Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiency and Robustness of Rosenbaum's Regression (Un)-Adjusted Rank-based Estimator in Randomized Experiments

Mean-based estimators of the causal effect in a completely randomized experiment (e.g., the difference-in-means estimator) may behave poorly if the potential outcomes have a heavy-tail, or contain outliers. We study an alternative estimator by Rosenbaum that estimates the constant additive treatment effect by inverting a randomization test using ranks. By investigating the breakdown point and asymptotic relative efficiency of this rank-based estimator, we show that it is provably robust against heavy-tailed potential outcomes, and has variance that is asymptotically, in the worst case, at most about 1.16 times that of the difference-in-means estimator; and its variance can be much smaller when the potential outcomes are not light-tailed. We further derive a consistent estimator of the asymptotic standard error for Rosenbaum's estimator which yields a readily computable confidence interval for the treatment effect. Further, we study a regression adjusted version of Rosenbaum's estimator to incorporate additional covariate information in randomization inference. We prove gain in efficiency by this regression adjustment method under a linear regression model. We illustrate through synthetic and real data that, unlike the mean-based estimators, these rank-based estimators (both unadjusted or regression adjusted) are efficient and robust against heavy-tailed distributions, contamination, and model misspecification.
Do Software Security Practices Yield Fewer Vulnerabilities?

Due to the ever-increasing number of security breaches, practitioners are motivated to produce more secure software. In the United States, the White House Office released a memorandum on Executive Order (EO) 14028 that mandates organizations provide self-attestation of the use of secure software development practices. The OpenSSF Scorecard project allows practitioners to measure the use of software security practices automatically. However, little research has been done to determine whether the use of security practices improves package security, particularly which security practices have the biggest impact on security outcomes. The goal of this study is to assist practitioners and researchers in making informed decisions on which security practices to adopt through the development of models between software security practice scores and security vulnerability counts.To that end, we developed five supervised machine learning models for npm and PyPI packages using the OpenSSF Scorecard security practices scores and aggregate security scores as predictors and the number of externally-reported vulnerabilities as a target variable. Our models found that four security practices (Maintained, Code Review, Branch Protection, and Security Policy) were the most important practices influencing vulnerability count. However, we had low R2 (ranging from 9% to 12%) when we tested the models to predict vulnerability counts. Additionally, we observed that the number of reported vulnerabilities increased rather than reduced as the aggregate security score of the packages increased. Both findings indicate that additional factors may influence the package vulnerability count. Other factors, such as the scarcity of vulnerability data, time to implicate security practices vs. time to detect vulnerabilities, and the need for more adequate scripts to detect security practices, may impede the data-driven studies to indicate that a practice can aid in the reduction of externally-reported vulnerabilities. We suggest that vulnerability count and security score data be refined such that these measures may be used to provide actionable guidance on security practices.
Convergence of one-level and multilevel unsymmetric collocation for second order elliptic boundary value problems

Thepaperprovesconvergenceofone-levelandmultilevelunsymmetriccollocationforsecondorderelliptic boundary value problems on the bounded domains. By using Schaback's linear discretization theory,L2 errors are obtained based on the kernel-based trial spaces generated by the compactly supported radial basis functions. For the one-level unsymmetric collocation case, we obtain convergence when the testing discretization is finer than the trial discretization. The convergence rates depend on the regularity of the solution, the smoothness of the computing domain, and the approximation of scaled kernel-based spaces. The multilevel process is implemented by employing successive refinement scattered data sets and scaled compactly supported radial basis functions with varying support radii. Convergence of multilevel collocation is further proved based on the theoretical results of one-level unsymmetric collocation. In addition to having the same dependencies as the one-level collocation, the convergence rates of multilevel unsymmetric collocation especially depends on the increasing rules of scattered data and the selection of scaling parameters.
OT-Net: A Reusable Neural Optimal Transport Solver

