Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting a Key Innovation in Evolutionary Biology: Felsenstein’s “Phylogenies and the Comparative Method”

The comparative method has long been a fundamental exploratory tool in evolutionary biology, but this venerable approach was revolutionized in 1985, when Felsenstein published “Phylogenies and the Comparative Method” in The American Naturalist. This article forced comparative biologists to start thinking phylogenetically when conducting statistical analyses of correlated trait evolution rather than simply applying conventional statistical methods that ignore evolutionary relationships. It did so by introducing a novel analytical method (phylogenetically “independent contrasts”) that required a phylogenetic topology with branch lengths and that assumed a Brownian motion model of trait evolution. Independent contrasts enabled comparative biologists to avoid the statistical dilemma of nonindependence of species values, arising from shared ancestry, but came at the cost of needing a detailed phylogeny and of accepting a specific model of character change. Nevertheless, this article not only revitalized comparative biology but even encouraged studies aimed at estimating phylogenies. Felsenstein’s characteristically lucid and concise statement of the problem (illustrated with powerful graphics), coupled with an oncoming flood of new molecular data and techniques for estimating phylogenies, led Felsenstein’s 1985 article to become the second most cited article in the history of this journal. Here we present a personal review of comparative biology before, during, and after Joe’s article. For historical context, we append a perspective written by Joe himself that describes how his article evolved, unedited transcripts of reviews of his submitted manuscript, and a guide to some nontrivial calculations. These additional materials help emphasize that the process of science does not always occur gradually or predictably.
Journal of the Mechanics and Physics of Solids

Interaction of nanoparticles (NPs) with cell membrane is a crucial issue in studying drug delivery, photodynamic therapy system and cytotoxicity. Single NP with relatively small size cannot be fully wrapped by the cell membrane, which prohibits its uptake. One feasible way is cooperative entry, i.e., recruiting and assembling multiple small NPs to form a larger NP cluster to enter into a cell. In this work, we present theoretical analysis about the cooperative entry of multiple NPs. Through free energy calculation we investigate how the NPs' size, shape, interval and NP/cell interfacial binding energy influence the feasibility of entry. Interestingly we find that the cooperative entry of oblate ellipsoidal NPs can get larger energy compensation than individual ones as well as spherical ones. We also propose that soft NPs have preference in cooperative entry of the cell. Our work can be used to actively design and transfer NPs in applications such as drug delivery as well as to understand the shape effect on toxic mechanism of ellipsoidal NPs. & 2014 Elsevier Ltd. All rights reserved.
Enhancing Default Prediction in P2P Lending using Random Forest and Grey Wolf Optimization-based Feature Selection

Online lending services such as Peer to Peer (P2P) loans provide convenience for lenders to transact directly without involving banks as intermediaries. Identifying potential loan recipients who are at risk of default is a crucial step in preventing financial losses, as lenders are responsible for default risk. However, predicting default risk becomes a challenge when P2P lending datasets have various complex features. Some features in P2P lending are redundant, while others do not significantly contribute to an effective solution. Therefore, feature selection is an important process to choose a relevant subset of features from input or target data. Traditional feature selection methods often fail to provide optimal results. A better approach is to use heuristic search algorithms capable of finding suboptimal feature subsets. We employ the Grey Wolf Optimization (GWO) technique, inspired by the hierarchy of leadership and grey wolf hunting mechanisms. Combined with Random Forest (RF), which has limitations in classifying data with very high dimensions, our GWO+RF combination has proven to enhance classification performance better than previous research. It achieves an accuracy score of 97.31%, compared to previous research with scores of only 67.72% for RBM+RF, 64% for Binary PSO+ERT, and 92% for GA+RF.
University of Birmingham Validity, reliability, and invariance of the Situational Motivation Scale (SIMS) across diverse physical activity contexts

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Rootcells® improve the establishment of Corylus colurna L. in a compacted car park substrate

Trees growing in an urban environment often experience very poor rooting environments that have a detrimental effect on the trees’ longevity and the benefits that the trees can provide. This study compares the condition of trees planted with RootCells® with trees planted without provision of any additional rooting volume, in a car park with a varying level of soil compaction across the site. Twelve Turkish hazels (Corylus colurna L.) were planted in compacted hard-core, half of which were supplied with RootCells® that increased the volume of these planting pits by approximately 34%. After 5 growing seasons, 2 trees had died (one control and one treatment tree) and we measured a selection of growth and health indicators in the remaining 10 trees, to assess the effect of the RootCells®. Mean stem girth at 1.5 m was 15.4% greater for the treatment group, and mean twig density was 18.3% greater for the canopies of the treatment trees. For a range of other tests, the trees growing with the RootCells® performed marginally better than the control group. However, chlorophyll fluorescence readings gave a contrary reading, which indicates that this method may not always be a reliable means of assessing successful tree establishment.
Transmission Tower category Identification from Airborne LiDAR Point Clouds based on Shape Curve

