Miracle-dz/UniFAR-FTUs huggingface dataset:
Template-Based Static Posterior Inference for Bayesian Probabilistic Programming

In Bayesian probabilistic programming, a central problem is to estimate the normalised posterior distribution (NPD) of a probabilistic program with conditioning. Prominent approximate approaches to address this problem include Markov chain Monte Carlo and variational inference, but neither can generate guaranteed outcomes within limited time. Moreover, most existing formal approaches that perform exact inference for NPD are restricted to programs with closed-form solutions or bounded loops/recursion. A recent work (Beutner et al., PLDI 2022) derived guaranteed bounds for NPD over programs with unbounded recursion. However, as this approach requires recursion unrolling, it suffers from the path explosion problem. Furthermore, previous approaches do not consider score-recursive probabilistic programs that allow score statements inside loops, which is non-trivial and requires careful treatment to ensure the integrability of the normalising constant in NPD. In this work, we propose a novel automated approach to derive bounds for NPD via polynomial templates. Our approach can handle probabilistic programs with unbounded while loops and continuous distributions with infinite supports. The novelties in our approach are three-fold: First, we use polynomial templates to circumvent the path explosion problem from recursion unrolling; Second, we derive a novel multiplicative variant of Optional Stopping Theorem that addresses the integrability issue in score-recursive programs; Third, to increase the accuracy of the derived bounds via polynomial templates, we propose a novel technique of truncation that truncates a program into a bounded range of program values. Experiments over a wide range of benchmarks demonstrate that our approach is time-efficient and can derive bounds for NPD that are comparable with (or tighter than) the recursion-unrolling approach (Beutner et al., PLDI 2022).
Remarks on projected solutions for generalized Nash games

In this work, we focus on the concept of projected solutions for generalized Nash equilibrium problems. We present new existence results by considering sets of strategies that are not necessarily compact. The relationship between projected solutions and Nash equilibria is studied for the generalized Nash game proposed by Rosen. Finally, we demonstrate that every projected solution of a game is associated with a Nash equilibrium, but in a different game.
QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models

Image quality remains a key problem for both traditional and deep learning (DL)-based approaches to retinal image analysis, but identifying poor quality images can be time consuming and subjective. Thus, automated methods for retinal image quality scoring (RIQS) are needed. The current state-of-the-art is MCFNet, composed of three Densenet121 backbones each operating in a different colour space. MCFNet, and the EyeQ dataset released by the same authors, was a huge step forward for RIQS. We present QuickQual, a simple approach to RIQS, consisting of a single off-the-shelf ImageNet-pretrained Densenet121 backbone plus a Support Vector Machine (SVM). QuickQual performs very well, setting a new state-of-the-art for EyeQ (Accuracy: 88.50% vs 88.00% for MCFNet; AUC: 0.9687 vs 0.9588). This suggests that RIQS can be solved with generic perceptual features learned on natural images, as opposed to requiring DL models trained on large amounts of fundus images. Additionally, we propose a Fixed Prior linearisation scheme, that converts EyeQ from a 3-way classification to a continuous logistic regression task. For this task, we present a second model, QuickQual MEga Minified Estimator (QuickQual-MEME), that consists of only 10 parameters on top of an off-the-shelf Densenet121 and can distinguish between gradable and ungradable images with an accuracy of 89.18% (AUC: 0.9537). Code and model are available on GitHub: https://github.com/justinengelmann/QuickQual . QuickQual is so lightweight, that the entire inference code (and even the parameters for QuickQual-MEME) is already contained in this paper.
GPT-3 Models are Few-Shot Financial Reasoners

Financial analysis is an important tool for evaluating company performance. Practitioners work to answer financial questions to make profitable investment decisions, and use advanced quantitative analyses to do so. As a result, Financial Question Answering (QA) is a question answering task that requires deep reasoning about numbers. Furthermore, it is unknown how well pre-trained language models can reason in the financial domain. The current state-of-the-art requires a retriever to collect relevant facts about the financial question from the text and a generator to produce a valid financial program and a final answer. However, recently large language models like GPT-3 [1] have achieved state-of-the-art performance on wide variety of tasks with just a few shot examples. We run several experiments with GPT-3 and find that a separate retrieval model and logic engine continue to be essential components to achieving SOTA performance in this task, particularly due to the precise nature of financial questions and the complex information stored in financial documents. With this understanding, our refined promptengineering approach on GPT-3 achieves near SOTA accuracy without any fine-tuning.
An Axiomatic Theory for Reversible Computation

