Miracle-dz/UniFAR-FTUs huggingface dataset:
High-resolution Imaging of a TESS Control Sample: Verifying a Deficit of Close-in Stellar Companions to Exoplanet Host Stars

The large number of exoplanets discovered with the Transiting Exoplanet Survey Satellite (TESS) means that any observational biases from TESS could influence the derived stellar multiplicity statistics of exoplanet host stars. To investigate this problem, we obtained speckle interferometry of 207 control stars whose properties in the TESS Input Catalog (TIC) closely match those of an exoplanetary host star in the TESS Object of Interest (TOI) catalog, with the objective of measuring the fraction of these stars that have companions within ∼1. ″2. Our main result is the identification of a bias in the creation of the control sample that prevents the selection of binaries with 0. ″1 ≲ ρ ≲ 1. ″2 and Δmag ≲3. This bias is the result of large astrometric residuals that cause binaries with these parameters to fail the quality checks used to create the TIC, which in turn causes them to have incomplete stellar parameters (and uncertainties) in the TIC. Any stellar multiplicity study that relies exclusively upon TIC stellar parameters to identify its targets will struggle to select unresolved binaries in this parameter space. Left uncorrected, this selection bias disproportionately excludes high-mass-ratio binaries, causing the mass-ratio distribution of the companions to deviate significantly from the uniform distribution expected of FGK-type field binaries. After accounting for this bias, the companion rate of the FGK control stars is consistent with the canonical 46% ± 2% rate from Raghavan et al., and the mass-ratio distribution agrees with that of binary TOI host stars. There is marginal evidence that the control-star companions have smaller projected orbital separations than TOI host stars from previous studies.
InfoVisDial: An Informative Visual Dialogue Dataset by Bridging Large Multimodal and Language Models

In this paper, we build a visual dialogue dataset, named InfoVisDial, which provides rich informative answers in each round even with external knowledge related to the visual content. Different from existing datasets where the answer is compact and short, InfoVisDial contains long free-form answers with rich information in each round of dialogue. For effective data collection, the key idea is to bridge the large-scale multimodal model (e.g., GIT) and the language models (e.g., GPT-3). GIT can describe the image content even with scene text, while GPT-3 can generate informative dialogue based on the image description and appropriate prompting techniques. With such automatic pipeline, we can readily generate informative visual dialogue data at scale. Then, we ask human annotators to rate the generated dialogues to filter the low-quality conversations.Human analyses show that InfoVisDial covers informative and diverse dialogue topics: $54.4\%$ of the dialogue rounds are related to image scene texts, and $36.7\%$ require external knowledge. Each round's answer is also long and open-ended: $87.3\%$ of answers are unique with an average length of $8.9$, compared with $27.37\%$ and $2.9$ in VisDial. Last, we propose a strong baseline by adapting the GIT model for the visual dialogue task and fine-tune the model on InfoVisDial. Hopefully, our work can motivate more effort on this direction.
The Conformal Manifold of S-folds in String Theory

We continue the holographic exploration of the conformal manifold of 3d $\mathcal{N}=2$ S-fold SCFTs constructed by gauging the flavor symmetry of the Gaiotto-Witten $T[U(N)]$ theory. We show how to uplift the two-parameter family of AdS$_4$ vacua dual to this conformal manifold to 10d backgrounds of type IIB supergravity. We use these uplifted solutions to shed new light on the mysterious nature of the infinite distance limit on the conformal manifold and to study probe strings and D3-branes. This analysis uncovers an intriguing structure of the $S^3$ partition function of the S-fold SCFTs which resembles the giant graviton expansion of the superconformal index of 4d $\mathcal{N}=4$ SYM. We also show how to each member of the family of supersymmetric AdS$_4$ vacua one can associate a consistent truncation to 4d $\mathcal{N}=2$ gauged supergravity and use this result, in conjunction with holography, to calculate the large $N$ partition function of the 3d S-fold SCFT on compact Euclidean manifolds. Finally, we generalize the supersymmetric AdS$_4$ vacua to a four-parameter family of non-supersymmetric AdS$_4$ solutions.
Development of Self-Shunted Josephson Junctions for a Ten-Superconductor-Layer Fabrication Process: Nb/NbNx/Nb Junctions

