Miracle-dz/UniFAR-FTUs huggingface dataset:
A computational model for synaptic message transmission

A computational model incorporating insights from quantum theory is proposed to describe and explain synaptic message transmission. We propose that together, neurotransmitters and their corresponding receptors, function as a physical"quantum decision tree"to"decide"whether to excite or inhibit the synapse. When a neurotransmitter binds to its corresponding receptor, it is the equivalent of randomly choosing different"strategies"; a"strategy"has two actions to take: excite or inhibit the synapse with a certain probability. The genetic programming can be applied for learning the observed data sequence to simulate the synaptic message transmission.
GPT-Driver: Learning to Drive with GPT

We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge in autonomous driving, aiming to plan a driving trajectory that is safe and comfortable. Existing motion planners predominantly leverage heuristic methods to forecast driving trajectories, yet these approaches demonstrate insufficient generalization capabilities in the face of novel and unseen driving scenarios. In this paper, we propose a novel approach to motion planning that capitalizes on the strong reasoning capabilities and generalization potential inherent to Large Language Models (LLMs). The fundamental insight of our approach is the reformulation of motion planning as a language modeling problem, a perspective not previously explored. Specifically, we represent the planner inputs and outputs as language tokens, and leverage the LLM to generate driving trajectories through a language description of coordinate positions. Furthermore, we propose a novel prompting-reasoning-finetuning strategy to stimulate the numerical reasoning potential of the LLM. With this strategy, the LLM can describe highly precise trajectory coordinates and also its internal decision-making process in natural language. We evaluate our approach on the large-scale nuScenes dataset, and extensive experiments substantiate the effectiveness, generalization ability, and interpretability of our GPT-based motion planner. Code is now available at https://github.com/PointsCoder/GPT-Driver.
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints

This paper studies the problem of distributed multi-agent Bayesian optimization with both coupled black-box constraints and known affine constraints. A primal-dual distributed algorithm is proposed that achieves similar regret/violation bounds as those in the single-agent case for the black-box objective and constraint functions. Additionally, the algorithm guarantees an $\mathcal{O}(N\sqrt{T})$ bound on the cumulative violation for the known affine constraints, where $N$ is the number of agents. Hence, it is ensured that the average of the samples satisfies the affine constraints up to the error $\mathcal{O}({N}/{\sqrt{T}})$. Furthermore, we characterize certain conditions under which our algorithm can bound a stronger metric of cumulative violation and provide best-iterate convergence without affine constraint. The method is then applied to both sampled instances from Gaussian processes and a real-world optimal power allocation problem for wireless communication; the results show that our method simultaneously provides close-to-optimal performance and maintains minor violations on average, corroborating our theoretical analysis.
Exact moments and re-entrant transitions in the inertial dynamics of active Brownian particles

In this study, we investigate the behavior of free inertial active Brownian particles in the presence of thermal noise. While finding a closed-form solution for the joint distribution of positions, orientations, and velocities using the Fokker–Planck equation is generally challenging, we utilize a Laplace transform method to obtain the exact temporal evolution of all dynamical moments in arbitrary dimensions. Our expressions in d dimensions reveal that inertia significantly impacts steady-state kinetic temperature and swim pressure while leaving the late-time diffusivity unchanged. Notably, as a function of activity and inertia, the steady-state velocity distribution exhibits a remarkable re-entrant crossover from ‘passive’ Gaussian to ‘active’ non-Gaussian behaviors. We construct a corresponding ‘phase diagram’ using the exact expression of the d-dimensional kurtosis. Our analytic expressions describe steady states and offer insights into time-dependent crossovers observed in moments of velocity and displacement. Our calculations can be extended to predict up to second-order moments for run-and-tumble particles and the active Ornstein–Uhlenbeck process (AOUP). Additionally, the kurtosis shows differences from AOUP.
MULTIVARIATE GEOSTATISTICAL METHODS FOR MAPPING SOIL SALINITY

Degradation of the lands by salinity under arid climate and poor drainage conditions can be inevitable. In the Harran plain total salt affected areas covers 10 % of total irrigated areas which are mainly located in the low lying parts of the plain where elevation ranges from 350 to 400 m. Soil salinity shows high spatial variability which requires intensive sampling and laboratory analyses. Geostatistical techniques such as simple or ordinary kriging can be used in explaining this spatial variability and estimating soil salinity parameters at unvisited locations. On the other hand, new approaches such as hybrid interpolation methods which incorporate secondary variables into primary variables can help improve the estimation. Estimating soil salinity is a vital issue in soil fertility and management. This study evaluated multivariate geostatistical methods such as regression kriging (RK) and kriging with external drift (KED) and compared them with ordinary kriging (OK) for the estimation of soil salinity parameters. Topographical parameters (i.e elevation, slope and topo wetness index (ln (A/tan))) as well as soil EC values at different depths were used as auxiliary variables. Overall results showed that estimation and mapping accuracy may be improved using multivariate geostatistical methods depending upon the power of the relationship between soil salinity parameters and environmental variables used as covariable.
Hybrid light-matter states in topological superconductors coupled to cavity photons

