Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation du paramétre de distribution de la distribution binomiale négative: A priori, effort d'échantillonnage, et information

Resume La distribution binomiale negative est frequemment utilisee pour modeliser la distribution des donnees d'abondance surdispersees. De ce fait, l'ajustement de la binomiale negative aux donnees d'abondance et l'estimation de ses parametres est un enjeu majeur pour de tres nombreuses etudes en ecologie, epidemiologie, actuariat, etc. qui reposent sur des donnees de ce type. L'estimation des parametres de la binomiale negative, et en particulier de son parametre de dispersion est neanmoins problematique, car les estimateurs disponibles sont biaises et peu efficaces, notamment lorsque les echantillons sont petits, que l'esperance de l'abondance est faible, et que sa variance est forte. Dans cette etude, nous tentons d'identifier la source de ces problemes d'estimation. Pour ce faire, nous utilisons l'entropie (Shannon, 1949) pour quantifier l'information qu'apportent les echantillons sur la dispersion, en fonction des caracteristiques de ces echantillons (taille, moyenne), et des valeurs reelles des parametres de la binomiale negative. On montre ainsi que le nombre total d'individus observes est un facteur cle de la qualite de l'estimation, et que pour un effort d'echantillonnage donne l'estimation de la dispersion est vraisemblablement tres peu fiable si la dispersion reelle se situe dans des gammes de valeurs extremes (notamment des valeurs fortes). Abstract The negative binomial distribiution is frequently used to model overdispersed count data. Adjusting the negative binomial and estimating its parameters is thus a major concern in ecology, epidemiology, actuaries, etc. that often deal with that type of data. However, the estimation of the parameters of the negative binomial, and the estimation of its dispersion parameter in particular, are often a problematic issue because available estimators are biased and have low efficiency. In particular, if samples are small, if the expected value of the distribution is low, or if its variance is high, the estimators are particularly unreliable. In this study, we try to identify the reasons behind these estimation problems. To do this, we use entropy (Shannon, 1949) to quantify the information about dispersion that the samples provide. Entropy depends on the characteristics of the samples (size, mean) and of the real values of the parameters of the negative binomial. We show that the total number of individuals observed in a sample is a key driver of estimation quality. We also highlight the fact that for a given sampling effort the estimation of dispersion is likely to be really unreliable if the real value of dispersion lies in extremes ranges of values (notably, high values).
Studies on the discretization of plasma transport equations

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Molecular docking and spectroscopic study of bovine serum albumin interaction with new anticancer Pt complex with isopentyl dithiocarbamate ligand

Abstract Although cisplatin is useful in the treatment of cancer, it has a series of side effects that limit its use. Dithiocarbamates reduce the toxicity of platinum due to their structure and the presence of S, and N donating groups. In this article, the interaction of [Pt(bpy)(isopentyl.dtc)]NO3, where bpy is bipyridine and isopentyl.dtc is isopentyl-dithiocarbamate, with BSA, bovine serum albumin has been studied. The molecular binding method, including UV-Vis and fluorescence titration, was carried out in conditions including pH = 7.4 and temperatures of 27 and 37 °C. The negative values of enthalpy (ΔH°b) and entropy (ΔS°b) show that the driving forces of this interaction are hydrogen and van der Waals, and the negative value of the Gibbs free energy, ΔG°b indicates that the interaction proceeds spontaneously. The fluorescence results showed that the quenching mechanism is the static type and the Stern Volmer constant, KSV, was also obtained. The fluorescence titration method data displayed that the quenching mechanism is static. Binding constant (Kb), binding point (n), Hill coefficients, nH, Hill constant, KH, number of binding sites, g, BSA melting temperature, Tm, were also obtained. Finally, the molecular docking method result shows the binding constant, Ki and binding free energy for the platinum complex are −6.53 and 16.39 kcal mol−1, respectively, and also the proper position of binding on BSA can be considered the site I in the subdomain IIA. Graphical Abstract
Neutrophil extracellular traps contributing to atherosclerosis: From pathophysiology to clinical implications

Neutrophil extracellular traps (NETs) are network-like structures of chromatin filaments decorated by histones, granules, and cytoplasmic-derived proteins expelled by activated neutrophils under multiple pathogenic conditions. NETs not only capture pathogens in innate immunity but also respond to sterile inflammatory stimuli in atherosclerosis, such as lipoproteins and inflammatory cytokines. Atherosclerosis is a lipid-driven chronic inflammatory disease characterized by the accumulation and transformation of inflammatory cells, and smooth muscle cells in the intimal space. NETs-derived extracellular components possess toxic and proinflammatory properties leading to cellular dysfunction and tissue damage, which may establish a link among lipid metabolism, inflammatory immunity, and atherosclerosis. In this review, we discuss recent advances regarding the role of NETs engaged in the pathogenesis of atherosclerosis, particularly focusing on the interaction with lipids and inflammasomes, crosstalk with smooth muscle cells and inflammatory cells, and the association with aging. We also evaluate the current knowledge on the potential of NETs as biomarkers and therapeutic targets for atherosclerosis and its related diseases in clinical practice.
Effect of Tibiofemoral Rotation Angle on Graft Failure After Anterior Cruciate Ligament Reconstruction

