Miracle-dz/UniFAR-FTUs huggingface dataset:
Hereditary classes of ordered sets of width at most two

This paper is a contribution to the study of hereditary classes of relational structures, these classes being quasi-ordered by embeddability. It deals with the speciﬁc case of ordered sets of width two and the corresponding bichains and incomparability graphs. Several open problems about hereditary classes of relational structures which have been considered over the years have positive answer in this case. For example, well-quasi-ordered hereditary classes of ﬁnite bipartite permutation graphs, respectively ﬁnite 321-avoiding permutations, have been characterized by Korpelainen, Lozin and Mayhill, respectively by Albert, Brignall, Ruˇskuc and Vatter. In this paper we present an overview of properties of these hereditary classes in the framework of the Theory of Relations as presented by Roland Fra¨ıss´e. We provide another proof of the results mentioned above. It is based on the existence of a countable universal poset of width two, obtained by the ﬁrst author in 1978, his notion of multichainability (1978) (a kind of analog to letter-graphs), and metric properties of incomparability graphs. Using Laver’s theorem (1971) on better-quasi-ordering (bqo) of countable chains we prove that a wqo hereditary class of ﬁnite or countable bipartite permutation graphs is necessarily bqo. This gives a positive answer to a conjecture of Nash-Williams (1965) in this case. We extend a previous result of Albert et al. by proving that if a hereditary class of ﬁnite, respectively countable, bipartite permutation graphs is wqo, respectively bqo, then the corresponding hereditary classes of posets of width at most two and bichains are wqo, respectively bqo. the case of bipartite permutation graphs, posets of width at most two and the corresponding bichains. We characterize hereditary classes of ﬁnite bipartite permutation graphs which remain wqo when labels from a wqo are added. Hereditary classes of posets of width two, bipartite permutation graphs and the corresponding bichains having ﬁnitely many bounds are also characterized. We prove that a hereditary class of ﬁnite bipartite permutation graphs is not wqo if and only if it embeds the poset of ﬁnite subsets of N ordered by set inclusion. This answers a long standing conjecture of the ﬁrst author in the case of bipartite permutation graphs.
Maximum-Likelihood Quantum State Tomography by Soft-Bayes

Quantum state tomography (QST), the task of estimating an unknown quantum state given measurement outcomes, is essential to building reliable quantum computing devices. Whereas computing the maximum-likelihood (ML) estimate corresponds to solving a finite-sum convex optimization problem, the objective function is not smooth nor Lipschitz, so most existing convex optimization methods lack sample complexity guarantees; moreover, both the sample size and dimension grow exponentially with the number of qubits in a QST experiment, so a desired algorithm should be highly scalable with respect to the dimension and sample size, just like stochastic gradient descent. In this paper, we propose a stochastic first-order algorithm that computes an $\varepsilon$-approximate ML estimate in $O( ( D \log D ) / \varepsilon ^ 2 )$ iterations with $O( D^3 )$ per-iteration time complexity, where $D$ denotes the dimension of the unknown quantum state and $\varepsilon$ denotes the optimization error. Our algorithm is an extension of Soft-Bayes to the quantum setup.
Building the Intent Landscape of Real-World Conversational Corpora with Extractive Question-Answering Transformers

For companies with customer service, mapping intents inside their conversational data is crucial in building applications based on natural language understanding (NLU). Nevertheless, there is no established automated technique to gather the intents from noisy online chats or voice transcripts. Simple clustering approaches are not suited to intent-sparse dialogues. To solve this intent-landscape task, we propose an unsupervised pipeline that extracts the intents and the taxonomy of intents from real-world dialogues. Our pipeline mines intent-span candidates with an extractive Question-Answering Electra model and leverages sentence embeddings to apply a low-level density clustering followed by a top-level hierarchical clustering. Our results demonstrate the generalization ability of an ELECTRA large model fine-tuned on the SQuAD2 dataset to understand dialogues. With the right prompting question, this model achieves a rate of linguistic validation on intent spans beyond 85%. We furthermore reconstructed the intent schemes of five domains from the MultiDoGo dataset with an average recall of 94.3%.
Lower bounds for the scalar curvatures of Ricci flow singularity models

Abstract In a series of papers, Bamler [5, 4, 6] further developed the high-dimensional theory of Hamilton’s Ricci flow to include new monotonicity formulas, a completely general compactness theorem, and a long-sought partial regularity theory analogous to Cheeger–Colding theory. In this paper we give an application of his theory to lower bounds for the scalar curvatures of singularity models for Ricci flow. In the case of 4-dimensional non-Ricci-flat steady soliton singularity models, we obtain as a consequence a quadratic decay lower bound for the scalar curvature.
Stabilization of nonvolatile memory operations using GaN/AlN resonant tunneling diodes by reducing structural inhomogeneity

