Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview

Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block, and it plays a crucial role in environmental perception. Conventional learning-based visual semantic segmentation approaches count heavily on large-scale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories. This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning. The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen-category from a few labeled or zero-labeled samples, which advances the extension to practical applications. Therefore, this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances. Specifically, the preliminaries on few/zero-shot visual semantic segmentation, including the problem definitions, typical datasets, and technical remedies, are briefly reviewed and discussed. Moreover, three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation, including image semantic segmentation, video object segmentation, and 3D segmentation. Finally, the future challenges of few/zero-shot visual semantic segmentation are discussed.
The Eternal Game Chromatic Number of a Graph

Game coloring is a well-studied two-player game in which each player properly colors one vertex of a graph at a time until all the vertices are colored. An `eternal' version of game coloring is introduced in this paper in which the vertices are colored and re-colored from a color set over a sequence of rounds. In a given round, each vertex is colored, or re-colored, once, so that a proper coloring is maintained. Player 1 wants to maintain a proper coloring forever, while player 2 wants to force the coloring process to fail. The eternal game chromatic number of a graph $G$ is defined to be the minimum number of colors needed in the color set so that player 1 can always win the game on $G$. We consider several variations of this new game and show its behavior on some elementary classes of graphs.
Improved Coresets for Euclidean k-Means

Given a set of $n$ points in $d$ dimensions, the Euclidean $k$-means problem (resp. the Euclidean $k$-median problem) consists of finding $k$ centers such that the sum of squared distances (resp. sum of distances) from every point to its closest center is minimized. The arguably most popular way of dealing with this problem in the big data setting is to first compress the data by computing a weighted subset known as a coreset and then run any algorithm on this subset. The guarantee of the coreset is that for any candidate solution, the ratio between coreset cost and the cost of the original instance is less than a $(1\pm \varepsilon)$ factor. The current state of the art coreset size is $\tilde O(\min(k^{2} \cdot \varepsilon^{-2},k\cdot \varepsilon^{-4}))$ for Euclidean $k$-means and $\tilde O(\min(k^{2} \cdot \varepsilon^{-2},k\cdot \varepsilon^{-3}))$ for Euclidean $k$-median. The best known lower bound for both problems is $\Omega(k \varepsilon^{-2})$. In this paper, we improve the upper bounds $\tilde O(\min(k^{3/2} \cdot \varepsilon^{-2},k\cdot \varepsilon^{-4}))$ for $k$-means and $\tilde O(\min(k^{4/3} \cdot \varepsilon^{-2},k\cdot \varepsilon^{-3}))$ for $k$-median. In particular, ours is the first provable bound that breaks through the $k^2$ barrier while retaining an optimal dependency on $\varepsilon$.
A Machine Learning Approach to Classifying Construction Cost Documents into the International Construction Measurement Standard

We introduce the first automated models for classifying natural language descriptions provided in cost documents called"Bills of Quantities"(BoQs) popular in the infrastructure construction industry, into the International Construction Measurement Standard (ICMS). The models we deployed and systematically evaluated for multi-class text classification are learnt from a dataset of more than 50 thousand descriptions of items retrieved from 24 large infrastructure construction projects across the United Kingdom. We describe our approach to language representation and subsequent modelling to examine the strength of contextual semantics and temporal dependency of language used in construction project documentation. To do that we evaluate two experimental pipelines to inferring ICMS codes from text, on the basis of two different language representation models and a range of state-of-the-art sequence-based classification methods, including recurrent and convolutional neural network architectures. The findings indicate a highly effective and accurate ICMS automation model is within reach, with reported accuracy results above 90% F1 score on average, on 32 ICMS categories. Furthermore, due to the specific nature of language use in the BoQs text; short, largely descriptive and technical, we find that simpler models compare favourably to achieving higher accuracy results. Our analysis suggest that information is more likely embedded in local key features in the descriptive text, which explains why a simpler generic temporal convolutional network (TCN) exhibits comparable memory to recurrent architectures with the same capacity, and subsequently outperforms these at this task.
Constraining axion and compact dark matter with interstellar medium heating

Cold interstellar gas systems have been used to constrain dark matter (DM) models by the condition that the heating rate from DM must be lower than the astrophysical cooling rate of the gas. Following the methodology of Wadekar and Farrar (2021), we use the interstellar medium of a gas-rich dwarf galaxy, Leo T, and a Milky Way-environment gas cloud, G33.4-8.0 to constrain DM. Leo T is a particularly strong system as its gas can have the lowest cooling rate among all the objects in the late Universe (owing to the low volume density and metallicity of the gas). Milky Way clouds, in some cases, provide complementary limits as the DM-gas relative velocity in them is much larger than that in Leo T. We derive constraints on the following scenarios in which DM can heat the gas: $(i)$ interaction of axions with hydrogen atoms or free electrons in the gas, $(ii)$ deceleration of relic magnetically charged DM in gas plasma, $(iii)$ dynamical friction from compact DM, $(iv)$ hard sphere scattering of composite DM with gas. Our limits are complementary to DM direct detection searches. Detection of more gas-rich low-mass dwarfs like Leo T from upcoming 21cm and optical surveys can improve our bounds.
Self-selecting vapor growth of transition-metal-halide single crystals