With the widespread application of optimal transport (OT), its calculation becomes essential, and various algorithms have emerged. However, the existing methods either have low efficiency or cannot represent discontinuous maps. A novel reusable neural OT solver OT-Net is thus presented, which first learns Brenier's height representation via the neural network to obtain its potential, and then gained the OT map by computing the gradient of the potential. The algorithm has two merits, 1) it can easily represent discontinuous maps, which allows it to match any target distribution with discontinuous supports and achieve sharp boundaries. This can well eliminate mode collapse in the generated models. 2) The OT map can be calculated straightly by the proposed algorithm when new target samples are added, which greatly improves the efficiency and reusability of the map. Moreover, the theoretical error bound of the algorithm is analyzed, and we have demonstrated the empirical success of our approach in image generation, color transfer, and domain adaptation.
Game-theoretic analysis of networks: Designing mechanisms for scheduling

Algorithmic mechanism design is an important area between computer science and economics. One of the most fundamental problems in this area is the problem of scheduling unrelated machines to minimize the makespan. The machines behave like selfish players: they have to get paid in order to process the tasks, and would lie about their processing times if they could increase their utility in this way. The problem was proposed and studied in the seminal paper of Nisan and Ronen, where it was shown that the approximation ratio of mechanisms is between 2 and n. In this thesis, we present some recent improvements of the lower bound to 1+ √ 2 for three or more machines and to 1 + φ for many machines. Since the gap between the lower bound of 2.618 and the upper bound of n is huge, we also propose an alternative approach to the problem, which first attempts to characterize all truthful mechanisms and then study their approximation ratio. Towards this goal, we show that the class of truthful mechanisms for two players (regardless of approximation ratio) is very limited: tasks can be partitioned in groups allocated by affine minimizers (a natural generalization of the well-known VCG mechanism) and groups allocated by threshold mechanisms. Finally we generalize a tool we have used in the proof of the 1+ √ 2 lower bound: we give a geometrical characterization of truthfulness for the case of three tasks, which we believe that might be useful for proving improved lower bounds and which provides a more complete understanding of truthfulness. SUBJECT AREA: Algorithms
Localization with Anticipation for Autonomous Urban Driving in Rain

This paper presents a localization algorithm for autonomous urban vehicles under rain weather conditions. In adverse weather, human drivers anticipate the location of the ego-vehicle based on the control inputs they provide and surrounding road contextual information. Similarly, in our approach for localization in rain weather, we use visual data, along with a global reference path and vehicle motion model for anticipating and better estimating the pose of the ego-vehicle in each frame. The global reference path contains useful road contextual information such as the angle of turn which can be potentially used to improve the localization accuracy especially when sensors are compromised. We experimented on the Oxford Robotcar Dataset and our internal dataset from Singapore to validate our localization algorithm in both clear and rain weather conditions. Our method improves localization accuracy by 50.83% in rain weather and 34.32% in clear weather when compared to baseline algorithms.
Geometric and Dynamic Properties of Entangled Polymer Chains in Athermal Solvents: A Coarse-Grained Molecular Dynamics Study

We used a coarse-grained model to study the geometric and dynamic properties of flexible entangled polymer chains dissolved in explicit athermal solvents. Our simulations successfully reproduced the geometrical properties including the scaling relationships between mean-square end-to-end distance $$, chain entanglement lengths $N_{e}$ and concentration $\Phi$. Specifically, we find that $\sim N*\Phi^{-1/4}$,$N_{e} = 30.01\Phi^{-5/4}+31.23$. Dynamically, our model confirmed the ratio of the dynamic critical entanglement $N_{c}$ and the geometric entanglement length $N_{e}$ is constant, with $N_{c}/N_{e} = 5\sim 6$. To account for the local swelling effect for chains confined in athermal solvents, we treated the chains using the concept of blobs where each blob occupies a volume $\Omega_{b}$, with length $g$. Direct MD simulations and scaling analysis showed that $g \sim \Phi^{-25/36}$, $\Omega_{b}\sim\Phi^{-5/4}$. Using these together with the concentration dependent packing length $p \sim \Phi^{-5/12}$, we obtained a modified the Lin-Noolandi ansatz for concentrated flexible polymer chains in athermal solvents: $G \sim \frac{\Phi}{\left(N_{e} / g\right) \Omega_{b}} \sim \Phi^{-2.28}$. We demonstrate this modified ansatz agrees well with our coarse-grained numerical simulations.
Skill-Critic: Refining Learned Skills for Reinforcement Learning

Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels. Promising results in sparse reward environments have been seen with skills, i.e. sequences of primitive actions. Typically, a skill latent space and policy are discovered from offline data, but the resulting low-level policy can be unreliable due to low-coverage demonstrations or distribution shifts. As a solution, we propose fine-tuning the low-level policy in conjunction with high-level skill selection. Our Skill-Critic algorithm optimizes both the low and high-level policies; these policies are also initialized and regularized by the latent space learned from offline demonstrations to guide the joint policy optimization. We validate our approach in multiple sparse RL environments, including a new sparse reward autonomous racing task in Gran Turismo Sport. The experiments show that Skill-Critic's low-level policy fine-tuning and demonstration-guided regularization are essential for optimal performance. Images and videos are available at https://sites.google. com/view/skill-critic. We plan to open source the code with the final version.
Hyperbolic Representation Learning: Revisiting and Advancing

The non-Euclidean geometry of hyperbolic spaces has recently garnered considerable attention in the realm of representation learning. Current endeavors in hyperbolic representation largely presuppose that the underlying hierarchies can be automatically inferred and preserved through the adaptive optimization process. This assumption, however, is questionable and requires further validation. In this work, we first introduce a position-tracking mechanism to scrutinize existing prevalent \hlms, revealing that the learned representations are sub-optimal and unsatisfactory. To address this, we propose a simple yet effective method, hyperbolic informed embedding (HIE), by incorporating cost-free hierarchical information deduced from the hyperbolic distance of the node to origin (i.e., induced hyperbolic norm) to advance existing \hlms. The proposed method HIE is both task-agnostic and model-agnostic, enabling its seamless integration with a broad spectrum of models and tasks. Extensive experiments across various models and different tasks demonstrate the versatility and adaptability of the proposed method. Remarkably, our method achieves a remarkable improvement of up to 21.4\% compared to the competing baselines.
Uncertain Logic for Modeling Human Language

This paper discusses uncertain set theory and proposes the concepts of membership function, variance, entropy and distance of uncertain sets. In order to determine membership functions, uncertain statistics is also suggested. Based on uncertain set theory, uncertain logic is presented for dealing with human language by using uncertain quantier, uncertain subject and uncertain predicate. As an application, uncertain logic provides a means for extracting linguistic summary from a collection of raw data. Finally, fuzzy logic will be discussed at the end of this paper. c
Cylinder water entry on a perturbed water surface

Abstract The perturbations existing on a breaking wavefront can be a potential explanation for the slamming pressure variability in wave impacts. Here, we investigate the effect of these perturbations by forced vertical slamming of a two-dimensional circular cylinder with constant downward velocity on standing waves. Through experimental modelling and numerical simulation, the slamming force is measured for several standing wave amplitudes and wavelengths. The standing wave phase is tuned such that the impact occurs symmetrically at the instant of maximum crest or trough. Our observations show that slamming coefficients vary with the standing wave amplitude when the wavelength is kept constant and vice versa. The trough impact slamming coefficient can be more than two times the flat impact, and up to four times the crest impact. The experimental results are reproduced by numerical simulations and they agree reasonably well in general. Two analytical approaches based on the von Kármán (NACA, vol. 321, 1929, pp. 1–8) and Wagner (Z. Angew. Math. Mech., vol. 12, 1932, pp. 913–215) methods, which consider the effect of water surface curvature, are introduced. The slamming coefficient calculated from these methods can provide a bound in which the slamming coefficient can be found for each standing wave amplitude and wavelength. Further insight is achieved by numerical simulations of impact on the shorter wavelength to diameter ratio of $0.05<\lambda /D<0.4$. As the wavelength to diameter ratio becomes smaller, the cylinder impacts the water surface at several locations. As a result, multiple peaks occur, and the trapped air at different locations between the cylinder and the water surface yields oscillations with different frequencies on the slamming coefficient time history.
Harmonising with Heaven and Earth: Reciprocal Harmony and Xunzi's Environmental Ethics

