Miracle-dz/UniFAR-FTUs huggingface dataset:
A Candidate for the Least-massive Black Hole in the First 1.1 Billion Years of the Universe

We report a candidate of a low-luminosity active galactic nucleus (AGN) at z = 5 that was selected from the first near-infrared images of the JWST CEERS project. This source, named CEERS-AGN-z5-1 at absolute 1450 Å magnitude M 1450 = −19.5 ± 0.3, was found via a visual selection of compact sources from a catalog of Lyman break galaxies at z > 4, taking advantage of the superb spatial resolution of the JWST/NIRCam images. The 20 photometric data available from CFHT, Hubble Space Telescope, Spitzer, and JWST suggest that the continuum shape of this source is reminiscent of that for an unobscured AGN, and there is a clear color excess in the filters where the redshifted Hβ+[O iii] and Hα are covered. The estimated line luminosity is L Hβ+[O III] = 1043.0 erg s−1 and L Hα = 1042.9 erg s−1 with the corresponding rest-frame equivalent width EWHβ+[O III] = 1100 Å and EWHα = 1600 Å, respectively. Our spectral energy distribution fitting analysis favors the scenario that this object is either a strong broad-line emitter or even a super-Eddington accreting black hole (BH), although a possibility of an extremely young galaxy with moderate dust attenuation is not completely ruled out. The bolometric luminosity, L bol = 2.5 ± 0.3 × 1044 erg s−1, is consistent with those of z < 0.35 broad-line AGNs with M BH ∼ 106 M ⊙ accreting at the Eddington limit. This new AGN population in the first 1.1 billion years of the universe may close the gap between the observed BH mass range at high redshift and that of BH seeds. Spectroscopic confirmation is awaited to secure the redshift and its AGN nature.
Example When Local Optimal Policies Contain Unstable Control

We provide a new perspective to understand why reinforcement learning (RL) struggles with robustness and generalization. We show, by examples, that local optimal policies may contain unstable control for some dynamic parameters and overfitting to such instabilities can deteriorate robustness and generalization. Contraction analysis of neural control reveals that there exists boundaries between stable and unstable control with respect to the input gradients of control networks. Ignoring those stability boundaries, learning agents may label the actions that cause instabilities for some dynamic parameters as high value actions if those actions can improve the expected return. The small fraction of such instabilities may not cause attention in the empirical studies, a hidden risk for real-world applications. Those instabilities can manifest themselves via overfitting, leading to failures in robustness and generalization. We propose stability constraints and terminal constraints to solve this issue, demonstrated with a proximal policy optimization example.
CLIPping Privacy: Identity Inference Attacks on Multi-Modal Machine Learning Models

As deep learning is now used in many real-world applications, research has focused increasingly on the privacy of deep learning models and how to prevent attackers from obtaining sensitive information about the training data. However, image-text models like CLIP have not yet been looked at in the context of privacy attacks. While membership inference attacks aim to tell whether a speciﬁc data point was used for training, we introduce a new type of privacy attack, named identity inference attack (IDIA), designed for multi-modal image-text models like CLIP. Using IDIAs, an attacker can reveal whether a particular person, was part of the training data by querying the model in a black-box fashion with different images of the same person. Letting the model choose from a wide variety of possible text labels, the attacker can probe the model whether it recognizes the person and, therefore, was used for training. Through several experiments on CLIP, we show that the attacker can identify individuals used for training with very high accuracy and that the model learns to connect the names with the depicted people. Our experiments show that a multi-modal image-text model indeed leaks sensitive information about its training data and, therefore, should be handled with care.
Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery?

Self-supervised pre-training is gaining increasingly more popularity in AI-aided drug discovery, leading to more and more pre-trained models with the promise that they can extract better feature representations for molecules. Yet, the quality of learned representations have not been fully explored. In this work, inspired by the two phenomena of Activity Cliffs (ACs) and Scaffold Hopping (SH) in traditional Quantitative Structure-Activity Relationship (QSAR) analysis, we propose a method named Representation-Property Relationship Analysis (RePRA) to evaluate the quality of the representations extracted by the pre-trained model and visualize the relationship between the representations and properties. The concepts of ACs and SH are generalized from the structure-activity context to the representation-property context, and the underlying principles of RePRA are analyzed theoretically. Two scores are designed to measure the generalized ACs and SH detected by RePRA, and therefore the quality of representations can be evaluated. In experiments, representations of molecules from 10 target tasks generated by 7 pre-trained models are analyzed. The results indicate that the state-of-the-art pre-trained models can overcome some shortcomings of canonical Extended-Connectivity FingerPrints (ECFP), while the correlation between the basis of the representation space and specific molecular substructures are not explicit. Thus, some representations could be even worse than the canonical fingerprints. Our method enables researchers to evaluate the quality of molecular representations generated by their proposed self-supervised pre-trained models. And our findings can guide the community to develop better pre-training techniques to regularize the occurrence of ACs and SH.
Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing

