Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring definitions of daily enteric methane emission phenotypes for genetic evaluations using a population of indoor-fed multi-breed growing cattle with feed intake data

Abstract Genetic selection has been identified as a promising approach for reducing enteric methane (CH4) emissions; a prerequisite for genetic evaluations; however, these are estimates of the necessary genetic parameters based on a population representative of where the genetic evaluations will be used. The objective of this study was, therefore, to derive genetic parameters for a series of definitions of CH4, carbon dioxide (CO2), and dry matter intake (DMI) as well as genetic correlations between CH4, CO2, and DMI in a bid to address the paucity of studies involving methane emissions measured in beef cattle using GreenFeed systems. Lastly, estimated breeding values (EBV) were generated for nine alternative definitions of CH4 using the derived genetic parameters; the EBV were validated against both phenotypic performance (adjusted for non-genetic effects) and the Legarra and Reverter method comparing EBV generated for a subset of the dataset compared to EBV generated from the entire dataset. Individual animal CH4 and CO2 records were available from a population of 1,508 multi-breed growing beef cattle using 10 GreenFeed Emission Monitoring systems. Nine trait definitions for CH4 and CO2 were derived: individual spot measures, the average of all spot measures within a 3-h, 6-h, 12-h, 1-d, 5-d, 10-d, and 15-d period and the average of all spot measures across the full test period (20 to 114 d on test). Heritability estimates from 1,155 animals, for CH4, increased as the length of the averaging period increased and ranged from 0.09 ± 0.03 for the individual spot measures trait to 0.43 ± 0.11 for the full test average trait; a similar trend existed for CO2 with the estimated heritability ranging from 0.17 ± 0.04 to 0.50 ± 0.11. Enteric CH4 was moderately to strongly genetically correlated with DMI with a genetic correlation of 0.72 ± 0.02 between the spot measures of CH4 and a 1-d average DMI. Correlations, adjusted for heritability, between the adjusted phenotype and (parental average) EBV ranged from 0.56 to 1.14 across CH4 definitions and the slope between the adjusted phenotype and EBV ranged from 0.92 to 1.16 (expectation = 1). Validation results from the Legarra and Reverter regression method revealed a level bias of between −0.81 and −0.45, a dispersion bias of between 0.93 and 1.17, and ratio accuracy (ratio of the partial evaluation accuracies on whole evaluation accuracies) from 0.28 to 0.38. While EBV validation results yielded no consensus, CH4 is a moderately heritable trait, and selection for reduced CH4 is achievable.
Coagulation Testing in Real-World Setting: Insights From a Comprehensive Survey

The objective of this survey was to gain a real-world perspective on coagulation testing by evaluating the availability of various coagulation laboratory tests, assessing specific analytic and postanalytic steps in clinical laboratories in Korea. Participants were surveyed using a 65-question questionnaire specifically focused on their coagulation testing practices related to prothrombin time (PT), activated partial thromboplastin time (aPTT), plasma-mixing studies, lupus anticoagulant (LA) tests, platelet function tests, coagulation factor assays, and the composition of hemostasis and thrombosis test panels. The survey was performed between July and September 2022. The survey achieved a 77.9% (81 of 104) response rate. PT or aPTT tests were performed directly at all participating institutions, followed by D-dimer and fibrinogen tests, platelet function test, and plasma-mixing studies in order of frequency. Variations existed in the performance of mixing test and LA assessment. Patterns of coagulating testing differed depending on the size of the hospital. The survey revealed that most laboratories conducted coagulation tests following the international guidelines such as Clinical Laboratory Standards Institute guidelines and the Korean Laboratory Certification system. However, some coagulation tests, including mixing test and LA tests, are yet to be standardized in Korea. Continuous education on coagulation test methods and internal and external quality control are required to encourage laboratories to enhance the performance of coagulation testing.
Effects of Traditional Chinese Medicine “Fuzheng Qingdu Decoction” on Autonomic Function and Cancer-Related Symptoms in Patients with Advanced Gastric Cancer undergoing Chemotherapy: A Controlled Trial