Xunzi's philosophy provides a rich resource for understanding how ethical relationships between humans and nature can be articulated in terms of harmony. In this paper, I build on his ideas to develop the concept of reciprocal harmony, which requires us to reciprocate those who make our lives liveable. In the context of the environment, I argue that reciprocal harmony generates moral obligations towards nature, in return for the existential debt that humanity owes towards heaven and earth. This can be used as a normative basis for an environmental ethic that enables humanity and nature to flourish together.
Genetic and Physiological Diversity of Marine Actinobacteria from the Okha Coast, Gujarat, India

Abstract Saline and alkaline habitats of the Okha coastline, Gujarat, India, were explored for the diversity of actinobacteria. Based on the phenotypic and genotypic heterogeneity, nine haloalkaliphilic actinobacterial strains were characterized and identified. The PCR amplification of 16S rDNA using universal and genus-specific primers corroborated the trends that emerged through cultural and physiological characterization. Streptomyces was the most abundant genus amplified with the Streptomyces-specific primers (StreptB/E, StreptB/F), whereas other actinobacterial strains were amplified by the universal primers U1 and U2. Further molecular diversity was investigated by the gradient PCR-DGGE as a fingerprinting tool that generated group-specific DGGE patterns. Based on the nucleotide homology and phylogenetic analysis, strains OK-1 and OK-2 were identified as Streptomyces somaliensis. At the same time, OK-3 and OK-7 were detected as Streptomyces sp. , while OK-5, OK-6, OK-8, OK-9, and OK-10 belonged to Nocardiopsis alba. Further, the cluster analysis using the UPGMA method generated 3 clusters based on biochemical characterization, sugar utilization, and enzyme production. The dendrogram based on the DGGE band pattern created with Jaccard-distance revealed two major clades with 33.33% similarity. Further, the study of alpha diversity calculation using phenotypic characteristics discloses highly diverse sugar utilization abilities. Moreover, a stress value of 0.1236 was obtained based on the NMDS analysis of the plots using Bary-Curties dissimilarity. Overall, the distinct phenotypic, metabolic, and molecular profiling illustrated the diversity among marine actinobacteria.
Gut-associated microbes are present and active in the pig nasal cavity

Background The nasal microbiota is a key contributor to animal health, and characterizing the nasal microbiota composition is an important step towards elucidating the role of its different members. Efforts to characterize the nasal microbiota composition of domestic pigs and other farm animals frequently report the presence of bacteria that are typically found in the gut, including many anaerobes from the Bacteroidales and Clostridiales orders. However, the in vivo role of these gut-microbiota associated taxa is currently unclear. Here, we tackled this issue by examining the prevalence, origin, and activity of these taxa in the nasal microbiota of piglets. Results First, analysis of the nasal microbiota of farm piglets sampled in this study, as well as various publicly available data sets, revealed that gut-microbiota associated taxa indeed constitute a substantial fraction of the pig nasal microbiota that is highly variable across individual animals. Second, comparison of herd-matched nasal and rectal samples at amplicon sequencing variant (ASV) level showed that these taxa are largely shared in the nasal and rectal microbiota, suggesting a common origin driven presumably by the transfer of fecal matter. Third, surgical sampling of the inner nasal tract showed that gut-microbiota associated taxa are found throughout the nasal cavity, indicating that these taxa do not stem from contaminations introduced during sampling with conventional nasal swabs. Finally, analysis of cDNA from the 16S rRNA gene in these nasal samples indicated that gut-microbiota associated taxa are indeed active in the pig nasal cavity. Conclusion This study shows that gut-microbiota associated taxa are not only present, but also active, in the nasal cavity of domestic pigs, and paves the way for future efforts to elucidate the in vivo function of these taxa within the nasal microbiota.
Covariate Selection for Linear Errors-in-Variables Regression Models