Using airborne LiDAR point cloud data to reconstruct the three-dimensional (3D) models of the transmission towers is crucial for ensuring power transmission safety. To enhance the models’ accuracy, knowing the tower categories is essential. However, there are few studies on the identification of tower categories at present. The existing studies have various shortcomings. Aiming at the problem of tower category identification in the application of airborne LiDAR point clouds in a smart grid, a transmission tower category identification method based on the shape curve is proposed. Firstly, a tower curve database is established based on relevant standards. Then, we employ three methods, namely dynamic time warping (DTW) distance, Hausdorff distance, and DTW-Hausdorff distance, to calculate the similarity indexes between the tower point cloud shape curves and those in the database. These indexes are used to determine the tower category. Finally, this method is tested using point clouds from transmission corridors. The experimental results show that the tower identification accuracy of DTW-Hausdorff distance is 94.0%. The tower category identification accuracy can reach 88.0%. The F-score used as the overall evaluation index of the tower identification effect is 90.9%. When employed as a similarity index, the tower identification effect is the best.
Configuration Design and Dynamics Simulation Analysis of Deployable Exposure Experiment Platform

Aiming at the problem that the traditional rigid exposure experiment platform takes up too much launch space and consumes high launch cost, a thick panel kirigami method is used to propose a deployable thick panel model for the exposure experiment platform, which realizes the plane of the thick panel. In the unfolding design, the unfolding process simulation of the structure was carried out, the influence of different hinge position drives on the dynamic behavior of the structure unfolding was studied, and the kinematics laws of the structure unfolding process under different driving modes were summarized. The new deployable exposure experiment platform provides new ideas for the development and application of practical engineering in the future, and the dynamic simulation also provides a driving strategy for it.
A Centiparsec-scale Compact Radio Core in the Nearby Galaxy M60

M60, an elliptical galaxy located 16.5 Mpc away, has an active nucleus with a very low luminosity and an extremely low accretion rate. Its central supermassive black hole (SMBH) has a mass of M BH ∼ 4.5 × 109 M ⊙ and a Schwarzschild radius corresponding to R S ∼ 5.4 μas. To investigate the nature of its innermost radio nucleus, data from the Very Long Baseline Array (VLBA) at 4.4 and 7.6 GHz were reduced. The VLBA images reveal a compact component with total flux densities of ∼20 mJy at both frequencies, a size of ≤0.27 mas (99.7% confidence level), about 0.022 pc (50 R S) at 7.6 GHz, and a brightness temperature of ≥6 × 109 K. This suggests that the observed centiparsec-scale compact core could be attributed to a nonthermal jet base or an advection-dominated accretion flow (ADAF) with nonthermal electrons. The extremely compact structure also supports the presence of an SMBH in the center. Our results indicate that M60 is a promising target for broadband very long baseline interferometry observations at millimeter wavelengths to probe ADAF scenarios and tightly constrain the potential photon ring (about 28 μas) around its SMBH.
On parametric generalizations of the Kardar-Parisi-Zhang equation and their integrability

There are analyzed two physically reasonable generalizations of the Kardar-Parisi-Zhang equation describing the spin glasses growth models and possessing important from physical point of view properties. The first one proved to be a completely integrable Hamiltonian dynamical system with an infinite hierarchy of commuting to each other conservation laws, and the second one proved to be linearized modulo some nonlinear constraints, imposed on its solutions.
Relevant Predictors in the Association Between Patients’ Functional Status and Scar Outcomes After Total Hip Arthroplasty