To increase the integration scale of superconductor electronics, we are developing a new node, titled SFQ7ee, of the fabrication process at MIT Lincoln Laboratory. In comparison to the existing SFQ5ee node, we increased the number of fully planarized superconducting layers to ten and utilized NbN and NbN/Nb kinetic inductors to increase the possible inductor number density above a hundred million inductors per cm<sup>2</sup>. Increasing the Josephson junction number density to the same level requires implementing self-shunted high-<italic>J</italic><sub>c</sub> Josephson junctions. We investigated the properties of Nb/NbN<sub>x</sub>/Nb trilayer junctions as a potential replacement of high-<italic>J</italic><sub>c</sub> Nb/Al-AlO<sub>x</sub>/Nb junctions, where NbN<sub>x</sub> is a disordered, overstoichiometric, nonsuperconducting nitride deposited by reactive sputtering. Dependences of the <italic>I<sub>c</sub>R<sub>n</sub></italic> product and Josephson critical current density, <italic>J</italic><sub>c</sub> on the NbN<sub>x</sub> barrier thickness and temperature were studied in the thickness range from 5 nm to 20 nm. The fabricated junctions can be well described by the microscopic theory of SNS junctions, assuming no suppression of the energy gap in Nb electrodes near the NbN<sub>x</sub> interfaces and a Cooper pair decay length in the NbN<sub>x</sub> barrier, <inline-formula><tex-math notation="LaTeX">${{\bm{\xi }}_{\bm{n}}}({{{\bm{T}}_{\bm{c}}}})$</tex-math></inline-formula>, of about 2.3 nm. Current-voltage characteristics of the junctions are well described by the resistively and capacitively shunted junction (RCSJ) model without excess current. In the studied range <italic>J</italic><sub>c</sub> < 10 mA/µm<sup>2</sup>, the Nb/NbN<sub>x</sub>/Nb junctions have lower values of the specific resistance <italic>R<sub>n</sub>A</italic>, lower <italic>I<sub>c</sub>R<sub>n</sub></italic> products, and a stronger dependence of the <italic>I<sub>c</sub>R<sub>n</sub></italic> on temperature than the self-shunted or critically damped externally shunted Nb/Al-AlO<sub>x</sub>/Nb junctions with the same critical current density; <italic>A</italic> is the junction area, <italic>I</italic><sub>c</sub> the junction critical current, and <italic>R<sub>n</sub></italic> the effective shunting resistance.
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks

Backpropagation (BP) has been a successful optimization technique for deep learning models. However, its limitations, such as backward- and update-locking, and its biological implausibility, hinder the concurrent updating of layers and do not mimic the local learning processes observed in the human brain. To address these issues, recent research has suggested using local error signals to asynchronously train network blocks. However, this approach often involves extensive trial-and-error iterations to determine the best configuration for local training. This includes decisions on how to decouple network blocks and which auxiliary networks to use for each block. In our work, we introduce a novel BP-free approach: a block-wise BP-free (BWBPF) neural network that leverages local error signals to optimize distinct sub-neural networks separately, where the global loss is only responsible for updating the output layer. The local error signals used in the BP-free model can be computed in parallel, enabling a potential speed-up in the weight update process through parallel implementation. Our experimental results consistently show that this approach can identify transferable decoupled architectures for VGG and ResNet variations, outperforming models trained with end-to-end backpropagation and other state-of-the-art block-wise learning techniques on datasets such as CIFAR-10 and Tiny-ImageNet. The code is released at https://github.com/Belis0811/BWBPF.
Limits on heavy neutral leptons, $Z'$ bosons and majorons from high-energy supernova neutrinos