Transition metal halides can host a large variety of novel phenomena, such as magnetism in the monolayer limit, quantum spin liquid and spiral spin liquid states, and topological magnons and phonons. Sizeable high quality single crystals are necessary for investigations of magnetic and lattice excitations by, for example, inelastic neutron scattering. In this paper, we review a less well-known vapor transport technique, self-selecting vapor growth, and report our growths of transition metal halides using this technique. We report the growth and characterizations of sizable single crystals of RuCl 3 , CrCl 3 , Ru 1 − x Cr x Cl 3 , and CrBr 3 . In order to expedite the conversion of starting powder to single crystals, we modiﬁed the technique by cooling the growth ampoule through an appropriate temperature range. Our work shows that the self-selecting vapor transport technique can provide large single crystals of transition metal halides, demonstrating its potential for providing high quality single crystals of quantum materials.
Linear Convergent Distributed Nash Equilibrium Seeking with Compression

Information compression techniques are majorly employed to address the concern of reducing communication cost over peer-to-peer links. In this paper, we investigate distributed Nash equilibrium (NE) seeking problems in a class of non-cooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain a NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compression operators, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors.
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information

Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in the training set and cannot perform well in predicting links concerning novel entities in the test set. Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities. Compared to KGs, TKGs require temporal reasoning techniques for modeling, which naturally increases the difficulty in dealing with novel, yet unseen entities. In this work, we focus on the inductive learning of unseen entities' representations on TKGs. We propose a few-shot out-of-graph (OOG) link prediction task for TKGs, where we predict the missing entities from the links concerning unseen entities by employing a meta-learning framework and utilizing the meta-information provided by only few edges associated with each unseen entity. We construct three new datasets for TKG few-shot OOG link prediction, and we propose a model that mines the concept-aware information among entities. Experimental results show that our model achieves superior performance on all three datasets and our concept-aware modeling component demonstrates a strong effect.
Operation-level Concurrent Transaction Execution for Blockchains

Despite the success in various scenarios, blockchain systems, especially EVM-compatible ones that serially execute transactions, still face the significant challenge of limited throughput. Concurrent transaction execution is a promising technique to accelerate transaction processing and increase the overall throughput. Existing concurrency control algorithms, however, fail to obtain enough speedups in real-world blockchains due to the high-contention workloads. In this paper, we propose a novel operation-level concurrency control algorithm designed for blockchains. The core idea behind our algorithm is that only operations depending on conflicts should be executed serially, while all other conflict-free operations can be executed concurrently. Therefore, in contrast to the traditional approaches, which block or abort the entire transaction when encountering conflicts, our algorithm introduces a redo phase to resolve conflicts at the operation level by re-executing conflicting operations only. We also develop a set of data dependency tracking mechanisms to achieve precise identification and speedy re-execution for conflicting operations. We implement an open-source prototype based on Go Ethereum and evaluate it using real-world Ethereum blocks. The evaluation results show that our algorithm achieves an average speedup of 4.28$\times$. If combined with state prefetching techniques, our approach can further accelerate the transaction execution by 7.11$\times$.
CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals

We present CardiacGen, a Deep Learning framework for generating synthetic but physiologically plausible cardiac signals like ECG. Based on the physiology of cardiovascular system function, we propose a modular hierarchical generative model and impose explicit regularizing constraints for training each module using multi-objective loss functions. The model comprises 2 modules, an HRV module focused on producing realistic Heart-Rate-Variability characteristics and a Morphology module focused on generating realistic signal morphologies for different modalities. We empirically show that in addition to having realistic physiological features, the synthetic data from CardiacGen can be used for data augmentation to improve the performance of Deep Learning based classifiers. CardiacGen code is available at https://github.com/SENSE-Lab-OSU/cardiac_gen_model.
Inverse Probability Weighting: from Survey Sampling to Evidence Estimation