Undoing computations of a concurrent system is beneficial in many situations, e.g., in reversible debugging of multi-threaded programs and in recovery from errors due to optimistic execution in parallel discrete event simulation. A number of approaches have been proposed for how to reverse formal models of concurrent computation including process calculi such as CCS, languages like Erlang, and abstract models such as prime event structures and occurrence nets. However it has not been settled what properties a reversible system should enjoy, nor how the various properties that have been suggested, such as the parabolic lemma and the causal-consistency property, are related. We contribute to a solution to these issues by using a generic labelled transition system equipped with a relation capturing whether transitions are independent to explore the implications between various reversibility properties. In particular, we show how all properties we consider are derivable from a set of axioms. Our intention is that when establishing properties of some formalism it will be easier to verify the axioms rather than proving properties such as the parabolic lemma directly. We also introduce two new properties related to causal consistent reversibility, namely causal liveness and causal safety, stating, respectively, that an action can be undone if (causal liveness) and only if (causal safety) it is independent from all the following actions. These properties come in three flavours: defined in terms of independent transitions, independent events, or via an ordering on events. Both causal liveness and causal safety are derivable from our axioms.
Advanced Numerical Simulation of Turbine Rim Seal Flows and Consideration for RANS Turbulence Modelling

This paper reports large-eddy simulations (LES) and unsteady Reynolds-averaged Navier-Stokes (URANS) calculations of a turbine rim seal configuration previously investigated experimentally. The configuration does not include any vanes, blades or external flows, but investigates inherent unsteady flow features and limitations of CFD modelling identified in engine representative studies. Compared to RANS and URANS CFD models, a sector LES model showed closer agreement with mean pressure measurements. LES models also showed agreement with measured pressure frequency spectra, but discrepancies were found between the LES and experiment in the speed and the circumferential lobe number of the unsteady flow structures. Sensitivity of predictions to modelling assumptions and differences with experimental data are investigated through CFD calculations considering sector size, interaction between the rim cavity and the inner cavity, outer annulus boundary conditions, and the coolant mass flow. Significant sensitivity to external flow conditions, which could contribute to differences with measurements, is shown, although some discrepancies remain. Further detailed analysis of the CFD solutions is given illustrating the complex flow physics. Possible improvement of a steady RANS model using a priori analysis of LES was investigated, but showed a rather small improvement in mean pressure prediction.
CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations

AI-driven drug design relies significantly on predicting molecular properties, which is a complex task. In current approaches, the most commonly used feature representations for training deep neural network models are based on SMILES and molecular graphs. While these methods are concise and efficient, they have limitations in capturing complex spatial information. Recently, researchers have recognized the importance of incorporating three-dimensional information of molecular structures into models. However, capturing spatial information requires the introduction of additional units in the generator, bringing additional design and computational costs. Therefore, it is necessary to develop a method for predicting molecular properties that effectively combines spatial structural information while maintaining the simplicity and efficiency of graph neural networks. In this work, we propose an embedding approach CTAGE, utilizing $k$-hop discrete Ricci curvature to extract structural insights from molecular graph data. This effectively integrates spatial structural information while preserving the training complexity of the network. Experimental results indicate that introducing node curvature significantly improves the performance of current graph neural network frameworks, validating that the information from k-hop node curvature effectively reflects the relationship between molecular structure and function.
The CarbonSat candidate mission: radiometric and spectral performances over spatially heterogeneous scenes

CarbonSat is one of the two candidate missions for the 8th cycle of European Space Agency (ESA) Earth Explorers, currently undergoing feasibility studies with two industrial consortia. The mission aims at quantifying the spatial distribution of carbon dioxide (CO2) and methane (CH4) with high precision (3.0 ppm for CO2 and 12.0 ppb for CH4) and accuracy (0.5 ppm for CO2 and 5 ppb for CH4) at a high spatial resolution (2km x 3km) and with global coverage above 40° latitude every 12 days. It consists of three pushbroom spectrometers measuring the Earth reflectance in each of the following bands: NIR (747nm- 773nm @0.1nm resolution), SWIR-1 (1590-1675nm @0.3nm) and SWIR-2 (1925-2095nm @0.55nm). Although most requirements for the CarbonSat phase A are defined over spatially homogeneous scenes, it is known from previous missions and studies that the observation of real, spatially heterogeneous scenes create specific measurement errors. One obvious mechanism is a distortion of the instrument spectral response function (ISRF) induced by a non-uniform slit illumination in the along-track (ALT) direction. This error has been analysed for several missions (OMI, Sentinel-4, Sentinel-5). The combination of spectrometer smile with across-track (ACT) scene non-uniformities induces similar errors. In this paper, we report about the analysis efforts carried out during CarbonSat preliminary phases to evaluate and mitigate these effects. In a first section, we introduce common concepts and notations for heterogeneous scenes analysis. An exhaustive list of known error mechanisms is presented. In section 2 we discuss the effect of inhomogeneous slit illumination, and describe hardware mitigation with a slit homogeniser. The combination of spectrometer smile and ACT heterogeneities is studied in section 3.
Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations: an Empirical Study

