Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthetic Latent Fingerprint Generator

Given a full fingerprint image (rolled or slap), we present CycleGAN models to generate multiple latent impressions of the same identity as the full print. Our models can control the degree of distortion, noise, blurriness and occlusion in the generated latent print images to obtain Good, Bad and Ugly latent image categories as introduced in the NIST SD27 latent database. The contributions of our work are twofold: (i) demonstrate the similarity of synthetically generated latent fingerprint images to crime scene latents in NIST SD27 and MSP databases as evaluated by the NIST NFIQ 2 quality measure and recognition accuracies obtained by a SOTA fingerprint matcher, and (ii) use of synthetic latents to augment small-size latent training databases in the public domain to improve the performance of DeepPrint, a SOTA fingerprint matcher designed for rolled to rolled fingerprint matching on three latent databases (NIST SD27, NIST SD302, and IIITD-SLF). As an example, with synthetic latent data augmentation, the Rank-1 retrieval performance of DeepPrint is improved from 15.50% to 29.07% on challenging NIST SD27 latent database. Our approach for generating synthetic latent fingerprints can be used to improve the recognition performance of any latent matcher and its individual components (e.g., enhancement, segmentation and feature extraction). https://prip-lab.github. io/Synthetic-Latent-Fingerprint-Generator/
Robust and Efficient Estimation in Ordinal Response Models using the Density Power Divergence

In real life, we frequently come across data sets that involve some independent explanatory variable(s) generating a set of ordinal responses. These ordinal responses may correspond to an underlying continuous latent variable, which is linearly related to the covariate(s), and takes a particular (ordinal) label depending on whether this latent variable takes value in some suitable interval specified by a pair of (unknown) cut-offs. The most efficient way of estimating the unknown parameters (i.e., the regression coefficients and the cut-offs) is the method of maximum likelihood (ML). However, contamination in the data set either in the form of misspecification of ordinal responses, or the unboundedness of the covariate(s), might destabilize the likelihood function to a great extent where the ML based methodology might lead to completely unreliable inferences. In this paper, we explore a minimum distance estimation procedure based on the popular density power divergence (DPD) to yield robust parameter estimates for the ordinal response model. This paper highlights how the resulting estimator, namely the minimum DPD estimator (MDPDE), can be used as a practical robust alternative to the classical procedures based on the ML. We rigorously develop several theoretical properties of this estimator, and provide extensive simulations to substantiate the theory developed.
The case for fully Bayesian optimisation in small-sample trials

While sample efficiency is the main motive for use of Bayesian optimisation when black-box functions are expensive to evaluate, the standard approach based on type II maximum likelihood (ML-II) may fail and result in disappointing performance in small-sample trials. The paper provides three compelling reasons to adopt fully Bayesian optimisation (FBO) as an alternative. First, failures of ML-II are more commonplace than implied by the existing studies using the contrived settings. Second, FBO is more robust than ML-II, and the price of robustness is almost trivial. Third, FBO has become simple to implement and fast enough to be practical. The paper supports the argument using relevant experiments, which reflect the current practice regarding models, algorithms, and software platforms. Since the benefits seem to outweigh the costs, researchers should consider adopting FBO for their applications so that they can guard against potential failures that end up wasting precious research resources.
A Circular Window-based Cascade Transformer for Online Action Detection

Online action detection aims at the accurate action prediction of the current frame based on long historical observations. Meanwhile, it demands real-time inference on online streaming videos. In this paper, we advocate a novel and efficient principle for online action detection. It merely updates the latest and oldest historical representations in one window but reuses the intermediate ones, which have been already computed. Based on this principle, we introduce a window-based cascade Transformer with a circular historical queue, where it conducts multi-stage attentions and cascade refinement on each window. We also explore the association between online action detection and its counterpart offline action segmentation as an auxiliary task. We find that such an extra supervision helps discriminative history clustering and acts as feature augmentation for better training the classifier and cascade refinement. Our proposed method achieves the state-of-the-art performances on three challenging datasets THUMOS'14, TVSeries, and HDD. Codes will be available after acceptance.
Updated constraints on WIMP dark matter by radio observations of M31 – other annihilation channels

