Miracle-dz/UniFAR-FTUs huggingface dataset:
Agent Coordination via Contextual Regression (AgentCONCUR) for Data Center Flexibility

A network of spatially distributed data centers can provide operational flexibility to power systems by shifting computing tasks among electrically remote locations. However, harnessing this flexibility in real-time through the standard optimization techniques is challenged by the need for sensitive operational datasets and substantial computational resources. To alleviate the data and computational requirements, this paper introduces a coordination mechanism based on contextual regression. This mechanism, abbreviated as AgentCONCUR, associates cost-optimal task shifts with public and trusted contextual data (e.g., real-time prices) and uses regression on this data as a coordination policy. Notably, regression-based coordination does not learn the optimal coordination actions from a labeled dataset. Instead, it exploits the optimization structure of the coordination problem to ensure feasible and cost-effective actions. A NYISO-based study reveals large coordination gains and the optimal features for the successful regression-based coordination.
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction

Graph neural network (GNN) link prediction is increasingly deployed in citation, collaboration, and online social networks to recommend academic literature, collaborators, and friends. While prior research has investigated the dyadic fairness of GNN link prediction, the within-group fairness and ``rich get richer'' dynamics of link prediction remain underexplored. However, these aspects have significant consequences for degree and power imbalances in networks. In this paper, we shed light on how degree bias in networks affects Graph Convolutional Network (GCN) link prediction. In particular, we theoretically uncover that GCNs with a symmetric normalized graph filter have a within-group preferential attachment bias. We validate our theoretical analysis on real-world citation, collaboration, and online social networks. We further bridge GCN's preferential attachment bias with unfairness in link prediction and propose a new within-group fairness metric. This metric quantifies disparities in link prediction scores between social groups, towards combating the amplification of degree and power disparities. Finally, we propose a simple training-time strategy to alleviate within-group unfairness, and we show that it is effective on citation, online social, and credit networks.
Enumerating minimal solution sets for metric graph problems

Problems from metric graph theory such as Metric Dimension, Geodetic Set, and Strong Metric Dimension have recently had a strong impact on the field of parameterized complexity by being the first problems in NP to admit double-exponential lower bounds in the treewidth, and even in the vertex cover number for the latter. We initiate the study of enumerating minimal solution sets for these problems and show that they are also of great interest in enumeration. More specifically, we show that enumerating minimal resolving sets in graphs and minimal geodetic sets in split graphs are equivalent to hypergraph dualization, arguably one of the most important open problems in algorithmic enumeration. This provides two new natural examples to a question that emerged in different works this last decade: for which vertex (or edge) set graph property $\Pi$ is the enumeration of minimal (or maximal) subsets satisfying $\Pi$ equivalent to hypergraph dualization? As only very few properties are known to fit within this context -- namely, properties related to minimal domination -- our results make significant progress in characterizing such properties, and provide new angles of approach for tackling hypergraph dualization. In a second step, we consider cases where our reductions do not apply, namely graphs with no long induced paths, and show these cases to be mainly tractable.
Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head

Accurate localization of cephalometric landmarks holds great importance in the fields of orthodontics and orthognathics due to its potential for automating key point labeling. In the context of landmark detection, particularly in cephalometrics, it has been observed that existing methods often lack standardized pipelines and well-designed bias reduction processes, which significantly impact their performance. In this paper, we revisit a related task, human pose estimation (HPE), which shares numerous similarities with cephalometric landmark detection (CLD), and emphasize the potential for transferring techniques from the former field to benefit the latter. Motivated by this insight, we have developed a robust and adaptable benchmark based on the well-established HPE codebase known as MMPose. This benchmark can serve as a dependable baseline for achieving exceptional CLD performance. Furthermore, we introduce an upscaling design within the framework to further enhance performance. This enhancement involves the incorporation of a lightweight and efficient super-resolution module, which generates heatmap predictions on high-resolution features and leads to further performance refinement, benefiting from its ability to reduce quantization bias. In the MICCAI CLDetection2023 challenge, our method achieves 1st place ranking on three metrics and 3rd place on the remaining one. The code for our method is available at https://github.com/5k5000/CLdetection2023.
The characteristics of LK-99 by Cu$_2$S removal using ammonia solution: A diamagnetic semiconductor