Objective: To evaluate the effects of Fuzheng Qingdu Decoction (FZQDD) on the autonomic function and cancer-related symptoms of patients with advanced gastric cancer undergoing chemotherapy to verify its clinical efficacy. Methods: Sixty-two patients with stage III or IV gastric cancer were included in this study. The patients were divided into 2 groups: the chemotherapy (33 patients) and chemotherapy with FZQDD (29 patients) groups. The primary outcome was the autonomic function of the patients before and after the interventions. The parameters that were used to assess autonomic function were deceleration capacity (DC) and acceleration capacity (AC) of heart rate and heart rate variability (HRV), which comprised standard deviation of the normal-normal interval (SDNN), root mean square of successive interval differences (RMSSD), low-frequency power (LF), high-frequency power (HF), total power (TP), and LF–HF ratio. The secondary outcomes were cancer-related symptoms and the quality of life. Results: DC and HRV parameters (ie, SDNN, RMSSD, LF, HF, and TP) were significantly decreased in the chemotherapy group; however, AC significantly increased after the interventions. No significant differences were observed in the DC, AC, and HRV parameters before and after the interventions in the chemotherapy with FZQDD group. Nevertheless, the changes in DC, AC, and HRV parameters (SDNN, RMSSD, HF, and TP) before and after the interventions were statistically significant between both the groups. FZQDD significantly improved the cancer-related symptoms and the quality of life of the patients. Conclusions: Oxaliplatin combined with S-1 (tegafur, gimeracil, and oteracil potassium) can impair autonomic modulation in patients with advanced gastric cancer. FZQDD can alleviate autonomic dysfunction by increasing the parasympathetic activity and decreasing the sympathetic tone, helping patients restore the dynamic sympathovagal balance, and significantly improving the cancer-related symptoms and the quality of life of patients.
Mapping Geological Events and Nitrogen Fixation Evolution Onto the Timetree of the Evolution of Nitrogen-Fixation Genes

Abstract Nitrogen is essential for all organisms, but biological nitrogen fixation (BNF) occurs only in a small fraction of prokaryotes. Previous studies divided nitrogenase-gene-carrying prokaryotes into Groups I to IV and provided evidence that BNF first evolved in bacteria. This study constructed a timetree of the evolution of nitrogen-fixation genes and estimated that archaea evolved BNF much later than bacteria and that nitrogen-fixing cyanobacteria evolved later than 1,900 MYA, considerably younger than the previous estimate of 2,200 MYA. Moreover, Groups III and II/I diverged ∼2,280 MYA, after the Kenorland supercontinent breakup (∼2,500–2,100 MYA) and the Great Oxidation Event (∼2,400–2,100 MYA); Groups III and Vnf/Anf diverged ∼2,086 MYA, after the Yarrabubba impact (∼2,229 MYA); and Groups II and I diverged ∼1,920 MYA, after the Vredefort impact (∼2,023 MYA). In summary, this study provided a timescale of BNF events and discussed the possible effects of geological events on BNF evolution.
The unique dual targeting of AGO1 by two types of PRMT enzymes promotes phasiRNA loading in Arabidopsis thaliana

Abstract Arginine/R methylation (R-met) of proteins is a widespread post-translational modification (PTM), deposited by a family of protein arginine/R methyl transferase enzymes (PRMT). Regulations by R-met are involved in key biological processes deeply studied in metazoan. Among those, post-transcriptional gene silencing (PTGS) can be regulated by R-met in animals and in plants. It mainly contributes to safeguard processes as protection of genome integrity in germlines through the regulation of piRNA pathway in metazoan, or response to bacterial infection through the control of AGO2 in plants. So far, only PRMT5 has been identified as the AGO/PIWI R-met writer in higher eukaryotes. We uncovered that AGO1, the main PTGS effector regulating plant development, contains unique R-met features among the AGO/PIWI superfamily, and outstanding in eukaryotes. Indeed, AGO1 contains both symmetric (sDMA) and asymmetric (aDMA) R-dimethylations and is dually targeted by PRMT5 and by another type I PRMT in Arabidopsis thaliana. We showed also that loss of sDMA didn’t compromise AtAGO1 subcellular trafficking in planta. Interestingly, we underscored that AtPRMT5 specifically promotes the loading of phasiRNA in AtAGO1. All our observations bring to consider this dual regulation of AtAGO1 in plant development and response to environment, and pinpoint the complexity of AGO1 post-translational regulation.
An Enhanced Genetic Algorithm and its Application on Nonlinear Geophysical Problems