This brief paper generalizes dark matter (DM) constraints recently derived in [A.E. Egorov, Phys. Rev. D 106, 023023 (2022)] by radio observations of M31 to all possible annihilation channels (ex-cept the cases of neutrinos and photons as primary annihilation products). All the methodology here exactly repeats that in the mentioned paper, where only two representative and popular annihilation channels – χχ → bb and χχ → τ + τ − – were considered. It is conﬁrmed here, that in the case of light primary annihilation products χχ → τ + τ − , µ + µ − , gg, cc, uu, dd, ss, e + e − , bb (and an arbitrary combination of them) the ﬁducial (averaged over uncertainties) lower mass limit for the thermal weakly interacting massive particle (WIMP) is conﬁned in the range ≈ (40–70) GeV, which is bounded by χχ → bb, τ + τ − cases. Heavier WIMP, which can annihilate to W + W − , Z 0 Z 0 , tt, hh ; can not be probed at the level of thermal cross section, unless one assumes the optimistic cases of DM density and magnetic ﬁeld distributions in M31. In conclusion, m x (cid:38) 40 GeV represents the ﬁducial channel-independent mass limit for the thermal WIMP with the full uncertainty range estimated to be ≈ (20–90) GeV.
Gilt Leather Artefacts: White Paper on Material Characterization and Improved Conservation Strategies within NICAS

Due to its sensitivity, the conservation and upkeep of gilt leather is often underestimated. It can sincerely be considered as endangered cultural heritage. This publication describes the current state of knowledge from the perspectives of Technical Art History, Material Dynamics, Conservation and Diagnostics, and sets out research directions for 2017-2025.
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning

Representation learning on temporal graphs has drawn considerable research attention owing to its fundamental importance in a wide spectrum of real-world applications. Though a number of studies succeed in obtaining time-dependent representations, it still faces significant challenges. On the one hand, most of the existing methods restrict the embedding space with a certain curvature. However, the underlying geometry in fact shifts among the positive curvature hyperspherical, zero curvature Euclidean and negative curvature hyperbolic spaces in the evolvement over time. On the other hand, these methods usually require abundant labels to learn temporal representations, and thereby notably limit their wide use in the unlabeled graphs of the real applications. To bridge this gap, we make the first attempt to study the problem of self-supervised temporal graph representation learning in the general Riemannian space, supporting the time-varying curvature to shift among hyperspherical, Euclidean and hyperbolic spaces. In this paper, we present a novel self-supervised Riemannian graph neural network (SelfℛGNN). Specifically, we design a curvature-varying Riemannian GNN with a theoretically grounded time encoding, and formulate a functional curvature over time to model the evolvement shifting among the positive, zero and negative curvature spaces. To enable the self-supervised learning, we propose a novel reweighting self-contrastive approach, exploring the Riemannian space itself without augmentation, and propose an edge-based self-supervised curvature learning with the Ricci curvature. Extensive experiments show the superiority of SelfRGNN, and moreover, the case study shows the time-varying curvature of temporal graph in reality.
Using Taylor-Approximated Gradients to Improve the Frank-Wolfe Method for Empirical Risk Minimization

The Frank-Wolfe method has become increasingly useful in statistical and machine learning applications, due to the structure-inducing properties of the iterates, and especially in settings where linear minimization over the feasible set is more computationally efficient than projection. In the setting of Empirical Risk Minimization -- one of the fundamental optimization problems in statistical and machine learning -- the computational effectiveness of Frank-Wolfe methods typically grows linearly in the number of data observations $n$. This is in stark contrast to the case for typical stochastic projection methods. In order to reduce this dependence on $n$, we look to second-order smoothness of typical smooth loss functions (least squares loss and logistic loss, for example) and we propose amending the Frank-Wolfe method with Taylor series-approximated gradients, including variants for both deterministic and stochastic settings. Compared with current state-of-the-art methods in the regime where the optimality tolerance $\varepsilon$ is sufficiently small, our methods are able to simultaneously reduce the dependence on large $n$ while obtaining optimal convergence rates of Frank-Wolfe methods, in both the convex and non-convex settings. We also propose a novel adaptive step-size approach for which we have computational guarantees. Last of all, we present computational experiments which show that our methods exhibit very significant speed-ups over existing methods on real-world datasets for both convex and non-convex binary classification problems.
Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies

: The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment eﬀect heterogeneity. However, estimating treatment eﬀects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the Subgroup Discovery for Longitudinal Data (SDLD) algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment eﬀects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeﬁciency virus (HIV) who are at higher risk of weight gain when receiving dolutegravir-containing antiretroviral therapies (ARTs) versus when receiving non dolutegravir-containing ARTs. used in the analysis before and after imputation. For binary or ordinal covariates, we present the number and proportion of participants falling in the listed category; for continuous covariates, we present the mean and standard deviation. For covariates with no missing values, we present one summary for before and after imputation. ART refers to antiretroviral therapy; TB refers to tuberculosis; NHIF refers to National Health Insurance Fund.
An Analysis of Abstracted Model-Based Reinforcement Learning

