Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of hyperparameters on variable selection in random forests

Random forests (RFs) are well suited for prediction modeling and variable selection in high-dimensional omics studies. The effect of hyperparameters of the RF algorithm on prediction performance and variable importance estimation have previously been investigated. However, how hyperparameters impact RF-based variable selection remains unclear. We evaluate the effects on the Vita and the Boruta variable selection procedures based on two simulation studies utilizing theoretical distributions and empirical gene expression data. We assess the ability of the procedures to select important variables (sensitivity) while controlling the false discovery rate (FDR). Our results show that the proportion of splitting candidate variables (mtry.prop) and the sample fraction (sample.fraction) for the training dataset influence the selection procedures more than the drawing strategy of the training datasets and the minimal terminal node size. A suitable setting of the RF hyperparameters depends on the correlation structure in the data. For weakly correlated predictor variables, the default value of mtry is optimal, but smaller values of sample.fraction result in larger sensitivity. In contrast, the difference in sensitivity of the optimal compared to the default value of sample.fraction is negligible for strongly correlated predictor variables, whereas smaller values than the default are better in the other settings. In conclusion, the default values of the hyperparameters will not always be suitable for identifying important variables. Thus, adequate values differ depending on whether the aim of the study is optimizing prediction performance or variable selection.
Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification

Developmental Canal Stenosis (DCS) quantification is crucial in cervical spondylosis screening. Compared with quantifying DCS manually, a more efficient and time-saving manner is provided by deep keypoint localization networks, which can be implemented in either the coordinate or the image domain. However, the vertebral visualization features often lead to abnormal topological structures during keypoint localization, including keypoint distortion with edges and weakly connected structures, which cannot be fully suppressed in either the coordinate or image domain alone. To overcome this limitation, a keypoint-edge and a reparameterization modules are utilized to restrict these abnormal structures in a cross-domain manner. The keypoint-edge constraint module restricts the keypoints on the edges of vertebrae, which ensures that the distribution pattern of keypoint coordinates is consistent with those for DCS quantification. And the reparameterization module constrains the weakly connected structures in image-domain heatmaps with coordinates combined. Moreover, the cross-domain network improves spatial generalization by utilizing heatmaps and incorporating coordinates for accurate localization, which avoids the trade-off between these two properties in an individual domain. Comprehensive results of distinct quantification tasks show the superiority and generability of the proposed Topology-inspired Cross-domain Network (TCN) compared with other competing localization methods.
Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning

The recent surge in large-scale foundation models has spurred the development of efficient methods for adapting these models to various downstream tasks. Low-rank adaptation methods, such as LoRA, have gained significant attention due to their outstanding parameter efficiency and no additional inference latency. This paper investigates a more general form of adapter module based on the analysis that parallel and sequential adaptation branches learn novel and general features during fine-tuning, respectively. The proposed method, named Hydra, due to its multi-head computational branches, combines parallel and sequential branch to integrate capabilities, which is more expressive than existing single branch methods and enables the exploration of a broader range of optimal points in the fine-tuning process. In addition, the proposed adaptation method explicitly leverages the pre-trained weights by performing a linear combination of the pre-trained features. It allows the learned features to have better generalization performance across diverse downstream tasks. Furthermore, we perform a comprehensive analysis of the characteristics of each adaptation branch with empirical evidence. Through an extensive range of experiments, encompassing comparisons and ablation studies, we substantiate the efficiency and demonstrate the superior performance of Hydra. This comprehensive evaluation underscores the potential impact and effectiveness of Hydra in a variety of applications. Our code is available on \url{https://github.com/extremebird/Hydra}
Can Whisper perform speech-based in-context learning

This paper investigates the in-context learning abilities of the Whisper automatic speech recognition (ASR) models released by OpenAI. A novel speech-based in-context learning (SICL) approach is proposed for test-time adaptation, which can reduce the word error rates (WERs) with only a small number of labelled speech samples without gradient descent. Language-level adaptation experiments using Chinese dialects showed that when applying SICL to isolated word ASR, consistent and considerable relative WER reductions can be achieved using Whisper models of any size on two dialects, which is on average 32.3%. A k-nearest-neighbours-based in-context example selection technique can be applied to further improve the efficiency of SICL, which can increase the average relative WER reduction to 36.4%. The findings are verified using speaker adaptation or continuous speech recognition tasks, and both achieved considerable relative WER reductions. Detailed quantitative analyses are also provided to shed light on SICL's adaptability to phonological variances and dialect-specific lexical nuances.
Cleaning Up the Streets: Understanding Motivations, Mental Models, and Concerns of Users Flagging Social Media Posts

