Miracle-dz/UniFAR-FTUs huggingface dataset:
4′-Deoxypyridoxine disrupts vitamin B6 homeostasis in Escherichia coli K12 through combined inhibition of cumulative B6 uptake and PLP-dependent enzyme activity

Pyridoxal 5′-phosphate (PLP) is the active form of vitamin B6 and a cofactor for many essential metabolic processes such as amino acid biosynthesis and one carbon metabolism. 4’-deoxypyridoxine (4dPN) is a long known B6 antimetabolite but its mechanism of action was not totally clear. By exploring different conditions in which PLP metabolism is affected in the model organism Escherichia coli K12, we showed that 4dPN cannot be used as a source of vitamin B6 as previously claimed and that it is toxic in several conditions where vitamin B6 homeostasis is affected, such as in a B6 auxotroph or in a mutant lacking the recently discovered PLP homeostasis gene, yggS. In addition, we found that 4dPN sensitivity is likely the result of multiple modes of toxicity, including inhibition of PLP-dependent enzyme activity by 4’-deoxypyridoxine phosphate (4dPNP) and inhibition of cumulative pyridoxine (PN) uptake. These toxicities are largely dependent on the phosphorylation of 4dPN by pyridoxal kinase (PdxK).
Linear extension operators for Sobolev spaces on radially symmetric binary trees

Abstract Let 1 < p < ∞ 1\lt p\lt \infty and suppose that we are given a function f f defined on the leaves of a weighted tree. We would like to extend f f to a function F F defined on the entire tree, so as to minimize the weighted W 1 , p {W}^{1,p} -Sobolev norm of the extension. An easy situation is when p = 2 p=2 , where the harmonic extension operator provides such a function F F . In this note, we record our analysis of the particular case of a radially symmetric binary tree, which is a complete, finite, binary tree with weights that depend only on the distance from the root. Neither the averaging operator nor the harmonic extension operator work here in general. Nevertheless, we prove the existence of a linear extension operator whose norm is bounded by a constant depending solely on p p . This operator is a variant of the standard harmonic extension operator, and in fact, it is harmonic extension with respect to a certain Markov kernel determined by p p and by the weights.
Link Prediction without Graph Neural Networks

Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications. As for several related problems, Graph Neural Networks (GNNs), which are based on an attribute-centric message-passing paradigm, have become the predominant framework for link prediction. GNNs have consistently outperformed traditional topology-based heuristics, but what contributes to their performance? Are there simpler approaches that achieve comparable or better results? To answer these questions, we first identify important limitations in how GNN-based link prediction methods handle the intrinsic class imbalance of the problem -- due to the graph sparsity -- in their training and evaluation. Moreover, we propose Gelato, a novel topology-centric framework that applies a topological heuristic to a graph enhanced by attribute information via graph learning. Our model is trained end-to-end with an N-pair loss on an unbiased training set to address class imbalance. Experiments show that Gelato is 145% more accurate, trains 11 times faster, infers 6,000 times faster, and has less than half of the trainable parameters compared to state-of-the-art GNNs for link prediction.
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent

Stein Variational Gradient Descent (SVGD) is a nonparametric particle-based deterministic sampling algorithm. Despite its wide usage, understanding the theoretical properties of SVGD has remained a challenging problem. For sampling from a Gaussian target, the SVGD dynamics with a bilinear kernel will remain Gaussian as long as the initializer is Gaussian. Inspired by this fact, we undertake a detailed theoretical study of the Gaussian-SVGD, i.e., SVGD projected to the family of Gaussian distributions via the bilinear kernel, or equivalently Gaussian variational inference (GVI) with SVGD. We present a complete picture by considering both the mean-field PDE and discrete particle systems. When the target is strongly log-concave, the mean-field Gaussian-SVGD dynamics is proven to converge linearly to the Gaussian distribution closest to the target in KL divergence. In the finite-particle setting, there is both uniform in time convergence to the mean-field limit and linear convergence in time to the equilibrium if the target is Gaussian. In the general case, we propose a density-based and a particle-based implementation of the Gaussian-SVGD, and show that several recent algorithms for GVI, proposed from different perspectives, emerge as special cases of our unified framework. Interestingly, one of the new particle-based instance from this framework empirically outperforms existing approaches. Our results make concrete contributions towards obtaining a deeper understanding of both SVGD and GVI.
Mapping Dark Matter in the Milky Way using Normalizing Flows and Gaia DR3

