Miracle-dz/UniFAR-FTUs huggingface dataset:
Group-Invariant Quantum Machine Learning

Quantum Machine Learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., with no assumptions about the problem embedded in the model) are likely to have trainability and generalization issues, especially for large problem sizes. As such, it is fundamental to develop schemes that encode as much information as available about the problem at hand. In this work we present a simple, yet powerful, framework where the underlying invariances in the data are used to build QML models that, by construction, respect those symmetries. These so-called group-invariant models produce outputs that remain invariant under the action of any element of the symmetry group G associated to the dataset. We present theoretical results underpinning the design of G -invariant models, and exemplify their application through several paradigmatic QML classiﬁcation tasks including cases when G is a continuous Lie group and also when it is a discrete symmetry group. Notably, our framework allows us to recover, in an elegant way, several well known algorithms for the literature, as well as to discover new ones. Taken together, we expect that our results will help pave the way towards a more geometric and group-theoretic approach to QML model design.
Near-Field Wideband Extremely Large-scale MIMO Transmission with Holographic Metasurface Antennas

Extremely large-scale multiple-input multiple-output (XL-MIMO) is the development trend of future wireless communications. However, the extremely large-scale antenna array could bring inevitable nearfield and dual-wideband effects that seriously reduce the transmission performance. This paper proposes an algorithmic framework to design the beam combining for the near-field wideband XL-MIMO uplink transmissions assisted by holographic metasurface antennas (HMAs). Firstly, we introduce a spherical-wave-based channel model that simultaneously takes into account both the near-field and dual-wideband effects. Based on such a model, we then formulate the HMA-based beam combining problem for the proposed XL-MIMO communications, which is challenging due to the nonlinear coupling of high dimensional HMA weights and baseband combiners. We further present a sum-mean-square-error-minimization-based algorithmic framework. Numerical results showcase that the proposed scheme can effectively alleviate the sum-rate loss caused by the near-field and dual-wideband effects in HMA-assisted XL-MIMO systems. Meanwhile, the proposed HMA-based scheme can achieve a higher sum rate than the conventional phase-shifter-based hybrid analog/digital one with the same array aperture.
Rooted America: Immobility and Segregation of the Intercounty Migration Network

Despite the popular narrative that the United States is a “land of mobility,” the country may have become a “rooted America” after a decades-long decline in migration rates. This article interrogates the lingering question about the social forces that limit migration, with an empirical focus on internal migration in the United States. We propose a systemic, network model of migration flows, combining demographic, economic, political, and geographic factors and network dependence structures that reflect the internal dynamics of migration systems. Using valued temporal exponential-family random graph models, we model the network of intercounty migration flows from 2011 to 2015. Our analysis reveals a pattern of segmented immobility, where fewer people migrate between counties with dissimilar political contexts, levels of urbanization, and racial compositions. Probing our model using “knockout experiments” suggests one would have observed approximately 4.6 million (27 percent) more intercounty migrants each year were the segmented immobility mechanisms inoperative. This article offers a systemic view of internal migration and reveals the social and political cleavages that underlie geographic immobility in the United States.
Quantum Spin Liquid Phase in the Shastry-Sutherland Model Detected by an Improved Level Spectroscopic Method

We study the spin-1/2 two-dimensional Shastry–Sutherland spin model by exact diagonalization of clusters with periodic boundary conditions, developing an improved level spectroscopic technique using energy gaps between states with different quantum numbers. The crossing points of some of the relative (composite) gaps have much weaker finite-size drifts than the normally used gaps defined only with respect to the ground state, thus allowing precise determination of quantum critical points even with small clusters. Our results support the picture of a spin liquid phase intervening between the well-known plaquette-singlet and antiferromagnetic ground states, with phase boundaries in almost perfect agreement with a recent density matrix renormalization group study, where much larger cylindrical lattices were used [J. Yang et al., Phys. Rev. B 105, L060409 (2022)]. The method of using composite low-energy gaps to reduce scaling corrections has potentially broad applications in numerical studies of quantum critical phenomena.
Transverse momentum distributions of valence quarks in light and heavy vector mesons