Sequential Recommender Systems (SRSs) are widely employed to model user behavior over time. However, their robustness in the face of perturbations in training data remains a largely understudied yet critical issue. A fundamental challenge emerges in previous studies aimed at assessing the robustness of SRSs: the Rank-Biased Overlap (RBO) similarity is not particularly suited for this task as it is designed for infinite rankings of items and thus shows limitations in real-world scenarios. For instance, it fails to achieve a perfect score of 1 for two identical finite-length rankings. To address this challenge, we introduce a novel contribution: Finite Rank-Biased Overlap (FRBO), an enhanced similarity tailored explicitly for finite rankings. This innovation facilitates a more intuitive evaluation in practical settings. In pursuit of our goal, we empirically investigate the impact of removing items at different positions within a temporally ordered sequence. We evaluate two distinct SRS models across multiple datasets, measuring their performance using metrics such as Normalized Discounted Cumulative Gain (NDCG) and Rank List Sensitivity. Our results demonstrate that removing items at the end of the sequence has a statistically significant impact on performance, with NDCG decreasing up to 60%. Conversely, removing items from the beginning or middle has no significant effect. These findings underscore the criticality of the position of perturbed items in the training data. As we spotlight the vulnerabilities inherent in current SRSs, we fervently advocate for intensified research efforts to fortify their robustness against adversarial perturbations.
Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection

Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and objects and predict triplets. Despite the challenges posed by the numerous interaction combinations, they also offer opportunities for multi-modal learning of visual texts. In this paper, we present a systematic and unified framework (RmLR) that enhances HOI detection by incorporating structured text knowledge. Firstly, we qualitatively and quantitatively analyze the loss of interaction information in the two-stage HOI detector and propose a re-mining strategy to generate more comprehensive visual representation. Secondly, we design more fine-grained sentence- and word-level alignment and knowledge transfer strategies to effectively address the many-to-many matching problem between multiple interactions and multiple texts. These strategies alleviate the matching confusion problem that arises when multiple interactions occur simultaneously, thereby improving the effectiveness of the alignment process. Finally, HOI reasoning by visual features augmented with textual knowledge substantially improves the understanding of interactions. Experimental results illustrate the effectiveness of our approach, where state-of-the-art performance is achieved on public benchmarks.
High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers

Robust distributed learning with Byzantine failures has attracted extensive research interests in recent years. However, most of existing methods suffer from curse of dimensionality, which is increasingly serious with the growing complexity of modern machine learning models. In this paper, we design a new method that is suitable for high dimensional problems, under arbitrary number of Byzantine attackers. The core of our design is a direct high dimensional semi-verified mean estimation method. Our idea is to identify a subspace first. The components of mean value perpendicular to this subspace can be estimated via gradient vectors uploaded from worker machines, while the components within this subspace are estimated using auxiliary dataset. We then use our new method as the aggregator of distributed learning problems. Our theoretical analysis shows that the new method has minimax optimal statistical rates. In particular, the dependence on dimensionality is significantly improved compared with previous works.
Precision Electroweak Tensions and a Dark Photon

We examine how different assumptions about the hadronic vacuum polarization, the $W$ boson mass, and the forward-backward asymmetry in $b$-quarks at the $Z$ pole can impact the precision electroweak fit. We study the implications for a kinetically mixed dark photon, addressing the complementarity of precision bounds and direct searches, particularly in the case where the dark photon can decay into the dark sector, and we consider implications for future Large Hadron Collider searches. We comment on cases where the precision effects of the dark photon may not be well-described by the oblique parameters.
Evidence for Low-power Radio Jet–ISM Interaction at 10 pc in the Dwarf AGN Host NGC 4395