We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning -- in the form of density estimation with normalizing flows -- to learn the underlying phase space distribution of 6 million nearby stars from the Gaia DR3 catalog. Solving the collisionless Boltzmann equation with the assumption of approximate equilibrium, we calculate -- for the first time ever -- a model-free, unbinned, fully 3D map of the local acceleration and mass density fields within a 3 kpc sphere around the Sun. As our approach makes no assumptions about symmetries, we can test for signs of disequilibrium in our results. We find our results are consistent with equilibrium at the 10% level, limited by the current precision of the normalizing flows. After subtracting the known contribution of stars and gas from the calculated mass density, we find clear evidence for dark matter throughout the analyzed volume. Assuming spherical symmetry and averaging mass density measurements, we find a local dark matter density of $0.47\pm 0.05\;\mathrm{GeV/cm}^3$. We fit our results to a generalized NFW, and find a profile broadly consistent with other recent analyses.
Scaling K2. VI. Reduced Small-planet Occurrence in High-galactic-amplitude Stars

In this study, we performed a homogeneous analysis of the planets around FGK dwarf stars observed by the Kepler and K2 missions, providing spectroscopic parameters for 310 K2 targets —including 239 Scaling K2 hosts—observed with Keck/HIRES. For orbital periods less than 40 days, we found that the distribution of planets as a function of orbital period, stellar effective temperature, and metallicity was consistent between K2 and Kepler, reflecting consistent planet formation efficiency across numerous ∼1 kpc sight-lines in the local Milky Way. Additionally, we detected a 3× excess of sub-Saturns relative to warm Jupiters beyond 10 days, suggesting a closer association between sub-Saturn and sub-Neptune formation than between sub-Saturn and Jovian formation. Performing a joint analysis of Kepler and K2 demographics, we observed diminishing super-Earth, sub-Neptune, and sub-Saturn populations at higher stellar effective temperatures, implying an inverse relationship between formation and disk mass. In contrast, no apparent host-star spectral-type dependence was identified for our population of Jupiters, which indicates gas-giant formation saturates within the FGK mass regimes. We present support for stellar metallicity trends reported by previous Kepler analyses. Using Gaia DR3 proper motion and radial velocity measurements, we discovered a galactic location trend; stars that make large vertical excursions from the plane of the Milky Way host fewer super-Earths and sub-Neptunes. While oscillation amplitude is associated with metallicity, metallicity alone cannot explain the observed trend, demonstrating that galactic influences are imprinted on the planet population. Overall, our results provide new insights into the distribution of planets around FGK dwarf stars and the factors that influence their formation and evolution.
USB: A Unified Summarization Benchmark Across Tasks and Domains

While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to control and reliability. We introduce a Wikipedia-derived benchmark, complemented by a rich set of crowd-sourced annotations, that supports $8$ interrelated tasks: (i) extractive summarization; (ii) abstractive summarization; (iii) topic-based summarization; (iv) compressing selected sentences into a one-line summary; (v) surfacing evidence for a summary sentence; (vi) predicting the factual accuracy of a summary sentence; (vii) identifying unsubstantiated spans in a summary sentence; (viii) correcting factual errors in summaries. We compare various methods on this benchmark and discover that on multiple tasks, moderately-sized fine-tuned models consistently outperform much larger few-shot prompted language models. For factuality-related tasks, we also evaluate existing heuristics to create training data and find that training on them results in worse performance than training on $20\times$ less human-labeled data. Our articles draw from $6$ domains, facilitating cross-domain analysis. On some tasks, the amount of training data matters more than the domain where it comes from, while for other tasks training specifically on data from the target domain, even if limited, is more beneficial.
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks

Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effective on various text classification tasks. However, most of the mixup methods do not consider the varying degree of learning difficulty in different stages of training and generate new samples with one hot labels, resulting in the model over confidence. In this paper, we propose a self evolution learning (SE) based mixup approach for data augmentation in text classification, which can generate more adaptive and model friendly pesudo samples for the model training. SE focuses on the variation of the model's learning ability. To alleviate the model confidence, we introduce a novel instance specific label smoothing approach, which linearly interpolates the model's output and one hot labels of the original samples to generate new soft for label mixing up. Through experimental analysis, in addition to improving classification accuracy, we demonstrate that SE also enhances the model's generalize ability.
GUARD: A Safe Reinforcement Learning Benchmark

Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-critical real-world applications, such as autonomous driving, human-robot interaction, robot manipulation, etc, where such errors are not tolerable. Recently, safe RL (i.e. constrained RL) has emerged rapidly in the literature, in which the agents explore the environment while satisfying constraints. Due to the diversity of algorithms and tasks, it remains difficult to compare existing safe RL algorithms. To fill that gap, we introduce GUARD, a Generalized Unified SAfe Reinforcement Learning Development Benchmark. GUARD has several advantages compared to existing benchmarks. First, GUARD is a generalized benchmark with a wide variety of RL agents, tasks, and safety constraint specifications. Second, GUARD comprehensively covers state-of-the-art safe RL algorithms with self-contained implementations. Third, GUARD is highly customizable in tasks and algorithms. We present a comparison of state-of-the-art safe RL algorithms in various task settings using GUARD and establish baselines that future work can build on.
Adversarial Catoptric Light: An Effective, Stealthy and Robust Physical-World Attack to DNNs

Deep neural networks (DNNs) have demonstrated exceptional success across various tasks, underscoring the need to evaluate the robustness of advanced DNNs. However, traditional methods using stickers as physical perturbations to deceive classifiers present challenges in achieving stealthiness and suffer from printing loss. Recent advancements in physical attacks have utilized light beams such as lasers and projectors to perform attacks, where the optical patterns generated are artificial rather than natural. In this study, we introduce a novel physical attack, adversarial catoptric light (AdvCL), where adversarial perturbations are generated using a common natural phenomenon, catoptric light, to achieve stealthy and naturalistic adversarial attacks against advanced DNNs in a black-box setting. We evaluate the proposed method in three aspects: effectiveness, stealthiness, and robustness. Quantitative results obtained in simulated environments demonstrate the effectiveness of the proposed method, and in physical scenarios, we achieve an attack success rate of 83.5%, surpassing the baseline. We use common catoptric light as a perturbation to enhance the stealthiness of the method and make physical samples appear more natural. Robustness is validated by successfully attacking advanced and robust DNNs with a success rate over 80% in all cases. Additionally, we discuss defense strategy against AdvCL and put forward some light-based physical attacks.
Probing Accretion Physics with Gravitational Waves

Gravitational-wave observations of extreme mass ratio inspirals (EMRIs) offer the opportunity to probe the environments of active galactic nuclei (AGN) through the torques that accretion disks induce on the binary. Within a Bayesian framework, we study how well such environmental effects can be measured using gravitational wave observations from the Laser Interferometer Space Antenna (LISA). We focus on the torque induced by planetary-type migration on quasicircular inspirals, and use different prescriptions for geometrically thin and radiatively efficient disks. We find that LISA could detect migration for a wide range of disk viscosities and accretion rates, for both $\alpha$ and $\beta$ disk prescriptions. For a typical EMRI with masses $50M_\odot+10^6M_\odot$, we find that LISA could distinguish between migration in $\alpha$ and $\beta$ disks and measure the torque amplitude with $\sim 20\%$ relative precision. Provided an accurate torque model, we also show how to turn gravitational-wave measurements of the torque into constraints on the disk properties. Furthermore, we show that, if an electromagnetic counterpart is identified, the multimessenger observations of the AGN EMRI system will yield direct measurements of the disk viscosity. Finally, we investigate the impact of neglecting environmental effects in the analysis of the gravitational-wave signal, finding 3$\sigma$ biases in the primary mass and spin, and showing that ignoring such effects can lead to false detection of a deviation from general relativity. This work demonstrates the scientific potential of gravitational observations as probes of accretion-disk physics, accessible so far through electromagnetic observations only.
Precise Object Sliding with Top Contact via Asymmetric Dual Limit Surfaces

In this paper, we discuss the mechanics and planning algorithms to slide an object on a horizontal planar surface via frictional patch contact made with its top surface. Here, we propose an asymmetric dual limit surface model to determine slip boundary conditions for both the top and bottom contact. With this model, we obtain a range of twists that can keep the object in sticking contact with the robot end-effector while slipping on the supporting plane. Based on these constraints, we derive a planning algorithm to slide objects with only top contact to arbitrary goal poses without slippage between end effector and the object. We validate the proposed model empirically and demonstrate its predictive accuracy on a variety of object geometries and motions. We also evaluate the planning algorithm over a variety of objects and goals demonstrate an orientation error improvement of 90\% when compared to methods naive to linear path planners.
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks

In this article, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS). The following persisting challenges motivated our research: (i) User and vehicular mobility, as well as redundant beam-reselections in mmWave networks, degrade the efficiency; (ii) Due to the large beamforming dimension at the BS, the beamforming weights predicted by the cutting-edge DL-based methods often do not suit the channel distributions; (iii) Co-located user devices may cause a severe beam conflict, thus deteriorating system performance. To address the aforementioned challenges, we exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation. In the first step, we propose a method for beam-quality prediction. It is based on deep learning and explores the relationship between high- and low-resolution beam images (energy). Afterward, we develop a DL-based allocation approach, which enables high-accuracy beam and power allocation with only a portion of the available time-sequential low-resolution images. Theoretical and numerical results verify the effectiveness of our proposed framework.
Improving Convergence and Generalization Using Parameter Symmetries

In many neural networks, different values of the parameters may result in the same loss value. Parameter space symmetries are loss-invariant transformations that change the model parameters. Teleportation applies such transformations to accelerate optimization. However, the exact mechanism behind this algorithm's success is not well understood. In this paper, we show that teleportation not only speeds up optimization in the short-term, but gives overall faster time to convergence. Additionally, teleporting to minima with different curvatures improves generalization, which suggests a connection between the curvature of the minimum and generalization ability. Finally, we show that integrating teleportation into a wide range of optimization algorithms and optimization-based meta-learning improves convergence. Our results showcase the versatility of teleportation and demonstrate the potential of incorporating symmetry in optimization.
Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases

The advancement of computer-aided detection systems had a significant impact on clinical analysis and decision-making on human disease. Lung cancer requires more attention among the numerous diseases being examined because it affects both men and women, increasing the mortality rate. LeNet, a deep learning model, is used in this study to detect lung tumors. The studies were run on a publicly available dataset made up of CT image data (IQ-OTH/NCCD). Convolutional neural networks (CNNs) were employed in the experiment for feature extraction and classification. The proposed system was evaluated on Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases datasets the success percentage was calculated as 99.51%, sensitivity (93%) and specificity (95%), and better results were obtained compared to the existing methods. Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.
Augmented Random Search for Multi-Objective Bayesian Optimization of Neural Networks

Deploying Deep Neural Networks (DNNs) on tiny devices is a common trend to process the increasing amount of sensor data being generated. Multi-objective optimization approaches can be used to compress DNNs by applying network pruning and weight quantization to minimize the memory footprint (RAM), the number of parameters (ROM) and the number of floating point operations (FLOPs) while maintaining the predictive accuracy. In this paper, we show that existing multi-objective Bayesian optimization (MOBOpt) approaches can fall short in finding optimal candidates on the Pareto front and propose a novel solver based on an ensemble of competing parametric policies trained using an Augmented Random Search Reinforcement Learning (RL) agent. Our methodology aims at finding feasible tradeoffs between a DNN's predictive accuracy, memory consumption on a given target system, and computational complexity. Our experiments show that we outperform existing MOBOpt approaches consistently on different data sets and architectures such as ResNet-18 and MobileNetV3.
Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree

Concerns regarding unfairness and discrimination in the context of artificial intelligence (AI) systems have recently received increased attention from both legal and computer science scholars. Yet, the degree of overlap between notions of algorithmic bias and fairness on the one hand, and legal notions of discrimination and equality on the other, is often unclear, leading to misunderstandings between computer science and law. What types of bias and unfairness does the law address when it prohibits discrimination? What role can fairness metrics play in establishing legal compliance? In this paper, we aim to illustrate to what extent European Union (EU) non-discrimination law coincides with notions of algorithmic fairness proposed in computer science literature and where they differ. The contributions of this paper are as follows. First, we analyse seminal examples of algorithmic unfairness through the lens of EU non-discrimination law, drawing parallels with EU case law. Second, we set out the normative underpinnings of fairness metrics and technical interventions and compare these to the legal reasoning of the Court of Justice of the EU. Specifically, we show how normative assumptions often remain implicit in both disciplinary approaches and explain the ensuing limitations of current AI practice and non-discrimination law. We conclude with implications for AI practitioners and regulators.