Miracle-dz/UniFAR-FTUs huggingface dataset:
The Floyd-Warshall Algorithm Re-implemented Using 3D-Tensors and Hardware Acceleration

The Floyd-Warshall(FW) algorithm, is an ancient but a largely important algorithm used to solve the all-pairs simple-paths(APSP) problem. While the algorithm is available for use in open-source graph optimization libraries such as NetworkX, they do not take advantage of modern parallel processing hardware such as Graphics Processing Units(GPUs), which would reduce compute time to a fraction of its iterative or recursive implementations. In this work, a re-implementation of the Floyd-Warshall algorithm using open-source GPU libraries such as PyTorch is presented. A further implementation of the R-Kleene is also described, a slightly newer algorithm used for solving the APSP problem in a divide-and-conquer, recursive but highly parallelized architecture. In addition, a random graph generator that generates a wide range of graphs of different scales is also contributed, where the densities and connectivities are controlled using some heuristics. The run-times of the GPU accelerated FW algorithm and R-Kleene on these heuristically generated graphs are evaluated against each other and to the widely used implementation from NetworkX. The code for the GPU implementation of the algorithms, the random graph generator, and the Blender animation file are available at https://github.com/tanjary21/APSP_GPU/.
Secondary Outflow Driven by the Protostar Ser-emb 15 in Serpens

We present the detection of a secondary outflow associated with a Class I source, Ser-emb 15, in the Serpens Molecular Cloud. We reveal two pairs of molecular outflows consisting of three lobes, that is, primary and secondary outflows, using Atacama Large Millimeter/submillimeter Array 12CO and SiO line observations at a resolution of ∼318 au. The secondary outflow is elongated approximately perpendicular to the axis of the primary outflow in the plane of the sky. We also identify two compact structures, Sources A and B, within an extended structure associated with Ser-emb 15 in the 1.3 mm continuum emission at a resolution of ∼40 au. The projected sizes of Sources A and B are 137 au and 60 au, respectively. Assuming a dust temperature of 20 K, we estimate the dust mass to be 2.4 × 10−3 M ⊙ for Source A and 3.3 × 10−4 M ⊙ for Source B. C18O line data imply rotational motion around the extended structure, but we cannot resolve rotational motion in Source A and/or B because the angular and frequency resolutions are insufficient. Therefore, we cannot conclude whether Ser-emb 15 is a single or binary system. Thus, either Source A or Source B could drive the secondary outflow. We discuss two scenarios that might explain the driving mechanism of the primary and secondary outflows: the Ser-emb 15 system is (1) a binary system composed of Sources A and B, or (2) a single-star system composed of Source A alone. In either case, the system could be a suitable target for investigating the disk and/or binary formation processes in complicated environments. Detecting these outflows should contribute to understanding complex star-forming environments, which may be common in the star formation processes.
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach

Adversarial training (AT) is a canonical method for enhancing the robustness of deep neural networks (DNNs). However, recent studies empirically demonstrated that it suffers from robust overfitting, i.e., a long time AT can be detrimental to the robustness of DNNs. This paper presents a theoretical explanation of robust overfitting for DNNs. Specifically, we non-trivially extend the neural tangent kernel (NTK) theory to AT and prove that an adversarially trained wide DNN can be well approximated by a linearized DNN. Moreover, for squared loss, closed-form AT dynamics for the linearized DNN can be derived, which reveals a new AT degeneration phenomenon: a long-term AT will result in a wide DNN degenerates to that obtained without AT and thus cause robust overfitting. Based on our theoretical results, we further design a method namely Adv-NTK, the first AT algorithm for infinite-width DNNs. Experiments on real-world datasets show that Adv-NTK can help infinite-width DNNs enhance comparable robustness to that of their finite-width counterparts, which in turn justifies our theoretical findings. The code is available at https://github.com/fshp971/adv-ntk.
Quantifying Zero-shot Coordination Capability with Behavior Preferring Partners

Zero-shot coordination (ZSC) is a new challenge focusing on generalizing learned coordination skills to unseen partners. Existing methods train the ego agent with partners from pre-trained or evolving populations. The agent's ZSC capability is typically evaluated with a few evaluation partners, including human and agent, and reported by mean returns. Current evaluation methods for ZSC capability still need to improve in constructing diverse evaluation partners and comprehensively measuring the ZSC capability. We aim to create a reliable, comprehensive, and efficient evaluation method for ZSC capability. We formally define the ideal 'diversity-complete' evaluation partners and propose the best response (BR) diversity, which is the population diversity of the BRs to the partners, to approximate the ideal evaluation partners. We propose an evaluation workflow including 'diversity-complete' evaluation partners construction and a multi-dimensional metric, the Best Response Proximity (BR-Prox) metric. BR-Prox quantifies the ZSC capability as the performance similarity to each evaluation partner's approximate best response, demonstrating generalization capability and improvement potential. We re-evaluate strong ZSC methods in the Overcooked environment using the proposed evaluation workflow. Surprisingly, the results in some of the most used layouts fail to distinguish the performance of different ZSC methods. Moreover, the evaluated ZSC methods must produce more diverse and high-performing training partners. Our proposed evaluation workflow calls for a change in how we efficiently evaluate ZSC methods as a supplement to human evaluation.
Study on the impact of uncertain design parameters on the performances of a permanent magnet assisted synchronous reluctance motor

