Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining exotic dark matter models with the dark ages 21-cm signal

The dark ages 21-cm signal is a powerful tool for precision cosmology and probing new physics. We study two non-standard models: an excess radio background (ERB) model (possibly generated by dark matter decay) and the millicharged dark matter (mDM) model. These models were inspired by the possible EDGES detection of a strong global 21-cm absorption during cosmic dawn, but more generally they provide a way to anticipate the potential discovery space. During the dark ages the 21-cm global signal in the ERB model reaches a saturated form for an amplitude Ar = 0.4, where Ar is the radio background intensity at cosmic dawn relative to the cosmic microwave background. This amplitude is one fifth of the minimum required to explain the EDGES signal, and corresponds to just 0.1 per cent of the observed extragalactic background; it would give a signal that can be detected at 5. 9σ significance (compared to 4.1σ for the standard signal) and can be distinguished from the standard (no ERB) signal at 8. 5σ, all with a 1000 h global signal measurement. The 21-cm power spectrum has potentially more information, but far greater resources would be required for comparable constraints. For the mDM model, over a range of viable parameters, the global signal detection significance would be $4.7-7.2\, \sigma$, and it could be distinguished from standard at $2.2-9.3\, \sigma$. With an array of global signal antennas achieving an effective 100 000 h integration, the significance would be 10× better. Our analysis helps motivate the development of lunar and space-based dark ages experiments.
Semantic Data Management in Data Lakes

In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontologybased data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies.
An Intra-pulse feedforward algorithm for improving pulsed microwave stability

During the pulsed operation of the linear accelerator in DCLS (Dalian Coherent Light Source), we found a strong correlation between the klystron modulator's high voltage and the klystron output microwave, with noticeable jitter among adjacent microwaves. Therefore, we propose an intra-pulse feedforward algorithm and implement it in LLRF (Low-Level Radiofrequency) systems. This algorithm assumes that the transfer model of the microwave system is linear within a small range of work points and measures the transfer coefficient of the microwave between the LLRF and klystron. For each pulsed microwave of the klystron output, the LLRF system first calculates the vector deviation between the initial measurement within its pulse and the target. The deviation will be compensated in the LLRF excitation so that the jitter in the later part of the pulsed microwave is suppressed. Experiments have shown that this algorithm can effectively suppress the jitter among adjacent microwaves, e.g., improving the amplitude and phase stability (RMS) from 0.11%/0.2{\deg} to 0.1%/0.05{\deg}. This algorithm can also be applied to other accelerators operating in pulsed modes.
G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X Data

Monocular depth inference is a fundamental problem for scene perception of robots. Specific robots may be equipped with a camera plus an optional depth sensor of any type and located in various scenes of different scales, whereas recent advances derived multiple individual sub-tasks. It leads to additional burdens to fine-tune models for specific robots and thereby high-cost customization in large-scale industrialization. This paper investigates a unified task of monocular depth inference, which infers high-quality depth maps from all kinds of input raw data from various robots in unseen scenes. A basic benchmark G2-MonoDepth is developed for this task, which comprises four components: (a) a unified data representation RGB+X to accommodate RGB plus raw depth with diverse scene scale/semantics, depth sparsity [0%, 100%] and errors (holes/noises/blurs), (b) a novel unified loss to adapt to diverse depth sparsity/errors of input raw data and diverse scales of output scenes, (c) an improved network to well propagate diverse scene scales from input to output, and (d) a data augmentation pipeline to simulate all types of real artifacts in raw depth maps for training. G2-MonoDepth is applied in three sub-tasks including depth estimation, depth completion with different sparsity, and depth enhancement in unseen scenes, and it always outperforms SOTA baselines on both real-world data and synthetic data.
Systematic AI Approach for AGI: Addressing Alignment, Energy, and AGI Grand Challenges