Since its inception in 1975, Genetics Algorithms (GAs) have been successfully used as a tool for global optimization on non-convex problems in a wide range of real world applications. Its creation was inspired by natural adaptation and selection mechanisms that evolve from one population of chromosomes to a ﬁtter population by means of an artiﬁcial natural selection dictated by the operators of elitism, crossover and mutation. An advanced Genetic Algorithm (aGA) was proposed by Sun et al. [2017], and this algorithm seeks the global maximum of n-th dimensional non-convex functions. However, convergence speed is a key factor for the success of a global optimization algorithm when it comes to scalability; in production even a slight eﬃciency improvement matters as it can easily takes weeks or even months to process a huge data set. Therefore, the goal of this project is to improve the convergence speed of the currently available aGA by simultaneously enhancing both its global and its local search capabilities. To this end, two solutions were proposed. The ﬁrst is a modiﬁed version of the well known Island model GAs and the second was named Self Adaptive Diﬀerential Evolution (SADE) ﬁne tuning scheme. After a successful demonstration of its improved performance on several multi-modal test functions, the enhanced Genetic Algorithm (eGA) is used to tackle two common non-linear geophysical problems: static correction and Common Reﬂection Surface (CRS) stacking. In the former, the near-surface related time-shifts are estimated without resorting to an explicit velocity-depth model; instead, the events of interest are aligned in a data-driven fashion by maximizing the stacking power. The latter is a novel alternative to the traditional Common Midpoint (CMP) stacking that has proven to yield higher quality images, specially when applied to low Signal to Noise Ratio (SNR) data or data with challenging structures like the anomalies encountered in the subsurface. This improvement in the quality of the stacked image is attributed to a non-local mean mentality, which enables using traces from diﬀerent CMP gathers to the CMP gather being resolved. Owing to the high non-linearity of both problems, they are ideal test beds for global optimization algorithms. The eﬀectiveness of the eGA is demonstrated on synthetic data sets with promising results in these problems.
PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels

The quadratic time and memory complexity inherent to self-attention mechanisms, with respect to sequence length, presents a critical computational bottleneck in the training and deployment of large-scale Transformer-based language models. Recent theoretical results indicate the intractability of sub-quadratic softmax attention approximation under reasonable complexity assumptions. This paper addresses this challenge by first demonstrating that polynomial attention with high degree can effectively replace softmax without sacrificing model quality. Next, we develop polynomial sketching techniques from numerical linear algebra to achieve linear-time polynomial attention with approximation guarantees. Crucially, our approach achieves this speedup without requiring the sparsification of attention matrices. We also present a block-based algorithm to apply causal masking efficiently. Combining these techniques, we provide \emph{PolySketchFormer}, a practical linear-time Transformer architecture for language modeling that offers provable guarantees. We validate PolySketchFormer empirically by training language models capable of handling long contexts. These experiments utilize both synthetic and real-world datasets (PG19, Wikipedia and C4) on Google Cloud TPUs. For context lengths of 32k and GPT-2 style models, our model achieves a 2.5-4x speedup in training compared to FlashAttention, with no observed degradation in quality across our experiments.
Fluctuating hydrodynamics of active particles interacting via chemotaxis and quorum sensing: static and dynamics

In this article we derive and test the fluctuating hydrodynamic description of active particles interacting via taxis and quorum sensing, both for mono-disperse systems and for mixtures of co-existing species of active particles. We compute the average steady-state density profile in the presence of spatial motility regulation, as well as the structure factor and intermediate scattering function for interacting systems. By comparing our predictions to microscopic numerical simulations, we show that our fluctuating hydrodynamics correctly predicts the large-scale static and dynamical properties of the system. We also discuss how the theory breaks down when structures emerge at scales smaller or comparable to the persistence length of the particles. When the density field is the unique hydrodynamic mode of the system, we show that active Brownian particles, run-and-tumble particles and active Ornstein-Uhlenbeck particles, interacting via quorum-sensing or chemotactic interactions, display undistinguishable large-scale properties. This form of universality implies an interesting robustness of the predicted physics but also that large-scale observations of patterns are insufficient to assess their microscopic origins.
LESS: Selecting Influential Data for Targeted Instruction Tuning

Instruction tuning has unlocked powerful capabilities in large language models (LLMs), effectively using combined datasets to develop generalpurpose chatbots. However, real-world applications often require a specialized suite of skills (e.g., reasoning). The challenge lies in identifying the most relevant data from these extensive datasets to effectively develop specific capabilities, a setting we frame as targeted instruction tuning. We propose LESS, an optimizer-aware and practically efficient algorithm to effectively estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. Crucially, LESS adapts existing influence formulations to work with the Adam optimizer and variable-length instruction data. LESS first constructs a highly reusable and transferable gradient datastore with low-dimensional gradient features and then selects examples based on their similarity to few-shot examples embodying a specific capability. Experiments show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Furthermore, the selected data is highly transferable: smaller models can be leveraged to select useful data for larger models and models from different families. Our qualitative analysis shows that our method goes beyond surface form cues to identify data that exemplifies the necessary reasoning skills for the intended downstream application.
Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation

This paper introduces a novel approach to leverage the generalizability capability of Diffusion Models for Source-Free Domain Adaptation (DM-SFDA). Our proposed DM-SFDA method involves fine-tuning a pre-trained text-to-image diffusion model to generate source domain images using features from the target images to guide the diffusion process. Specifically, the pre-trained diffusion model is fine-tuned to generate source samples that minimize entropy and maximize confidence for the pre-trained source model. We then apply established unsupervised domain adaptation techniques to align the generated source images with target domain data. We validate our approach through comprehensive experiments across a range of datasets, including Office-31, Office-Home, and VisDA. The results highlight significant improvements in SFDA performance, showcasing the potential of diffusion models in generating contextually relevant, domain-specific images.
Chatbots in Knowledge-Intensive Contexts: Comparing Intent and LLM-Based Systems