Many methods for Model-based Reinforcement learning (MBRL) provide guarantees for both the accuracy of the Markov decision process (MDP) model they can deliver and the learning efﬁciency. At the same time, state abstraction techniques allow for a reduction of the size of an MDP while maintaining a bounded loss with respect to the original problem. It may come as a surprise, therefore, that no such guarantees are available when combining both techniques, i.e., where MBRL merely observes abstract states. Our theoretical analysis shows that abstraction can introduce a dependence between samples collected online (e.g., in the real world), which means that most results for MBRL can not be directly extended to this setting. The new results in this work show that concentration inequalities for martingales can be used to overcome this problem and allows for extending the re-sults of algorithms such as R-MAX to the setting with abstraction. Thus producing the ﬁrst performance guarantees for ‘Abstracted RL’: model-based reinforcement learning with an abstracted model.
A family of natural equilibrium measures for Sinai billiard flows

The Sinai billiard flow on the two-torus, i.e., the periodic Lorentz gas, is a continuous flow, but it is not everywhere differentiable. Assuming finite horizon, we relate the equilibrium states of the flow with those of the Sinai billiard map $T$ -- which is a discontinuous map. We propose a definition for the topological pressure $P_*(T,g)$ associated to a potential $g$. We prove that for any piecewise H\"older potential $g$ satisfying a mild assumption, $P_*(T,g)$ is equal to the definitions of Bowen using spanning or separating sets. We give sufficient conditions under which a potential gives rise to equilibrium states for the Sinai billiard map. We prove that in this case the equilibrium state $\mu_g$ is unique, Bernoulli, adapted and gives positive measure to all nonempty open sets. For this, we make use of a well chosen transfer operator acting on anisotropic Banach spaces, and construct the measure by pairing its maximal eigenvectors. Last, we prove that the flow invariant probability measure $\bar \mu_g$, obtained by taking the product of $\mu_g$ with the Lebesgue measure along orbits, is Bernoulli and flow adapted. We give examples of billiard tables for which there exists an open set of potentials satisfying those sufficient conditions.
Faithful Reasoning Using Large Language Models

Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. This entails an unwelcome degree of opacity and compromises performance, especially on problems that are inherently multi-step. To address these limitations, we show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem. Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned LMs, one for selection and one for inference, to produce a valid reasoning trace. Our method carries out a beam search through the space of reasoning traces to improve reasoning quality. We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy, and generates humanly interpretable reasoning traces whose validity can be checked by the user.
Integration on the Surreals

Conway’s real closed field No of surreal numbers is a sweeping generalization of the real numbers and the ordinals to which a number of elementary functions such as log and exponentiation have been shown to extend. The problems of identifying significant classes of functions that can be so extended and of defining integration for them have proven to be formidable. In this paper, we address this and related unresolved issues by showing that extensions to No, and thereby integrals, exist for most functions arising in practical applications. In particular, we show they exist for a large subclass of the resurgent functions, a subclass that contains the functions that at ∞ are semi-algebraic, semi-analytic, analytic, meromorphic, and Borel summable as well as generic solutions to linear and nonlinear systems of ODEs possibly having irregular singularities. We further establish a sufficient condition for the theory to carry over to ordered exponential subfields of No more generally and illustrate the result with structures familiar from the surreal literature. We work in NBG less the Axiom of Choice (for both sets and proper classes), with the result that the extensions of functions and integrals that concern us here have a “constructive” nature in this sense. In the Appendix it is shown that the existence of such constructive extensions and integrals of substantially more general types of functions (e.g. smooth functions) is obstructed by considerations from the foundations of mathematics.
SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes

Intrinsic image decomposition (IID) is an under-constrained problem. Therefore, traditional approaches use hand crafted priors to constrain the problem. However, these constraints are limited when coping with complex scenes. Deep learning-based approaches learn these constraints implicitly through the data, but they often suffer from dataset biases (due to not being able to include all possible imaging conditions). In this paper, a combination of the two is proposed. Component specific priors like semantics and invariant features are exploited to obtain semantically and physically plausible reflectance transitions. These transitions are used to steer a progressive CNN with implicit homogeneity constraints to decompose reflectance and shading maps. An ablation study is conducted showing that the use of the proposed priors and progressive CNN increase the IID performance. State of the art performance on both our proposed dataset and the standard real-world IIW dataset shows the effectiveness of the proposed method. Code is made available at https://github.com/Morpheus3000/SIGNet
Transformers with Learnable Activation Functions