In this letter, we address blockage detection and precoder design for multiple-input multiple-output (MIMO) links, without communication overhead required. Blockage detection is achieved by classifying light detection and ranging (LIDAR) data through a physics-based graph neural network (GNN). For precoder design, a preliminary channel estimate is obtained by running ray tracing on a 3D surface obtained from LIDAR data. This estimate is successively refined and the precoder is designed accordingly. Numerical simulations show that blockage detection is successful with 95% accuracy. Our digital precoding achieves 90% of the capacity and analog precoding outperforms previous works exploiting LIDAR for precoder design.
A case for DOT: theoretical foundations for objects with pattern matching and GADT-style reasoning

Many programming languages in the OO tradition now support pattern matching in some form. Historical examples include Scala and Ceylon, with the more recent additions of Java, Kotlin, TypeScript, and Flow. But pattern matching on generic class hierarchies currently results in puzzling type errors in most of these languages. Yet this combination of features occurs naturally in many scenarios, such as when manipulating typed ASTs. To support it properly, compilers needs to implement a form of subtyping reconstruction: the ability to reconstruct subtyping information uncovered at runtime during pattern matching. We introduce cDOT, a new calculus in the family of Dependent Object Types (DOT) intended to serve as a formal foundation for subtyping reconstruction. Being descended from pDOT, itself a formal foundation for Scala, cDOT can be used to encode advanced object-oriented features such as generic inheritance, type constructor variance, F-bounded polymorphism, and first-class recursive modules. We demonstrate that subtyping reconstruction subsumes GADTs by encoding λ2,Gµ, a classical constraint-based GADT calculus, into cDOT.
Medical Image Segmentation using LeViT-UNet++: A Case Study on GI Tract Data

Gastro-Intestinal Tract cancer is considered a fatal malignant condition of the organs in the GI tract. Due to its fatality, there is an urgent need for medical image segmentation techniques to segment organs to reduce the treatment time and enhance the treatment. Traditional segmentation techniques rely upon handcrafted features and are computationally expensive and inefficient. Vision Transformers have gained immense popularity in many image classification and segmentation tasks. To address this problem from a transformers’ perspective, we introduced a hybrid CNN-transformer architecture to segment the different organs from an image. The proposed solution is robust, scalable, and computationally efficient, with a Dice and Jaccard coefficient of 0.79 and 0.72, respectively. The proposed solution also depicts the essence of deep learning-based automation to improve the effectiveness of the treatment.
Uncertainty propagation for nonlinear dynamics: A polynomial optimization approach

We use Lyapunov-like functions and convex optimization to propagate uncertainty in the initial condition of nonlinear systems governed by ordinary differential equations. We consider the full nonlinear dynamics without approximation, producing rigorous bounds on the expected future value of a quantity of interest even when only limited statistics of the initial condition (e.g., mean and variance) are known. For dynamical systems evolving in compact sets, the best upper (lower) bound coincides with the largest (smallest) expectation among all initial state distributions consistent with the known statistics. For systems governed by polynomial equations and polynomial quantities of interest, one-sided estimates on the optimal bounds can be computed using tools from polynomial optimization and semidefinite programming. Moreover, these numerical bounds provably converge to the optimal ones in the compact case. We illustrate the approach on a van der Pol oscillator and on the Lorenz system in the chaotic regime.
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

Pre-trained vision-language models (e.g., CLIP) have shown promising zero-shot generalization in many downstream tasks with properly designed text prompts. Instead of relying on hand-engineered prompts, recent works learn prompts using the training data from downstream tasks. While effective, training on domain-specific data reduces a model's generalization capability to unseen new domains. In this work, we propose test-time prompt tuning (TPT), a method that can learn adaptive prompts on the fly with a single test sample. For image classification, TPT optimizes the prompt by minimizing the entropy with confidence selection so that the model has consistent predictions across different augmented views of each test sample. In evaluating generalization to natural distribution shifts, TPT improves the zero-shot top-1 accuracy of CLIP by 3.6% on average, surpassing previous prompt tuning approaches that require additional task-specific training data. In evaluating cross-dataset generalization with unseen categories, TPT performs on par with the state-of-the-art approaches that use additional training data. Project page: https://azshue.github. io/TPT.
Some arithmetical properties of convergents to algebraic numbers