In this paper, deterministic and robust design optimizations of a permanent magnet assisted synchronous reluctance motor were performed to study the impact of different uncertain input parameters on the design. These optimizations were carried out using a surrogate model based on 2-D finite element simulations. Different robust optimizations considering geometric and magnetic uncertain parameters were compared to the deterministic optimization. It was noticed that both geometrical and magnetic properties tolerances greatly impact the machines’ mean torque and torque ripple, where the magnetic properties tolerances had a more significant impact on the mean torque. In such a case, robust optimization is essential to find optimal and robust electric motors designs.
Constructive Large Language Models Alignment with Diverse Feedback

In recent research on large language models (LLMs), there has been a growing emphasis on aligning these models with human values to reduce the impact of harmful content. However, current alignment methods often rely solely on singular forms of human feedback, such as preferences, annotated labels, or natural language critiques, overlooking the potential advantages of combining these feedback types. This limitation leads to suboptimal performance, even when ample training data is available. In this paper, we introduce Constructive and Diverse Feedback (CDF) as a novel method to enhance LLM alignment, inspired by constructivist learning theory. Our approach involves collecting three distinct types of feedback tailored to problems of varying difficulty levels within the training dataset. Specifically, we exploit critique feedback for easy problems, refinement feedback for medium problems, and preference feedback for hard problems. By training our model with this diversified feedback, we achieve enhanced alignment performance while using less training data. To assess the effectiveness of CDF, we evaluate it against previous methods in three downstream tasks: question answering, dialog generation, and text summarization. Experimental results demonstrate that CDF achieves superior performance even with a smaller training dataset.
Generative quantum machine learning via denoising diffusion probabilistic models

Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Denoising diffusion probabilistic models (DDPMs) have recently gained much attention due to their ability to generate diverse and high-quality samples in many computer vision tasks, as well as to incorporate flexible model architectures and a relatively simple training scheme. Quantum generative models, empowered by entanglement and superposition, have brought new insight to learning classical and quantum data. Inspired by the classical counterpart, we propose the quantum denoising diffusion probabilistic model (QuDDPM) to enable efficiently trainable generative learning of quantum data. QuDDPM adopts sufficient layers of circuits to guarantee expressivity, while it introduces multiple intermediate training tasks as interpolation between the target distribution and noise to avoid barren plateau and guarantee efficient training. We provide bounds on the learning error and demonstrate QuDDPM's capability in learning correlated quantum noise model, quantum many-body phases, and topological structure of quantum data. The results provide a paradigm for versatile and efficient quantum generative learning.
ANALISIS DE LA ALIMENTACI0N DE LA NUTRIA LONTRA LONGICAUDIS (MAMMALIA: CARNIVORA) EN UN SECTOR DEL RIO LOS PESCADOS, VERACRUZ, MEXICO

Se presentan los resultados del análisis de 474 excretas de nutria, colectadas en el río Los Pescados, Veracruz, entre diciembre de 1994 y agosto de 1995. Los grupos de presas que presentaron los más altos porcentajes de aparición fueron los peces (54.1%) y los crustáceos (30.8%); en menor grado estuvieron representados los insectos (7.5%), los reptiles (6.2%) y las aves (1.4%). Las especies con el mayor porcentaje de aparición fueron los langostinos Macrobrachium spp (21.3%) y los peces Agonostomus monticola (15.2%), Cichlasoma meeki (11.8%) y Sicydium gymnogaster (11.0%). Se detectó variación estacional en la dieta de la nutria, considerando las épocas seca y de lluvias.
The 1 $\rightarrow$ 3 Massive Splitting Functions from QCD Factorization and SCET

Splitting functions are universal functions describing the collinear dynamics of gauge theories, and as such are crucial ingredients for a wide variety of calculations in perturbative QCD. We present analytic results for the triple collinear splitting functions in QCD with a single massive parton. We derive the splitting functions using two distinct methods; first by expanding the squared matrix elements in the collinear limit, and secondly by using soft-collinear effective theory with massive quarks. We find agreement between these two approaches, providing a strong check of our results. Additionally, we also check all iterated and soft limits of our results, finding agreement with predictions from factorization. Our results provide an important ingredient for higher order perturbative calculations involving massive partons, and for the description of the collinear dynamics of heavy flavor jets.
Small batch deep reinforcement learning