AI faces a trifecta of grand challenges the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume unsustainable amounts of energy during model training and daily operations. Making things worse, the amount of computation required to train each new AI model has been doubling every 2 months since 2020, directly translating to increases in energy consumption. The leap from AI to AGI requires multiple functional subsystems operating in a balanced manner, which requires a system architecture. However, the current approach to artificial intelligence lacks system design; even though system characteristics play a key role in the human brain from the way it processes information to how it makes decisions. Similarly, current alignment and AI ethics approaches largely ignore system design, yet studies show that the brains system architecture plays a critical role in healthy moral decisions. In this paper, we argue that system design is critically important in overcoming all three grand challenges. We posit that system design is the missing piece in overcoming the grand challenges. We present a Systematic AI Approach for AGI that utilizes system design principles for AGI, while providing ways to overcome the energy wall and the alignment challenges.
A Neuro-Mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization

Over the last few years, large neural generative models, capable of synthesizing semantically rich passages of text or producing complex images, have recently emerged as a popular representation of what has come to be known as ``generative artificial intelligence'' (generative AI). Beyond opening the door to new opportunities as well as challenges for the domain of statistical machine learning, the rising popularity of generative AI brings with it interesting questions for Cognitive Science, which seeks to discover the nature of the processes that underpin minds and brains as well as to understand how such functionality might be acquired and instantianted in biological (or artificial) substrate. With this goal in mind, we argue that a promising research program lies in the crafting of cognitive architectures, a long-standing tradition of the field, cast fundamentally in terms of neuro-mimetic generative building blocks. Concretely, we discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition in terms of Hebbian adaptation operating in service of optimizing a variational free energy.
UI Layout Generation with LLMs Guided by UI Grammar

The recent advances in Large Language Models (LLMs) have stimulated interest among researchers and industry professionals, particularly in their application to tasks concerning mobile user interfaces (UIs). This position paper investigates the use of LLMs for UI layout generation. Central to our exploration is the introduction of UI grammar -- a novel approach we proposed to represent the hierarchical structure inherent in UI screens. The aim of this approach is to guide the generative capacities of LLMs more effectively and improve the explainability and controllability of the process. Initial experiments conducted with GPT-4 showed the promising capability of LLMs to produce high-quality user interfaces via in-context learning. Furthermore, our preliminary comparative study suggested the potential of the grammar-based approach in improving the quality of generative results in specific aspects.
A practical approach to computing Lyapunov exponents of renewal and delay equations

We propose a method for computing the Lyapunov exponents of renewal equations (delay equations of Volterra type) and of coupled systems of renewal and delay differential equations. The method consists of the reformulation of the delay equation as an abstract differential equation, the reduction of the latter to a system of ordinary differential equations via pseudospectral collocation and the application of the standard discrete QR method. The effectiveness of the method is shown experimentally and a MATLAB implementation is provided.
PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network

Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but also maintaining adequate image quality. Recent methods using convolutional neural networks (CNNs) to generate synthesized high-quality PET images from low-dose counterparts have been reported to be state-of-the-art for low-to-high image recovery methods. However, these methods are prone to exhibiting discrepancies in texture and structure between synthesized and real images. Furthermore, the distribution shift between low-dose PET and standard PET has not been fully investigated. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN). We introduce (1) An adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input, and (2) A self-supervised pre-training strategy to enhance the feature representation of the coarse generator. Our experiments with a public benchmark dataset of total-body PET images show that SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.
Rydberg-atom-based single-photon detection for haloscope axion searches

We propose a Rydberg-atom-based single-photon detector for signal readout in dark matter haloscope experiments between 40 ${\mu}$eV and 200 ${\mu}$eV (10 GHz and 50 GHz). At these frequencies, standard haloscope readout using linear amplifiers is limited by quantum measurement noise, which can be avoided by using a single-photon detector. Our single-photon detection scheme can offer scan rate enhancements up to a factor of $10^4$ over traditional linear amplifier readout, and is compatible with many different haloscope cavities. We identify multiple haloscope designs that could use our Rydberg-atom-based single-photon detector to search for QCD axions with masses above 40 ${\mu}$eV (10 GHz), currently a minimally explored parameter space.
An Optimal Ansatz Space for Moving Least Squares Approximation on Spheres