We consider the class of inverse probability weight (IPW) estimators, including the popular Horvitz-Thompson and Hajek estimators used routinely in survey sampling, causal inference and evidence estimation for Bayesian computation. We focus on the 'weak paradoxes' for these estimators due to two counterexamples by Basu [1988] and Wasserman [2004] and investigate the two natural Bayesian answers to this problem: one based on binning and smoothing : a 'Bayesian sieve' and the other based on a conjugate hierarchical model that allows borrowing information via exchangeability. We compare the mean squared errors for the two Bayesian estimators with the IPW estimators for Wasserman's example via simulation studies on a broad range of parameter configurations. We also prove posterior consistency for the Bayes estimators under missing-completely-at-random assumption and show that it requires fewer assumptions on the inclusion probabilities. We also revisit the connection between the different problems where improved or adaptive IPW estimators will be useful, including survey sampling, evidence estimation strategies such as Conditional Monte Carlo, Riemannian sum, Trapezoidal rules and vertical likelihood, as well as average treatment effect estimation in causal inference.
EMMI rapid reaction task force on ?Thermalization in non-Abelian plasmas?

Recently, different proposals have been put forward on how thermalization proceeds in heavy-ion collisions in the idealized limit of very large nuclei at sufficiently high energy. Important aspects of the parametric estimates at weak coupling may be tested using well-established classical-statistical lattice simulations of the far-from-equilibrium gluon dynamics. This has to be confronted with strong coupling scenarios in related theories based on gauge-string dualities. Furthermore, closely related questions about far-from-equilibrium dynamics arise in early-universe cosmology and in non-relativistic systems of ultracold atoms. These were central topics of the EMMI Rapid Reaction Task Force meeting held on 12–14 December 2011, at the University of Heidelberg, which we report on. Communicated by Professor Achim Schwenk
Spectral energy distribution of M-subdwarfs: A study of their atmospheric properties

Context. M-type subdwarfs are metal-poor low-mass stars and probe for the old populations in our Galaxy. Accurate knowledge of their atmospheric parameters and especially the composition is essential for understanding the chemical history of our Galaxy. Aims. The purpose of this work is to perform a detailed study of M-subdwarf spectra covering the full wavelength range from the optical to the near-infrared. It allows to do a more detailed analysis of the atmospheric composition in order to determine the stellar parameters, and to constrain the atmospheric models. The study will allow us to further understand physical and chemical processes such as increasing condensation of gas into dust, to point out the missing continuum opacities and see how the main band features are reproduced by the models. The spectral resolution and the large wavelength coverage used is a unique combination to constrain the process that occur in cool atmosphere. Methods. We obtained medium-resolution (R = 5000-7000) spectra over the wavelength range 0.3-2.5 {\mu}m of ten M-type subdwarfs with X-SHOOTER at VLT. These data constitute an unique atlas of M-subdwarfs from optical to near-infrared. We performed spectral synthesis analysis using a full grid of synthetic spectra computed from BT-Settl models and obtained consistent stellar parameters such as effective temperature, surface gravity and metallicity. Results. We show that state-of the-art atmospheric models correctly represent the overall shape of their spectral energy distribution, as well as atomic and molecular line profiles both in the optical and near-infrared. We found that the actual fitted gravities of almost all our sample are consistent with old objects, except for LHS 73 where it is found surprisingly low.
An explicit Ricci potential for the universal moduli space of vector bundle

In this paper we modify the coordinate construction in our previous paper on the universal moduli space of pair consisting of a Riemann Surfaces and a stable holomorphic bundles on the Riemann Surface, so as to produce a new set of coordinates, which are in fact K\" ahler coordinates on this universal moduli space. Further, we give a functional determinant formula for the Ricci potential of the universal moduli space.
New N-bearing species towards OH 231.8+4.2. HNCO, HNCS, HC_{3}N, and NO

OH231.8+4.2 is a well studied O-rich CSE around an intermediate-mass evolved star that displays bipolar molecular outflows accelerated up to 400 km/s. OH231 also presents an exceptional molecular richness probably due to shock-induced chemical processes. We report the first detection in this source of HNCO, HNCS, HC3N and NO, with the IRAM-30m telescope in a sensitive mm-wavelength survey towards this target. HNCO and HNCS are also first detections in CSEs. The observed line profiles show that the emission arises in the massive central component of the envelope and at the base of the fast lobes. The NO profiles are broader than those of HNCO, HNCS, and HC3N, and most importantly, broader than the line profiles of 13CO. This indicates that the NO abundance is enhanced in the fast lobes relative to the slow, central parts. From LTE and non-LTE excitation analysis, we estimate beam-average rotational temperatures of 15-30 K (and, maybe, up to 55 K for HC3N) and fractional abundances of X(HNCO)=[0.8-1]E-7, X(HNCS)=[0.9-1]E-8, X(HC3N)=[5-7]E-9 and X(NO)=[1-2]E-6. NO is, therefore, amongst the most abundant N-bearing species in OH231. We have performed thermodynamical chemical equilibrium and chemical kinetics models to investigate the formation of these N-bearing species in OH231. The model underestimates the observed abundances for HNCO, HNCS, and HC3N by several orders of magnitude, which indicates that these molecules can hardly be products of standard UV-photon and/or cosmic-ray induced chemistry in OH231, and that other processes (e.g. shocks) play a major role in their formation. For NO, the model abundance is compatible with the observed average value. The new detections presented in this work corroborate the particularly rich chemistry of OH231, which is likely profoundly influenced by shock-induced processes, as proposed in earlier works.
EXTENDED STAR CLUSTERS IN THE REMOTE HALO OF THE INTRIGUING DWARF GALAXY NGC 6822

