Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Reveals Large-scale Impact of Posidonia Oceanica on Mediterranean Sea Water

Posidonia oceanica is a protected endemic seagrass of Mediterranean sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset of 174 features compiled from diverse data sources. Through machine learning analysis, we discovered the existence of a robust correlation between the exact location of P. oceanica and water biogeochemical properties. The model's feature importance, showed that carbon-related variables as net biomass production and downward surface mass flux of carbon dioxide have their values altered in the areas with P. oceanica, which in turn can be used for indirect location of P. oceanica meadows. The study provides the evidence of the plant's ability to exert a global impact on the environment and underscores the crucial role of this plant in sea ecosystems, emphasizing the need for its conservation and management.
Human-machine social systems

From fake accounts on social media and generative-AI bots such as ChatGPT to high-frequency trading algorithms on financial markets and self-driving vehicles on the streets, robots, bots, and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions, and transportation arteries. Networks of multiple interdependent and interacting humans and autonomous machines constitute complex adaptive social systems where the collective outcomes cannot be simply deduced from either human or machine behavior alone. Under this paradigm, we review recent experimental, theoretical, and observational research from across a range of disciplines - robotics, human-computer interaction, web science, complexity science, computational social science, finance, economics, political science, social psychology, and sociology. We identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion, and collective decision-making, and contextualize them in four prominent existing human-machine communities: high-frequency trading markets, the social media platform formerly known as Twitter, the open-collaboration encyclopedia Wikipedia, and the news aggregation and discussion community Reddit. We conclude with suggestions for the research, design, and governance of human-machine social systems, which are necessary to reduce misinformation, prevent financial crashes, improve road safety, overcome labor market disruptions, and enable a better human future.
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. Different from previous weight pruning methods that rely on specifically designed hardware, this paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques. Specifically, we propose, for the first time to our best knowledge, post-training approaches for task-agnostic and task-specific expert pruning and skipping of MoE LLMs, tailored to improve deployment efficiency while maintaining model performance across a wide range of tasks. Extensive experiments show that our proposed methods can simultaneously reduce model sizes and increase the inference speed, while maintaining satisfactory performance. Data and code will be available at https://github.com/Lucky-Lance/Expert_Sparsity.
AuroraMag: Twin Explorer of Asymmetry in Aurora and Solar Wind-Magnetosphere Coupling

In the present-day context, small satellites and their constellations consisting of varying sizes (nano, micro, pico satellites) are being favored for remote sensing and in situ probing of the heliosphere and terrestrial magnetosphere-ionosphere system. We introduce a mission concept aimed at concurrently observing Earth's northern and southern auroral ovals while conducting in situ measurements of particles, fields, and temperature. The mission concept consists of two small satellites, each having an identical auroral X-ray imager, an in situ particle detector, a magnetometer pair, and an electron temperature analyzer onboard in an elliptical polar orbit (400X1000 km ). This mission would assist the space weather community in primarily answering important questions about the formation, morphology, and hemispherical asymmetries that we observe in the X-ray aurora, the fluxes of precipitating particles, Solar Energetic Particles, currents, and cusp dynamics. Once realized, this would be the first dedicated twin spacecraft mission of such kind to simultaneously study hemispheric asymmetries of solar-wind magnetosphere coupling. This study reveals the intricacies of the mission concept, encompassing orbital details, potential payloads, and its underlying scientific objectives. By leveraging the capabilities of small satellites, this mission concept is poised to make significant contributions to space weather monitoring and research.
On Organizational Principles of Neural Systems

How do we understand natural intelligence? Inspired by classical embodied cognition and the emerging multimodal interaction, we study the organizational principles of neural systems at three levels (device/implementation, circuit/algorithm, and system/computational) in this survey paper. Our main contributions consist of 1) Device/Implementation level: reproducibility of macroscopic states in polychronization neural group (PNG) serves as the physical basis of associative memory; 2) Circuit/Algorithm level: canonical microcircuits implement a universal predictive coding algorithm underlying all high-level cognitive functions; 3) System/Computational level: modeling sensorimotor interaction for embodied cognition plays a fundamental role in understanding natural intelligence. At each level, we use mathematical models as our abstractions and study their organizational principles (e.g., entropy reduction, predictive coding, and coordinate transformation). The unifying theme is that natural intelligence evolves by recycling a simple navigation trick in different coordinates (reference frames) hundreds of thousands of times. By representing space as a latent sequence, the neocortex solves the embodied cognition problem by building parallel cognitive maps at a large scale. We hope this survey article can inspire new research at the intersection of neuroscience and learning systems, helping bridge the gap between natural and artificial intelligence.
On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation

This study addresses the application of encoder-only Pre-trained Language Models (PLMs) in keyphrase generation (KPG) amidst the broader availability of domain-tailored encoder-only models compared to encoder-decoder models. We investigate three core inquiries: (1) the efficacy of encoder-only PLMs in KPG, (2) optimal architectural decisions for employing encoder-only PLMs in KPG, and (3) a performance comparison between in-domain encoder-only and encoder-decoder PLMs across varied resource settings. Our findings, derived from extensive experimentation in two domains reveal that with encoder-only PLMs, although KPE with Conditional Random Fields slightly excels in identifying present keyphrases, the KPG formulation renders a broader spectrum of keyphrase predictions. Additionally, prefix-LM fine-tuning of encoder-only PLMs emerges as a strong and data-efficient strategy for KPG, outperforming general-domain seq2seq PLMs. We also identify a favorable parameter allocation towards model depth rather than width when employing encoder-decoder architectures initialized with encoder-only PLMs. The study sheds light on the potential of utilizing encoder-only PLMs for advancing KPG systems and provides a groundwork for future KPG methods. Our code and pre-trained checkpoints are released at https://github.com/uclanlp/DeepKPG.
LLMs with Industrial Lens: Deciphering the Challenges and Prospects - A Survey

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions.
Strategies and safety simulations for ultrasonic cervical spinal cord neuromodulation

Focused ultrasound spinal cord neuromodulation studies have demonstrated spinal cord neuromodulation in small animals. The safe and efficacious translation of these approaches to human scale requires an understanding of ultrasound propagation and heat deposition within the human spine. To address this, combined acoustic and thermal modelling was used to assess the pressure and heat distributions produced by a 500 kHz source focused to the C5/C6 level of the cervical spine via two approaches a) the posterior acoustic window between vertebral posterior arches, or b) the lateral intervertebral foramen from which the C6 spinal nerve exits. Pulse trains of 150 0.1 s pulses with a pulse repetition frequency of 0.33 Hz and free-field spatial peak pulse-averaged intensity of 10 W/cm^2 were simulated for the CT volumes of four subjects and for $\pm$10 mm translational and $\pm$10{\deg} rotational source positioning errors. Target pressures ranged between 20% and 70% of free-field spatial peak pressures with the posterior approach, and 20% and 100% with the lateral approach. When the source was optimally positioned with the posterior approach, peak spine heating values were below 1{\deg}C, but source mis-positioning resulted in bone heating up to 4{\deg}C. Heating with the lateral approach did not exceed 2{\deg}C within the mispositioning range. There were substantial inter-subject differences in target pressures and peak heating values. Target pressure varied three to four-fold between subjects, depending on approach, while peak heating varied approximately two-fold between subjects. This results in a near ten-fold range in the target pressure achieved per degree of peak heating between subjects. This highlights the importance of developing trans-spine ultrasound simulation software for the assurance of subject-specific safety and efficacy of focused ultrasound spinal cord therapies.
YOLO-TLA: An Efficient and Lightweight Small Object Detection Model based on YOLOv5

Object detection, a crucial aspect of computer vision, has seen significant advancements in accuracy and robustness. Despite these advancements, practical applications still face notable challenges, primarily the inaccurate detection or missed detection of small objects. In this paper, we propose YOLO-TLA, an advanced object detection model building on YOLOv5. We first introduce an additional detection layer for small objects in the neck network pyramid architecture, thereby producing a feature map of a larger scale to discern finer features of small objects. Further, we integrate the C3CrossCovn module into the backbone network. This module uses sliding window feature extraction, which effectively minimizes both computational demand and the number of parameters, rendering the model more compact. Additionally, we have incorporated a global attention mechanism into the backbone network. This mechanism combines the channel information with global information to create a weighted feature map. This feature map is tailored to highlight the attributes of the object of interest, while effectively ignoring irrelevant details. In comparison to the baseline YOLOv5s model, our newly developed YOLO-TLA model has shown considerable improvements on the MS COCO validation dataset, with increases of 4.6% in mAP@0.5 and 4% in mAP@0.5:0.95, all while keeping the model size compact at 9.49M parameters. Further extending these improvements to the YOLOv5m model, the enhanced version exhibited a 1.7% and 1.9% increase in mAP@0.5 and mAP@0.5:0.95, respectively, with a total of 27.53M parameters. These results validate the YOLO-TLA model's efficient and effective performance in small object detection, achieving high accuracy with fewer parameters and computational demands.
Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization

In the rapidly advancing arena of large language models (LLMs), a key challenge is to enhance their capabilities amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets, with a specific focus on selective retention of samples that incur moderately high losses. These samples are deemed informative and beneficial for model refinement, contrasting with the highest-loss samples, which would be discarded due to their correlation with data noise and complexity. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization (IR-DRO). IR-DRO is designed to dynamically prioritize the training focus on informative samples through an instance reweighting mechanism, streamlined by a closed-form solution for straightforward integration into established training protocols. Through rigorous experimentation with various models and datasets, our findings indicate that our sample-targeted methods significantly improve LLM performance across multiple benchmarks, in both continual pre-training and instruction tuning scenarios. Our codes are available at https://github.com/VITA-Group/HardFocusTraining.
SecurePose: Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings

Movement disorders are typically diagnosed by consensus-based expert evaluation of clinically acquired patient videos. However, such broad sharing of patient videos poses risks to patient privacy. Face blurring can be used to de-identify videos, but this process is often manual and time-consuming. Available automated face blurring techniques are subject to either excessive, inconsistent, or insufficient facial blurring - all of which can be disastrous for video assessment and patient privacy. Furthermore, assessing movement disorders in these videos is often subjective. The extraction of quantifiable kinematic features can help inform movement disorder assessment in these videos, but existing methods to do this are prone to errors if using pre-blurred videos. We have developed an open-source software called SecurePose that can both achieve reliable face blurring and automated kinematic extraction in patient videos recorded in a clinic setting using an iPad. SecurePose, extracts kinematics using a pose estimation method (OpenPose), tracks and uniquely identifies all individuals in the video, identifies the patient, and performs face blurring. The software was validated on gait videos recorded in outpatient clinic visits of 116 children with cerebral palsy. The validation involved assessing intermediate steps of kinematics extraction and face blurring with manual blurring (ground truth). Moreover, when SecurePose was compared with six selected existing methods, it outperformed other methods in automated face detection and achieved ceiling accuracy in 91.08% less time than a robust manual face blurring method. Furthermore, ten experienced researchers found SecurePose easy to learn and use, as evidenced by the System Usability Scale. The results of this work validated the performance and usability of SecurePose on clinically recorded gait videos for face blurring and kinematics extraction.
Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering

Recent progress with LLM-based agents has shown promising results across various tasks. However, their use in answering questions from knowledge bases remains largely unexplored. Implementing a KBQA system using traditional methods is challenging due to the shortage of task-specific training data and the complexity of creating task-focused model structures. In this paper, we present Triad, a unified framework that utilizes an LLM-based agent with three roles for KBQA tasks. The agent is assigned three roles to tackle different KBQA subtasks: agent as a generalist for mastering various subtasks, as a decision maker for the selection of candidates, and as an advisor for answering questions with knowledge. Our KBQA framework is executed in four phases, involving the collaboration of the agent's multiple roles. We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11.8% and 20.7%, respectively.
Chern-Simons gravity and PT Symmetry

This paper considers a string inspired effective axion anomalously coupled to Abelian gauge fields and gravity. By considering the renormalisation group flows in the flat space limit it is observed that a Hermitian parity symmetric phase of the theory can flow into a non-Hermitian parity-time symmetric phase. This behavior has implications for Chern-Simons gravity. The repulsive nature of gravity, usually attributed to the existence of a positive cosmological constant, is reinterpreted at large scales as a flow from a Hermitian (attractive) gravitational theory, to a PT-symmetric (repulsive) gravity in the infrared. The discussion here is presented in the context of a Chern-Simons gravitational theory but it may be valid more generally in gravity with torsion. The validity of such a scenario in realistic theories might alleviate the need for de Sitter phases in the current epoch of the cosmological evolution, thus avoiding their associated conceptual and technical complications.
Edge Caching Based on Deep Reinforcement Learning and Transfer Learning