Social media platforms offer flagging, a technical feature that empowers users to report inappropriate posts or bad actors, to reduce online harms. While flags are often presented as flimsy icons, their simple interface disguises complex underlying interactions among users, algorithms, and moderators. Through semi-structured interviews with 22 active social media users who had recently flagged, we examine their understanding of flagging procedures, explore the factors that motivate and demotivate them from engaging in flagging, and surface their emotional, cognitive, and privacy concerns. Our findings show that a belief in generalized reciprocity motivates flag submissions, but deficiencies in procedural transparency create gaps in users' mental models of how platforms process flags. We highlight how flags raise questions about the distribution of labor and responsibility between platforms and users for addressing online harm. We recommend innovations in the flagging design space that assist user comprehension and facilitate granular status checks while aligning with their privacy and security expectations.
Dynamic NeRFs for Soccer Scenes

The long-standing problem of novel view synthesis has many applications, notably in sports broadcasting. Photorealistic novel view synthesis of soccer actions, in particular, is of enormous interest to the broadcast industry. Yet only a few industrial solutions have been proposed, and even fewer that achieve near-broadcast quality of the synthetic replays. Except for their setup of multiple static cameras around the playfield, the best proprietary systems disclose close to no information about their inner workings. Leveraging multiple static cameras for such a task indeed presents a challenge rarely tackled in the literature, for a lack of public datasets: the reconstruction of a large-scale, mostly static environment, with small, fast-moving elements. Recently, the emergence of neural radiance fields has induced stunning progress in many novel view synthesis applications, leveraging deep learning principles to produce photorealistic results in the most challenging settings. In this work, we investigate the feasibility of basing a solution to the task on dynamic NeRFs, i.e., neural models purposed to reconstruct general dynamic content. We compose synthetic soccer environments and conduct multiple experiments using them, identifying key components that help reconstruct soccer scenes with dynamic NeRFs. We show that, although this approach cannot fully meet the quality requirements for the target application, it suggests promising avenues toward a cost-efficient, automatic solution. We also make our work dataset and code publicly available, with the goal to encourage further efforts from the research community on the task of novel view synthesis for dynamic soccer scenes. For code, data, and video results, please see https://soccernerfs.isach. be.
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization

Federated Learning (FL) has been successfully adopted for distributed training and inference of large-scale Deep Neural Networks (DNNs). However, DNNs are characterized by an extremely large number of parameters, thus, yielding significant challenges in exchanging these parameters among distributed nodes and managing the memory. Although recent DNN compression methods (e.g., sparsification, pruning) tackle such challenges, they do not holistically consider an adaptively controlled reduction of parameter exchange while maintaining high accuracy levels. We, therefore, contribute with a novel FL framework (coined FedDIP), which combines (i) dynamic model pruning with error feedback to eliminate redundant information exchange, which contributes to significant performance improvement, with (ii) incremental regularization that can achieve extreme sparsity of models. We provide convergence analysis of FedDIP and report on a comprehensive performance and comparative assessment against state-of-the-art methods using benchmark data sets and DNN models. Our results showcase that FedDIP not only controls the model sparsity but efficiently achieves similar or better performance compared to other model pruning methods adopting incremental regularization during distributed model training. The code is available at: https://github.com/EricLoong/feddip.
Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices

The recent surge of interest surrounding Multimodal Neural Networks (MM-NN) is attributed to their ability to effectively process and integrate multiscale information from diverse data sources. MM-NNs extract and fuse features from multiple modalities using adequate unimodal backbones and specific fusion networks. Although this helps strengthen the multimodal information representation, designing such networks is labor-intensive. It requires tuning the architectural parameters of the unimodal backbones, choosing the fusing point, and selecting the operations for fusion. Furthermore, multimodality AI is emerging as a cutting-edge option in Internet of Things (IoT) systems where inference latency and energy consumption are critical metrics in addition to accuracy. In this paper, we propose Harmonic-NAS, a framework for the joint optimization of unimodal backbones and multimodal fusion networks with hardware awareness on resource-constrained devices. Harmonic-NAS involves a two-tier optimization approach for the unimodal backbone architectures and fusion strategy and operators. By incorporating the hardware dimension into the optimization, evaluation results on various devices and multimodal datasets have demonstrated the superiority of Harmonic-NAS over state-of-the-art approaches achieving up to 10.9% accuracy improvement, 1.91x latency reduction, and 2.14x energy efficiency gain.
Benchmarking Procedural Language Understanding for Low-Resource Languages: A Case Study on Turkish

