Miracle-dz/UniFAR-FTUs huggingface dataset:
Resolved near-UV hydrogen emission lines at 40-Myr super-Jovian protoplanet Delorme 1 (AB)b

Context. Accretion at planetary-mass companions (PMCs) suggests the presence of a protoplanetary disc in the system, likely accompanied by a circumplanetary disc. High-resolution spectroscopy of accreting PMCs is very difficult due to their proximity to bright host stars. For well-separated companions, however, such spectra are feasible and they are unique windows into accretion. Aims. We have followed up on our observations of the 40-Myr, and still accreting, circumbinary PMC Delorme 1 (AB)b. We used high-resolution spectroscopy to characterise the accretion process further by accessing the wealth of emission lines in the near-UV. Methods. We have used the UVES spectrograph on the ESO VLT/UT2 to obtain Rλ ≈ 50 000 spectroscopy, at 3300–4520 Å, of Delorme 1 (AB)b. After separating the emission of the companion from that of the M5 low-mass binary, we performed a detailed emission-line analysis, which included planetary accretion shock modelling. Results. We reaffirm ongoing accretion in Delorme 1 (AB)b and report the first detections in a (super-Jovian) protoplanet of resolved hydrogen line emission in the near-UV (Hγ, Hδ, Hϵ, H8, and H9). We tentatively detect H11, H12, He I, and Ca II H/K. The analysis strongly favours a planetary accretion shock with a line-luminosity-based accretion rate of Ṁ = 2 × 10−8 MJ yr−1. The lines are asymmetric and are well described by sums of narrow and broad components with different velocity shifts. The overall line shapes are best explained by a pre-shock velocity of v0 = 170 ± 30 km s−1, implying a planetary mass of MP = 13 ± 5 MJ, and number densities of n0 ≳ 1013 cm−3 or n0 ∼ 1011 cm−3. The higher density implies a small line-emitting area of ∼1% relative to the planetary surface. This favours magnetospheric accretion, a case potentially strengthened by the presence of blueshifted emission in the line profiles. Conclusions. High-resolution spectroscopy offers the opportunity to resolve line profiles, which are crucial for studying the accretion process in depth. The super-Jovian protoplanet Delorme 1 (AB)b is still accreting at ∼40 Myr. Thus, Delorme 1 belongs to the growing family of ‘Peter Pan disc’ systems with (a) protoplanetary and/or circumplanetary disc(s) far beyond the typically assumed disc lifetimes. Further observations of this benchmark companion and its presumed disc(s) will help answer key questions about the accretion geometry in PMCs.
Higher-rank sectors in the hexagon formalism and marginal deformations

: The hexagon approach provides an integrability framework for the computation of structure constants in N = 4 super Yang–Mills theory in four dimensions. Three-point functions are cut into two hexagonal patches, on which the excitations of the long-range Bethe ansatz of the spectrum problem scatter. To this end, the Bethe states representing the operators also need to be cut into two parts. In rank-one sectors such entangled states are fairly straightforward to construct so that most applications of the method have so far been restricted to this simplest case. In this article we construct entangled states for operators in psu (1 , 1 | 2) sectors, importing a minimum of information from the nested Bethe ansatz. The idea is successfully tested against free ﬁeld theory for a sample set of correlators with up to three higher-rank operators. Further, we take a look at the same correlators in the presence of marginal deformations of the theory. While a systematic modiﬁcation of the hexagon procedure remains out of reach for now, in practical applications the undeformed amplitudes are surprisingly eﬃcient, especially for a certain one-parameter deformation.
LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based Retrieval

Natural language inference has trended toward studying contexts beyond the sentence level. An important application area is law: past cases often do not foretell how they apply to new situations and implications must be inferred. This paper introduces LawngNLI, constructed from U.S. legal opinions with automatic labels with high human-validated accuracy. Premises are long and multigranular. Experiments show two use cases. First, LawngNLI can benchmark for in-domain generalization from short to long contexts. It has remained unclear if large-scale long-premise NLI datasets actually need to be constructed: near-top performance on long premises could be achievable by fine-tuning using short premises. Without multigranularity, benchmarks cannot distinguish lack of fine-tuning on long premises versus domain shift between short and long datasets. In contrast, our long and short premises share the same examples and domain. Models fine-tuned using several past NLI datasets and/or our short premises fall short of top performance on our long premises. So for at least certain domains (such as ours), large-scale long-premise datasets are needed. Second, LawngNLI can benchmark for implication-based retrieval. Queries are entailed or contradicted by target documents, allowing users to move between arguments and evidence. Leading retrieval models perform reasonably zero shot on a LawngNLI-derived retrieval task. We compare different systems for re-ranking, including lexical overlap and cross-encoders fine-tuned using a modified LawngNLI or past NLI datasets. LawngNLI can train and test systems for implication-based case retrieval and argumentation.
Applying Opponent Modeling for Automatic Bidding in Online Repeated Auctions