We consider a one-dimensional topological superconductor hosting Majorana bound states at its ends coupled to a single mode cavity. In the strong light-matter coupling regime, electronic and photonic degrees of freedom hybridize resulting in the formation of polaritons. We find the polariton spectrum by calculating the cavity photon spectral function of the coupled electron-photon system. In the topological phase the lower in energy polariton modes are formed by the bulk-Majorana transitions coupled to cavity photons and are also sensitive to the Majorana parity. In the trivial phase the lower polariton modes emerge due to the coupling of the bulk-bulk transitions across the gap to photons. Our work demonstrates the formation of polaritons in topological superconductors coupled to photons that contain information on the features of the Majorana bound states.
EAST: Environment Aware Safe Tracking using Planning and Control Co-Design

This paper considers the problem of autonomous robot navigation in unknown environments with moving obstacles. We propose a new method that systematically puts planning, motion prediction and safety metric design together to achieve environmental adaptive and safe navigation. This algorithm balances optimality in travel distance and safety with respect to passing clearance. Robot adapts progress speed adaptively according to the sensed environment, being fast in wide open areas and slow down in narrow passages and taking necessary maneuvers to avoid dangerous incoming obstacles. In our method, directional distance measure, conic-shape motion prediction and custom costmap are integrated properly to evaluate system risk accurately with respect to local geometry of surrounding environments. Using such risk estimation, reference governor technique and control barrier function are worked together to enable adaptive and safe path tracking in dynamical environments. We validate our algorithm extensively both in simulation and challenging real-world environments.
Numerical modelling of the lobes of radio galaxies in cluster environments – III. Powerful relativistic and non-relativistic jets

We present results from two suites of simulations of powerful radio galaxies in poor cluster environments, with a focus on the formation and evolution of the radio lobes. One suite of models uses relativistic hydrodynamics and the other relativistic magnetohydrodynamics;both are set up to cover a range of jet powers and velocities. The dynamics of the lobes are shown to be in good agreement with analytical models and with previous numerical models, confirming in the relativistic regime that the observed widths of radio lobes may be explained if they are driven by very light jets. The ratio of energy stored in the radio lobes to that put into the intracluster gas is seen to be the same regardless of jet power, jet velocity or simulation type, suggesting that we have a robust understanding of the work done on the ambient gas by this type of radio source. For the most powerful jets, we at times find magnetic field amplification by up to a factor of 2 in energy, but mostly the magnetic energy in the lobes is consistent with the magnetic energy injected. We confirm our earlier result that for jets with a toroidally injected magnetic field, the field in the lobes is predominantly aligned with the jet axis once the lobes are well developed, and that this leads to radio flux anisotropies of up to a factor of about two for mature sources. We reproduce the relationship between 151 MHz luminosity and jet power determined analytically in the literature.
Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection

Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality. Prior work in deep AD is predominantly based on a familiarity hypothesis, where familiar features serve as the reference in a pre-trained embedding space. While this strategy has proven highly successful, it turns out that it causes consistent false negatives when anomalies consist of truly novel features that are not well captured by the pre-trained encoding. We propose a novel approach to AD using explainability to capture such novel features as unexplained observations in the input space. We achieve strong performance across a wide range of anomaly benchmarks by combining familiarity and novelty in a hybrid approach. Our approach establishes a new state-of-the-art across multiple benchmarks, handling diverse anomaly types while eliminating the need for expensive background models and dense matching. In particular, we show that by taking account of novel features, we reduce false negative anomalies by up to 40% on challenging benchmarks compared to the state-of-the-art. Our method gives visually inspectable explanations for pixel-level anomalies.
Prompt-tuning latent diffusion models for inverse problems

We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors. Existing methods using latent diffusion models for inverse problems typically rely on simple null text prompts, which can lead to suboptimal performance. To address this limitation, we introduce a method for prompt tuning, which jointly optimizes the text embedding on-the-fly while running the reverse diffusion process. This allows us to generate images that are more faithful to the diffusion prior. In addition, we propose a method to keep the evolution of latent variables within the range space of the encoder, by projection. This helps to reduce image artifacts, a major problem when using latent diffusion models instead of pixel-based diffusion models. Our combined method, called P2L, outperforms both image- and latent-diffusion model-based inverse problem solvers on a variety of tasks, such as super-resolution, deblurring, and inpainting.
Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance

The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions. However, existing state-of-the-art (SOTA) approaches usually incorporate scene text detection and recognition simply by pretraining on natural scene text datasets, which do not directly exploit the intermediate feature representations between multiple domains. Here, we investigate the problem of domain-adaptive scene text spotting, i.e., training a model on multi-domain source data such that it can directly adapt to target domains rather than being specialized for a specific domain or scenario. Further, we investigate a transformer baseline called Swin-TESTR to focus on solving scene-text spotting for both regular and arbitrary-shaped scene text along with an exhaustive evaluation. The results clearly demonstrate the potential of intermediate representations to achieve significant performance on text spotting benchmarks across multiple domains (e.g. language, synth-to-real, and documents). both in terms of accuracy and efficiency.
Unaffordable Fare: The cost of public transportation for low-income commuters working at three airports

For airport employers, making sure the many low-income people they employ as baggage handlers and retail salespeople, amongst others, can get to work ensures the continued efficient operations of the airport and the prosperity of the regional economy. However high and increasing costs coupled with low-wages make commutes unaffordable and constrain employees' ability to get to their job. Using case studies of Chicago Midway International Airport (MDW), Denver International Airport (DEN), and San Diego International Airport (SAN), this research measures the extent to which low-income employees commuting to work at the airport by public transportation can afford their commute in 2011. The results of this analysis suggest that the cost of commuting on public transportation is beyond the means of a low-income budget. Furthermore, fewer low-income commuters take public transportation as the affordability of public transportation declines, suggesting that affordability contributes to outcomes of spatial mismatch. These results promote interventions to increase affordability and expand accessibility to public transportation for low-income commuters to the airport. Thesis Supervisor: Amy Glasmeier Title: Professor of City Planning
The Gevrey class implicit mapping theorem with application to UQ of semilinear elliptic PDEs

This article is concerned with a regularity analysis of parametric operator equations with a perspective on uncertainty quantification. We study the regularity of mappings between Banach spaces near branches of isolated solutions that are implicitly defined by a residual equation. Under $s$-Gevrey assumptions on on the residual equation, we establish $s$-Gevrey bounds on the Fr\'echet derivatives of the local data-to-solution mapping. This abstract framework is illustrated in a proof of regularity bounds for a semilinear elliptic partial differential equation with parametric and random field input.
ETGraph: A Pioneering Dataset Bridging Ethereum and Twitter

While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data. This constraint limits the incorporation of relevant social network data into blockchain analysis, thereby diminishing the breadth and depth of insight that can be derived. To address the above limitation, we introduce ETGraph, a novel dataset that authentically links Ethereum and Twitter, marking the first and largest dataset of its kind. ETGraph combines Ethereum transaction records (2 million nodes and 30 million edges) and Twitter following data (1 million nodes and 3 million edges), bonding 30,667 Ethereum addresses with verified Twitter accounts sourced from OpenSea. Detailed statistical analysis on ETGraph highlights the structural differences between Twitter-matched and nonTwitter-matched Ethereum addresses. Extensive experiments, including Ethereum link prediction, wash-trading Ethereum addresses detection, and Twitter-Ethereum matching link prediction, emphasize the significant role of Twitter data in enhancing Ethereum analysis. ETGraph is available at https://etgraph.deno. dev/.
Scalable Statistical Inference in Non-parametric Least Squares

Stochastic approximation (SA) is a powerful and scalable computational method for iteratively estimating the solution of optimization problems in the presence of randomness, particularly well-suited for large-scale and streaming data settings. In this work, we propose a theoretical framework for stochastic approximation (SA) applied to non-parametric least squares in reproducing kernel Hilbert spaces (RKHS), enabling online statistical inference in non-parametric regression models. We achieve this by constructing asymptotically valid pointwise (and simultaneous) confidence intervals (bands) for local (and global) inference of the nonlinear regression function, via employing an online multiplier bootstrap approach to functional stochastic gradient descent (SGD) algorithm in the RKHS. Our main theoretical contributions consist of a unified framework for characterizing the non-asymptotic behavior of the functional SGD estimator and demonstrating the consistency of the multiplier bootstrap method. The proof techniques involve the development of a higher-order expansion of the functional SGD estimator under the supremum norm metric and the Gaussian approximation of suprema of weighted and non-identically distributed empirical processes. Our theory specifically reveals an interesting relationship between the tuning of step sizes in SGD for estimation and the accuracy of uncertainty quantification.
ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions

Unfamiliar decisions -- decisions where people lack adequate domain knowledge or expertise -- specifically increase the complexity and uncertainty of the process of searching for, understanding, and making decisions with online information. Through our formative study (n=14), we observed users' challenges in accessing diverse perspectives, identifying relevant information, and deciding the right moment to make the final decision. We present ChoiceMates, a system that enables conversations with a dynamic set of LLM-powered agents for a holistic domain understanding and efficient discovery and management of information to make decisions. Agents, as opinionated personas, flexibly join the conversation, not only providing responses but also conversing among themselves to elicit each agent's preferences. Our between-subjects study (n=36) comparing ChoiceMates to conventional web search and single-agent showed that ChoiceMates was more helpful in discovering, diving deeper, and managing information compared to Web with higher confidence. We also describe how participants utilized multi-agent conversations in their decision-making process.
A Bayesian approach to infer radial models of temperature and anisotropy in the transition zone from surface wave dispersion curves

Mineralogical transformations and material transfers within the Earth’s mantle make the 350– 1000 km depth range (referred here as the mantle transition zone) highly heterogeneous and anisotropic. Most of the 3-D global tomographic models are anchored on small perturbations from 1-D models such as PREM, and are secondly interpreted in terms of temperature and composition distributions. However, the degree of heterogeneity in the transition zone can be strong enough so that the concept of a 1-D reference seismic model must be addressed. To avoid the use of any seismic reference model, we present in this paper a Markov chain Monte Carlo algorithm to directly interpret surface wave dispersion curves in terms of temperature and radial anisotropy distributions, here considering a given composition of the mantle. These interpretations are based on laboratory measurements of elastic moduli and Birch–Murnaghan equation of state. An originality of the algorithm is its ability to explore both smoothly varying models and ﬁrst-order discontinuities, using C1-B´ezier curves, which interpolate the randomly chosen values for depth, temperature and radial anisotropy. This parametrization is able to generate a self-adapting parameter space exploration while reducing the computing time. Thanks to a Bayesian exploration, the probability distributions on temperature and anisotropy are governed by uncertainties on the data set. The method is applied to both synthetic data and real dispersion curves. Though surface wave data are weakly sensitive to the sharpness of the of the mid-mantle seismic discontinuities, the interpretation of the temperature distribution is highly related to the chosen composition and to the modelling of mineralogical phase transformations. Surface wave measurements along the Vanuatu–California path suggest a strong anisotropy above 400 km depth, which decreases below, and a monotonous temperature distribution between 350 and 1000 km depth.
Revisiting Link Prediction: A Data Perspective

Link prediction, a fundamental task on graphs, has proven indispensable in various applications, e.g., friend recommendation, protein analysis, and drug interaction prediction. However, since datasets span a multitude of domains, they could have distinct underlying mechanisms of link formation. Evidence in existing literature underscores the absence of a universally best algorithm suitable for all datasets. In this paper, we endeavor to explore principles of link prediction across diverse datasets from a data-centric perspective. We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity. We then unearth relationships among those factors where (i) global structural proximity only shows effectiveness when local structural proximity is deficient. (ii) The incompatibility can be found between feature and structural proximity. Such incompatibility leads to GNNs for Link Prediction (GNN4LP) consistently underperforming on edges where the feature proximity factor dominates. Inspired by these new insights from a data perspective, we offer practical instruction for GNN4LP model design and guidelines for selecting appropriate benchmark datasets for more comprehensive evaluations.
Anomalous Hall Transport by Optically Injected Isospin Degree of Freedom in Dirac Semimetal Thin Film.

Chirality of massless fermions emerging in condensed matter is a key to understand their characteristic behavior as well as to exploit their functionality. However, the chiral nature of massless fermions in Dirac semimetals has remained elusive, due to equivalent occupation of carriers with the opposite chirality in thermal equilibrium. Here, we show that the isospin degree of freedom, which labels the chirality of massless carriers from a crystallographic point of view, can be injected by circularly polarized light. Terahertz Faraday rotation spectroscopy successfully detects the anomalous Hall conductivity by a light-induced isospin polarization in a three-dimensional Dirac semimetal, Cd3As2. Spectral analysis of the Hall conductivity reveals a long scattering time and a long decay time, which are characteristic of the isospin. The long-lived, robust, and reversible character of the isospin promises a potential application of Dirac semimetals in future information technology.