In value-based deep reinforcement learning with replay memories, the batch size parameter specifies how many transitions to sample for each gradient update. Although critical to the learning process, this value is typically not adjusted when proposing new algorithms. In this work we present a broad empirical study that suggests {\em reducing} the batch size can result in a number of significant performance gains; this is surprising, as the general tendency when training neural networks is towards larger batch sizes for improved performance. We complement our experimental findings with a set of empirical analyses towards better understanding this phenomenon.
Locality-Aware Generalizable Implicit Neural Representation

Generalizable implicit neural representation (INR) enables a single continuous function, i.e., a coordinate-based neural network, to represent multiple data instances by modulating its weights or intermediate features using latent codes. However, the expressive power of the state-of-the-art modulation is limited due to its inability to localize and capture fine-grained details of data entities such as specific pixels and rays. To address this issue, we propose a novel framework for generalizable INR that combines a transformer encoder with a locality-aware INR decoder. The transformer encoder predicts a set of latent tokens from a data instance to encode local information into each latent token. The locality-aware INR decoder extracts a modulation vector by selectively aggregating the latent tokens via cross-attention for a coordinate input and then predicts the output by progressively decoding with coarse-to-fine modulation through multiple frequency bandwidths. The selective token aggregation and the multi-band feature modulation enable us to learn locality-aware representation in spatial and spectral aspects, respectively. Our framework significantly outperforms previous generalizable INRs and validates the usefulness of the locality-aware latents for downstream tasks such as image generation.
MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents

Significant advancements have occurred in the application of Large Language Models (LLMs) for various tasks and social simulations. Despite this, their capacities to coordinate within task-oriented social contexts are under-explored. Such capabilities are crucial if LLMs are to effectively mimic human-like social behavior and produce meaningful results. To bridge this gap, we introduce collaborative generative agents, endowing LLM-based Agents with consistent behavior patterns and task-solving abilities. We situate these agents in a simulated job fair environment as a case study to scrutinize their coordination skills. We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills. Our evaluation demonstrates that these agents show promising performance. However, we also uncover limitations that hinder their effectiveness in more complex coordination tasks. Our work provides valuable insights into the role and evolution of LLMs in task-oriented social simulations.
Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models

Over the last decade, several regulatory bodies have started requiring the disclosure of non-financial information from publicly listed companies, in light of the investors' increasing attention to Environmental, Social, and Governance (ESG) issues. Such information is publicly released in a variety of non-structured and multi-modal documentation. Hence, it is not straightforward to aggregate and consolidate such data in a cohesive framework to further derive insights about sustainability practices across companies and markets. Thus, it is natural to resort to Information Extraction (IE) techniques to provide concise, informative and actionable data to the stakeholders. Moving beyond traditional text processing techniques, in this work we leverage Large Language Models (LLMs), along with prominent approaches such as Retrieved Augmented Generation and in-context learning, to extract semantically structured information from sustainability reports. We then adopt graph-based representations to generate meaningful statistical, similarity and correlation analyses concerning the obtained findings, highlighting the prominent sustainability actions undertaken across industries and discussing emerging similarity and disclosing patterns at company, sector and region levels. Lastly, we investigate which factual aspects impact the most on companies' ESG scores using our findings and other company information.
STOPNet: Multiview-based 6-DoF Suction Detection for Transparent Objects on Production Lines

In this work, we present STOPNet, a framework for 6-DoF object suction detection on production lines, with a focus on but not limited to transparent objects, which is an important and challenging problem in robotic systems and modern industry. Current methods requiring depth input fail on transparent objects due to depth cameras' deficiency in sensing their geometry, while we proposed a novel framework to reconstruct the scene on the production line depending only on RGB input, based on multiview stereo. Compared to existing works, our method not only reconstructs the whole 3D scene in order to obtain high-quality 6-DoF suction poses in real time but also generalizes to novel environments, novel arrangements and novel objects, including challenging transparent objects, both in simulation and the real world. Extensive experiments in simulation and the real world show that our method significantly surpasses the baselines and has better generalizability, which caters to practical industrial needs.
Efficient Graduated Non-Convexity for Pose Graph Optimization

We propose a novel approach to Graduated Non-Convexity (GNC) and demonstrate its efficacy through its application in robust pose graph optimization, a key component in SLAM backends. Traditional GNC methods often rely on heuristic methods for GNC schedule, updating control parameter {\mu} for escalating the non-convexity. In contrast, our approach leverages the properties of convex functions and convex optimization to identify the boundary points beyond which convexity is no longer guaranteed, thereby eliminating redundant optimization steps in existing methodologies and enhancing both speed and robustness. We show that our method outperforms the state-of-the-art method in terms of speed and accuracy when used for robust back-end pose graph optimization via GNC. Our work builds upon and enhances the open-source riSAM framework. Our implementation can be accessed from: https://github.com/SNU-DLLAB/EGNC-PGO
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design