In this study, we re-evaluated the superconducting properties of LK-99. The LK-99 samples were synthesized using the process proposed by the original Korean team. Additionally, we examined whether the results of the Korean team are related to Cu$_2$S by using ammonia solution (NH$_3$-H$_2$O) to remove Cu$_2$S . Through x-ray diffraction (XRD) analysis, a distinct Cu$_2$S phase was identified in the LK-99 samples. A subsequent treatment using an ammonia solution effectively eliminated this phase. The appearance of blue Cu$^{+2}$ ions in the solution and the elimination of the Cu$_2$S peak in XRD support the conclusion. The magnetic and electrical properties of LK-99 with and without Cu$_2$S postulate that the superconducting-like behavior in LK-99 predominantly arises from a transition in resistivity due to the influence of Cu$_2$S . As such, LK-99 is better classified as a diamagnetic semiconductor than a room-temperature superconductor. The room-temperature superconductors still require further research.
Symmetry-breaking bifurcations and excitations of solitons in linearly coupled NLS equations with PT-symmetric potentials

We address symmetry-breaking bifurcations (SBBs) in the ground state (GS) and dipole-mode (DM) solitons of the one-dimensional linearly coupled NLS equations, modelling the propagation of light in a dual-core planar waveguide with the Kerr nonlinearity and two types of PT-symmetric potentials. The PT-symmetric potential is employed to obtain different types of solutions. A supercritical pitchfork bifurcation occurs in families of symmetric solutions of both the GS and DM types. A novel feature of the system is interplay between breakings of the PT and inter-core symmetries. Stability of symmetric GS and DM modes and their asymmetric counterparts, produced by SBBs of both types, is explored via the linear-stability analysis and simulations. It is found that the instability of PT-symmetric solutions takes place prior to the inter-core symmetry breaking. Surprisingly, stable inter-core-symmetric GS solutions may remain stable while the PT symmetry is broken. Fully asymmetric GS and DM solitons are only partially stable. Moreover, we construct symmetric and asymmetric GS solitons under the action of a pure imaginary localized potential, for which the SBB is subcritical. These results exhibit that stable solitons can still be found in dissipative systems. Finally, excitations of symmetric and asymmetric GS solitons are investigated by making the potential’s parameters or the system’s coupling constant functions, showing that GS solitons can be converted from an asymmetric shape onto a symmetric one under certain conditions. These results may pave the way for the study of linear and nonlinear phenomena in a dual-core planar waveguide with PT potential and related experimental designs.
Simultaneous Synchronization and Calibration for Wide-baseline Stereo Event Cameras

Event-based cameras are increasingly utilized in various applications, owing to their high temporal resolution and low power consumption. However, a fundamental challenge arises when deploying multiple such cameras: they operate on independent time systems, leading to temporal misalignment. This misalignment can significantly degrade performance in downstream applications. Traditional solutions, which often rely on hardware-based synchronization, face limitations in compatibility and are impractical for long-distance setups. To address these challenges, we propose a novel algorithm that exploits the motion of objects in the shared field of view to achieve millisecond-level synchronization among multiple event-based cameras. Our method also concurrently estimates extrinsic parameters. We validate our approach in both simulated and real-world indoor/outdoor scenarios, demonstrating successful synchronization and accurate extrinsic parameters estimation.
A Direct k-Way Hypergraph Partitioning Algorithm for Optimizing the Steiner Tree Metric

Minimizing wire-lengths is one of the most important objectives in circuit design. The process involves initially placing the logical units (cells) of a circuit onto a physical layout, and subsequently routing the wires to connect the cells. Hypergraph partitioning (HGP) has been long used as a placement strategy in this process. However, it has been replaced by other methods due to the limitation that common HGP objective funtions only optimize wire-lengths implicitly. In this work, we present a novel HGP formulation that maps a hypergraph $H$, representing a logical circuit, onto a routing layout represented by a weighted graph $G$. The objective is to minimize the total length of all wires induced by the hyperedges of $H$ on $G$. To capture wire-lengths, we compute minimal Steiner trees - a metric commonly used in routing algorithms. For this formulation, we present the first direct $k$-way multilevel mapping algorithm that incorporates techniques used by the highest-quality partitioning algorithms. We contribute a greedy mapping algorithm to compute an initial solution and three refinement algorithms to improve the initial: Two move-based local search heuristics (based on label propagation and the FM algorithm) and a refinement algorithm based on max-flow min-cut computations. Our experiments demonstrate that our new algorithm achieves an improvement in the Steiner tree metric by 7% (median) on VLSI instances when compared to the best performing partitioning algorithm that optimizes the mapping in a postprocessing step. Although computing Steiner trees is an NP-hard problem, we achieve this improvement with only a 2-3 times slowdown in partitioning time compared to optimizing the connectivity metric.
Laboratory simulations of radiatively induced entrainment in stratiform clouds