A new nonvolatile memory based on intersubband transitions and electron accumulation in GaN/AlN resonant tunneling diodes (RTDs) was investigated toward the realization of a high-speed nonvolatile random-access memory (RAM) operating on picosecond time scales. It was shown that the endurance of write/erase memory operations can be enhanced by reducing the structural inhomogeneity of GaN/AlN RTDs. Neither mechanical damage nor significant degradation of current–voltage characteristics was observed after repeated write/erase memory operations using GaN/AlN RTDs with small degrees of inhomogeneity. High-endurance nonvolatile RAMs for computing systems are expected to be realized by further improvement in the crystal quality of GaN/AlN RTDs.
Prompt Tuning with Soft Context Sharing for Vision-Language Models

Vision-language models have recently shown great potential on many computer vision tasks. Meanwhile, prior work demonstrates prompt tuning designed for vision-language models could acquire superior performance on few-shot image recognition compared to linear probe, a strong baseline. In real-world applications, many few-shot tasks are correlated, particularly in a specialized area. However, such information is ignored by previous work. Inspired by the fact that modeling task relationships by multi-task learning can usually boost performance, we propose a novel method SoftCPT (Soft Context Sharing for Prompt Tuning) to fine-tune pre-trained vision-language models on multiple target few-shot tasks, simultaneously. Specifically, we design a task-shared meta network to generate prompt vector for each task using pre-defined task name together with a learnable meta prompt as input. As such, the prompt vectors of all tasks will be shared in a soft manner. The parameters of this shared meta network as well as the meta prompt vector are tuned on the joint training set of all target tasks. Extensive experiments on three multi-task few-shot datasets show that SoftCPT outperforms the representative single-task prompt tuning method CoOp [78] by a large margin, implying the effectiveness of multi-task learning in vision-language prompt tuning. The source code and data will be made publicly available.
Pasture intake protects against commercial diet-induced lipopolysaccharide production facilitated by gut microbiota through activating intestinal alkaline phosphatase enzyme in meat geese

Introduction Diet strongly affects gut microbiota composition, and gut bacteria can influence the intestinal barrier functions and systemic inflammation through metabolic endotoxemia. In-house feeding system (IHF, a low dietary fiber source) may cause altered cecal microbiota composition and inflammatory responses in meat geese via increased endotoxemia (lipopolysaccharides) with reduced intestinal alkaline phosphatase (ALP) production. The effects of artificial pasture grazing system (AGF, a high dietary fiber source) on modulating gut microbiota architecture and gut barrier functions have not been investigated in meat geese. Therefore, this study aimed to investigate whether intestinal ALP could play a critical role in attenuating reactive oxygen species (ROS) generation and ROS facilitating NF-κB pathway-induced systemic inflammation in meat geese. Methods The impacts of IHF and AGF systems on gut microbial composition via 16 sRNA sequencing were assessed in meat geese. The host markers analysis through protein expression of serum and cecal tissues, hematoxylin and eosin (H&E) staining, localization of NF-қB and Nrf2 by immunofluorescence analysis, western blotting analysis of ALP, and quantitative PCR of cecal tissues was evaluated. Results and Discussion In the gut microbiota analysis, meat geese supplemented with pasture showed a significant increase in commensal microbial richness and diversity compared to IHF meat geese demonstrating the antimicrobial, antioxidant, and anti-inflammatory ability of the AGF system. A significant increase in intestinal ALP-induced Nrf2 signaling pathway was confirmed representing LPS dephosphorylation mediated TLR4/MyD88 induced ROS reduction mechanisms in AGF meat geese. Further, the correlation analysis of top 44 host markers with gut microbiota showed that artificial pasture intake protected gut barrier functions via reducing ROS-mediated NF-κB pathway-induced gut permeability, systemic inflammation, and aging phenotypes. In conclusion, the intestinal ALP functions to regulate gut microbial homeostasis and barrier function appear to inhibit pro-inflammatory cytokines by reducing LPS-induced ROS production in AGF meat geese. The AGF system may represent a novel therapy to counteract the chronic inflammatory state leading to low dietary fiber-related diseases in animals.
Superconductivity from repulsive interactions on the kagome lattice