Black-hole-driven outflows in galaxies hosting active galactic nuclei (AGN) may interact with their interstellar medium (ISM) affecting star formation (SF). Such feedback processes, reminiscent of those seen in massive galaxies, have been reported recently in some dwarf galaxies. However, such studies have usually been on kiloparsec and larger scales and our knowledge of the smallest spatial scales to which these feedback processes can operate is unclear. Here we demonstrate radio jet−ISM interaction on the scale of an asymmetric triple radio structure of ∼10 pc size in NGC 4395. This triple radio structure is seen in the 15 GHz continuum image and the two asymmetric jet-like structures are situated on either side of the radio core that coincides with the optical Gaia position. The high-resolution radio image and the extended [O iii]λ5007 emission, indicative of an outflow, are spatially coincident and are consistent with the interpretation of a low-power radio jet interacting with the ISM. Modelling of the spectral lines using MAPPINGS, and estimation of temperature using optical integral field spectroscopic data suggest shock ionization of the gas. The continuum emission at 237 GHz, though weak, was found to spatially coincide with the AGN. However, the CO(2−1) line emission was found to be displaced by around 20 pc northward of the AGN core. The spatial coincidence of molecular H2 λ2. 4085 along the jet direction, the morphology of ionized [O iii]λ5007, and displacement of the CO(2−1) emission argues for conditions less favorable for SF in the central ∼10 pc region.
Finding Money Launderers Using Heterogeneous Graph Neural Networks

Current anti-money laundering (AML) systems, predominantly rule-based, exhibit notable shortcomings in efficiently and precisely detecting instances of money laundering. As a result, there has been a recent surge toward exploring alternative approaches, particularly those utilizing machine learning. Since criminals often collaborate in their money laundering endeavors, accounting for diverse types of customer relations and links becomes crucial. In line with this, the present paper introduces a graph neural network (GNN) approach to identify money laundering activities within a large heterogeneous network constructed from real-world bank transactions and business role data belonging to DNB, Norway's largest bank. Specifically, we extend the homogeneous GNN method known as the Message Passing Neural Network (MPNN) to operate effectively on a heterogeneous graph. As part of this procedure, we propose a novel method for aggregating messages across different edges of the graph. Our findings highlight the importance of using an appropriate GNN architecture when combining information in heterogeneous graphs. The performance results of our model demonstrate great potential in enhancing the quality of electronic surveillance systems employed by banks to detect instances of money laundering. To the best of our knowledge, this is the first published work applying GNN on a large real-world heterogeneous network for anti-money laundering purposes.
High-Dimensional Bayesian Likelihood Normalisation for CRESST's Background Model

Using CaWO$_4$ crystals as cryogenic calorimeters, the CRESST experiment searches for nuclear recoils caused by the scattering of potential Dark Matter particles. A reliable identification of a potential signal crucially depends on an accurate background model. In this work we introduce an improved normalisation method for CRESST's model of the electromagnetic backgrounds. Spectral templates, based on Geant4 simulations, are normalised via a Bayesian likelihood fit to experimental background data. Contrary to our previous work, no assumption of partial secular equilibrium is required, which results in a more robust and versatile applicability. Furthermore, considering the correlation between all background components allows us to explain 82.7% of the experimental background within [1 keV, 40 keV], an improvement of 18.6% compared to our previous method.
Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions

Chain-of-thought (CoT) prompting has been shown to empirically improve the accuracy of large language models (LLMs) on various question answering tasks. While understanding why CoT prompting is effective is crucial to ensuring that this phenomenon is a consequence of desired model behavior, little work has addressed this; nonetheless, such an understanding is a critical prerequisite for responsible model deployment. We address this question by leveraging gradient-based feature attribution methods which produce saliency scores that capture the influence of input tokens on model output. Specifically, we probe several open-source LLMs to investigate whether CoT prompting affects the relative importances they assign to particular input tokens. Our results indicate that while CoT prompting does not increase the magnitude of saliency scores attributed to semantically relevant tokens in the prompt compared to standard few-shot prompting, it increases the robustness of saliency scores to question perturbations and variations in model output.
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework

The Path-Dependent Neural Jump Ordinary Differential Equation (PD-NJ-ODE) is a model for predicting continuous-time stochastic processes with irregular and incomplete observations. In particular, the method learns optimal forecasts given irregularly sampled time series of incomplete past observations. So far the process itself and the coordinate-wise observation times were assumed to be independent and observations were assumed to be noiseless. In this work we discuss two extensions to lift these restrictions and provide theoretical guarantees as well as empirical examples for them. In particular, we can lift the assumption of independence by extending the theory to much more realistic settings of conditional independence without any need to change the algorithm. Moreover, we introduce a new loss function, which allows us to deal with noisy observations and explain why the previously used loss function did not lead to a consistent estimator.