This paper addresses the escalating challenge of redundant data transmission in networks. The surge in traffic has strained backhaul links and backbone networks, prompting the exploration of caching solutions at the edge router. Existing work primarily relies on Markov Decision Processes (MDP) for caching issues, assuming fixed-time interval decisions; however, real-world scenarios involve random request arrivals, and despite the critical role of various file characteristics in determining an optimal caching policy, none of the related existing work considers all these file characteristics in forming a caching policy. In this paper, first, we formulate the caching problem using a semi-Markov Decision Process (SMDP) to accommodate the continuous-time nature of real-world scenarios allowing for caching decisions at random times upon file requests. Then, we propose a double deep Q-learning-based caching approach that comprehensively accounts for file features such as lifetime, size, and importance. Simulation results demonstrate the superior performance of our approach compared to a recent Deep Reinforcement Learning-based method. Furthermore, we extend our work to include a Transfer Learning (TL) approach to account for changes in file request rates in the SMDP framework. The proposed TL approach exhibits fast convergence, even in scenarios with increased differences in request rates between source and target domains, presenting a promising solution to the dynamic challenges of caching in real-world environments.
INSTRAUG: Automatic Instruction Augmentation for Multimodal Instruction Fine-tuning

Fine-tuning large language models (LLMs) on multi-task instruction-following data has been proven to be a powerful learning paradigm for improving their zero-shot capabilities on new tasks. Recent works about high-quality instruction-following data generation and selection require amounts of human labor to conceive model-understandable instructions for the given tasks and carefully filter the LLM-generated data. In this work, we introduce an automatic instruction augmentation method named INSTRAUG in multimodal tasks. It starts from a handful of basic and straightforward meta instructions but can expand an instruction-following dataset by 30 times. Results on two popular multimodal instructionfollowing benchmarks MULTIINSTRUCT and InstructBLIP show that INSTRAUG can significantly improve the alignment of multimodal large language models (MLLMs) across 12 multimodal tasks, which is even equivalent to the benefits of scaling up training data multiple times.
From Large to Small Datasets: Size Generalization for Clustering Algorithm Selection

In clustering algorithm selection, we are given a massive dataset and must efficiently select which clustering algorithm to use. We study this problem in a semi-supervised setting, with an unknown ground-truth clustering that we can only access through expensive oracle queries. Ideally, the clustering algorithm's output will be structurally close to the ground truth. We approach this problem by introducing a notion of size generalization for clustering algorithm accuracy. We identify conditions under which we can (1) subsample the massive clustering instance, (2) evaluate a set of candidate algorithms on the smaller instance, and (3) guarantee that the algorithm with the best accuracy on the small instance will have the best accuracy on the original big instance. We provide theoretical size generalization guarantees for three classic clustering algorithms: single-linkage, k-means++, and (a smoothed variant of) Gonzalez's k-centers heuristic. We validate our theoretical analysis with empirical results, observing that on real-world clustering instances, we can use a subsample of as little as 5% of the data to identify which algorithm is best on the full dataset.
Diverse Oxygen Abundance in Early Galaxies Unveiled by Auroral Line Analysis with JWST

We present deep JWST NIRSpec observations in the sightline of MACS J1149.5+2223, a massive cluster of galaxies at $z=0.54$. We report the spectroscopic redshift of 28 sources at $3<z<9.1$, including 9 sources with the detection of the [OIII]4363 auroral line. Combining these with 16 [OIII]4363-detected sources from publicly available JWST data, our sample consists of 25 galaxies with robust gas-phase metallicity measurements via the direct method. We observe a positive correlation between stellar mass and metallicity, with a $\sim0.5$\,dex offset down below the local relation. Interestingly, we find a larger than expected scatter of $\sim0.3$\,dex around the relation, which cannot be explained by redshift evolution among our sample or other third parameter. The scatter increases at higher redshift, and we attribute this to the enrichment process having higher stochasticity due to shallower potential wells, more intense feedback processes, and a higher galaxy merger rate. Despite reaching to a considerably low-mass regime ($\log M_*/M_\odot \sim7.3$), our samples have metallicity of $\log$(O/H)$+12>7$, i.e. comparable to the most metal poor galaxies in the local Universe. The search of primordial galaxies may be accomplished by extending toward a lower mass and/or by investigating inhomogeneities at smaller spatial scales. Lastly, we investigate potential systematics caused by the limitation of JWST's MSA observations. Caution is warranted when the target exceeds the slit size, as this situation could allow an overestimation of ``global"metallicity, especially under the presence of strong negative metallicity gradient.