The discovery of superconductivity in layered vanadium-based kagome metals $A$V$_3$Sb$_5$ ($A$: K, Rb, Cs) has added a new family of materials to the growing class of possible unconventional superconductors. However, the nature of the superconducting pairing in these materials remains elusive. We present a microscopic theoretical study of the leading superconducting instabilities on the kagome lattice based on spin- and charge-fluctuation mediated Cooper pairing. The applied methodology includes effects of both on-site and nearest-neighbor repulsive Coulomb interactions. Near the upper van Hove filling -- relevant for the $A$V$_3$Sb$_5$ materials -- we find a rich phase diagram with several pairing symmetries being nearly degenerate. In particular, while a substantial fraction of the phase diagram is occupied by a spin-singlet order parameter transforming as a two-dimensional irreducible representation of the point group, several nodal spin-triplet pairing states remain competitive. We compute the band and interaction parameter-dependence of the hierarchy of the leading superconducting instabilities, and determine the detailed momentum dependence of the resulting preferred gap structures. Crucially, for moderate values of the interaction parameters, the individual pairing states depend strongly on momentum and exhibit multiple nodes on the Fermi surface. We discuss the properties of these superconducting gap structures in light of recent experimental developments of the $A$V$_3$Sb$_5$ materials.
Two-dimensional ferromagnetic semiconductors of rare-earth Janus 2H-GdIBr monolayers with large valley polarization.

Based on a rare-earth Gd atom with 4f electrons, through first-principles calculations, we demonstrate that a Janus 2H-GdIBr monolayer exhibits an intrinsic ferromagnetic (FM) semiconductor character with an indirect band gap of 0.75 eV, a high Curie temperature Tc of 260 K, a significant magnetic moment of 8μB per f.u. (f.u. = formula unit), in-plane magnetic anisotropy (IMA) and a large spontaneous valley polarization of 118 meV. The MAE, inter-atomic distance or angle, and Tc can be efficiently modulated by in-plane strains and charge carrier doping. Under a strain range from -5% to 5% and charge carrier doping from -0.3 e to 0.3 e per f.u. , the system still retains its FM ordering and the corresponding Tc can be modulated by strains from 233 K to 281 K and by charge carrier doping from 140 K to 245 K. Interestingly, under various strains, the matrix element differences (dz2, dyz), (dx2-y2, dxy) and (px, py) of Gd atoms dominate the MAE behaviors, which originates from the competition between the contributions of the Gd-d orbitals, Gd-p orbitals, and p orbitals of halogen atoms based on the second-order perturbation theory. Inequivalent Dirac valleys are not energetically degenerate due to the time-reversal symmetry breaking in the Janus 2H-GdIBr monolayer. A considerable valley gap between the Berry curvature at the K and K' points provides an opportunity to selectively control the valley freedom and states. External tensile (compressive) strain further increases (decreases) the valley gap up to a maximum (minimum) value of 158 (37) meV, indicating that the valley polarization in the Janus 2H-GdIBr monolayer is robust to external strains. This study provides a novel paradigm and platform to design spintronic devices for next-generation quantum information technology.
Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding

In sequence-to-sequence learning, e.g., natural language generation, the decoder relies on the attention mechanism to efficiently extract information from the encoder. While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information. Nonetheless, the decoder still obtains only a single view of the source sequences, which might lead to insufficient training of the encoder layer stack due to the hierarchy bypassing problem. In this work, we propose layer-wise multi-view decoding , where for each decoder layer, together with the representations from the last encoder layer, which serve as a global view, those from other encoder layers are supplemented for a stereoscopic view of the source sequences. Systematic experiments and analyses show that we successfully address the hierarchy bypassing problem, require almost negligible parameter increase, and substantially improve the performance of sequence-to-sequence learning with deep representations on five diverse tasks, i.e., machine translation, abstractive summarization, image captioning, video captioning, medical report generation, and paraphrase generation. In particular, our approach achieves new state-of-the-art results on ten benchmark datasets, including a low-resource machine translation dataset and two low-resource medical report generation datasets. 1
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions

Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is to convert a natural language (NL) question to its corresponding structured query language (SQL) based on the evidences provided by relational databases. Early text-to-SQL parsing systems from the database community achieved a noticeable progress with the cost of heavy human engineering and user interactions with the systems. In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query. Subsequently, the large pre-trained language models have taken the state-of-the-art of the text-to-SQL parsing task to a new level. In this survey, we present a comprehensive review on deep learning approaches for text-to-SQL parsing. First, we introduce the text-to-SQL parsing corpora which can be categorized as single-turn and multi-turn. Second, we provide a systematical overview of pre-trained language models and existing methods for text-to-SQL parsing. Third, we present readers with the challenges faced by text-to-SQL parsing and explore some potential future directions in this field.
Adversarial Robustness for Tabular Data through Cost and Utility Awareness