We study the leading-twist time-reversal even transverse momentum dependent parton distribution functions (TMDs) of light and heavy vector mesons, i.e., the 𝜌 , 𝐽 / 𝜓 and Υ . We employ the leading Fock-state light front wave functions (LF-LFWFs) of 𝜌 and 𝐽 / 𝜓 from our recent study, and supplement with Υ ’s LF-LFWFs. These LF-LFWFs are extracted from dynamically solved Bethe-Salpeter wave functions. The vector meson TMDs are then studied with the light front overlap representation at leading Fock-state. All the obtained TMDs are non-vanishing and evolve with current quark mass, in particular the tensor polarized TMDs 𝑓 1 𝐿𝑇 and 𝑓 1 𝑇𝑇 which undergo a sign ﬂip. The 𝜌 TMDs are compared with other model studies and agreement is found, aside from 𝑓 1 𝐿𝑇 and 𝑓 1 𝑇𝑇 . Finally, the collinear PDFs of vector mesons are studied. The 𝜌 ’s valence PDFs 𝑓 1 ,𝑣 ( 𝑥 ) and 𝑔 1 𝐿,𝑣 ( 𝑥 ) are evolved to the scale of 2.4 GeV, with their ﬁrst three moments compared to lattice QCD prediction. The qualitative behavior of tensor polarized PDF 𝑓 1 𝐿𝐿 ( 𝑥 ) in 𝜌 at large 𝑥 is also discussed.
cuHARM: A New GPU-accelerated GRMHD Code and Its Application to ADAF Disks

We introduce a new GPU-accelerated general relativistic magnetohydrodynamic code based on HARM, which we call cuHARM. The code is written in CUDA-C and uses OpenMP to parallelize multi-GPU setups. Our code allows us to run high-resolution simulations of accretion disks and the formation and structure of jets without a need for multinode supercomputer infrastructure. A 2563 simulation is well within the reach of an Nvidia DGX-V100 server, with the computation being about 10 times faster than if only the CPU is used. We use this code to examine several disk structures all in the SANE state. We find that (i) increasing the magnetic field in the SANE state does not affect the mass accretion rate; (ii) simultaneously increasing the disk size and the magnetic field, while keeping the ratio of energies fixed, leads to the destruction of the jet once the magnetic flux through the horizon decreases below a certain limit (this demonstrates that the existence of a jet is a function of the initial mass distribution, and not of the initial intensity of the magnetic field, since the magnetorotational instability dictates the evolution of the magnetic field); and (iii) the structure of the jet is a weak function of the adiabatic index of the gas, with relativistic gas tending to have a wider jet.
Asymptotic analysis of diabatic surface hopping algorithm in the adiabatic and non-adiabatic limits

Surface hopping algorithms, as an important class of quantum dynamics simulation algorithms for non-adiabatic dynamics, are typically performed in the adiabatic representation, which can break down in the presence of ill-defined adiabatic potential energy surfaces (PESs) and adiabatic coupling term. Another issue of surface hopping algorithms is the difficulty in capturing the correct scaling of the transition rate in the Marcus (weak-coupling/non-adiabatic) regime. Though the first issue can be circumvented by exploiting the diabatic representation, diabatic surface hopping algorithms usually lack justification on the theoretical level. We consider the diabatic surface hopping algorithm proposed in [Fang, Lu. Multiscale Model. Simul. 16:4, 1603-1622, 2018] and provide the asymptotic analysis of the transition rate in the Marcus regime that justifies the correct scaling for the spin-boson model. We propose two conditions that guarantee the correctness for general potentials. In the opposite (strong-coupling/adiabatic) regime, we derive the asymptotic behavior of the algorithm that interestingly matches a type of mean-field description. The techniques used here may shed light on the analysis for other diabatic-based algorithms.
Organizing Crime: an Empirical Analysis of the Sicilian Mafia