Background: We aimed to investigate the relevant predictors in the association between the functional status and the consequences of the persistence of scars in patients with traumatic versus non-traumatic coxarthrosis after total hip arthroplasty (THA). Methods: A total of 203 patients undergoing THA after traumatic or non-traumatic coxarthrosis were asked to complete the Mekeres' Psychosocial Internalization Scale (MPIS), in which they self-evaluated on a Likert scale (between one and five) by selecting the rating that corresponded to their personal opinion and the activities of daily living (ADL) form at six months postoperative. The statistical data were processed using the IBM SPSS Statistics software version 22.0 (IBM Corp., Armonk, NY). A combined assessment of the internalization of scars using MPIS and ADL forms after THA allowed for the identification of relevant predictors of the quality of life six months post-surgery in patients with traumatic or non-traumatic coxarthrosis. Results: Depending on the coxarthrosis etiology (traumatic or non-traumatic), the results were further processed by a univariate ANOVA, considering the independent variables represented by symptoms, the number of surgical procedures, and the postoperative evolution, which are acting on the outcomes of physical functioning (the dependent variable) in the postoperative phase. In the case of the traumatic group, our results suggest that the number of surgical interventions, the ability to internalize scars, and autonomy in terms of body care are predictors of the quality of life. In patients with non-traumatic coxarthrosis, an important role in predicting quality of life is played by the administered treatment and the ability to maintain their autonomy regarding self-hygiene six months post-surgery. Conclusions: The predictive regression equation suggests that the quality of life in patients with traumatic coxarthrosis can be predicted by the number of surgical interventions, the administered treatment, the ability to internalize scars, and the autonomy regarding body care activities. On the other hand, for patients with non-traumatic coxarthrosis, an important role in predicting the quality of life is played by the treatment and the ability to maintain autonomy in terms of body hygiene activities.
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning

Ocean and climate research benefits from global ocean observation initiatives such as Argo, GLOSS, and EMSO. The Argo network, dedicated to ocean profiling, generates a vast volume of observatory data. However, data quality issues from sensor malfunctions and transmission errors necessitate stringent quality assessment. Existing methods, including machine learning, fall short due to limited labeled data and imbalanced datasets. To address these challenges, we propose an Outlier Detection-Enhanced Active Learning (ODEAL) framework for ocean data quality assessment, employing Active Learning (AL) to reduce human experts’ workload in the quality assessment workflow and leveraging outlier detection algorithms for effective model initialization. We also conduct extensive experiments on five large-scale realistic Argo datasets to gain insights into our proposed method, including the effectiveness of AL query strategies and the initial set construction approach. The results suggest that our framework enhances quality assessment efficiency by up to 465.5% with the uncertainty-based query strategy compared to random sampling and minimizes overall annotation costs by up to 76.9% using the initial set built with outlier detectors.
More on greedy construction heuristics for the MAX-CUT problem

A cut of a graph can be represented in many different ways. Here we propose to represent a cut through a ``relation tree'', which is a spanning tree with signed edges. We show that this picture helps to classify the main greedy heuristics for the maximum cut problem, in analogy with the minimum spanning tree problem. Namely, all versions of the Sahni-Gonzalez~(SG) algorithms could be classified as the Prim class, while various Edge-Contraction~(EC) algorithms are of the Kruskal class. We further elucidate the relation of this framework to the stabilizer formalism in quantum computing, and point out that the recently proposed \textit{ADAPT-Clifford} algorithm is a reformulation of a refined version of the SG algorithm, SG3. Numerical performance of the typical algorithms from the two classes are studied with various kinds of graphs. It turns out that, the Prim-class algorithms perform better for general dense graphs, and the Kruskal-class algorithms performs better when the graphs are sparse enough.
CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems

We present CrystalBox, a novel, model-agnostic, posthoc explainability framework for Deep Reinforcement Learning (DRL) controllers in the large family of input-driven environments which includes computer systems. We combine the natural decomposability of reward functions in input-driven environments with the explanatory power of decomposed returns. We propose an efficient algorithm to generate future-based explanations across both discrete and continuous control environments. Using applications such as adaptive bitrate streaming and congestion control, we demonstrate CrystalBox's capability to generate high-fidelity explanations. We further illustrate its higher utility across three practical use cases: contrastive explanations, network observability, and guided reward design, as opposed to prior explainability techniques that identify salient features.
Multi-Label Classification of COVID-Tweets Using Large Language Models

Vaccination is important to minimize the risk and spread of various diseases. In recent years, vaccination has been a key step in countering the COVID-19 pandemic. However, many people are skeptical about the use of vaccines for various reasons, including the politics involved, the potential side effects of vaccines, etc. The goal in this task is to build an effective multi-label classifier to label a social media post (particularly, a tweet) according to the specific concern(s) towards vaccines as expressed by the author of the post. We tried three different models-(a) Supervised BERT-large-uncased, (b) Supervised HateXplain model, and (c) Zero-Shot GPT-3.5 Turbo model. The Supervised BERT-large-uncased model performed best in our case. We achieved a macro-F1 score of 0.66, a Jaccard similarity score of 0.66, and received the sixth rank among other submissions. Code is available at-https://github.com/anonmous1981/AISOME
High-$p_T$ Suppression in Small Systems