Understanding procedural natural language (e.g., step-by-step instructions) is a crucial step to execution and planning. However, while there are ample corpora and downstream tasks available in English, the field lacks such resources for most languages. To address this gap, we conduct a case study on Turkish procedural texts. We first expand the number of tutorials in Turkish wikiHow from 2,000 to 52,000 using automated translation tools, where the translation quality and loyalty to the original meaning are validated by a team of experts on a random set. Then, we generate several downstream tasks on the corpus, such as linking actions, goal inference, and summarization. To tackle these tasks, we implement strong baseline models via fine-tuning large language-specific models such as TR-BART and BERTurk, as well as multilingual models such as mBART, mT5, and XLM. We find that language-specific models consistently outperform their multilingual models by a significant margin across most procedural language understanding (PLU) tasks. We release our corpus, downstream tasks and the baseline models with https://github.com/ GGLAB-KU/turkish-plu.
Electromagnetic form factors of the nucleon from $N_f = 2 + 1$ lattice QCD

There is a long-standing discrepancy between different measurements of the electric and magnetic radii of the proton. Lattice QCD calculations are a well-suited tool for theoretical investigations of the structure of the nucleon from first principles. However, all previous lattice studies of the proton's electromagnetic radii have either neglected quark-disconnected contributions or were not extrapolated to the continuum and infinite-volume limit. Here, we present results for the electromagnetic form factors of the proton and neutron computed on the $(2 + 1)$-flavor Coordinated Lattice Simulations (CLS) ensembles including both quark-connected and -disconnected contributions. From simultaneous fits to the $Q^2$-, pion-mass, lattice-spacing, and finite-volume dependence of the form factors, we determine the electric and magnetic radii and the magnetic moments of the proton and neutron. For the proton, we obtain as our final values $\langle r_E^2 \rangle^p = (0.672 \pm 0.014$ (stat)${} \pm 0.018$ (syst)$)$ fm${}^2$, $\langle r_M^2 \rangle^p = (0.658 \pm 0.012$ (stat)${} \pm 0.008$ (syst)$)$ fm${}^2$, and $\mu_M^p = 2.739 \pm 0.063$ (stat)${} \pm 0.018$ (syst). The magnetic moment is in good agreement with the experimental value, as is the one of the neutron. On the one hand, our result for the electric (charge) radius of the proton clearly points towards a small value, as favored by muonic hydrogen spectroscopy and the recent $ep$-scattering experiment by PRad. Our estimate for the magnetic radius, on the other hand, is well compatible with that inferred from the A1 $ep$-scattering experiment.
Occurrence of Chaceon Larvae in Plankton Samples from Slope Waters of the Northeastern Gulf of Mexico

Geographic, seasonal, and bathymetric distributions of Chaceon larvae are described for the northeastem Gulf of Mexico. INTRODUCTION From 1986 to 1988 a cooperative research project to define geographic, seasonal and bathymetric distribution and abundance of geryonid crabs in the northeastem Gulf of Mexico was carried out by personnel of the University of Florida, the University of South Florida, and the Gulf Coast Research Laboratory (Lockhart et al. 1990). Four seasonal cruises (May 1987, August 1987, December, 1987, and February 1988) were conducted in five distinct geographic sampling areas (Figure 1). Three depth contours (311, 494, and 677 m) were sampled in each area. MATERIALS AND METHODS Plankton tows were made at each depth contour in Areas 1 through 5. Oblique tows were taken with closing nets (1 meter diameter, 333 micron mesh) equipped with Niskin double-trip devices. Deployment rate was approximately 8 meters per minute. Tow depth and duration varied with station depth. The upper 200 meters of the water column were sampled at all depth contours (this was the only portion of the water column sampled at the 311 meter stations). At the 494 and 677 depth contours, bottom tow depth range was 200 to 400 meters and 200 to 500 meters, respectively. Samples were preserved in the field in 5% formalin and taken to Gulf Coast Research Laboratory for analysis. Samples in May, August, November/December 1987 Manuscript received November 5 , 1990; accepted November 21, 1990. and February 1988 were taken aboard the Gulf Coast Research Laboratory vessel R/V TOMMY MUNRO . An additional set of samples was taken in Area 5 in March 1988 by personnel of the University of South Florida aboard the R/V SUNCOASTER (Florida Institute of Oceanography). RESULTS Chaceon larvae occurred infrequently and in small numbers. Larvae of the red crab, Chaceon quinquedens, occurred in samples in February. Zoeal stages I, 11, and III were identified from samples taken in the upper 200 meters of the water column (Table 1). Zoeae occurred in all areas except Area 4. The fourth zoeal stage and the megalopal stage were not collected. Zoeae of Chaceon fenneri (Stuck et al. In manuscript) were more limited in distribution. Seven stage I1 zoeae were collected in a surface tow in Area 2 in February. All other occurrences were in Area 5 in March 1988; a single stage I zoea and two stage II zoeae were identified from surface samples, and one stage I1 zoea was taken in a bottom tow. TABLE 1 Number of zoeae of Chaceon quinquedens by area and developmental stage. Area Zoea I Zoea JI Zoea IU 5 1 3 3 4 3 2 6 6 1 1 9 4 1
The role of cognitive factors in second language writing and writing to learn a second language