In this article we study the organizational structure of a large group of members of the Sicilian Maﬁa by means of social network analysis and an econometric analysis of link formation. Our mains results are the following. i) The Maﬁa network is a small-world network adjusted by its criminal nature, and is strongly disassortative. ii) Maﬁa bosses are not always central in the network. In particular, consistent with a prediction of Baccara and Bar-Isaac (2008), we identify a “cell-dominated hierarchy” in the network: a key member is not central, but is connected to a relative with a central position. iii) The probability of link formation between two agents is higher if the two agents belong to the same Mandamento , if they share a high number of similar tasks, while being a “boss” reduces the probability of link formation between them. iv) The probability of link formation for an individual agent is higher if he is in charge of keeping connections outside his Mandamento , of collecting protection money and or having a directive role, while age has modest role. These results are interpreted in the light of the eﬃciency/security trade-oﬀ faced by the Maﬁa and of its known hierarchical structure.
Theories of “Gender” in NLP Bias Research

The rise of concern around Natural Language Processing (NLP) technologies containing and perpetuating social biases has led to a rich and rapidly growing area of research. Gender bias is one of the central biases being analyzed, but to date there is no comprehensive analysis of how “gender” is theorized in the field. We survey nearly 200 articles concerning gender bias in NLP to discover how the field conceptualizes gender both explicitly (e.g. through definitions of terms) and implicitly (e.g. through how gender is operationalized in practice). In order to get a better idea of emerging trajectories of thought, we split these articles into two sections by time. We find that the majority of the articles do not make their theorization of gender explicit, even if they clearly define “bias. ” Almost none use a model of gender that is intersectional or inclusive of nonbinary genders; and many conflate sex characteristics, social gender, and linguistic gender in ways that disregard the existence and experience of trans, nonbinary, and intersex people. There is an increase between the two time-sections in statements acknowledging that gender is a complicated reality, however, very few articles manage to put this acknowledgment into practice. In addition to analyzing these findings, we provide specific recommendations to facilitate interdisciplinary work, and to incorporate theory and methodology from Gender Studies. Our hope is that this will produce more inclusive gender bias research in NLP.
Subverting Fair Image Search with Generative Adversarial Perturbations

In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to make the ranking model behave unfairly without having access to the model or training data? To investigate this question, we present a case study in which we develop and then attack a state-of-the-art, fairness-aware image search engine using images that have been maliciously modified using a Generative Adversarial Perturbation (GAP) model [75]. These perturbations attempt to cause the fair re-ranking algorithm to unfairly boost the rank of images containing people from an adversary-selected subpopulation. We present results from extensive experiments demonstrating that our attacks can successfully confer significant unfair advantage to people from the majority class relative to fairly-ranked baseline search results. We demonstrate that our attacks are robust across a number of variables, that they have close to zero impact on the relevance of search results, and that they succeed under a strict threat model. Our findings highlight the danger of deploying fair machine learning algorithms in-the-wild when (1) the data necessary to achieve fairness may be adversarially manipulated, and (2) the models themselves are not robust against attacks.
Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains Using Attention-based and Pixel-wise Supervised Learning

Iris Presentation Attack Detection (PAD) is essential to secure iris recognition systems. Recent iris PAD solutions achieved good performance by leveraging deep learning techniques. However, most results were reported under intra-database scenarios and it is unclear if such solutions can generalize well across databases and capture spectra. These PAD methods run the risk of overfitting because of the binary label supervision during the network training, which serves global information learning but weakens the capture of local discriminative features. This chapter presents a novel attention-based deep pixel-wise binary supervision (A-PBS) method. A-PBS utilizes pixel-wise supervision to capture the fine-grained pixel/patch-level cues and attention mechanism to guide the network to automatically find regions where most contribute to an accurate PAD decision. Extensive experiments are performed on six NIR and one visible-light iris databases to show the effectiveness and robustness of proposed A-PBS methods. We additionally conduct extensive experiments under intra-/cross-database and intra-/cross-spectrum for detailed analysis. The results of our experiments indicates the generalizability of the A-PBS iris PAD approach.
Nucleon-antinucleon interaction

