Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Scrubbing of Demographic Information for Text Classification

Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task. We aim to scrub such undesirable attributes and learn fair representations while maintaining performance on the target task. In this paper, we present an adversarial learning framework “Adversarial Scrubber” (AdS), to debias contextual representations. We perform theoretical analysis to show that our framework converges without leaking demographic information under certain conditions. We extend previous evaluation techniques by evaluating debiasing performance using Minimum Description Length (MDL) probing. Experimental evaluations on 8 datasets show that AdS generates representations with minimal information about demographic attributes while being maximally informative about the target task.
Weighted Conditional EL⊥ Knowledge Bases with Integer Weights: an ASP Approach

Weighted knowledge bases for description logics with typicality have been recently considered under a"concept-wise"multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of Multilayer Perceptrons. In this paper we consider weighted conditional EL^bot knowledge bases in the two-valued case, and exploit ASP and asprin for encoding concept-wise multipreference entailment for weighted KBs with integer weights.
Soliton asymptotics for KdV shock waves via classical inverse scattering

Abstract. We show how the inverse scattering transform can be used as a convenient tool to derive the long-time asymptotics of shock waves for the Korteweg–de Vries (KdV) equation in the soliton region. In particular, we improve the results previously obtained via the nonlinear steepest decent approach both with respect to the decay of the initial conditions as well as the region where they are valid.
Hyperbolic balance laws: residual distribution, local and global fluxes

This paper describes a class of scheme named"residual distribution schemes"or"fluctuation splitting schemes". They are a generalization of Roe's numerical flux in fluctuation form. The so-called multidimensional fluctuation schemes have historically first been developed for steady homogeneous hyperbolic systems. Their application to unsteady problems and conservation laws has been really understood only relatively recently. This understanding has allowed to make of the residual distribution framework a powerful playground to develop numerical discretizations embedding some prescribed constraints. This paper describes in some detail these techniques, with several examples, ranging from the compressible Euler equations to the Shallow Water equations.
Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

We use Gaussian stochastic weight averaging (SWAG) to assess the model-form uncertainty associated with neural-network-based function approximation relevant to ﬂuid ﬂows. SWAG approximates a posterior Gaussian distribution of each weight, given training data, and a constant learning rate. Having access to this distribution, it is able to create multiple models with various combinations of sampled weights, which can be used to obtain ensemble predictions. The average of such an ensemble can be regarded as the ‘mean estimation’, whereas its standard deviation can be used to construct ‘conﬁdence intervals’, which enable us to perform uncertainty quantiﬁcation (UQ) with regard to the training process of neural networks. We utilize representative neural-network-based function approximation tasks for the following cases: (i) a two-dimensional circular-cylinder wake; (ii) the DayMET dataset (maximum daily temperature in North America); (iii) a three-dimensional square-cylinder wake; and (iv) urban ﬂow, to assess the generalizability of the present idea for a wide range of complex datasets. SWAG-based UQ can be applied regardless of the network architecture, and therefore, we demonstrate the applicability of the method for two types of neural networks: (i) global ﬁeld reconstruction from sparse sensors by combining convolutional neural network (CNN) and multi-layer perceptron (MLP); and (ii) far-ﬁeld state estimation from sectional data with two-dimensional CNN. We ﬁnd that SWAG
CLEAR: Emission-line Ratios at Cosmic High Noon

We use Hubble Space Telescope Wide Field Camera 3 G102 and G141 grism spectroscopy to measure rest-frame optical emission-line ratios of 533 galaxies at z ∼ 1.5 in the CANDELS Lyα Emission at Reionization survey. We compare [O iii]/Hβ versus [S ii]/(Hα + [N ii]) as an “unVO87” diagram for 461 galaxies and [O iii]/Hβ versus [Ne iii]/[O ii] as an “OHNO” diagram for 91 galaxies. The unVO87 diagram does not effectively separate active galactic nuclei (AGN) and [Ne v] sources from star-forming galaxies, indicating that the unVO87 properties of star-forming galaxies evolve with redshift and overlap with AGN emission-line signatures at z > 1. The OHNO diagram does effectively separate X-ray AGN and [Ne v]-emitting galaxies from the rest of the population. We find that the [O iii]/Hβ line ratios are significantly anticorrelated with stellar mass and significantly correlated with log(LHβ) , while [S ii]/(Hα + [N ii]) is significantly anticorrelated with log(LHβ) . Comparison with MAPPINGS V photoionization models indicates that these trends are consistent with lower metallicity and higher ionization in low-mass and high-star formation rate (SFR) galaxies. We do not find evidence for redshift evolution of the emission-line ratios outside of the correlations with mass and SFR. Our results suggest that the OHNO diagram of [O iii]/Hβ versus [Ne iii]/[O ii] will be a useful indicator of AGN content and gas conditions in very high-redshift galaxies to be observed by the James Webb Space Telescope.
Oxidative Capacity and Radical Chemistry in a Semi-arid and Petrochemical-industrialized City, Northwest China

