Miracle-dz/UniFAR-FTUs huggingface dataset:
Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery

The recent advancement in deep Convolutional Neural Network (CNN) has brought insight into the automation of X-ray security screening for aviation security and beyond. Here, we explore the viability of two recent end-to-end object detection CNN architectures, Cascade R-CNN and FreeAnchor, for prohibited item detection by balancing processing time and the impact of image data compression from an operational viewpoint. Overall, we achieve maximal detection performance using a FreeAnchor architecture with a ResNet50 backbone, obtaining mean Average Precision (mAP) of 87.7 and 85.8 for using the OPIXray and SIXray benchmark datasets, showing superior performance over prior work on both. With fewer parameters and less training time, FreeAnchor achieves the highest detection inference speed of $\sim 13$ fps (3.9 ms per image). Furthermore, we evaluate the impact of lossy image compression upon detector performance. The CNN models display substantial resilience to the lossy compression, resulting in only a 1.1% decrease in mAP at the JPEG compression level of 50. Additionally, a thorough evaluation of data augmentation techniques is provided, including adaptions of MixUp and CutMix strategy as well as other standard transformations, further improving the detection accuracy.
Self-supervised Learning is More Robust to Dataset Imbalance

Self-supervised learning (SSL) is a scalable way to learn general visual representations since it learns without labels. However, large-scale unlabeled datasets in the wild often have long-tailed label distributions, where we know little about the behavior of SSL. In this work, we systematically investigate self-supervised learning under dataset imbalance. First, we find out via extensive experiments that off-the-shelf self-supervised representations are already more robust to class imbalance than supervised representations. The performance gap between balanced and imbalanced pre-training with SSL is significantly smaller than the gap with supervised learning, across sample sizes, for both in-domain and, especially, out-of-domain evaluation. Second, towards understanding the robustness of SSL, we hypothesize that SSL learns richer features from frequent data: it may learn label-irrelevant-but-transferable features that help classify the rare classes and downstream tasks. In contrast, supervised learning has no incentive to learn features irrelevant to the labels from frequent examples. We validate this hypothesis with semi-synthetic experiments and theoretical analyses on a simplified setting. Third, inspired by the theoretical insights, we devise a re-weighted regularization technique that consistently improves the SSL representation quality on imbalanced datasets with several evaluation criteria, closing the small gap between balanced and imbalanced datasets with the same number of examples.
Training Transition Policies via Distribution Matching for Complex Tasks

Humans decompose novel complex tasks into simpler ones to exploit previously learned skills. Analogously, hierarchical reinforcement learning seeks to leverage lower-level policies for simple tasks to solve complex ones. However, because each lower-level policy induces a different distribution of states, transitioning from one lower-level policy to another may fail due to an unexpected starting state. We introduce transition policies that smoothly connect lower-level policies by producing a distribution of states and actions that matches what is expected by the next policy. Training transition policies is challenging because the natural reward signal -- whether the next policy can execute its subtask successfully -- is sparse. By training transition policies via adversarial inverse reinforcement learning to match the distribution of expected states and actions, we avoid relying on task-based reward. To further improve performance, we use deep Q-learning with a binary action space to determine when to switch from a transition policy to the next pre-trained policy, using the success or failure of the next subtask as the reward. Although the reward is still sparse, the problem is less severe due to the simple binary action space. We demonstrate our method on continuous bipedal locomotion and arm manipulation tasks that require diverse skills. We show that it smoothly connects the lower-level policies, achieving higher success rates than previous methods that search for successful trajectories based on a reward function, but do not match the state distribution.
Human Values in Mobile App Development: An Empirical Study on Bangladeshi Agriculture Mobile Apps

Given the ubiquity of mobile applications (apps) in daily lives, understanding and reflecting end-users' human values (e.g., transparency, privacy, social recognition etc.) in apps has become increasingly important. Violations of end users' values by software applications have been reported in the media and have resulted in a wide range of difficulties for end users. Value violations may bring more and lasting problems for marginalized and vulnerable groups of end-users. This research aims to understand the extent to which the values of Bangladeshi female farmers, marginalized and vulnerable end-users, who are less studied by the software engineering community, are reflected in agriculture apps in Bangladesh. Further to this, we aim to identify possible strategies to embed their values in those apps. To this end, we conducted a mixed-methods empirical study consisting of 13 interviews with app practitioners and four focus groups with 20 Bangladeshi female farmers. The accumulated results from the interviews and focus groups identified 22 values of Bangladeshi female farmers, which the participants expect to be reflected in the agriculture apps. Among these 22 values, 15 values (e.g., accuracy, independence) are already reflected and 7 values (e.g., accessibility, pleasure) are ignored/violated in the existing agriculture apps. We also identified 14 strategies (e.g.,"applying human-centered approaches to elicit values","establishing a dedicated team/person for values concerns") to address Bangladeshi female farmers' values in agriculture apps.
Low amplitude and high frequency mechanical stimulation does not affect directly cell differentiation during bone healing