A review is presented of the antinucleon-nucleon interaction, and some related issues such as fundamental symmetries, annihilation mechanisms, antinucleon-nucleus scattering, antiprotonic atoms and neutron-antineutron oscillations. The overall perspective is historical but the modern approaches are also presented.
Dancing in the dark: detecting a population of distant primordial black holes

Primordial black holes (PBHs) are compact objects proposed to have formed in the early Universe from the collapse of small-scale over-densities. Their existence may be detected from the observation of gravitational waves (GWs) emitted by PBH mergers, if the signals can be distinguished from those produced by the merging of astrophysical black holes. In this work, we forecast the capability of the Einstein Telescope, a proposed third-generation GW observatory, to identify and measure the abundance of a subdominant population of distant PBHs, using the difference in the redshift evolution of the merger rate of the two populations as our discriminant. We carefully model the merger rates and generate realistic mock catalogues of the luminosity distances and errors that would be obtained from GW signals observed by the Einstein Telescope. We use two independent statistical methods to analyse the mock data, finding that, with our more powerful, likelihood-based method, PBH abundances as small as f PBH ≈ 7 × 10-6 (f PBH ≈ 2×10-6) would be distinguishable from f PBH = 0 at the level of 3σ with a one year (ten year) observing run of the Einstein Telescope. Our mock data generation code, darksirens, is fast, easily extendable and publicly available on GitLab.
A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration

The cross-entropy objective has proved to be an all-purpose training objective for autoregressive language models (LMs). However, without considering the penalization of problematic tokens, LMs trained using cross-entropy exhibit text degeneration. To address this, unlikelihood training has been proposed to reduce the probability of unlikely tokens predicted by LMs. But unlikelihood does not consider the relationship between the label tokens and unlikely token candidates, thus showing marginal improvements in degeneration. We propose a new contrastive token learning objective that inherits the advantages of cross-entropy and unlikelihood training and avoids their limitations. The key idea is to teach a LM to generate high probabilities for label tokens and low probabilities of negative candidates. Comprehensive experiments on language modeling and open-domain dialogue generation tasks show that the proposed contrastive token objective yields much less repetitive texts, with a higher generation quality than baseline approaches, achieving the new state-of-the-art performance on text degeneration.
Low-Level Bi-Abduction

The paper proposes a new static analysis designed to handle open programs, i.e., fragments of programs, with dynamic pointer-linked data structures - in particular, various kinds of lists - that employ advanced low-level pointer operations. The goal is to allow such programs be analysed without a need of writing analysis harnesses that would first initialise the structures being handled. The approach builds on a special flavour of separation logic and the approach of bi-abduction. The code of interest is analyzed along the call tree, starting from its leaves, with each function analysed just once without any call context, leading to a set of contracts summarizing the behaviour of the analysed functions. In order to handle the considered programs, methods of abduction existing in the literature are significantly modified and extended in the paper. The proposed approach has been implemented in a tool prototype and successfully evaluated on not large but complex programs.
Rate-Splitting Multiple Access for 6G—Part I: Principles, Applications and Future Works