Laboratory experiments have been conducted to simulate entrainment in stratiform clouds. In particular, the case of entrainment across a capping temperature inversion and induced by cloud top cooling has been simulated. This geometry is termed interfacial convection, and its physics differ from the more thoroughly studied case of penetrative convection. The dimensionless entrainment rate associated with interfacial convection has been found to vary inversely with a bulk Richardson number over a broad range of Richardson numbers. A dependence of the entrainment rate on the diffusivity of the stratifying agent has also been found. This dependence is explained in terms of Taylor layers. A physical model for the dynamics of interfacial convection is proposed. In the laboratory case, a stably stratified interface separates two fluid layers. Convection is driven in the upper layer by the deposition of radiation near the interface. After sufficient energy has been deposited, buoyant fluid rises and induces formation of entraining cusps at the interface. The spacing between cusps is determined by equating a buoyancy instability timescale with a heating timescale. When the depth of the convecting layer is large compared to the distance separating the cusps, a larger-scale circulation also develops. In such cases, eddies of size comparable to the depth of the convecting layer advect the cusps horizontally. Despite Reynolds numbers that differ by 4 orders of magnitude or more between the laboratory simulation and the real atmosphere, it is argued that the entrainment dynamics are analogous. Dimensionless entrainment rates measured in the laboratory are within 1 order of magnitude of those measured in the atmosphere for a given Richardson number. Thinner Taylor layers and the lack of evaporative effects in the laboratory may account for the difference.
Symmetry Leads to Structured Constraint of Learning

Due to common architecture designs, symmetries exist extensively in contemporary neural networks. In this work, we unveil the importance of the loss function symmetries in affecting, if not deciding, the learning behavior of machine learning models. We prove that every mirror symmetry of the loss function leads to a structured constraint, which becomes a favored solution when either the weight decay or gradient noise is large. As direct corollaries, we show that rescaling symmetry leads to sparsity, rotation symmetry leads to low rankness, and permutation symmetry leads to homogeneous ensembling. Then, we show that the theoretical framework can explain the loss of plasticity and various collapse phenomena in neural networks and suggest how symmetries can be used to design algorithms to enforce hard constraints in a differentiable way.
APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds

In this paper, we focus on semantic segmentation method for point clouds of urban scenes. Our fundamental concept revolves around the collaborative utilization of diverse scene representations to benefit from different context information and network architectures. To this end, the proposed network architecture, called APNet, is split into two branches: a point cloud branch and an aerial image branch which input is generated from a point cloud. To leverage the different properties of each branch, we employ a geometry-aware fusion module that is learned to combine the results of each branch. Additional separate losses for each branch avoid that one branch dominates the results, ensure the best performance for each branch individually and explicitly define the input domain of the fusion network assuring it only performs data fusion. Our experiments demonstrate that the fusion output consistently outperforms the individual network branches and that APNet achieves state-of-the-art performance of 65.2 mIoU on the SensatUrban dataset. Upon acceptance, the source code will be made accessible.
Circular-Line Trajectory Tracking Controller for Mobile Robot using Multi-Pixy2 Sensors

This study suggests a novel tracking method that employs three Pixy2 sensors to identify the desired line trajectories instead of traditional perceiving means. Firstly, the kinematic model of the mobile robot is derived from the information gathered by three Pixy2 sensors. Secondly, the sliding mode controller is implemented to regulate the tracking error. Finally, simulation results are analyzed to show the effectiveness of the proposed method.
Memory in Plain Sight: A Survey of the Uncanny Resemblances between Diffusion Models and Associative Memories

Diffusion Models (DMs) have recently set state-of-the-art on many generation benchmarks. However, there are myriad ways to describe them mathematically, which makes it difficult to develop a simple understanding of how they work. In this survey, we provide a concise overview of DMs from the perspective of dynamical systems and Ordinary Differential Equations (ODEs) which exposes a mathematical connection to the highly related yet often overlooked class of energy-based models, called Associative Memories (AMs). Energy-based AMs are a theoretical framework that behave much like denoising DMs, but they enable us to directly compute a Lyapunov energy function on which we can perform gradient descent to denoise data. We then summarize the 40 year history of energy-based AMs, beginning with the original Hopfield Network, and discuss new research directions for AMs and DMs that are revealed by characterizing the extent of their similarities and differences
Neural Lithography: Close the Design-to-Manufacturing Gap in Computational Optics with a 'Real2Sim' Learned Photolithography Simulator

We introduce neural lithography to address the 'design-to-manufacturing' gap in computational optics. Computational optics with large design degrees of freedom enable advanced functionalities and performance beyond traditional optics. However, the existing design approaches often overlook the numerical modeling of the manufacturing process, which can result in significant performance deviation between the design and the fabricated optics. To bridge this gap, we, for the first time, propose a fully differentiable design framework that integrates a pre-trained photolithography simulator into the model-based optical design loop. Leveraging a blend of physics-informed modeling and data-driven training using experimentally collected datasets, our photolithography simulator serves as a regularizer on fabrication feasibility during design, compensating for structure discrepancies introduced in the lithography process. We demonstrate the effectiveness of our approach through two typical tasks in computational optics, where we design and fabricate a holographic optical element (HOE) and a multi-level diffractive lens (MDL) using a two-photon lithography system, showcasing improved optical performance on the task-specific metrics.
ONNXExplainer: an ONNX Based Generic Framework to Explain Neural Networks Using Shapley Values