Abstract This article revisits the role of cognitive individual differences in creating synergies between second language (L2) writing and second language acquisition research that were proposed by Kormos (2012). It takes stock of the advances in research findings, in the past decade, on the role of working memory and language learning aptitude in L2 writing processes and performance. The article offers an overview of how cognitive factors can mediate learning gains when L2 users engage in writing. Using theoretical accounts of cognitive individual differences, the article discusses how the characteristics of writing tasks can interact with individual variation in cognitive functioning. The article concludes by proposing a Task-Mediated Cognitive Model of L2 Writing and Writing to Learn that describes the role of cognitive factors in L2 writing processes and in learning through writing, and an outline of a research agenda for future studies.
Invariant ideals and their applications to the turnpike theory

Abstract In this paper, the turnpike property is established for a nonconvex optimal control problem in discrete time. The functional is defined by the notion of the ideal convergence and can be considered as an analogue of the terminal functional defined over infinite-time horizon. The turnpike property states that every optimal solution converges to some unique optimal stationary point in the sense of ideal convergence if the ideal is invariant under translations. This kind of convergence generalizes, for example, statistical convergence and convergence with respect to logarithmic density zero sets.
Why and How to Account for Sex and Gender in Brain and Behavioral Research

Long overlooked in neuroscience research, sex and gender are increasingly included as key variables potentially impacting all levels of neurobehavioral analysis. Still, many neuroscientists do not understand the difference between the terms “sex” and “gender,” the complexity and nuance of each, or how to best include them as variables in research designs. This TechSights article outlines rationales for considering the influence of sex and gender across taxa, and provides technical guidance for strengthening the rigor and reproducibility of such analyses. This guidance includes the use of appropriate statistical methods for comparing groups as well as controls for key covariates of sex (e.g., total intracranial volume) and gender (e.g., income, caregiver stress, bias). We also recommend approaches for interpreting and communicating sex- and gender-related findings about the brain, which have often been misconstrued by neuroscientists and the lay public alike.
Endoscopic Third Ventriculostomy versus Ventriculoperitoneal Shunt in Patients with Obstructive Hydrocephalus: An Updated Systematic Review and Meta-Analysis

Abstract Endoscopic third ventriculostomy (ETV) and ventriculoperitoneal shunt (VPS) are surgical methods for treating obstructive hydrocephalus. However, there is still disagreement regarding the most effective technique, in terms of both operative success and postoperative complications. Therefore, we performed a systematic review and meta-analysis to compare the efficacy and safety of these two methods in patients with obstructive hydrocephalus. We performed a systematic search of the PubMed, Scopus, and Cochrane Library databases. Randomized clinical trials (RCTs) comparing ETV and VPS in pediatric or adult patients with obstructive hydrocephalus were included. The outcomes included were operative success, postoperative cerebrospinal fluid leak, postoperative infection, postoperative or intraoperative bleeding, blockage rate, and mortality. The risk ratio (RR) was calculated with a 95% confidence interval (CI). Heterogeneity was evaluated with I 2 statistics. We used a fixed-effects model for outcomes with I 2 < 25% and DerSimonian and Laird random-effects model for other conditions. The Cochrane collaboration tool for assessing the risk of bias in randomized trials was used for risk-of-bias assessment. R, version 4.2. 1, was used for statistical analyses. Of the 2,353 identified studies, 5 RCTs were included, involving 310 patients with obstructive hydrocephalus, of which 163 underwent ETV. There was a significant difference in favor of ETV for postoperative infection (risk ratio [RR]: 0.11; 95% confidence interval [CI]: 0.04–0.33; p < 0.0001; I 2 = 0%) and blockage rate (RR: 0.15; 95% CI: 0.03–0.75; p = 0.02; I 2 = 53%). Meanwhile, there was no significant difference between groups for the postoperative or intraoperative bleeding (RR: 0.44; 95% CI: 0.17–1.15; p = 0.09; I 2 = 0%), postoperative cerebrospinal fluid leak (RR: 0.65; 95% CI: 0.22–1.92; p = 0.44; I 2 = 18%), operative success (RR: 1.18; 95% CI: 0.77–1.82; p = 0.44; I 2 = 84%), and mortality (RR: 0.19; 95% CI: 0.03–1.09; p = 0.06; I 2 = 0%). Three RCTs had some concerns about the risk of bias and one RCT had a high risk of bias due to the process of randomization and selection of reported results. Thus, this meta-analysis of RCTs evaluating ETV compared with VPS demonstrated that although there is no superiority of ETV in terms of operative success, the incidence of complications was significantly higher in patients who underwent VPS. Our results suggest that the use of ETV provides greater benefits for the treatment of obstructive hydrocephalus. However, more RCTs are needed to corroborate the superiority of ETV.