This letter is the first part of a three-part tutorial focusing on rate-splitting multiple access (RSMA) for 6G. As Part I of the tutorial, the letter presents the basics of RSMA and its applications in light of 6G. To begin with, we first delineate the design principle and basic transmission frameworks of downlink and uplink RSMA. We then illustrate the applications of RSMA for addressing the challenges of various potential enabling technologies and use cases, consequently making it a promising next generation multiple access (NGMA) scheme for future networks such as 6G and beyond. We briefly discuss the challenges of RSMA and conclude the letter. In continuation of Part I, we will focus on the interplay of RSMA with integrated sensing and communication, and reconfigurable intelligent surfaces, respectively in Part II and Part III of this tutorial.
Meta-learning Feature Representations for Adaptive Gaussian Processes via Implicit Differentiation

We propose Adaptive Deep Kernel Fitting (ADKF), a general framework for learning deep kernels by interpolating between meta-learning and conventional learning. Our approach employs a bilevel optimization objective where we meta-learn feature representations that are generally useful across tasks, in the sense that task-speciﬁc Gaussian process models estimated on top of such features achieve the lowest possible predictive loss on average across tasks. We solve the resulting nested optimization problem using the implicit function theorem. We show that ADKF contains Deep Kernel Learning and Deep Kernel Transfer as special cases. Although ADKF is a completely general method, we argue that it is especially well-suited for drug discovery problems and demonstrate that it signiﬁcantly outperforms previous state-of-the-art methods on a variety of real-world few-shot molecular property prediction tasks and out-of-domain molecular optimization tasks. Kernel Fitting (ADKF), a novel framework for deep kernel ﬁtting that interpolates between meta-learning and conventional learning. ADKF meta-learns a feature extractor across tasks such that the task-speciﬁc GP models estimated on top of the extracted feature representations can achieve low prediction error on average across tasks. ADKF is implemented by solving a bilevel optimization objective via implicit differentiation. We have showed that ADKF learns generally useful feature representations, achieving state-of-the-art performance on real-world few-shot molecular prediction tasks and on out-of-domain molecular optimization tasks.
Asymptotic Efficiency Bounds for a Class of Experimental Designs

We consider an experimental design setting in which units are assigned to treatment after being sampled sequentially from an inﬁnite population. We derive asymptotic ef-ﬁciency bounds that apply to data from any experiment that assigns treatment as a (possibly randomized) function of covariates and past outcome data, including stratiﬁcation on covariates and adaptive designs. For estimating the average treatment eﬀect of a binary treatment, our results show that no further ﬁrst order asymptotic eﬃciency improvement is possible relative to an estimator that achieves the Hahn (1998) bound in an experimental design where the propensity score is chosen to minimize this bound. Our results also apply to settings with multiple treatments with possible constraints on treatment, as well as covariate based sampling of a single outcome.
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions. During a conversation involving various people, a person’s emotions are influenced by the other speaker’s utterances and their own emotional state over the utterances. In this paper, we propose COntextualized Graph Neural Network based Multi- modal Emotion recognitioN (COGMEN) system that leverages local information (i.e., inter/intra dependency between speakers) and global information (context). The proposed model uses Graph Neural Network (GNN) based architecture to model the complex dependencies (local and global information) in a conversation. Our model gives state-of-the- art (SOTA) results on IEMOCAP and MOSEI datasets, and detailed ablation experiments show the importance of modeling information at both levels.
Choosing exogeneity assumptions in potential outcome models

There are many kinds of exogeneity assumptions. How should researchers choose among them? When exogeneity is imposed on an unobservable like a potential outcome, we argue that the form of exogeneity should be chosen based on the kind of selection on unobservables it allows. Consequently, researchers can assess the plausibility of any exogeneity assumption by studying the distributions of treatment given the unobservables that are consistent with that assumption. We use this approach to study two common exogeneity assumptions: quantile and mean independence. We show that both assumptions require a kind of non-monotonic relationship between treatment and the potential outcomes. We discuss how to assess the plausibility of this kind of treatment selection. We also show how to define a new and weaker version of quantile independence that allows for monotonic selection on unobservables. We then show the implications of the choice of exogeneity assumption for identification. We apply these results in an empirical illustration of the effect of child soldiering on wages.