Activation functions can have a significant impact on reducing the topological complexity of input data and therefore, improving a model’s performance. However, the choice of activation functions is seldom discussed or explored in Transformer-based language models. As a common practice, commonly used activation functions like Gaussian Error Linear Unit (GELU) are chosen beforehand and then remain fixed from pre-training to fine-tuning. In this paper, we investigate the impact of activation functions on Transformer-based models by utilizing rational activation functions (RAFs). In contrast to fixed activation functions (FAF), RAFs are capable of learning the optimal activation functions from data. Our experiments show that the RAF-based Transformer model (RAFT) achieves a better performance than its FAF-based counterpart ({baseline). For instance, we find that RAFT outperforms {baseline on the GLUE benchmark by 5.71 points when using only 100 training examples and by 2.05 points on SQuAD with all available data. Analyzing the shapes of the learned RAFs further unveils that they vary across different layers and different tasks; opening a promising way to better analyze and understand large, pre-trained language models.
Image Classification by Throwing Quantum Kitchen Sinks at Tensor Networks

Several variational quantum circuit approaches to machine learning have been proposed in recent years, with one promising class of variational algorithms involving tensor networks operating on states resulting from local feature maps. In contrast, a random feature approach known as quantum kitchen sinks provides comparable performance, but leverages non-local feature maps. Here we combine these two approaches by proposing a new circuit ansatz where a tree tensor network coherently processes the non-local feature maps of quantum kitchen sinks, and we run numerical experiments to empirically evaluate the performance of the new ansatz on image classiﬁcation. From the perspective of classiﬁcation performance, we ﬁnd that simply combining quantum kitchen sinks with tensor networks yields no qualitative improvements. However, the addition of feature optimization greatly boosts performance, leading to state-of-the-art quantum circuits for image classiﬁcation, requiring only shallow circuits and a small number of qubits – both well within reach of near-term quantum devices.
Liouville conformal field theory on even-dimensional spheres

Initiated by Polyakov in his 1981 seminal work, the study of two-dimensional Liouville conformal field theory has drawn considerable attention over the past few decades. Recent progress in the understanding of conformal geometry in dimension higher than two has naturally led to a generalization of the Polyakov formalism to higher dimensions based on conformally invariant operators: Graham–Jenne–Mason–Sparling operators and the Q-curvature. This article is dedicated to providing a rigorous construction of Liouville conformal field theory on even-dimensional spheres. This is done at the classical level in terms of a generalized uniformization problem and at the quantum level, thanks to a probabilistic construction based on log-correlated fields and Gaussian multiplicative chaos. The properties of the objects thus defined are in agreement with the ones expected in the physics literature.
Common Idiosyncratic Quantile Risk

We identify a new type of risk that is characterised by commonalities in the quantiles of the cross-sectional distribution of asset returns. Our newly proposed quantile risk factor is associated with a quantile-specific risk premium and provides new insights into how upside and downside risks are priced by investors. In contrast to the previous literature, we recover the common structure in cross-sectional quantiles without making confounding assumptions or aggregating potentially non-linear information. We discuss how the new quantile-based risk factor differs from popular volatility and downside risk factors, and we identify where the quantile-dependent risks deserve greater compensation. Quantile factors also have predictive power for aggregate market returns.
DTAC-ADMM: Delay-Tolerant Augmented Consensus ADMM-based Algorithm for Distributed Resource Allocation

Latency is inherent in almost all real-world net-worked applications. In this paper, we propose a distributed resource allocation strategy over multi-agent networks with delayed communications. The state of each agent (or node) represents its share of assigned resources out of a fixed amount (equal to the overall demand). Every node locally updates its state towards optimizing a global allocation cost function via received information of its neighbouring nodes even when the data exchange over the network is heterogeneously delayed at different links. The update is based on the alternating direction method of multipliers (ADMM) formulation subject to both sum-preserving coupling-constraint and local box-constraints. The solution is derivative-free and holds for general (not necessarily differentiable) convex cost models. We use the notion of augmented consensus over undirected networks to model delayed information-exchange for convergence analysis. We simulate our delay-tolerant algorithm for optimal energy reservation-production scheduling.