A significant amount of protein function requires binding small molecules, including enzymatic catalysis. As such, designing binding pockets for small molecules has several impactful applications ranging from drug synthesis to energy storage. Towards this goal, we first develop HarmonicFlow, an improved generative process over 3D protein-ligand binding structures based on our self-conditioned flow matching objective. FlowSite extends this flow model to jointly generate a protein pocket's discrete residue types and the molecule's binding 3D structure. We show that HarmonicFlow improves upon the state-of-the-art generative processes for docking in simplicity, generality, and performance. Enabled by this structure modeling, FlowSite designs binding sites substantially better than baseline approaches and provides the first general solution for binding site design.
Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates

Across various sectors such as healthcare, criminal justice, national security, finance, and technology, large-scale machine learning (ML) and artificial intelligence (AI) systems are being deployed to make critical data-driven decisions. Many have asked if we can and should trust these ML systems to be making these decisions. Two critical components are prerequisites for trust in ML systems: interpretability, or the ability to understand why the ML system makes the decisions it does, and fairness, which ensures that ML systems do not exhibit bias against certain individuals or groups. Both interpretability and fairness are important and have separately received abundant attention in the ML literature, but so far, there have been very few methods developed to directly interpret models with regard to their fairness. In this paper, we focus on arguably the most popular type of ML interpretation: feature importance scores. Inspired by the use of decision trees in knowledge distillation, we propose to leverage trees as interpretable surrogates for complex black-box ML models. Specifically, we develop a novel fair feature importance score for trees that can be used to interpret how each feature contributes to fairness or bias in trees, tree-based ensembles, or tree-based surrogates of any complex ML system. Like the popular mean decrease in impurity for trees, our Fair Feature Importance Score is defined based on the mean decrease (or increase) in group bias. Through simulations as well as real examples on benchmark fairness datasets, we demonstrate that our Fair Feature Importance Score offers valid interpretations for both tree-based ensembles and tree-based surrogates of other ML systems.
Supernova Remnants in the Irregular Galaxy NGC 4449

The nearby irregular galaxy NGC 4449 has a star formation rate of ∼0.4M ☉ yr−1 and should host of order 70 supernova remnants (SNRs) younger than 20,000 yr, a typical age for SNRs expanding into an interstellar medium (ISM) with a density of 1 cm−3 to reach the radiative phase. We have carried out an optical imaging and spectroscopic survey in an attempt to identify these SNRs. This task is challenging because diffuse gas with elevated ratios of [S ii]:Hα is omnipresent in NGC 4449, causing confusion when using this common diagnostic for SNRs. Using narrowband interference-filter images, we first identified 49 objects that have elevated [S ii]:Hα ratios compared to nearby H ii regions. Using Gemini-N and GMOS, we then obtained high-resolution spectra of 30 of these SNR candidates, 25 of which have [S ii]:Hα ratios greater than 0.5. Of these, 15 nebulae are almost certainly SNRs, based on a combination of characteristics: higher [O i]:Hα ratios and broader line widths than observed from H ii regions. The remainder are good candidates as well, but need additional confirmation. Surprisingly, despite having superior imaging and spectroscopic data sets to examine, we are unable to confirm most of the candidates suggested by Leonidaki et al. While NGC 4449 is likely an extreme case because of the high surface brightness and elevated [S ii]:Hα ratio of diffuse gas, it highlights the need for sensitive high-resolution optical spectroscopy, or high spatial resolution radio or X-ray observations that can ensure accurate SNR identifications in external galaxies.
Safe-by-Construction Autonomous Vehicle Overtaking using Control Barrier Functions and Model Predictive Control

Ensuring safety for vehicle overtaking systems is one of the most fundamental and challenging tasks in autonomous driving. This task is particularly intricate when the vehicle must not only overtake its front vehicle safely but also consider the presence of potential opposing vehicles in the opposite lane that it will temporarily occupy. In order to tackle the overtaking task in such challenging scenarios, we introduce a novel integrated framework tailored for vehicle overtaking maneuvers. Our approach integrates the theories of varying-level control barrier functions (CBF) and time-optimal model predictive control (MPC). The main feature of our proposed overtaking strategy is that it is safe-by-construction, which enables rigorous mathematical proof and validation of the safety guarantees. We show that the proposed framework is applicable when the opposing vehicle is either fully autonomous or driven by human drivers. To demonstrate our framework, we perform a set of simulations for overtaking scenarios under different settings. The simulation results show the superiority of our framework in the sense that it ensures collision-free and achieves better safety performance compared with the standard MPC-based approach without safety guarantees.