Understanding why a neural network model makes certain decisions can be as important as the inference performance. Various methods have been proposed to help practitioners explain the prediction of a neural network model, of which Shapley values are most popular. SHAP package is a leading implementation of Shapley values to explain neural networks implemented in TensorFlow or PyTorch but lacks cross-platform support, one-shot deployment and is highly inefficient. To address these problems, we present the ONNXExplainer, which is a generic framework to explain neural networks using Shapley values in the ONNX ecosystem. In ONNXExplainer, we develop its own automatic differentiation and optimization approach, which not only enables One-Shot Deployment of neural networks inference and explanations, but also significantly improves the efficiency to compute explanation with less memory consumption. For fair comparison purposes, we also implement the same optimization in TensorFlow and PyTorch and measure its performance against the current state of the art open-source counterpart, SHAP. Extensive benchmarks demonstrate that the proposed optimization approach improves the explanation latency of VGG19, ResNet50, DenseNet201, and EfficientNetB0 by as much as 500%.
Hydrogen and carbon isotopic measurements of methane from agricultural combustion: Implications for isotopic signatures of global biomass burning sources

[1] The hydrogen and carbon isotope ratios of CH4 emitted from one bonfire and two laboratory combustions of agricultural residues were determined in this study as −196 to −262‰ for δD and −19.9 to −35.1‰ for δ13C. The isotopic difference between CH4 emitted from biomass burning and fuel biomass, that is, the apparent isotopic fractionation occurring during combustion (ɛBurn), was within −101 to −174‰ for ɛBurnD and +6.9 to −8.6‰ for ɛBurn13C. That difference varied according to burning conditions: flaming and smoldering. Variation in ɛBurn is correlated with combustion efficiency (CE), defined here as the ratio of emitted CO2 to the sum of emitted CO2 and CO. In light of the previously reported global distributions of CE, the implied ɛBurn values for global biomass burning sources were −121‰ for ɛBurnD and +2.2‰ for ɛBurn13C. Using the relationship between ɛBurn and CE and global distributions of CE and isotopic ratios of fuel biomass, we estimated the global isotopic signature for biomass burning sources as −169‰ for δD and −23.6‰ for δ13C.
Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks

Stowing, the task of placing objects in cluttered shelves or bins, is a common task in warehouse and manufacturing operations. However, this task is still predominantly carried out by human workers as stowing is challenging to automate due to the complex multi-object interactions and long-horizon nature of the task. Previous works typically involve extensive data collection and costly human labeling of semantic priors across diverse object categories. This paper presents a method to learn a generalizable robot stowing policy from predictive model of object interactions and a single demonstration with behavior primitives. We propose a novel framework that utilizes Graph Neural Networks to predict object interactions within the parameter space of behavioral primitives. We further employ primitive-augmented trajectory optimization to search the parameters of a predefined library of heterogeneous behavioral primitives to instantiate the control action. Our framework enables robots to proficiently execute long-horizon stowing tasks with a few keyframes (3-4) from a single demonstration. Despite being solely trained in a simulation, our framework demonstrates remarkable generalization capabilities. It efficiently adapts to a broad spectrum of real-world conditions, including various shelf widths, fluctuating quantities of objects, and objects with diverse attributes such as sizes and shapes.
Duality Principle and Biologically Plausible Learning: Connecting the Representer Theorem and Hebbian Learning

A normative approach called Similarity Matching was recently introduced for deriving and understanding the algorithmic basis of neural computation focused on unsupervised problems. It involves deriving algorithms from computational objectives and evaluating their compatibility with anatomical and physiological observations. In particular, it introduces neural architectures by considering dual alternatives instead of primal formulations of popular models such as PCA. However, its connection to the Representer theorem remains unexplored. In this work, we propose to use teachings from this approach to explore supervised learning algorithms and clarify the notion of Hebbian learning. We examine regularized supervised learning and elucidate the emergence of neural architecture and additive versus multiplicative update rules. In this work, we focus not on developing new algorithms but on showing that the Representer theorem offers the perfect lens to study biologically plausible learning algorithms. We argue that many past and current advancements in the field rely on some form of dual formulation to introduce biological plausibility. In short, as long as a dual formulation exists, it is possible to derive biologically plausible algorithms. Our work sheds light on the pivotal role of the Representer theorem in advancing our comprehension of neural computation.