Online auction scenarios, such as bidding searches on advertising platforms, often require bidders to participate repeatedly in auctions for identical or similar items. Most previous studies have only considered the process by which the seller learns the prior-dependent optimal mechanism in a repeated auction. However, in this paper, we define a multiagent reinforcement learning environment in which strategic bidders and the seller learn their strategies simultaneously and design an automatic bidding algorithm that updates the strategy of bidders through online interactions. We propose Bid Net to replace the linear shading function as a representation of the strategic bidders' strategy, which effectively improves the utility of strategy learned by bidders. We apply and revise the opponent modeling methods to design the PG (pseudo-gradient) algorithm, which allows bidders to learn optimal bidding strategies with predictions of the other agents' strategy transition. We prove that when a bidder uses the PG algorithm, it can learn the best response to static opponents. When all bidders adopt the PG algorithm, the system will converge to the equilibrium of the game induced by the auction. In experiments with diverse environmental settings and varying opponent strategies, the PG algorithm maximizes the utility of bidders. We hope that this article will inspire research on automatic bidding strategies for strategic bidders.
Observing circumplanetary disks with METIS

Context: A full understanding of the planet and moon formation process requires observations that probe the circumplanetary environment of accreting giant planets. The mid-infrared ELT imager and spectrograph (METIS) will provide a unique capability to detect warm-gas emission lines from circumplanetary disks. Aims: We aim to demonstrate the capability of the METIS instrument on the Extremely Large Telescope (ELT) to detect circumplanetary disks (CPDs) with fundamental v=1-0 transitions of $^{12}$CO from 4.5-5 $\mu$m. Methods: We consider the case of the well-studied HD 100546 pre-transitional disk to inform our disk modeling approach. We use the radiation-thermochemical disk modeling code ProDiMo to produce synthetic spectral channel maps. The observational simulator SimMETIS is employed to produce realistic data products with the integral field spectroscopic (IFU) mode. Results: The detectability of the CPD depends strongly on the level of external irradiation and the physical extent of the disk, favoring massive (~10 M$_{\rm J}$) planets and spatially extended disks with radii approaching the planetary Hill radius. The majority of $^{12}$CO line emission originates from the outer disk surface, and thus the CO line profiles are centrally peaked. The planetary luminosity does not contribute significantly to exciting disk gas line emission. If CPDs are dust-depleted, the $^{12}$CO line emission is enhanced as external radiation can penetrate deeper into the line emitting region. Conclusions: UV-bright star systems with pre-transitional disks are ideal candidates to search for CO-emitting CPDs with ELT/METIS. METIS will be able to detect a variety of circumplanetary disks via their fundamental $^{12}$CO ro-vibrational line emission in only 60 s of total detector integration time.
DiffTune+: Hyperparameter-Free Auto-Tuning using Auto-Differentiation

Controller tuning is a vital step to ensure the controller delivers its designed performance. DiffTune has been proposed as an automatic tuning method that unrolls the dynamical system and controller into a computational graph and uses auto-differentiation to obtain the gradient for the controller's parameter update. However, DiffTune uses the vanilla gradient descent to iteratively update the parameter, in which the performance largely depends on the choice of the learning rate (as a hyperparameter). In this paper, we propose to use hyperparameter-free methods to update the controller parameters. We find the optimal parameter update by maximizing the loss reduction, where a predicted loss based on the approximated state and control is used for the maximization. Two methods are proposed to optimally update the parameters and are compared with related variants in simulations on a Dubin's car and a quadrotor. Simulation experiments show that the proposed first-order method outperforms the hyperparameter-based methods and is more robust than the second-order hyperparameter-free methods.
The distribution of large quadratic character sums and applications

In this paper, we investigate the distribution of the maximum of character sums over the family of primitive quadratic characters attached to fundamental discriminants $|d|\leq x$. In particular, our work improves results of Montgomery and Vaughan, and gives strong evidence that the Omega result of Bateman and Chowla for quadratic character sums is optimal. We also obtain similar results for real characters with prime discriminants up to $x$, and deduce the interesting consequence that almost all primes with large Legendre symbol sums are congruent to $3$ modulo $4$. Our results are motivated by a recent work of Bober, Goldmakher, Granville and Koukoulopoulos, who proved similar results for the family of non-principal characters modulo a large prime. However, their method does not seem to generalize to other families of Dirichlet characters. Instead, we use a different and more streamlined approach, which relies mainly on the quadratic large sieve. As an application, we consider a question of Montgomery concerning the positivity of sums of Legendre symbols.
Fine-tuning a Subtle Parsing Distinction Using a Probabilistic Decision Tree: the Case of Postnominal “that” in Noun Complement Clauses vs. Relative Clauses

In this paper we investigated two different methods to parse relative and noun complement clauses in English and resorted to distinct tags for their corresponding that as a relative pronoun and as a complementizer. We used an algorithm to relabel a corpus parsed with the GUM Treebank using Universal Dependency. Our second experiment consisted in using TreeTagger, a Probabilistic Decision Tree, to learn the distinction between the two complement and relative uses of postnominal"that". We investigated the effect of the training set size on TreeTagger accuracy and how representative the GUM Treebank files are for the two structures under scrutiny. We discussed some of the linguistic and structural tenets of the learnability of this distinction.
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?

Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information. However, policies learned through Deep Reinforcement Learning (DRL) are susceptible to adversarial state perturbation attacks. In this work, we propose a State-Adversarial Markov Game (SAMG) and make the first attempt to investigate the fundamental properties of MARL under state uncertainties. Our analysis shows that the commonly used solution concepts of optimal agent policy and robust Nash equilibrium do not always exist in SAMGs. To circumvent this difficulty, we consider a new solution concept called robust agent policy, where agents aim to maximize the worst-case expected state value. We prove the existence of robust agent policy for finite state and finite action SAMGs. Additionally, we propose a Robust Multi-Agent Adversarial Actor-Critic (RMA3C) algorithm to learn robust policies for MARL agents under state uncertainties. Our experiments demonstrate that our algorithm outperforms existing methods when faced with state perturbations and greatly improves the robustness of MARL policies. Our code is public on https://songyanghan.github. io/what_is_solution/.
An Online Newton’s Method for Time-Varying Linear Equality Constraints

We consider online optimization problems with time-varying linear equality constraints. In this framework, an agent makes sequential decisions using only prior information. At every round, the agent suffers an environment-determined loss and must satisfy time-varying constraints. Both the loss functions and the constraints can be chosen adversarially. We propose the Online Projected Equality-constrained Newton Method (OPEN-M) to tackle this family of problems. We obtain sublinear dynamic regret and constraint violation bounds for OPEN-M under mild conditions. Namely, smoothness of the loss function and boundedness of the inverse Hessian at the optimum are required, but not convexity. Finally, we show OPEN-M outperforms state-of-the-art online constrained optimization algorithms in a numerical network flow application.
On the Kinetic Model Description of Rayleigh–Brillouin Scattering from Molecular Gases

We analyze in detail, in the kinetic regime, the behavior of a model kinetic equation previously used by us for the description of the density fluctuation spectrum in molecular gases. We find that over a small range of wavelengths and frequencies the predicted spectrum contains an unphysical feature, namely a shallow dip in the Rayleigh line. This effect is traced to the modelled elastic collision operator and has gone unnoticed in similar models of monatomic gases. We propose a modified model which corrects this feature and which appears to reproduce the details seen in light-scattering experiments.
ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion

Knowledge base completion (KBC) aims to predict the missing links in knowledge graphs. Previous KBC tasks and approaches mainly focus on the setting where all test entities and relations have appeared in the training set. However, there has been limited research on the zero-shot KBC settings, where we need to deal with unseen entities and relations that emerge in a constantly growing knowledge base. In this work, we systematically examine different possible scenarios of zero-shot KBC and develop a comprehensive benchmark, ZeroKBC, that covers these scenarios with diverse types of knowledge sources. Our systematic analysis reveals several missing yet important zero-shot KBC settings. Experimental results show that canonical and state-of-the-art KBC systems cannot achieve satisfactory performance on this challenging benchmark. By analyzing the strength and weaknesses of these systems on solving ZeroKBC, we further present several important observations and promising future directions.11Work was done during the internship at Tencent AI lab. The data and code are available at: https://github.com/brickee/ZeroKBC
Texture metric of halftone images

Texture plays an important role in image features, in both display and perception of halftone images. We propose a quantitative measure for the occurrence of textures in binary images. This metric considers the fact that textures may be disturbed by missing or spurious pixels and that texels may overlap. Moreover, the texture may be black on white or white on black, which is also considered in the texture metric. We analyze various binary images with different textural characteristics with respect to different textures, to demonstrate the properties of the texture metric.
DisTRaC: Accelerating High Performance Compute Processing for Temporary Data Storage

—High Performance Compute (HPC) clusters often produce intermediate ﬁles as part of code execution and message passing is not always possible to supply data to these cluster jobs. In these cases, I/O goes back to central distributed storage to allow cross node data sharing. These systems are often high performance and characterised by their high cost per TB and sensitivity to workload type such as being tuned to small or large ﬁle I/O. However, compute nodes often have large amounts of RAM, so when dealing with intermediate ﬁles where longevity or reliability of the system is not as important, local RAM disks can be used to obtain performance beneﬁts. In this paper we show how this problem was tackled by creating a RAM block that could interact with the object storage system Ceph, as well as creating a deployment tool to deploy Ceph on HPC infrastructure effectively. This work resulted in a system that was more performant than the central high performance distributed storage system used at Diamond reducing I/O overhead and processing time for Savu, a tomography data processing application, by 81.04% and 8.32% respectively.
Risk management in the Artificial Intelligence Act