Bone fractures has high incidence and despite its relevance and frequency, some bone healing process features are still unknown. In this work, it is computationally investigated the influence of low amplitude and high frequency mechanical stimulation on cell differentiation during bone healing, using a cell differentiation theory that relates two mechanical variables (strain and flow velocity of interstitial fluid) with the cell fate. For this purpose, a finite element model was developed to study three hypothetical situations, to determine in which proportion external stimulation influences bone healing. Firstly, the mechanical stimulus was computed as 20% of external mechanical stimulus and 80% of the stimulus during gait. Secondly, it was considered 50% external mechanical stimulus and 50% gait stimulus. Finally, it was considered a proportion of 80% external mechanical stimulus and 20% gait stimulus. The results indicated that hypothesis considering high proportions of external stimulation results in unreal delayed healing process and the first hypothetical situation proved to be that which best represents the real process. From the results obtained, it was concluded that external mechanical stimulation does not affected directly cell differentiation during bone healing. Thus, other processes such as flow of oxygen, nutrients or wastes must be considered.
DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing

Text discourse parsing weighs importantly in understanding information flow and argumentative structure in natural language, making it beneficial for downstream tasks. While previous work significantly improves the performance of RST discourse parsing, they are not readily applicable to practical use cases: (1) EDU segmentation is not integrated into most existing tree parsing frameworks, thus it is not straightforward to apply such models on newly-coming data. (2) Most parsers cannot be used in multilingual scenarios, because they are developed only in English. (3) Parsers trained from single-domain treebanks do not generalize well on out-of-domain inputs. In this work, we propose a document-level multilingual RST discourse parsing framework, which conducts EDU segmentation and discourse tree parsing jointly. Moreover, we propose a cross-translation augmentation strategy to enable the framework to support multilingual parsing and improve its domain generality. Experimental results show that our model achieves state-of-the-art performance on document-level multilingual RST parsing in all sub-tasks.
Cyclic and well-rounded lattices

We focus on two important classes of lattices, the well-rounded and the cyclic. We show that every well-rounded lattice in the plane is similar to a cyclic lattice, and use this cyclic parameterization to count planar wellrounded similarity classes defined over a fixed number field with respect to height. We then investigate cyclic properties of the irreducible root lattices in arbitrary dimensions, in particular classifying those that are simple cyclic, i.e. generated by rotation shifts of a single vector. Finally, we classify cyclic, simple cyclic and well-rounded cyclic lattices coming from rings of integers of Galois algebraic number fields.
Inter-Correlation among the Hydrophilic–Lipophilic Balance, Surfactant System, Viscosity, Particle Size, and Stability of Candelilla Wax-Based Dispersions

Owing to a decrease in mineral oil resources, it is crucial to develop packaging materials based on renewable resources. Hence, a water vapor-barrier coating is developed as a natural wax-based dispersion. This dispersion should be stable over the storage time. In this study, the physical stability of a wax-based melt dispersion was analyzed (24 h and 21 days after production), and instability phenomena such as agglomeration, coalescence, and flotation were identified. Furthermore, the inter-correlations among the particle size, viscosity of the continuous phase, physical stability, surfactant chemistry, and hydrophilic–lipophilic balance value were characterized. Particle sizes were described by volume/surface mean d3,2, volume moment mean d4,3, and number mean d1,0 diameter, as well as the span of the volume and number distribution. Stability was characterized by the flotation rate, emulsion stability index, and Turbiscan stability index. Coalescence and agglomeration were not observed after the solidification of the wax particles. A significant correlation was observed for the emulsion stability index, with d3,2, and for flotation rate, with d1,0, d4,3, and viscosity as well, with d1,0, d3,2. Surfactants with hydrophilic–lipophilic balance values of 11–13.5 seem to be the most suitable for stabilizing candelilla wax-in-water suspensions. Particles were smaller, and wax suspensions were better stabilized using Tween 20 and Span 20, compared with Tween 80 and Span 80.
Finding Second-Order Stationary Point for Nonconvex-Strongly-Concave Minimax Problem