Background: Coronal and sagittal malalignment of the knee are well-recognized risk factors for failure after anterior cruciate ligament (ACL) reconstruction (ACLR). However, the effect of axial malalignment on graft survival after ACLR is yet to be determined. Purpose: To evaluate whether increased tibiofemoral rotational malalignment, namely, tibiofemoral rotation angle (TFA) and tibial tubercle–trochlear groove (TT-TG) distance, is associated with graft failure after ACLR. Study Design: Cohort study; Level of evidence, 3. Methods: In this retrospective matched control study of a single center’s database, 151 patients who underwent revision ACLR because of graft failure (ACLR failure group, defined as symptomatic patients with anterior knee instability and an ACL graft tear appreciated on magnetic resonance imaging [MRI] and confirmed during arthroscopic surgery) were compared with a matched control group of 151 patients who underwent primary ACLR with no evidence of failure after ≥2-year follow-up (intact ACLR group). Patients were matched by sex, age, and meniscal injury during primary ACLR. Axial malalignment was assessed on preoperative MRI through the TFA and the TT-TG distance. Sagittal alignment was measured through the posterior tibial slope on MRI. The optimal TFA cutoff associated with graft failure was identified by a receiver operating characteristic curve. The Kaplan-Meier curve with log-rank analysis was performed to evaluate the influence of the TFA on ACLR longevity. Results: The mean age was 25.7 ± 10.4 years for the ACLR failure group and 25.9 ± 10.0 years for the intact ACLR group. Among all the included patients, 174 (57.6%) were male. In the ACLR failure group, the mean TFA was 5.8°± 4.5° (range, −5° to 16°), while it was 3.0°± 3.3° (range, −3° to 15°) in the intact ACLR group (P < .001). Neither the TT-TG distance nor the posterior tibial slope presented statistical differences between the groups. The receiver operating characteristic curve suggested an optimal TFA cutoff of 4.5° for graft failure (area under the curve = 0.71; P < .001; sensitivity, 68.2%; specificity, 75.5%). Considering this a threshold, patients who had a TFA ≥4.5° had 6.6 times higher odds of graft failure compared with patients with a TFA <4.5° (P < .001). Survival analysis demonstrated a 5-year survival rate of 81% in patients with a TFA <4.5°, while it was 44% in those with a TFA ≥4.5° (P < .001). Conclusion: An increased TFA was associated with increased odds of ACLR failure when the TFA was ≥4.5°. Measuring the TFA in patients with ACL tears undergoing reconstruction may inform the surgeon about additional factors that may require consideration before ACLR for a successful outcome.
A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models

The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), poses a significant challenge for clinicians and researchers to access clinical information efficiently. To tackle this challenge, biomedical text summarization (BTS) has been proposed as a solution to support clinical information retrieval and management. BTS aims at generating concise summaries that distill key information from single or multiple biomedical documents. In recent years, the rapid advancement of fundamental natural language processing (NLP) techniques, from pre-trained language models (PLMs) to large language models (LLMs), has greatly facilitated the progress of BTS. This growth has led to numerous proposed summarization methods, datasets, and evaluation metrics, raising the need for a comprehensive and up-to-date survey for BTS. In this paper, we present a systematic review of recent advancements in BTS, leveraging cutting-edge NLP techniques from PLMs to LLMs, to help understand the latest progress, challenges, and future directions. We begin by introducing the foundational concepts of BTS, PLMs and LLMs, followed by an in-depth review of available datasets, recent approaches, and evaluation metrics in BTS. We finally discuss existing challenges and promising future directions in the era of LLMs. To facilitate the research community, we line up open resources including available datasets, recent approaches, codes, evaluation metrics, and the leaderboard in a public project: https://github.com/KenZLuo/Biomedical-Text-Summarization-Survey/tree/master. We believe that this survey will be a useful resource to researchers, allowing them to quickly track recent advancements and provide guidelines for future BTS research within the research community.
Kernel t-distributed stochastic neighbor embedding