In this article, we provide a procedure to select the significant covariates of the linear regression models in which some or all covariates are measured with errors. The proposed method is based on the combination of a non concave penalization and a corrected least squares, and it simultaneously selects significant covariates and estimates the unknown regression coefficients. Same as Fan and Li (2001), we show the resulted estimator has an oracle property with a proper choice of regularization parameters and penalty function. Some simulation studies are conducted to illustrate the finite sample performance of the proposed method.
Membrane extraction in native lipid nanodiscs reveals dynamic regulation of Cdc42 complexes during cell polarization

Embryonic development requires the establishment of cell polarity to enable cell fate segregation and tissue morphogenesis. This process is regulated by Par complex proteins, which partition into polarized membrane domains and direct downstream polarized cell behaviors. The kinase aPKC (along with its cofactor Par6) is a key member of this network and can be recruited to the plasma membrane by either the small GTPase Cdc42 or the scaffolding protein Par3. Although in vitro interactions among these proteins are well established, much is still unknown about the complexes they form during development. Here, to enable the study of membrane-associated complexes in vivo, we used a maleic acid copolymer to rapidly isolate membrane proteins from single C. elegans zygotes into lipid nanodiscs. We show that native lipid nanodisc formation enables detection of endogenous complexes involving Cdc42, which are undetectable when cells are lysed in detergent. We found that Cdc42 interacts more strongly with aPKC/Par6 during polarity maintenance than polarity establishment, two developmental stages that are separated by only a few minutes. We further show that Cdc42 and Par3 do not bind aPKC/Par6 simultaneously, confirming recent in vitro findings in an in vivo context. Our findings establish a new tool for studying membrane-associated signaling complexes and reveal an unexpected mode of polarity regulation via Cdc42.
Impaired motor-to-sensory transformation mediates auditory hallucinations

Distinguishing reality from hallucinations requires efficient monitoring of agency. It has been hypothesized that a copy of motor signals, termed efference copy (EC) or corollary discharge (CD), suppresses sensory responses to yield the sense of agency; impairment of the inhibitory function leads to hallucinations. However, how can the sole absence of inhibition yield positive symptoms of hallucinations? We hypothesize that selective impairments in functionally distinct signals of CD and EC during motor- to-sensory transformation cause the positive symptoms of hallucinations. In an electroencephalography (EEG) experiment with a delayed articulation paradigm in schizophrenic patients with (AVHs) and without auditory verbal hallucinations (non- AVHs), we found that preparing to speak without knowing the contents (general preparation) did not suppress auditory responses in both patient groups, suggesting the absent of inhibitory function of CD. Whereas, preparing to speak a syllable (specific preparation) enhanced the auditory responses to the prepared syllable in non-AVHs, whereas AVHs showed enhancement in responses to unprepared syllables, opposite to the observations in the normal population, suggesting that the enhancement function of EC is not precise in AVHs. A computational model with a virtual lesion of an inhibitory inter-neuron and disproportional sensitization of auditory cortices fitted the empirical data and further quantified the distinct impairments in motor-to-sensory transformation in AVHs. These results suggest that ‘broken’ CD plus ‘noisy’ EC causes erroneous monitoring on the imprecise generation of internal auditory representation and yields auditory hallucinations. Specific impairments in functional granularity of motor-to-sensory transformation mediate positivity symptoms of agency abnormality in mental disorders.