Light hypothetical particles with masses up to $\mathcal{O}(100)\ {\rm MeV}$ can be produced in the core of supernovae. Their subsequent decays to neutrinos can produce a flux component with higher energies than the standard flux. We study the impact of heavy neutral leptons, $Z'$ bosons, in particular ${\rm U(1)}_{L_\mu-L_\tau}$ and ${\rm U(1)}_{B-L}$ gauge bosons, and majorons coupled to neutrinos flavor-dependently. We obtain new strong limits on these particles from no events of high-energy SN 1987A neutrinos and their future sensitivities from observations of galactic supernova neutrinos.
Rupture mechanism and seismotectonics of the Ms6.5 Ludian earthquake inferred from three‐dimensional magnetotelluric imaging

A three‐dimensional (3‐D) resistivity model around the 2014 Ms6.5 Ludian earthquake was obtained. The model shows that the aftershocks were mainly distributed in a shallow inverse L‐shaped conductive angular region surrounded by resistive structures. The presences of this shallow conductive zone may be the key factor leading to the severe damage and surface rupture of the Ludian earthquake. A northwest trending local resistive belt along the Baogunao‐Xiaohe fault interrupts the northeast trending conductive zone at the Zhaotong‐Lianfeng fault zone in the middle crust, which may be the seismogenic structure of the main shock. Based on the 3‐D electrical model, combining with GPS, thermal structure, and seismic survey results, a geodynamic model is proposed to interpret the seismotectonics, deep seismogenic background, and deformation characterized by a sinistral strike slip with a tensile component of the Ludian earthquake.
The second quandle homology group of the knot $n$-quandle

The knot quandle is a complete invariant for oriented classical knots in the $3$-sphere up to orientation. Eisermann computed the second quandle homology group of the knot quandle and showed that it characterizes the unknot. In this paper, we compute the second quandle homology group of the knot $n$-quandle completely, where the knot $n$-quandle is a certain quotient of the knot quandle for each integer $n$ greater than one. Although the knot $n$-quandle is weaker than the knot quandle, the second quandle homology group of the former is found to have more information than that of the latter. As one of the consequences, it follows that the second quandle homology group of the knot $3$-quandle characterizes the unknot, the trefoil and the cinquefoil.
An EFT approach to baryon number violation: lower limits on the new physics scale and correlations between nucleon decay modes

Baryon number is an accidental symmetry of the Standard Model at the Lagrangian level. Its violation is arguably one of the most compelling phenomena predicted by physics beyond the Standard Model. Furthermore, there is a large experimental effort to search for it including the Hyper-K, DUNE, JUNO, and THEIA experiments. Therefore, an agnostic, model-independent, analysis of baryon number violation using the power of Effective Field Theory is very timely. In particular, in this work we study the contribution of dimension six and seven effective operators to $|\Delta (B-L)|=0, \, 2$ nucleon decays taking into account the effects of Renormalisation Group Evolution. We obtain lower limits on the energy scale of each operator and study the correlations between different decay modes. We find that for some operators the effect of running is very significant.
Intel PMDK Transactions: Specification, Validation and Concurrency (Extended Version)

Software Transactional Memory (STM) is an extensively studied paradigm that provides an easy-to-use mechanism for thread safety and concurrency control. With the recent advent of byte-addressable persistent memory, a natural question to ask is whether STM systems can be adapted to support failure atomicity. In this article, we answer this question by showing how STM can be easily integrated with Intel's Persistent Memory Development Kit (PMDK) transactional library (which we refer to as txPMDK) to obtain STM systems that are both concurrent and persistent. We demonstrate this approach using known STM systems, TML and NOrec, which when combined with txPMDK result in persistent STM systems, referred to as PMDK-TML and PMDK-NORec, respectively. However, it turns out that existing correctness criteria are insufficient for specifying the behaviour of txPMDK and our concurrent extensions. We therefore develop a new correctness criterion, dynamic durable opacity, that extends the previously defined notion of durable opacity with dynamic memory allocation. We provide a model of txPMDK, then show that this model satisfies dynamic durable opacity. Moreover, dynamic durable opacity supports concurrent transactions, thus we also use it to show correctness of both PMDK-TML and PMDK-NORec.
Iteration on the Higgs-portal for vector Dark Matter and its effective field theory description