We present first results for leading hadron suppression in small collision systems, from a convolved radiative and collisional pQCD energy loss model which receives a short path length correction to the radiative energy loss. We find that the short path length correction is exceptionally large for light flavor final states in both small and large collision systems, due to the disproportionate size of the correction for gluons. We examine various assumptions underlying the energy loss model through the calculation of energy loss weighted expectation values of ratios assumed small by the various assumptions. This calculation shows that the large formation time assumption, which is utilized by most contemporary energy loss models, is invalid for a large portion of the phenomenologically relevant parameter space.
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations

Decentralized learning has emerged as an alternative method to the popular parameter-server framework which suffers from high communication burden, single-point failure and scalability issues due to the need of a central server. However, most existing works focus on a single shared model for all workers regardless of the data heterogeneity problem, rendering the resulting model performing poorly on individual workers. In this work, we propose a novel personalized decentralized learning algorithm named DePRL via shared representations. Our algorithm relies on ideas from representation learning theory to learn a low-dimensional global representation collaboratively among all workers in a fully decentralized manner, as well as a user-specific low-dimensional local head leading to a personalized solution for each worker. We show that DePRL achieves, for the first time, a provable \textit{linear speedup for convergence} with general non-linear representations (i.e., the convergence rate is improved linearly with respect to the number of workers). Experimental results support our theoretical findings showing the superiority of our method in data heterogeneous environments.
Rheological Analysis and Evaluation of Measurement Techniques for the Curing Polymethylmethacrylate Bone Cement in Vertebroplasty

Vertebroplasty is a minimally invasive surgical procedure used to treat vertebral fractures, which conventionally involves injection of a polymethylmethacrylate (PMMA) bone cement into the fractured vertebra. A common risk associated with vertebroplasty is that of cement leaking out of the vertebra during the injection. This could happen since the flow behaviour of the bone cement is not conventional, but it varies with many factors like curing time, shear rate, and temperature. Therefore, experiments to quantify the cement's flow properties are necessary to be able to understand and properly handle the bone cement. In this study, we aimed to characterize the behaviour of the PMMA bone cement in its curing stages, in order to obtain parameters that govern the flow behaviour during injection flow. The measurements were done using rotational and oscillatory rheometry as well as using a custom-made injetor setup replicating a typical vertebroplasty setting. Our results showed that the complex viscoelastic behaviour of the bone cement, which evolves over a two-phase curing process, can be significantly affected by deformations and temperature. We found that the results from rotational tests were affected by wall slip and"ridge"-like formations in the test sample, which we confirmed visually by microscopical assessment. We also found that the Cox-Merz rule to be conditionally valid, which impacts the use of oscillatory tests to obtain shear-thinning characteristics for the bone cement.
Data Contamination Issues in Brain-to-Text Decoding

Decoding non-invasive cognitive signals to natural language has long been the goal of building practical brain-computer interfaces (BCIs). Recent major milestones have successfully decoded cognitive signals like functional Magnetic Resonance Imaging (fMRI) and electroencephalogram (EEG) into text under open vocabulary setting. However, how to split the datasets for training, validating, and testing in cognitive signal decoding task still remains controversial. In this paper, we conduct systematic analysis on current dataset splitting methods and find the existence of data contamination largely exaggerates model performance. Specifically, first we find the leakage of test subjects' cognitive signals corrupts the training of a robust encoder. Second, we prove the leakage of text stimuli causes the auto-regressive decoder to memorize information in test set. The decoder generates highly accurate text not because it truly understands cognitive signals. To eliminate the influence of data contamination and fairly evaluate different models' generalization ability, we propose a new splitting method for different types of cognitive datasets (e.g. fMRI, EEG). We also test the performance of SOTA Brain-to-Text decoding models under the proposed dataset splitting paradigm as baselines for further research.
Steady-state Dirichlet approximation of the Wright-Fisher model using the prelimit generator comparison approach of Stein's method

The Wright-Fisher model, originating in Wright (1931) is one of the canonical probabilistic models used in mathematical population genetics to study how genetic type frequencies evolve in time. In this paper we bound the rate of convergence of the stationary distribution for a finite population Wright-Fisher Markov chain with parent independent mutation to the Dirichlet distribution. Our result improves the rate of convergence established in Gan et al. (2017) from $O(1/\sqrt{N})$ to $O(1/N)$. The results are derived using Stein's method, in particular, the prelimit generator comparison method.