Heavy ozone (O-3) pollution is often observed in chemically industrialized Lanzhou and other capital cities located in the semi-arid and mountainous provinces of northwestern China. There are large knowledge gaps regarding the relationship between radical budgets and photochemistry in these cities. To gain insights into this relationship, a photochemical box model based on the Master Chemical Mechanism (MCM v3.3) was applied to investigate oxidative capacity and radical chemistry in the city of Lanzhou. The budgets of ROx (OH + HO2 + RO2) radicals were quantified, and the initiation, propagation, and termination process of the diurnal variation in the ozone chemistry were examined. The MCM model was constrained by in situ measurements at two sampling sites in the city, one located in the City downtown area (S1) and the other in the heavy petrochemical industrial area (S2) in the western suburb of the City, characterized by significant differences in volatile organic compounds (VOCs) and NOx concentrations between the two sites. Results showed that during the high O-3 episodes in summer, OH initiation was dominated by the reaction of excited oxygen atoms O(D-1) with water and the photolysis of nitrous acid (HONO) at the S1 site. At the S2 site, the most important production of OH was the reaction of O(D-1) + H2O, followed by the reaction of O-3 with VOCs. HONO photolysis was mostly identified at 7:00-13:00 local time at the S2 site, which is less important than that at the S1 site during the daytime. The photolysis of HCHO and OVOCs (except for HCHO) were the primary sources contributing to the initiation of HO2 and RO2 radicals at both sites. Results also revealed that the ROx termination could be attributed to the reactions of ROx with NO and NO2. The self-reactions between radicals also played an essential role at the S2 site. Overall OH was found to be the predominant oxidant, and NO3 was a major oxidant in the nighttime chemistry in the city.
Spurious Quasi-Resonances in Boundary Integral Equations for the Helmholtz Transmission Problem

We consider the Helmholtz transmission problem with piecewise-constant material coefficients, and the standard associated direct boundary integral equations. For certain coefficients and geometries, the norms of the inverses of the boundary integral operators grow rapidly through an increasing sequence of frequencies, even though this is not the case for the solution operator of the transmission problem; we call this phenomenon that of spurious quasi-resonances. We give a rigorous explanation of why and when spurious quasi-resonances occur, and propose modified boundary integral equations that are not affected by them.
Benchmarking Feature-Based Algorithm Selection Systems for Black-Box Numerical Optimization

Feature-based algorithm selection aims to automatically find the best one from a portfolio of optimization algorithms on an unseen problem based on its landscape features. Feature-based algorithm selection has recently received attention in the research field of black-box numerical optimization. However, there is still room for the analysis of algorithm selection for black-box optimization. Most previous studies have focused only on whether an algorithm selection system can outperform the single-best solver (SBS) in a portfolio. In addition, a benchmarking methodology for algorithm selection systems has not been well investigated in the literature. In this context, this article analyzes algorithm selection systems on the 24 noiseless black-box optimization benchmarking functions. First, we demonstrate that the first successful performance measure is more reliable than the expected runtime measure for benchmarking algorithm selection systems. Then, we examine the influence of randomness on the performance of algorithm selection systems. We also show that the performance of algorithm selection systems can be significantly improved by using sequential least squares programming as a presolver. We point out that the difficulty of outperforming the SBS depends on algorithm portfolios, cross-validation methods, and dimensions. Finally, we demonstrate that the effectiveness of algorithm portfolios depends on various factors. These findings provide fundamental insights for algorithm selection for black-box optimization.
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications

Sentence-level Quality estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels. Recent QE models have achieved previously-unseen levels of correlation with human judgments, but they rely on large multilingual contextualized language models that are computationally expensive and make them infeasible for real-world applications. In this work, we evaluate several model compression techniques for QE and find that, despite their popularity in other NLP tasks, they lead to poor performance in this regression setting. We observe that a full model parameterization is required to achieve SoTA results in a regression task. However, we argue that the level of expressiveness of a model in a continuous range is unnecessary given the downstream applications of QE, and show that reframing QE as a classification problem and evaluating QE models using classification metrics would better reflect their actual performance in real-world applications.
Regularized Training of Nearest Neighbor Language Models