We reanalyze the effective field theory (EFT) approach for the scenario in which the particles that account for the dark matter (DM) in the universe are vector states that interact only through the Standard Model-like Higgs boson. These DM particles are searched for in direct and indirect detection in astrophysical experiments and in invisible Higgs decays at the LHC. The constraints obtained in these two search types are complementary and correlated. In recent years, it has been advocated that the EFT approach is problematic for small DM mass and that it does not capture all the aspects of vector DM; one should thus rather interpret the searches in ultraviolet complete theories that are more realistic. In this note, we show that a more appropriate definition of the EFT with the introduction of an effective New Physics scale parameter, can encompass such issues. We illustrate this by matching the EFT to two examples of ultraviolet completions for it: the U(1) model with a dark photon and a model that was recently adopted by the LHC experiments in which vector-like fermions generate an effective interaction between the Higgs and the DM states at the one-loop level. Additionally, we find that the region of parameter space that is relevant for DM phenomenology is well inside the range of validity of the EFT. It thus provides a general parametrization of the effects of any ultraviolet model in the regime under exploration, making it the ideal framework for model-independent analyses of the vector DM Higgs-portal.
Extreme Value Statistics for Analysing Simulated Environmental Extremes

We present the methods employed by team `Uniofbathtopia' as part of the Data Challenge organised for the 13th International Conference on Extreme Value Analysis (EVA2023), including our winning entry for the third sub-challenge. Our approaches unite ideas from extreme value theory, which provides a statistical framework for the estimation of probabilities/return levels associated with rare events, with techniques from unsupervised statistical learning, such as clustering and support identification. The methods are demonstrated on the data provided for the Data Challenge -- environmental data sampled from the fantasy country of `Utopia' -- but the underlying assumptions and frameworks should apply in more general settings and applications.
Leakage-Resilient Hardness Equivalence to Logspace Derandomization

Efficient derandomization has long been a goal in complexity theory, and a major recent result by Yanyi Liu and Rafael Pass identifies a new class of hardness assumption under which it is possible to perform time-bounded derandomization efficiently: that of ''leakage-resilient hardness. '' They identify a specific form of this assumption which is $\textit{equivalent}$ to $\mathsf{prP} = \mathsf{prBPP}$. In this paper, we pursue a an equivalence to derandomization of $\mathsf{prBP{\cdot}L}$ (logspace promise problems with two-way randomness) through techniques analogous to Liu and Pass. We are able to obtain an equivalence between a similar ''leakage-resilient hardness'' assumption and a slightly stronger statement than derandomization of $\mathsf{prBP{\cdot}L}$, that of finding ''non-no'' instances of ''promise search problems. ''
HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models

Recent progress in text-guided image inpainting, based on the unprecedented success of text-to-image diffusion models, has led to exceptionally realistic and visually plausible results. However, there is still significant potential for improvement in current text-to-image inpainting models, particularly in better aligning the inpainted area with user prompts and performing high-resolution inpainting. Therefore, we introduce HD-Painter, a training free approach that accurately follows prompts and coherently scales to high resolution image inpainting. To this end, we design the Prompt-Aware Introverted Attention (PAIntA) layer enhancing self-attention scores by prompt information resulting in better text aligned generations. To further improve the prompt coherence we introduce the Reweighting Attention Score Guidance (RASG) mechanism seamlessly integrating a post-hoc sampling strategy into the general form of DDIM to prevent out-of-distribution latent shifts. Moreover, HD-Painter allows extension to larger scales by introducing a specialized super-resolution technique customized for inpainting, enabling the completion of missing regions in images of up to 2K resolution. Our experiments demonstrate that HD-Painter surpasses existing state-of-the-art approaches quantitatively and qualitatively across multiple metrics and a user study. Code is publicly available at: https://github.com/Picsart-AI-Research/HD-Painter
Metalearning with Very Few Samples Per Task