We study the smooth minimax optimization problem min x max y f ( x , y ), where f is (cid:96) -smooth, strongly-concave in y but possibly nonconvex in x . Most of existing works focus on ﬁnding the ﬁrst-order stationary point of the function f ( x , y ) or its primal function P ( x ) (cid:44) max y f ( x , y ), but few of them focus on achieving the second-order stationary point. In this paper, we propose a novel approach for minimax optimization, called Minimax Cubic Newton (MCN), which could ﬁnd an O (cid:0) ε, κ 1 . 5 √ ρε (cid:1) -second-order stationary point of P ( x ) with calling O (cid:0) κ 1 . 5 √ ρε − 1 . 5 (cid:1) times of second-order oracles and ˜ O (cid:0) κ 2 √ ρε − 1 . 5 (cid:1) times of ﬁrst-order oracles, where κ is the condition number and ρ is the Lipschitz continuous constant for the Hessian of f ( x , y ). In addition, we propose an inexact variant of MCN for high-dimensional problems to avoid calling the expensive second-order oracles. Instead, our method solves the cubic sub-problem inexactly via gradient descent and matrix Chebyshev expansion. This strategy still obtains the desired approximate second-order stationary point with high probability but only requires ˜ O (cid:0) κ 1 . 5 (cid:96)ε − 2 (cid:1) Hessian-vector oracle calls and ˜ O (cid:0) κ 2 √ ρε − 1 . 5 (cid:1) ﬁrst-order oracle calls. To the best of our knowledge, this is the ﬁrst work that considers the non-asymptotic convergence behavior of ﬁnding second-order stationary points for minimax problems without the convex-concave assumptions.
Automation of Beam and Jet functions at NNLO

We present a novel framework to streamline the calculation of jet and beam functions to next-to-next-to-leading order (NNLO) in perturbation theory. By exploiting the infrared behaviour of the collinear splitting functions, we factorise the singularities with suitable phase-space parametrisations and perform the observable-dependent integrations numerically. We have implemented our approach in the publicly available code pySecDec and present first results for sample jet and beam functions.
An Empirical Study on Compressed Decentralized Stochastic Gradient Algorithms with Overparameterized Models

This paper considers decentralized optimization with application to machine learning on graphs. The growing size of neural network (NN) models has motivated prior works on decentralized stochastic gradient algorithms to incorporate communication compression. On the other hand, recent works have demonstrated the favorable convergence and generalization properties of overparameterized NN s. In this work, we present an empirical analysis on the performance of compressed decentral-ized stochastic gradient (DSG) algorithms with overparameter-ized NNs. Through simulations on an MPI network environment, we observe that the convergence rates of popular compressed DSG algorithms are robust to the size of NNs. Our findings suggest a gap between theories and practice of the compressed DSG algorithms in the existing literature.
Left regular representations of Garside categories I. C*-algebras and groupoids

Abstract We initiate the study of C*-algebras and groupoids arising from left regular representations of Garside categories, a notion which originated from the study of Braid groups. Every higher rank graph is a Garside category in a natural way. We develop a general classification result for closed invariant subspaces of our groupoids as well as criteria for topological freeness and local contractiveness, properties which are relevant for the structure of the corresponding C*-algebras. Our results provide a conceptual explanation for previous results on gauge-invariant ideals of higher rank graph C*-algebras. As another application, we give a complete analysis of the ideal structures of C*-algebras generated by left regular representations of Artin–Tits monoids.
Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent

Follow-the-Regularized-Leader (FTRL) and Online Mirror Descent (OMD) are regret minimization algorithms for Online Convex Optimization (OCO), they are mathematically elegant but less practical in solving Extensive-Form Games (EFGs). Counterfactual Regret Minimization (CFR) is a technique for approximating Nash equilibria in EFGs. CFR and its variants have a fast convergence rate in practice, but their theoretical results are not satisfactory. In recent years, researchers have been trying to link CFRs with OCO algorithms, which may provide new theoretical results and inspire new algorithms. How-ever, existing analysis is restricted to local decision points. In this paper, we show that CFRs with Regret Matching and Regret Matching+ are equivalent to special cases of FTRL and OMD, respectively. According to these equivalences, a new FTRL and a new OMD algorithm, which can be considered as extensions of vanilla CFR and CFR+, are derived. The experimental results show that the two variants converge faster than conventional FTRL and OMD, even faster than vanilla CFR and CFR+ in some EFGs.
Mass spectra and strong decays of charmed and charmed-strange mesons