Including memory banks in a natural language processing architecture increases model capacity by equipping it with additional data at inference time. In this paper, we build upon kNN-LM (CITATION), which uses a pre-trained language model together with an exhaustive kNN search through the training data (memory bank) to achieve state-of-the-art results. We investigate whether we can improve the kNN-LM performance by instead training a LM with the knowledge that we will be using a kNN post-hoc. We achieved significant improvement using our method on language modeling tasks on WIKI-2 and WIKI-103. The main phenomenon that we encounter is that adding a simple L2 regularization on the activations (not weights) of the model, a transformer, improves the post-hoc kNN classification performance. We explore some possible reasons for this improvement. In particular, we find that the added L2 regularization seems to improve the performance for high-frequency words without deteriorating the performance for low frequency ones.
New Students on Sesame Street: What Order-Aware Matrix Embeddings Can Learn from BERT

Large-scale pretrained language models (PreLMs) are revolutionizing natural language processing across all benchmarks. However, their sheer size is prohibitive in low-resource or large-scale applications. While common approaches reduce the size of PreLMs via same-architecture distillation or pruning, we explore distilling PreLMs into more efﬁcient order-aware embedding models. Our results on the GLUE benchmark show that embedding-centric students, which have learned from BERT, yield scores comparable to DistilBERT on QQP and RTE, often match or exceed the scores of ELMo, and only fall behind on detecting linguistic acceptability.
Interpretable Local Tree Surrogate Policies

High-dimensional policies, such as those represented by neural networks, cannot be reasonably interpreted by humans. This lack of interpretability reduces the trust users have in policy behavior, limiting their use to low-impact tasks such as video games. Unfortunately, many methods rely on neural network representations for effective learning. In this work, we propose a method to build predictable policy trees as surrogates for policies such as neural networks. The policy trees are easily human interpretable and provide quantitative predictions of future behavior. We demonstrate the performance of this approach on several simulated tasks.
Deeply Buried Nuclei in the Infrared-luminous Galaxies NGC 4418 and Arp 220. II. Line Forests at λ = 1.4–0.4 mm and Circumnuclear Gas Observed with ALMA

We present the line observations in our Atacama Millimeter-Submillimeter Array imaging spectral scan toward three deeply buried nuclei in NGC 4418 and Arp 220. We cover 67 GHz in f rest = 215–697 GHz at about 0. ″2 (30, 80 pc) resolution. All the nuclei show dense line forests; we report our initial line identification using 55 species. The line velocities generally indicate gas rotation around each nucleus, tracing nuclear disks of ∼100 pc in size. We confirmed the counter-rotation of the nuclear disks in Arp 220 and that of the nuclear disk and the galactic disk in NGC 4418. While the brightest lines exceed 100 K, most of the major lines and many 13C isotopologues show absorption against even brighter continuum cores of the nuclei. The lines with higher upper-level energies, including those from vibrationally excited molecules, tend to arise from smaller areas, indicating radially varying conditions in these nuclei. The outflows from the two Arp 220 nuclei cause blueshifted line absorption below the continuum level. The absorption mostly has small spatial offsets from the continuum peaks to indicate the outflow orientations. The bipolar outflow from the western nucleus is also imaged in multiple emission lines, showing the extent of ∼1″ (400 pc). Redshifted line absorption against the nucleus of NGC 4418 indicates either an inward gas motion or a small collimated outflow slanted to the nuclear disk. We also resolved some previous confusions due to line blending and misidentification.
Strategic Ranking

Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals. However, the existing literature does not consider the effect of competition among individuals as induced by the algorithm design. Motivated by constrained allocation settings such as college admissions, we introduce strategic ranking, in which the (designed) individual reward depends on an applicant's post-effort rank in a measurement of interest. Our results illustrate how competition among applicants affects the resulting equilibria and model insights. We analyze how various ranking reward designs, belonging to a family of step functions, trade off applicant, school, and societal utility, as well as how ranking design counters inequities arising from disparate access to resources. In particular, we find that randomization in the reward design can mitigate two measures of disparate impact, welfare gap and access.
Version Age of Information in Clustered Gossip Networks

