because the membrane permeability factor plays a dominant role to control reversible and irreversible electroporation. The obtained dipole parameters through simulation for various electric field allows for an accurate determination of the quantitative changes in the membrane and external conductivity during the process of electroporation.
Teacher knowledge and teaching in Panama and Costa Rica: A comparative study in Primary and Secondary education

This study examines the relationship between mathematical knowledge for teaching, the preparation of and classroom practices of third and seventh grade teachers in Panama and Costa Rica. Mathematical content and pedagogical knowledge are measured with an instrument that includes three levels: basic content, advanced content and content situated in the teaching learning context. The quality of teacher practice was evaluated via a rubric applied to videotaped lessons. In Panama and Costa Rica, responses to questions designed to measure specialized knowledge for teaching and evaluation of the teacher practice revealed deficiencies in this critical domain. In addition, some significant relationships were found between each of these aspects of teacher knowledge and characteristics of the teachers.
On the Integration of Self-Sovereign Identity with TLS 1.3 Handshake to Build Trust in IoT Systems

The centralized PKI is not a suitable solution to provide identities in large-scale IoT systems. The main problem is the high cost of managing X.509 certificates throughout their lifecycle, from installation to regular updates and revocation. The Self-Sovereign Identity (SSI) is a decentralised option that reduces the need for human intervention, and therefore has the potential to significantly reduce the complexity and cost associated to identity management in large-scale IoT systems. However, to leverage the full potential of SSI, the authentication of IoT nodes needs to be moved from the application to the Transport Layer Security (TLS) level. This paper contributes to the adoption of SSI in large-scale IoT systems by addressing, for the first time, the extension of the original TLS 1.3 handshake to support two new SSI authentication modes while maintaining the interoperability with nodes implementing the original handshake protocol. The open source implementation of the new TLS 1.3 handshake protocol in OpenSSL is used to experimentally prove the feasibility of the approach.