A semi-relativistic potential model is adopted to calculate the mass spectra of charmed and charmed-strange meson states up to the $2D$ excitations.The strong decay properties are further analyzed with a chiral quark model by using the numerical wave functions obtained from the potential model. By using the strong decay amplitudes extracted from the chiral quark model, we also systematically study the coupled-channel effects on the bare masses of the $1P$-wave states, since the masses of $D^*_{s0}(2317)$ and $D_{s1}(2460)$ cannot be explained with bare $1P$-wave states within the potential model. Based on our good descriptions of the mass and decay properties for the low-lying well-established states, we give a quark model classification for the high mass resonances observed in recent years. In the $D$-meson family, $D_0(2550)$ can be classified as the radially excited state $D(2^1S_0)$; $D_3^*(2750)$ and $D_2(2740)$ can be classified as the second orbital excitations $D(1^3D_3)$ and $D(1D'_2)$, respectively; $D_J^*(3000)$ may be a candidate of $D(1^3F_4)$ or $D(2^3P_2)$; while $D_J(3000)$ may favor the high mass mixed state $D(2P'_1)$; however, there still exist puzzles for understanding the natures of $D_1^*(2600)$ and $D_1^*(2760)$, whose decay properties cannot be well explained with either pure $D(2^3S_1)$ and $D(1^3D_1)$ states or their mixing. In the $D_s$-meson family, $D_{s3}^*(2860)$ favors the $D_s(1^3D_3)$ assignment; $D_{s1}^*(2700)$ and $D_{s1}^*(2860)$ may favor the mixed states $|(SD)_1\rangle_L$ and $|(SD)_1\rangle_H$ via the $2^3S_1$-$1^3D_1$ mixing, respectively; $D_{sJ}(3040)$ may favor $D_s(2P_1)$ or $D_s(2P_1')$, or corresponds to a structure contributed by both $D_s(2P_1)$ and $D_s(2P_1')$.
AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation

Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such as the heterogeneity in CT images and variation in nodule size, shape, and location, which limit their accuracy. In an attempt to handle these challenges, this article proposes a fully automated deep learning framework that consists of lung nodule detection and segmentation models. Our proposed system comprises two cascaded stages: (1) nodule detection based on fine-tuned Faster R-CNN to localize the nodules in CT images, and (2) nodule segmentation based on the U-Net architecture with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule feature representations. The experimental results demonstrate that the proposed system yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly available LUNA16 and LIDC-IDRI datasets, respectively.
β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap

As an important problem in causal inference, we discuss the identification and estimation of treatment effects (TEs) under limited overlap; that is, when subjects with certain features belong to a single treatment group. We use a latent variable to model a prognostic score which is widely used in biostatistics and sufficient for TEs; i.e., we build a generative prognostic model. We prove that the latent variable recovers a prognostic score, and the model identifies individualized treatment effects. The model is then learned as \beta-Intact-VAE--a new type of variational autoencoder (VAE). We derive the TE error bounds that enable representations balanced for treatment groups conditioned on individualized features. The proposed method is compared with recent methods using (semi-)synthetic datasets.
Chaos as an interpretable benchmark for forecasting and data-driven modelling

The striking fractal geometry of strange attractors underscores the generative nature of chaos: like probability distributions, chaotic systems can be repeatedly measured to produce arbitrarily-detailed information about the underlying attractor. Chaotic systems thus pose a unique challenge to modern statistical learning techniques, while retaining quantifiable mathematical properties that make them controllable and interpretable as benchmarks. Here, we present a growing database currently comprising 131 known chaotic dynamical systems spanning fields such as astrophysics, climatology, and biochemistry. Each system is paired with precomputed multivariate and univariate time series. Our dataset has comparable scale to existing static time series databases; however, our systems can be re-integrated to produce additional datasets of arbitrary length and granularity. Our dataset is annotated with known mathematical properties of each system, and we perform feature analysis to broadly categorize the diverse dynamics present across the collection. Chaotic systems inherently challenge forecasting models, and across extensive benchmarks we correlate forecasting performance with the degree of chaos present. We also exploit the unique generative properties of our dataset in several proof-of-concept experiments: surrogate transfer learning to improve time series classification, importance sampling to accelerate model training, and benchmarking symbolic regression algorithms.
Deploying Containerized QuantEx Quantum Simulation Software on HPC Systems

The simulation of quantum circuits using the tensor network method is very computationally demanding and requires significant High Performance Computing (HPC) resources to find an efficient contraction order and to perform the contraction of the large tensor networks. In addition, the researchers want a workflow that is easy to customize, reproduce and migrate to different HPC systems. In this paper, we discuss the issues associated with the deployment of the QuantEX quantum computing simulation software within containers on different HPC systems. Also, we compare the performance of the containerized software with the software running on bare metal.
Characterization of the scintillation time response of liquid argon detectors for dark matter search

The scintillation time response of liquid argon has a key role in the discrimination of electronic backgrounds in dark matter search experiments. However, its extraordinary rejection power can be affected by various detector effects such as the delayed light emission of TetraPhenyl Butadiene, the most commonly used wavelength shifter, and the electric drift field applied in Time Projection Chambers. In this work, we characterized the TetraPhenyl Butadiene delayed response and the dependence of the pulse shape discrimination on the electric field, exploiting the data acquired with the ARIS, a small-scale single-phase liquid argon detector exposed to monochromatic neutron and gamma sources at the ALTO facility of IJC Lab in Orsay.