This paper presents a kernelized version of the t-SNE algorithm, capable of mapping high-dimensional data to a low-dimensional space while preserving the pairwise distances between the data points in a non-Euclidean metric. This can be achieved using a kernel trick only in the high dimensional space or in both spaces, leading to an end-to-end kernelized version. The proposed kernelized version of the t-SNE algorithm can offer new views on the relationships between data points, which can improve performance and accuracy in particular applications, such as classification problems involving kernel methods. The differences between t-SNE and its kernelized version are illustrated for several datasets, showing a neater clustering of points belonging to different classes.
Energetic cost of microswimmer navigation: The role of body shape

We study the energetic efficiency of navigating microswimmers by explicitly taking into account the geometry of their body. We show that, as their shape transitions from prolate to oblate, non-steering microswimmers rotated by flow gradients naturally follow increasingly time-optimal trajectories. At the same time, they also require larger dissipation to swim. The coupling between body geometry and hydrodynamics thus leads to a generic trade-off between the energetic costs associated with propulsion and navigation, which is accompanied by the selection of a finite optimal aspect ratio. We derive from optimal control theory the steering policy ensuring overall minimum energy dissipation, and characterize how navigation performances vary with the swimmer shape. Our results highlight the important role of the swimmer geometry in realistic navigation problems.
Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

In this paper, we explore the potential of using a large language model (LLM) to understand the driving environment in a human-like manner and analyze its ability to reason, interpret, and memorize when facing complex scenarios. We argue that traditional optimization-based and modular autonomous driving (AD) systems face inherent performance limitations when dealing with long-tail corner cases. To address this problem, we propose that an ideal AD system should drive like a human, accumulating experience through continuous driving and using common sense to solve problems. To achieve this goal, we identify three key abilities necessary for an AD system: reasoning, interpretation, and memorization. We demonstrate the feasibility of employing an LLM in driving scenarios by building a closed-loop system to showcase its comprehension and environment-interaction abilities. Our extensive experiments show that the LLM exhibits the impressive ability to reason and solve long-tailed cases, providing valuable insights for the development of human-like autonomous driving. The related code are available at https://github.com/PJLab-ADG/DriveLikeAHuman .
Proof of Training (PoT): Harnessing Crypto Mining Power for Distributed AI Training

In the midst of the emerging trend of integrating artificial intelligence (AI) with crypto mining, we identify three major challenges that create a gap between these two fields. To bridge this gap, we introduce the proof-of-training (PoT) protocol, an approach that combines the strengths of both AI and blockchain technology. The PoT protocol utilizes the practical Byzantine fault tolerance (PBFT) consensus mechanism to synchronize global states. To evaluate the performance of the protocol design, we present an implementation of a decentralized training network (DTN) that adopts the PoT protocol. Our results indicate that the protocol exhibits considerable potential in terms of task throughput, system robustness, and network security.
Equality conditions for the fractional Brunn-Minkowski-Lyusternik inequality in one-dimension

While studying set function properties of Lebesgue measure, Franck Barthe and Mokshay Madiman proved that Lebesgue measure is fractionally superadditive on compact sets in $\mathbb{R}^n$. In doing this they proved a fractional generalization of the Brunn-Minkowski-Lyusternik (BML) inequality in dimension $n=1$. In this article a complete characterization of the equality conditions for the fractional BML inequality in one-dimension will be given. It will be shown that aside from some trivial cases, that for a fractional partition $(\mathcal{G},\beta)$ and non-empty compact sets $A_1,\dots,A_m\subset\mathbb{R}$ equality holds if and only if for each $S\in\mathcal{G}$ the set $\sum_{i\in S}A_i$ is either an interval with positive measure or consists of exactly one point.
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation

It is imperative to ensure the robustness of deep learning models in critical applications such as, healthcare. While recent advances in deep learning have improved the performance of volumetric medical image segmentation models, these models cannot be deployed for real-world applications immediately due to their vulnerability to adversarial attacks. We present a 3D frequency domain adversarial attack for volumetric medical image segmentation models and demonstrate its advantages over conventional input or voxel domain attacks. Using our proposed attack, we introduce a novel frequency domain adversarial training approach for optimizing a robust model against voxel and frequency domain attacks. Moreover, we propose frequency consistency loss to regulate our frequency domain adversarial training that achieves a better tradeoff between model's performance on clean and adversarial samples. Code is publicly available at https://github.com/asif-hanif/vafa.
SafeDreamer: Safe Reinforcement Learning with World Models

The deployment of Reinforcement Learning (RL) in real-world applications is constrained by its failure to satisfy safety criteria. Existing Safe Reinforcement Learning (SafeRL) methods, which rely on cost functions to enforce safety, often fail to achieve zero-cost performance in complex scenarios, especially vision-only tasks. These limitations are primarily due to model inaccuracies and inadequate sample efficiency. The integration of world models has proven effective in mitigating these shortcomings. In this work, we introduce SafeDreamer, a novel algorithm incorporating Lagrangian-based methods into world model planning processes within the superior Dreamer framework. Our method achieves nearly zero-cost performance on various tasks, spanning low-dimensional and vision-only input, within the Safety-Gymnasium benchmark, showcasing its efficacy in balancing performance and safety in RL tasks. Further details and resources are available on the project website: https://sites.google. com/view/safedreamer.
Monte Carlo graph search for quantum circuit optimization