Many safety-critical applications of machine learning, such as fraud or abuse detection, use data in tabular domains. Adversarial examples can be particularly damaging for these applications. Yet, existing works on adversarial robustness primarily focus on machine-learning models in image and text domains. We argue that, due to the differences between tabular data and images or text, existing threat models are not suitable for tabular domains. These models do not capture that the costs of an attack could be more significant than imperceptibility, or that the adversary could assign different values to the utility obtained from deploying different adversarial examples. We demonstrate that, due to these differences, the attack and defense methods used for images and text cannot be directly applied to tabular settings. We address these issues by proposing new cost and utility-aware threat models that are tailored to the adversarial capabilities and constraints of attackers targeting tabular domains. We introduce a framework that enables us to design attack and defense mechanisms that result in models protected against cost and utility-aware adversaries, for example, adversaries constrained by a certain financial budget. We show that our approach is effective on three datasets corresponding to applications for which adversarial examples can have economic and social implications.
Exact spectral function of the Tonks-Girardeau gas at finite temperature

We report on the derivation of determinant representations for the Green's functions and spectral function of the trapped Tonks-Girardeau gas on the lattice and in the continuum. Our results are valid for any type of statistics of the constituent particles, at zero and finite temperature and arbitrary confining potentials, including nonequilibrium scenarios induced by sudden changes of the external potential. In addition, they are also extremely efficient and easy to implement numerically with the main computational effort being represented by the calculation of partial overlaps of the dynamically evolved single particle wavefunctions. In the lattice case we show that the spectral function of a system with a strong harmonic potential presents only two singular lines compared with three singular lines in the case of a homogeneous system.
K\"ahler-Einstein metrics and obstruction flatness of circle bundles

Obstruction flatness of a strongly pseudoconvex hypersurface $\Sigma$ in a complex manifold refers to the property that any (local) K\"ahler-Einstein metric on the pseudoconvex side of $\Sigma$, complete up to $\Sigma$, has a potential $-\log u$ such that $u$ is $C^\infty$-smooth up to $\Sigma$. In general, $u$ has only a finite degree of smoothness up to $\Sigma$. In this paper, we study obstruction flatness of hypersurfaces $\Sigma$ that arise as unit circle bundles $S(L)$ of negative Hermitian line bundles $(L, h)$ over K\"ahler manifolds $(M, g). $ We prove that if $(M,g)$ has constant Ricci eigenvalues, then $S(L)$ is obstruction flat. If, in addition, all these eigenvalues are strictly less than one and $(M,g)$ is complete, then we show that the corresponding disk bundle admits a complete K\"ahler-Einstein metric. Finally, we give a necessary and sufficient condition for obstruction flatness of $S(L)$ when $(M, g)$ is a K\"ahler surface $(\dim M=2$) with constant scalar curvature.
Explainability of Deep Learning models for Urban Space perception

Deep learning based computer vision models are increasingly used by urban planners to support decision making for shaping urban environments. Such models predict how people perceive the urban environment quality in terms of e.g. its safety or beauty. However, the blackbox nature of deep learning models hampers urban planners to understand what landscape objects contribute to a particularly high quality or low quality urban space perception. This study investigates how computer vision models can be used to extract relevant policy information about peoples' perception of the urban space. To do so, we train two widely used computer vision architectures; a Convolutional Neural Network and a transformer, and apply GradCAM -- a well-known ex-post explainable AI technique -- to highlight the image regions important for the model's prediction. Using these GradCAM visualizations, we manually annotate the objects relevant to the models' perception predictions. As a result, we are able to discover new objects that are not represented in present object detection models used for annotation in previous studies. Moreover, our methodological results suggest that transformer architectures are better suited to be used in combination with GradCAM techniques. Code is available on Github.
Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) is an emerging task aiming to extract potential pairs of emotions and corresponding causes from documents. Previous approaches have focused on modeling the pair-to-pair relationship and achieved promising results. However, the clause-to-clause relationship, which fundamentally symbolizes the underlying structure of a document, has still been in its research infancy. In this paper, we define a novel clause-to-clause relationship. To learn it applicably, we propose a general clause-level encoding model named EA-GAT comprising E-GAT and Activation Sort. E-GAT is designed to aggregate information from different types of clauses; Activation Sort leverages the individual emotion/cause prediction and the sort-based mapping to propel the clause to a more favorable representation. Since EA-GAT is a clause-level encoding model, it can be broadly integrated with any previous approach. Experimental results show that our approach has a significant advantage over all current approaches on the Chinese and English benchmark corpus, with an average of $2.1\%$ and $1.03\%$.
A Quantitative and Qualitative Analysis of the Robustness of (Real-World) Election Winners

Contributing to the toolbox for interpreting election results, we evaluate the robustness of election winners to random noise. We compare the robustness of different voting rules and evaluate the robustness of real-world election winners from the Formula 1 World Championship and some variant of political elections. We find many instances of elections that have very non-robust winners and numerous delicate robustness patterns that cannot be identified using classical and simpler approaches.