The proposed Artificial Intelligence Act (AI Act) is the first comprehensive attempt to regulate artificial intelligence (AI) in a major jurisdiction. This article analyses Article 9, the key risk management provision in the AI Act. It gives an overview of the regulatory concept behind the norm, determines its purpose and scope of application, offers a comprehensive interpretation of the specific risk management requirements and outlines ways in which the requirements can be enforced. This article can help providers of high-risk systems to comply with the requirements set out in Article 9. In addition, it can inform revisions of the current draft of the AI Act and efforts to develop harmonised standards on AI risk management.
Generalized amoebas for subvarieties of $GL_n(\mathbb{C})$

This paper is a report based on the results obtained during a three months internship at the University of Pittsburgh by the ﬁrst author and under the mentorship of the second author. In [KM22] and [KM19, Section 7], the notion of an amoeba of a subvariety in a torus ( C ∗ ) n has been extended to subvarieties of the general linear group GL n ( C ). In this paper, we show some basic properties of these matrix amoebas, e.g. any such amoeba is closed and the connected components of its complement are convex when the variety is a hypersurface. We also extend the notion of Ronkin function to this setting. For hypersurfaces, we show how to describe the asymptotic directions of the matrix amoebas using a notion of Newton polytope. Finally, we partially extend the classical statement that the amoebas converge to the tropical variety. We also discuss a few examples. Our matrix amoeba should be considered as the Archimedean version of the spherical tropicalization of Tevelev-Vogiannou for the variety GL n ( C ) regarded as a spherical homogeneous space for the left-right action of GL n ( C ) × GL n ( C ).
Generative probabilistic matrix model of data with different low-dimensional linear latent structures

We construct a generative probabilistic matrix model of large data based on mixing of linear latent features distributed following Gaussian and Dirichlet distributions. Key ingredient of our model is that we allow for statistical dependence between the mixing coefficients, as well as latent features with a statistically dependent structure. Latent dimensionality and correlation patterns of the data are controlled by two model parameters. The model’s data patterns include (overlapping) clusters, sparse mixing, and constrained (non-negative) mixing. We describe the correlation and the eigenvalue distributions of these patterns. As a possible application of our model, we discuss how it can be used to generate structured training data for supervised learning.
STABILITY OF RICCI FLOWS BASED ON KILLING CONDITIONS

C. Guenther studied the stability of DeTurck flows by using maximal regularity theory and center manifolds, but these arguments can not solve the stability of Ricci flows because the Ricci flow equation is not strictly parabolic. Recognizing this deficiency, the present paper considers and obtains the stability of Ricci flows, and of quasi-Ricci flows in view of some Killing conditions.
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization

Advancement in large pretrained language models has significantly improved their performance for conditional language generation tasks including summarization albeit with hallucinations. To reduce hallucinations, conventional methods proposed improving beam search or using a fact checker as a postprocessing step. In this paper, we investigate the use of the Natural Language Inference (NLI) entailment metric to detect and prevent hallucinations in summary generation. We propose an NLI-assisted beam re-ranking mechanism by computing entailment probability scores between the input context and summarization model-generated beams during saliency-enhanced greedy decoding. Moreover, a diversity metric is introduced to compare its effectiveness against vanilla beam search. Our proposed algorithm significantly outperforms vanilla beam decoding on XSum and CNN/DM datasets.
A Large-Scale Analysis of Phishing Websites Hosted on Free Web Hosting Domains

Free Website Building services (FWBs) provide individuals with a cost-effective and convenient way to create a website without requiring advanced technical knowledge or coding skills. However, malicious actors often abuse these services to host phishing websites. In this work, we propose FreePhish, a scalable framework to continuously identify phishing websites that are created using FWBs. Using FreePhish, we were able to detect and characterize more than 31.4K phishing URLs that were created using 17 unique free website builder services and shared on Twitter and Facebook over a period of six months. We find that FWBs provide attackers with several features that make it easier to create and maintain phishing websites at scale while simultaneously evading anti-phishing countermeasures. Our study indicates that anti-phishing blocklists and browser protection tools have significantly lower coverage and high detection time against FWB phishing attacks when compared to regular (self-hosted) phishing websites. While our prompt disclosure of these attacks helped some FWBs to remove these attacks, we found several others who were slow at removal or did not remove them outright, with the same also being true for Twitter and Facebook. Finally, we also provide FreePhish as a free Chromium web extension that can be utilized to prevent end-users from accessing potential FWB-based phishing attacks.