Cognitive assistants (CA) are chatbots that provide context-aware support to human workers in knowledge-intensive tasks. Traditionally, cognitive assistants respond in specific ways to predefined user intents and conversation patterns. However, this rigidness does not handle the diversity of natural language well. Recent advances in natural language processing (NLP), powering large language models (LLM) such as GPT-4, Llama2, and Gemini, could enable CAs to converse in a more flexible, human-like manner. However, the additional degrees of freedom may have unforeseen consequences, especially in knowledge-intensive contexts where accuracy is crucial. As a preliminary step to assessing the potential of using LLMs in these contexts, we conducted a user study comparing an LLM-based CA to an intent-based system regarding interaction efficiency, user experience, workload, and usability. This revealed that LLM-based CAs exhibited better user experience, task completion rate, usability, and perceived performance than intent-based systems, suggesting that switching NLP techniques should be investigated further.
Pedagogical Alignment of Large Language Models

In this paper, we introduce the novel concept of pedagogically aligned Large Language Models (LLMs) that signifies a transformative shift in the application of LLMs within educational contexts. Rather than providing direct responses to user queries, pedagogically-aligned LLMs function as scaffolding tools, breaking complex problems into manageable subproblems and guiding students towards the final answer through constructive feedback and hints. The objective is to equip learners with problem-solving strategies that deepen their understanding and internalization of the subject matter. Previous research in this field has primarily applied the supervised finetuning approach without framing the objective as an alignment problem, hence not employing reinforcement learning through human feedback (RLHF) methods. This study reinterprets the narrative by viewing the task through the lens of alignment and demonstrates how RLHF methods emerge naturally as a superior alternative for aligning LLM behaviour. Building on this perspective, we propose a novel approach for constructing a reward dataset specifically designed for the pedagogical alignment of LLMs. We apply three state-of-the-art RLHF algorithms and find that they outperform SFT significantly. Our qualitative analyses across model differences and hyperparameter sensitivity further validate the superiority of RLHF over SFT. Also, our study sheds light on the potential of online feedback for enhancing the performance of pedagogically-aligned LLMs, thus providing valuable insights for the advancement of these models in educational settings.
Critical axis of Ruelle resonances for Anosov flow with a potential

We combine methods from microlocal analysis and dimension theory to study resonances with largest real part for an Anosov flow with smooth real valued potential. We show that the resonant states are closely related to special systems of measures supported on the stable manifolds introduced by Climenhaga. As a result, we relate the presence of the resonances on the critical axis to mixing properties of the flow with respect to certain equilibrium measures and show that these equilibrium measures can be reconstructed from the spectral theory of the Anosov flow.
Noise Map Guidance: Inversion with Spatial Context for Real Image Editing

Text-guided diffusion models have become a popular tool in image synthesis, known for producing high-quality and diverse images. However, their application to editing real images often encounters hurdles primarily due to the text condition deteriorating the reconstruction quality and subsequently affecting editing fidelity. Null-text Inversion (NTI) has made strides in this area, but it fails to capture spatial context and requires computationally intensive per-timestep optimization. Addressing these challenges, we present Noise Map Guidance (NMG), an inversion method rich in a spatial context, tailored for real-image editing. Significantly, NMG achieves this without necessitating optimization, yet preserves the editing quality. Our empirical investigations highlight NMG's adaptability across various editing techniques and its robustness to variants of DDIM inversions.
Curvature-Informed SGD via General Purpose Lie-Group Preconditioners

We present a novel approach to accelerate stochastic gradient descent (SGD) by utilizing curvature information obtained from Hessian-vector products or finite differences of parameters and gradients, similar to the BFGS algorithm. Our approach involves two preconditioners: a matrix-free preconditioner and a low-rank approximation preconditioner. We update both preconditioners online using a criterion that is robust to stochastic gradient noise and does not require line search or damping. To preserve the corresponding symmetry or invariance, our preconditioners are constrained to certain connected Lie groups. The Lie group's equivariance property simplifies the preconditioner fitting process, while its invariance property eliminates the need for damping, which is commonly required in second-order optimizers. As a result, the learning rate for parameter updating and the step size for preconditioner fitting are naturally normalized, and their default values work well in most scenarios. Our proposed approach offers a promising direction for improving the convergence of SGD with low computational overhead. We demonstrate that Preconditioned SGD (PSGD) outperforms SoTA on Vision, NLP, and RL tasks across multiple modern deep-learning architectures. We have provided code for reproducing toy and large scale experiments in this paper.