We consider a network consisting of a single source and <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> receiver nodes that are grouped into equal-sized clusters. Each cluster corresponds to a distinct community such that nodes that belong to different communities cannot exchange information. We use dedicated cluster heads in each cluster to facilitate communication between the source and the nodes within that cluster. Inside clusters, nodes are connected to each other according to a given network topology. Based on the connectivity levels within clusters, each node relays its current stored version of the source update to its neighboring nodes by <italic>local gossiping</italic>. We consider disconnected, ring, and fully connected network topologies for each cluster. For each of these network topologies, we characterize the average version age at each node and find the average version age scaling as a function of the network size <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. Next, by allowing gossiping among the cluster heads, we improve the version age scaling at the receiver nodes. Then, focusing on a ring network topology in each cluster, we introduce hierarchy to the considered clustered gossip network model without using dedicated cluster heads. Finally, we find the version age-optimum cluster sizes as a function of the source, cluster head, and node update rates through numerical evaluations.
Superconducting fluctuations observed far above T$_\mathrm{c}$ in the isotropic superconductor K$_3$C$_{60}$

Alkali-doped fullerides are strongly correlated organic superconductors that exhibit high transition temperatures, exceptionally large critical magnetic fields and a number of other unusual properties. The proximity to a Mott insulating phase is thought to be a crucial ingredient of the underlying physics, and may also affect precursors of superconductivity in the normal state above T$_\text{c}$. We report on the observation of a sizeable magneto-thermoelectric (Nernst) effect in the normal state of K$_3$C$_{60}$, which displays the characteristics of superconducting fluctuations. The anomalous Nernst effect emerges from an ordinary quasiparticle background below a temperature of 80K, far above T$_\text{c}$ = 20K. At the lowest fields and close to T$_\text{c}$, the scaling of the effect is captured by a model based on Gaussian fluctuations. The temperature up to which we observe fluctuations is exceptionally high for a three-dimensional isotropic system, where fluctuation effects are usually suppressed.
PrivateFetch: Scalable Catalog Delivery in Privacy-Preserving Advertising

In order to preserve the possibility of an Internet that is free at the point of use, attention is turning to new solutions that would allow targeted advertisement delivery based on behavioral information such as user preferences, without compromising user privacy. Recently, explorations in devising such systems either take approaches that rely on semantic guarantees like $k$-anonymity -- which can be easily subverted when combining with alternative information, and do not take into account the possibility that even knowledge of such clusters is privacy-invasive in themselves. Other approaches provide full privacy by moving all data and processing logic to clients -- but which is prohibitively expensive for both clients and servers. In this work, we devise a new framework called PrivateFetch for building practical ad-delivery pipelines that rely on cryptographic hardness and best-case privacy, rather than syntactic privacy guarantees or reliance on real-world anonymization tools. PrivateFetch utilizes local computation of preferences followed by high-performance single-server private information retrieval (PIR) to ensure that clients can pre-fetch ad content from servers, without revealing any of their inherent characteristics to the content provider. When considering an database of $>1,000,000$ ads, we show that we can deliver $30$ ads to a client in 40 seconds, with total communication costs of 192KB. We also demonstrate the feasibility of PrivateFetch by showing that the monetary cost of running it is less than 1% of average ad revenue. As such, our system is capable of pre-fetching ads for clients based on behavioral and contextual user information, before displaying them during a typical browsing session. In addition, while we test PrivateFetch as a private ad-delivery, the generality of our approach means that it could also be used for other content types.
A Fairness Analysis on Private Aggregation of Teacher Ensembles

The Private Aggregation of Teacher Ensembles (PATE) is an important private machine learning framework. It combines multiple learning models used as teachers for a student model that learns to predict an output chosen by noisy voting among the teachers. The resulting model satisfies differential privacy and has been shown effective in learning high-quality private models in semisupervised settings or when one wishes to protect the data labels. This paper asks whether this privacy-preserving framework introduces or exacerbates bias and unfairness and shows that PATE can introduce accuracy disparity among individuals and groups of individuals. The paper analyzes which algorithmic and data properties are responsible for the disproportionate impacts, why these aspects are affecting different groups disproportionately, and proposes guidelines to mitigate these effects. The proposed approach is evaluated on several datasets and settings.
Lattice star and acyclic branched polymer vertex exponents in 3d

Numerical values of lattice star entropic exponents γ f , and star vertex exponents σ f , are estimated using parallel implementations of the PERM and Wang–Landau algorithms. Our results show that the numerical estimates of the vertex exponents deviate from predictions of the ϵ-expansion and confirms and improves on estimates in the literature. We also estimate the entropic exponents γG of a few acyclic branched lattice networks with comb and brush connectivities. In particular, we confirm within numerical accuracy the Duplantier scaling relation γG−1=∑f⩾1mfσf for a comb and two brushes (where m f is the number of nodes of degree f in the network) using our independent estimates of σ f .