We revisit the moving least squares (MLS) approximation scheme on the sphere $\mathbb S^{d-1} \subset \mathbb R^d$, where $d>1$. It is well known that using the spherical harmonics up to degree $L \in \mathbb N$ as ansatz space yields for functions in $\mathcal C^{L+1}(\mathbb S^{d-1})$ the approximation order $\mathcal O \left( h^{L+1} \right)$, where $h$ denotes the fill distance of the sampling nodes. In this paper we show that the dimension of the ansatz space can be almost halved, by including only spherical harmonics of even or odd degree up to $L$, while preserving the same order of approximation. Numerical experiments indicate that using the reduced ansatz space is essential to ensure the numerical stability of the MLS approximation scheme as $h \to 0$. Finally, we compare our approach with an MLS approximation scheme that uses polynomials on the tangent space as ansatz space.
Correlation functions for open strings and chaos

We study the holographic interpretation of the bulk instability, i.e. the bulk Lyapunov exponent in the motion of open classical bosonic strings in AdS black hole/brane/string backgrounds. In the vicinity of homogeneous and isotropic horizons the bulk Lyapunov exponent saturates the MSS chaos bound but in fact has nothing to do with chaos as our string configurations live in an integrable sector. In the D1-D5-p black string background, the bulk Lyapunov exponent is deformed away from the MSS value both by the rotation (the infrared deformation) and the existence of an asymptotically flat region (the ultraviolet deformation). The dynamics is still integrable and has nothing to do with chaos (either in gravity or in field theory). Instead, the bulk Lyapunov scale captures the imaginary part of quasinormal mode frequencies. Therefore, the meaning of the bulk chaos is that it determines the thermal decay rate due to the coupling to the heat bath, i.e. the horizon.
A Roadmap of Emerging Trends Discovery in Hydrology: A Topic Modeling Approach

In the new global era, determining trends can play an important role in guiding researchers, scientists, and agencies. The main faced challenge is to track the emerging topics among the stacked publications. Therefore, any study done to propose the trend topics in a field to foresee upcoming subjects is crucial. In the current study, the trend topics in the field of"Hydrology"have been attempted to evaluate. To do so, the model is composed of three key components: a gathering of data, preprocessing of the article's significant features, and determining trend topics. Various topic models including Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and Latent Semantic Analysis (LSA) have been implemented. Comparing the obtained results with respect to the $C_V$ coherence score, in 2022, the topics of"Climate change","River basin","Water management","Natural hazards/erosion", and"Hydrologic cycle"have been obtained. According to a further analysis, it is shown that these topics keep their impact on the field in 2023, as well.
A roadmap to the efficient and robust characterization of temperate terrestrial planet atmospheres with JWST

Ultra-cool dwarf stars are abundant, long-lived, and uniquely suited to enable the atmospheric study of transiting terrestrial companions with JWST. Amongst them, the most prominent is the M8.5V star TRAPPIST-1 and its seven planets, which have been the favored targets of eight JWST Cycle 1 programs. While Cycle 1 observations have started to yield preliminary insights into the planets, they have also revealed that their atmospheric exploration requires a better understanding of their host star. Here, we propose a roadmap to characterize the TRAPPIST-1 system -- and others like it -- in an efficient and robust manner. We notably recommend that -- although more challenging to schedule -- multi-transit windows be prioritized to constrain stellar heterogeneities and gather up to 2$\times$ more transits per JWST hour spent. We conclude that in such systems planets cannot be studied in isolation by small programs, thus large-scale community-supported programs should be supported to enable the efficient and robust exploration of terrestrial exoplanets in the JWST era.
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit

We study the real-valued combinatorial pure exploration of the multi-armed bandit in the fixed-budget setting. We first introduce the Combinatorial Successive Asign (CSA) algorithm, which is the first algorithm that can identify the best action even when the size of the action class is exponentially large with respect to the number of arms. We show that the upper bound of the probability of error of the CSA algorithm matches a lower bound up to a logarithmic factor in the exponent. Then, we introduce another algorithm named the Minimax Combinatorial Successive Accepts and Rejects (Minimax-CombSAR) algorithm for the case where the size of the action class is polynomial, and show that it is optimal, which matches a lower bound. Finally, we experimentally compare the algorithms with previous methods and show that our algorithm performs better.
Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation

Knowing how to end and resume conversations over time is a natural part of communication, allowing for discussions to span weeks, months, or years. The duration of gaps between conversations dictates which topics are relevant and which questions to ask, and dialogue systems which do not explicitly model time may generate responses that are unnatural. In this work we explore the idea of making dialogue models aware of time, and present GapChat, a multi-session dialogue dataset in which the time between each session varies. While the dataset is constructed in real-time, progress on events in speakers' lives is simulated in order to create realistic dialogues occurring across a long timespan. We expose time information to the model and compare different representations of time and event progress. In human evaluation we show that time-aware models perform better in metrics that judge the relevance of the chosen topics and the information gained from the conversation.
Thermodynamic formalism for entire transcendental maps with hyperbolic Baker Domains

We provide a version of a thermodynamic formalism of entire transcendental maps that exhibit Baker domains, denoted as $f_{\ell, c}: \mathbb C\to \mathbb C$ and defined by $f_{\ell, c}(z)= c-(\ell-1)\log c+ \ell z- e^z$, where $\ell \in \mathbb N$, with $\ell \geq 2 $ and $c$ belongs to the disk $ D(\ell, 1)$ in the complex plane. We show in particular the existence and uniqueness of conformal measures and that the Hausdorff dimension is the unique zero of the pressure function $t\to P(t)$, for $t>1,$ where $J_r(f)$ is the radial Julia set.
Hierarchical Event Descriptor library schema for EEG data annotation

Standardizing terminology to describe electrophysiological events can improve both clinical care and computational research. Sharing data enriched by such standardized terminology can support advances in neuroscientific data exploration, from single-subject to mega-analysis. Machine readability of electrophysiological event annotations is essential for performing such analyses efficiently across software tools and packages. Hierarchical Event Descriptors (HED) provide a framework for describing events in neuroscience experiments. HED library schemas extend the standard HED schema vocabulary to include specialized vocabularies, such as standardized clinical terms for electrophysiological events. The Standardized Computer-based Organized Reporting of EEG (SCORE) defines terms for annotating EEG events, including artifacts. This study makes SCORE machine-readable by incorporating it into a HED library schema. We demonstrate the use of the HED-SCORE library schema to annotate events in example EEG data stored in Brain Imaging Data Structure (BIDS) format. Clinicians and researchers worldwide can now use the HED-SCORE library schema to annotate and then compute on electrophysiological data obtained from the human brain.
qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling

Classical evolutionary approaches for multiobjective optimization are quite effective but incur a lot of queries to the objectives; this can be prohibitive when objectives are expensive oracles. A sample-efficient approach to solving multiobjective optimization is via Gaussian process (GP) surrogates and Bayesian optimization (BO). Multiobjective Bayesian optimization (MOBO) involves the construction of an acquisition function which is optimized to acquire new observation candidates. This ``inner'' optimization can be hard due to various reasons: acquisition functions being nonconvex, nondifferentiable and/or unavailable in analytical form; the success of MOBO heavily relies on this inner optimization. We do away with this hard acquisition function optimization step and propose a simple, but effective, Thompson sampling based approach ($q\texttt{POTS}$) where new candidate(s) are chosen from the Pareto frontier of random GP posterior sample paths obtained by solving a much cheaper multiobjective optimization problem. To further improve computational tractability in higher dimensions we propose an automated active set of candidates selection combined with a Nystr\"{o}m approximation. Our approach applies to arbitrary GP prior assumptions and demonstrates strong empirical performance over the state of the art, both in terms of accuracy and computational efficiency, on synthetic as well as real-world experiments.
Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces

The ability to identify and control different kinds of linguistic information encoded in vector representations of words has many use cases, especially for explainability and bias removal. This is usually done via a set of simple classification tasks, termed probes, to evaluate the information encoded in the embedding space. However, the involvement of a trainable classifier leads to entanglement between the probe's results and the classifier's nature. As a result, contemporary works on probing include tasks that do not involve training of auxiliary models. In this work we introduce the term indicator tasks for non-trainable tasks which are used to query embedding spaces for the existence of certain properties, and claim that this kind of tasks may point to a direction opposite to probes, and that this contradiction complicates the decision on whether a property exists in an embedding space. We demonstrate our claims with two test cases, one dealing with gender debiasing and another with the erasure of morphological information from embedding spaces. We show that the application of a suitable indicator provides a more accurate picture of the information captured and removed compared to probes. We thus conclude that indicator tasks should be implemented and taken into consideration when eliciting information from embedded representations.