Metalearning and multitask learning are two frameworks for solving a group of related learning tasks more efficiently than we could hope to solve each of the individual tasks on their own. In multitask learning, we are given a fixed set of related learning tasks and need to output one accurate model per task, whereas in metalearning we are given tasks that are drawn i.i. d. from a metadistribution and need to output some common information that can be easily specialized to new, previously unseen tasks from the metadistribution. In this work, we consider a binary classification setting where tasks are related by a shared representation, that is, every task $P$ of interest can be solved by a classifier of the form $f_{P} \circ h$ where $h \in H$ is a map from features to some representation space that is shared across tasks, and $f_{P} \in F$ is a task-specific classifier from the representation space to labels. The main question we ask in this work is how much data do we need to metalearn a good representation? Here, the amount of data is measured in terms of both the number of tasks $t$ that we need to see and the number of samples $n$ per task. We focus on the regime where the number of samples per task is extremely small. Our main result shows that, in a distribution-free setting where the feature vectors are in $\mathbb{R}^d$, the representation is a linear map from $\mathbb{R}^d \to \mathbb{R}^k$, and the task-specific classifiers are halfspaces in $\mathbb{R}^k$, we can metalearn a representation with error $\varepsilon$ using just $n = k+2$ samples per task, and $d \cdot (1/\varepsilon)^{O(k)}$ tasks. Learning with so few samples per task is remarkable because metalearning would be impossible with $k+1$ samples per task, and because we cannot even hope to learn an accurate task-specific classifier with just $k+2$ samples per task.
Empowering Few-Shot Recommender Systems With Large Language Models-Enhanced Representations

Recommender systems utilizing explicit feedback have witnessed significant advancements and widespread applications over the past years. However, generating recommendations in few-shot scenarios remains a persistent challenge. Recently, large language models (LLMs) have emerged as a promising solution for addressing natural language processing (NLP) tasks, thereby offering novel insights into tackling the few-shot scenarios encountered by explicit feedback-based recommender systems. To bridge recommender systems and LLMs, we devise a prompting template that generates user and item representations based on explicit feedback. Subsequently, we integrate these LLM-processed representations into various recommendation models to evaluate their significance across diverse recommendation tasks. Our ablation experiments and case study analysis collectively demonstrate the effectiveness of LLMs in processing explicit feedback, highlighting that LLMs equipped with generative and logical reasoning capabilities can effectively serve as a component of recommender systems to enhance their performance in few-shot scenarios. Furthermore, the broad adaptability of LLMs augments the generalization potential of recommender models, despite certain inherent constraints. We anticipate that our study can inspire researchers to delve deeper into the multifaceted dimensions of LLMs’ involvement in recommender systems and contribute to the advancement of the explicit feedback-based recommender systems field.
How Does Stake Distribution Influence Consensus? Analyzing Blockchain Decentralization

In the PoS blockchain landscape, the challenge of achieving full decentralization is often hindered by a disproportionate concentration of staked tokens among a few validators. This study analyses this challenge by first formalizing decentralization metrics for weighted consensus mechanisms. An empirical analysis across ten permissionless blockchains uncovers significant weight concentration among validators, underscoring the need for an equitable approach. To counter this, we introduce the Square Root Stake Weight (SRSW) model, which effectively recalibrates staking weight distribution. Our examination of the SRSW model demonstrates notable improvements in the decentralization metrics: the Gini index improves by 37.16% on average, while Nakamoto coefficients for liveness and safety see mean enhancements of 101.04% and 80.09%, respectively. This research is a pivotal step toward a more fair and equitable distribution of staking weight, advancing the decentralization in blockchain consensus mechanisms.