Let $\xi$ be an irrational algebraic real number and $(p_k / q_k)_{k \ge 1}$ denote the sequence of its convergents. Let $(u_n)_{n \geq 1}$ be a non-degenerate linear recurrence sequence of integers, which is not a polynomial sequence. We show that if the intersection of the sequences $(q_k)_{k \ge 1}$ and $(u_n)_{n \geq 1}$ is infinite, then $\xi$ is a quadratic number. We also discuss several arithmetical properties of the sequence $(q_k)_{k \ge 1}$.
MDE for machine learning-enabled software systems: a case study and comparison of MontiAnna & ML-Quadrat

In this paper, we propose to adopt the MDE paradigm for the development of Machine Learning (ML)-enabled software systems with a focus on the Internet of Things (IoT) domain. We illustrate how two state-of-the-art open-source modeling tools, namely MontiAnna and ML-Quadrat can be used for this purpose as demonstrated through a case study. The case study illustrates using ML, in particular deep Artificial Neural Networks (ANNs), for automated image recognition of handwritten digits using the MNIST reference dataset, and integrating the machine learning components into an IoT-system. Subsequently, we conduct a functional comparison of the two frameworks, setting out an analysis base to include a broad range of design considerations, such as the problem domain, methods for the ML integration into larger systems, and supported ML methods, as well as topics of recent intense interest to the ML community, such as AutoML and MLOps. Accordingly, this paper is focused on elucidating the potential of the MDE approach in the ML domain. This supports the ML-engineer in developing the (ML/software) model rather than implementing the code, and additionally enforces reusability and modularity of the design through enabling the out-of-the-box integration of ML functionality as a component of the IoT or cyber-physical systems.
Quasi-Arithmetic Mixtures, Divergence Minimization, and Bregman Information

Markov Chain Monte Carlo methods for sampling from complex distributions and estimating normalization constants often simulate samples from a sequence of intermediate distributions along an annealing path , which bridges between a tractable initial distribution and a target density of interest. Prior works have constructed annealing paths using quasi-arithmetic means, and interpreted the resulting intermediate densities as minimizing an expected divergence to the endpoints. To analyze these variational representations of annealing paths, we extend the results of Banerjee et al. [11] showing that the arithmetic mean over arguments minimizes the expected Bregman divergence to a single representative point. In particular, we obtain an analogous result for quasi-arithmetic means, when the inputs to the Bregman divergence are transformed under a monotonic embedding function. Our analysis highlights the interplay between quasi-arithmetic means, parametric families, and divergence functionals using the rho-tau Bregman divergence framework of Zhang [86, 87], and associates common divergence functionals with intermediate densities along an annealing path.
Quantum simulation of spin-1/2 XYZ model using solid-state spin centers

In this work we propose a novel solid-state platform for creating quantum simulators based on implanted spin centers in semiconductors. We show that under the presence of an external magnetic field, an array of $S=1$ spin centers interacting through magnetic dipole-dipole interaction can be mapped into an effective spin-half system equivalent to the XYZ model in an external magnetic field. Interestingly, this system presents a wide range of quantum phases and critical behaviors that can be controlled via magnetic field and orientational arrangement of the spin centers. We demonstrate our interacting spin chain can be tuned to both isotropic Heisenberg model and transverse-field Ising universality class. Notably, our model contains a line where the system is in a critical floating phase that terminates at Berezinskii-Kosterlitz-Thouless and Pokrovsky-Talapov transition points. We propose this system as the first solid-state quantum simulator for the floating phase based on spin centers.
Steady state propulsion of isotropic active colloids along a wall

Active drops emit/absorb chemical solutes, whose concentration gradients cause interfacial ﬂows driving their own transport and the propulsion of the droplet. Such non-linear coupling enables active drops to achieve directed self-propulsion despite their isotropy, if the ratio of advective-to-diﬀusive solute transport, i.e. the P´eclet number Pe, is larger than a ﬁnite critical threshold. In most experimental situations, active drops are non-neutrally buoyant and thus swim along rigid sur-faces; yet theoretical descriptions of their non-linear motion focus almost exclusively on unbounded domains to circumvent geometric complexity. To overcome this gap in understanding, we investigate the spontaneous emergence and nonlinear saturation of propulsion of an isotropic phoretic colloid along a rigid wall, to which it is conﬁned by a constant external force (e.g., gravity). This phoretic particle model is considered here as a limiting case for a viscous active drop. We show that, for moderate Pe, the particle motion and associated chemical transport reduce the chemically-induced wall repulsion, thereby causing the particle to swim progressively closer to the wall as Pe increases. Far from hindering self-propulsion, this reduction in the particle-wall separation is accompanied by a wall-induced eﬃcient rearrangement of the solute concentration gradients driving the particle, thus augmenting its swimming speed.
A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks

We introduce the \emph{temporal graphlet kernel} for classifying dissemination processes in labeled temporal graphs. Such dissemination processes can be spreading (fake) news, infectious diseases, or computer viruses in dynamic networks. The networks are modeled as labeled temporal graphs, in which the edges exist at specific points in time, and node labels change over time. The classification problem asks to discriminate dissemination processes of different origins or parameters, e.g., infectious diseases with different infection probabilities. Our new kernel represents labeled temporal graphs in the feature space of temporal graphlets, i.e., small subgraphs distinguished by their structure, time-dependent node labels, and chronological order of edges. We introduce variants of our kernel based on classes of graphlets that are efficiently countable. For the case of temporal wedges, we propose a highly efficient approximative kernel with low error in expectation. We show that our kernels are faster to compute and provide better accuracy than state-of-the-art methods.
Higher Harmonics In Vacuum From Nonlinear QED Effects without Low-Mass Intermediate Particles

We show that in the presence of a slowly rotating strong transverse magnetic field there is an infinite spectrum of harmonic wave functions An due to the first order QED correction (in alpha2) given by the Euler-Heisenberg Lagrangian. The frequency shifts are integer multiples +/- omega(0)n of the magnetic field angular frequency rotation omega(0)=2pinu(m), and the several modes n are coupled to the nearest harmonics n+/-1. This is a new effect due to QED vacuum fluctuations, not exploited before, that can explain, both qualitatively and quantitatively, the recent experimental results of the PVLAS collaboration without the need of a low-mass intermediate particle, hence may dismiss the recent claim of the discovery of the axion.
A flickering study of nova-like systems KR Aur and UU Aqr

We present a study of the flickering activity in two nova-like systems, KR Aur and UU Aqr. We applied a statistical model of flickering simulations in accretion discs based on turbulent angular momentum transport between two adjacent rings with an exponential distribution of the turbulence dimension scale. The model is based on a steady-state disc model, which is satisfied in the case of hot ionized discs of nova-like cataclysmic variables. Our model successfully fits the observed power-density spectrum of KR Aur with the disc parameter α= 0.10–0.40 and an inner-disc truncation radius in the range Rin= 0.88–1.67 × 109 cm. The exact values depend on the mass-transfer rate in the sense that α decreases and Rin increases with mass-transfer rate. In any case, the inner-disc radius found for KR Aur is considerably smaller than those for quiescent dwarf novae, as predicted by the disc instability model. On the other hand, our simulations fail to reproduce the power-density spectrum of UU Aqr. A tantalizing explanation involves the possible presence of spiral waves, which are expected in UU Aqr because of its low mass ratio but not in KR Aur. In general our model predicts the observed concentration of flickering in the central disc. We explain this by the radial dependence of the angular-momentum gradient.
Simulation-Based Critical Gap Distribution Estimation Using Genetic Algorithms

Gap acceptance is a process that occurs in different traffic situations, such as to cross a road, to enter a roundabout or to perform an overtaking maneuver on a bi-directional road. The minimum gap that a driver will accept is generally called the critical gap. Mathematical representations of the gap acceptance process are an important part of for instance traffic simulation models. For a more realistic representation, these mathematical descriptions have become more complex, including behavior parameters, such as ‘impatience’ or ‘aggressiveness’. Since these parameters influence gap acceptance behavior, they should therefore be included in critical gap distribution estimations. This paper presents a new simulation-based approach to estimate distribution of critical gaps. The proposed, so called, KS-GA method enables estimation of other behavior parameters simultaneously with the critical gap distribution parameters. The approach is based on minimization of the Kolmogorov-Smirnov distance between the simulated and observed accepted gaps, and the simulated and observed rejected gaps. The optimization is performed using a Genetic Algorithm. By applying the approach to synthetic data, the model is verified. The estimation results of the KS-GA method are comparable to the performance of the maximum likelihood procedure; a well accepted and frequently applied critical gap distribution estimation method. If intra-driver changes are included, the KS-GA method performs better. The KS-GA method is applied using real life overtaking gap acceptance data on a bi-directional two-lane road.