The building blocks of quantum algorithms and software are quantum gates, with the appropriate combination of quantum gates leading to a desired quantum circuit. Deep expert knowledge is necessary to discover effective combinations of quantum gates to achieve a desired quantum algorithm for solving a specific task. This is especially challenging for quantum machine learning and signal processing. For example, it is not trivial to design a quantum Fourier transform from scratch. This work proposes a quantum architecture search algorithm which is based on a Monte Carlo graph search and measures of importance sampling. It is applicable to the optimization of gate order, both for discrete gates, as well as gates containing continuous variables. Several numerical experiments demonstrate the applicability of the proposed method for the automatic discovery of quantum circuits.
Universal lower bound for community structure of sparse graphs

We prove new lower bounds on the modularity of graphs. Specifically, the modularity of a graph $G$ with average degree $\bar d$ is $\Omega(\bar{d}^{-1/2})$, under some mild assumptions on the degree sequence of $G$. The lower bound $\Omega(\bar{d}^{-1/2})$ applies, for instance, to graphs with a power-law degree sequence or a near-regular degree sequence. It has been suggested that the relatively high modularity of the Erd\H{o}s-R\'enyi random graph $G_{n,p}$ stems from the random fluctuations in its edge distribution, however our results imply high modularity for any graph with a degree sequence matching that typically found in $G_{n,p}$. The proof of the new lower bound relies on certain weight-balanced bisections with few cross-edges, which build on ideas of Alon [Combinatorics, Probability and Computing (1997)] and may be of independent interest.
AIC-AB NET: A Neural Network for Image Captioning with Spatial Attention and Text Attributes

Image captioning is a significant field across computer vision and natural language processing. We propose and present AIC-AB NET, a novel Attribute-Information-Combined Attention-Based Network that combines spatial attention architecture and text attributes in an encoder-decoder. For caption generation, adaptive spatial attention determines which image region best represents the image and whether to attend to the visual features or the visual sentinel. Text attribute information is synchronously fed into the decoder to help image recognition and reduce uncertainty. We have tested and evaluated our AICAB NET on the MS COCO dataset and a new proposed Fashion dataset. The Fashion dataset is employed as a benchmark of single-object images. The results show the superior performance of the proposed model compared to the state-of-the-art baseline and ablated models on both the images from MSCOCO and our single-object images. Our AIC-AB NET outperforms the baseline adaptive attention network by 0.017 (CIDEr score) on the MS COCO dataset and 0.095 (CIDEr score) on the Fashion dataset.
Safe Reinforcement Learning as Wasserstein Variational Inference: Formal Methods for Interpretability

Reinforcement learning can provide effective reasoning for sequential decision-making problems with variable dynamics. Such reasoning in practical implementation, however, poses a persistent challenge in interpreting the reward function and the corresponding optimal policy. Consequently, representing sequential decision-making problems as probabilistic inference can have considerable value, as, in principle, the inference offers diverse and powerful mathematical tools to infer the stochastic dynamics whilst suggesting a probabilistic interpretation of policy optimization. In this study, we propose a novel Adaptive Wasserstein Variational Optimization, namely AWaVO, to tackle these interpretability challenges. Our approach uses formal methods to achieve the interpretability for convergence guarantee, training transparency, and intrinsic decision-interpretation. To demonstrate its practicality, we showcase guaranteed interpretability with an optimal global convergence rate in simulation and in practical quadrotor tasks. In comparison with state-of-the-art benchmarks including TRPO-IPO, PCPO and CRPO, we empirically verify that AWaVO offers a reasonable trade-off between high performance and sufficient interpretability.
Local elimination in the traveling salesman problem

Hougardy and Schroeder (WG 2014) proposed a combinatorial technique for pruning the search space in the traveling salesman problem, establishing that, for a given instance, certain edges cannot be present in any optimal tour. We describe an implementation of their technique, employing an exact TSP solver to locate k-opt moves in the elimination process. In our computational study, we combine LP reduced-cost elimination together with the new combinatorial algorithm. We report results on a set of geometric instances, with the number of points n ranging from 3,038 up to 115,475. The test set includes all TSPLIB instances having at least 3,000 points, together with 250 randomly generated instances, each with 10,000 points, and three currently unsolved instances having 100,000 or more points. In all but two of the test instances, the complete-graph edge sets were reduced to under 3n edges. For the three large unsolved instances, repeated runs of the elimination process reduced the graphs to under 2.5n edges.