We present a study on four new star clusters discovered in the halo of the intriguing dwarf irregular galaxy NGC 6822 from a wide-field survey covering 3° × 3° area carried out with MegaCam at the Canada–France–Hawaii Telescope. The star clusters have extended structures with half-light radii Rh ≈ 7.5–14.0 pc, larger than typical Galactic globular clusters and other known globular clusters in NGC 6822. The integrated colors and color–magnitude diagrams of resolved stars suggest that the new star clusters are 2–10 Gyr old and relatively metal poor with Z = 0.0001–0.004 based on the comparison with theoretical models. The projected distance of each star cluster from the galaxy center ranges from 10. ′7 (≈1.5 kpc) to 77′ (≈11 kpc), far beyond the optical body of the galaxy. Interestingly, the new star clusters are aligned along the elongated old stellar halo of NGC 6822, which is almost perpendicular to the H i gas distribution where young stellar populations exist. We also find that the colors and half-light radii of the new clusters are correlated with the galactocentric distance: clusters farther from the galaxy center are larger and bluer than those closer to the galaxy center. We discuss the stellar structure and evolution of NGC 6822 implied by these new extended star clusters in the halo. We also discuss the current status of observational and theoretical understandings regarding the origin of extended star clusters in NGC 6822 and other galaxies.
New branch of Kaluza-Klein compactification

We found a new branch of solutions in Freund-Rubin type flux compactifications. The geometry of these solutions is described as the external space which has a de Sitter symmetry and the internal space which is topologically spherical. However, it is not a simple form of ${\mathrm{dS}}_{p}\ifmmode\times\else\texttimes\fi{}{S}^{q}$ but a warped product of de Sitter space and a deformed sphere. We explicitly constructed numerical solutions for a specific case with $p=4$ and $q=4$. We show that the new branch of solutions emanates from the marginally stable solution in the branch of ${\mathrm{dS}}_{4}\ifmmode\times\else\texttimes\fi{}{S}^{4}$ solutions.
Analysis of mycotoxins in grain samples using 3D covalent organic frameworks and stable isotope labeling technique

Abstract A new three-dimensional (3D) covalent organic framework (COF) tris (4-formylphenyl) amine-tetra (4-aminophenyl) methane (TFPA-TAM) was designed and synthesized. It was used in the dispersed solid-phase extraction (DSPE) of mycotoxins from grain sample extractant. The adsorption performance of 3D COF (TFPA-TAM) was compared with four kinds of different COFs with various spatial structures and steric hindrances to illustrate the key factors in the adsorption process. Stable isotope labeling (SIL) strategy with dansyl chloride (DNS-Cl) and deuterated dansyl chloride (d6-DNS-Cl) as labeling agents was applied for the first time in the high-performance liquid chromatography-tandem mass spectrometry (HPLC–MS/MS) quantitation of mycotoxins. SIL strategy enhanced the mass spectrometry (MS) sensitivity and minimized matrix effects. The optimized adsorption conditions were sample solution pH 7; adsorbent amount, 20 mg; desorption time, 5 min; elution volume, 1 mL. The limits of detection (LODs) of mycotoxins in grain samples were in the range of 4–7 µg/kg, while limits of quantification (LOQs) were in the range of 13–24 µg/kg. The linearity coefficients for all analytes were higher than 0.9977. The recoveries of this method were in the range of 90.3–94.3% with relative standard deviations of <6.3%, and the matrix effects were in the range of 93.5–98.8%. Graphical Abstract
The Unintended Consequences of Federal Direct Student Aid After Five Decades of Practice

currently serves as a Senior Faculty Fellow at the Education Policy Center at the University of Alabama and a Faculty Affiliate at the Cornell University Higher Education Research Institute. He has published widely in academic journals and contributed to many media sources including The Economist, Washington Post, Financial Times, Politico, Inside Higher Education, the Chronicle of Higher Education, and the Hechinger Report. By F. King Alexander