Miracle-dz/UniFAR-FTUs huggingface dataset:
Unleashing the potential of price-based congestion management schemes: a unifying approach to compare alternative models under multiple objectives

A wide range of price-based congestion management schemes were proposed in the literature ranging from marginal cost road pricing to trip based multimodal pricing. The underlying models were formulated under different theoretical assumptions and with varying, and sometimes conflicting objectives. This paper presents a unifying framework under which different approaches can be compared based on their respective assumptions. The unifying modelling framework is referred to as trip pricing model – which extends path-differentiated pricing to multimodal paths, i.e., trips on whatever mode or combinations of modes, and generalizes road pricing schemes (which are shown to be a special case). By setting both positive (tolls) and negative (incentives) prices, revenue-neutral schemes are also shown to be special cases, with no a-priori assumption on which paths/modes to toll/incentivize. For model comparison, the pricing design problem is formulated in a multi-objective optimization framework, which combines traffic efficiency, environmental sustainability, users’ acceptance, social and spatial equity, as pricing objectives. The trip pricing scheme is compared with traditional road pricing schemes, and with their revenue-neutral variants. First-best and second-best pricing schemes, designed in single- and multi-objective optimization frameworks, are compared on the Nguyen-Dupuis network. Results suggest a vast potential for multi-objective trip congestion pricing over single-objective road pricing. In addition, the application of both positive and negative prices is shown to significantly increase the expected acceptance of pricing schemes and to preserve efficiency and sustainability objectives. The results should promote the design of more effective pricing policies, i.e., set of pricing rules easier to communicate, based on now available technologies of passengers’ tracking and pricing.
Enhancing Document-level Relation Extraction by Entity Knowledge Injection

Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. Specifically, we introduce coreference distillation to inject coreference knowledge, endowing an RE model with the more general capability of coreference reasoning. We also employ representation reconciliation to inject factual knowledge and aggregate KG representations and document representations into a unified space. The experiments on two benchmark datasets validate the generalization of our entity knowledge injection framework and the consistent improvement to several document-level RE models.
Disruption of Dark Matter Minihalos in the Milky Way Environment: Implications for Axion Miniclusters and Early Matter Domination

Many theories of dark matter beyond the weakly interacting massive particles paradigm feature an enhanced matter power spectrum on subparsec scales, leading to the formation of dense dark matter minihalos. Future local observations are promising to search for and constrain such substructures. The survival probability of these dense minihalos in the Milky Way environment is crucial for interpreting local observations. In this work, we investigate two environmental effects: stellar disruption and (smooth) tidal disruption. These two mechanisms are studied using semianalytic models and idealized N-body simulations. For stellar disruption, we perform a series of N-body simulations of isolated minihalo–star encounters to test and calibrate analytic models of stellar encounters before applying the model to the realistic Milky Way disk environment. For tidal disruption, we perform N-body simulations to confirm the effectiveness of the analytic treatment. Finally, we propose a framework to combine the hierarchical assembly and infall of minihalos to the Milky Way with the late-time disruption mechanisms. We make predictions for the mass functions of minihalos in the Milky Way. The mass survival fraction (at M mh ≥ 10−12 M ⊙) of dense dark matter minihalos, e.g., for axion miniclusters and minihalos from early matter domination, is ∼60% with the relatively low-mass, compact population surviving. The survival fraction is insensitive to the detailed model parameters. We discuss various implications of the framework and future direct detection prospects.
Optimal Utilization of Third-Party Demand Response Resources in Vertically Integrated Utilities: A Game Theoretic Approach

This paper studies the optimal mechanisms for the vertically integrated utility to dispatch and incentivize the third-party demand response (DR) providers in its territory. A framework is proposed, with three-layer coupled Stackelberg and simultaneous games, to study the interactions and competitions among the profit-seeking process of the utility, the third-party DR providers, and the individual end users (EUs) in the DR programs. Two coupled single-leader-multiple-followers Stackelberg games with a three-layer structure are proposed to capture the interactions among the utility (modeled in the upper layer), the third-party DR providers (modeled in the middle layer), and the EUs in each DR program (modeled in the lower layer). The competitions among the EUs in each DR program is captured through a non-cooperative simultaneous game. An inconvenience cost function is proposed to model the DR provision willingness and capacity of different EUs. The Stackelberg game between the middle-layer DR provider and the lower-layer EUs is solved by converting the original bi-level programming to a singlelevel programming. This converted single-level programming is embedded in an iterative algorithm toward solving the entire coupled games framework. Case studies are performed on IEEE 34-bus and IEEE 69-bus test systems to illustrate the application of the proposed framework.
A Bayesian method for estimating gene‐level polygenicity under the framework of transcriptome‐wide association study

Polygenicity refers to the phenomenon that multiple genetic variants have a nonzero effect on a complex trait. It is defined as the proportion of genetic variants with a nonzero effect on the trait. Evaluation of polygenicity can provide valuable insights into the genetic architecture of the trait. Several recent works have attempted to estimate polygenicity at the single nucleotide polymorphism level. However, evaluating polygenicity at the gene level can be biologically more meaningful. We propose the notion of gene‐level polygenicity, defined as the proportion of genes having a nonzero effect on the trait under the framework of a transcriptome‐wide association study. We introduce a Bayesian approach genepoly to estimate this quantity for a trait. The method is based on spike and slab prior and simultaneously estimates the subset of non‐null genes. Our simulation study shows that genepoly efficiently estimates gene‐level polygenicity. The method produces a downward bias for small choices of trait heritability due to a non‐null gene, which diminishes rapidly with an increase in the genome‐wide association study (GWAS) sample size. While identifying the subset of non‐null genes, genepoly offers a high level of specificity and an overall good level of sensitivity—the sensitivity increases as the sample size of the reference panel expression and GWAS data increase. We applied the method to seven phenotypes in the UK Biobank, integrating expression data. We find height to be the most polygenic and asthma to be the least polygenic.
Hosting Capacity Approach

This chapter proposes an evolved concept of"hosting capacity"using the term of"feasible region". Through converting the grid model into a more compact one,"hosting capacity region"not only is promising to further exploit the grid potential for power delivery, but also benefits grid operation feasibility investigation with concise formulas. The hosting capacity region assessment schemes are exploited as well. Facing the derived hosting capacity, originally complicated energy storage optimization problems can be represented algebraically, which is more efficient and friendly for computer processing. Case studies based on a 10.5kV Dutch grid have been implemented, eventually confirming the validity of relevant assessment and optimization methods.
Adaptive Marginalized Semantic Hashing for Unpaired Cross-Modal Retrieval

In recent years, Cross-Modal Hashing (CMH) has attracted much attention due to its fast query speed and efficient storage. Previous studies have achieved promising results for Cross-Modal Retrieval (CMR) by discovering discriminative hash codes and modality-specific hash functions. Nonetheless, most existing CMR works are subjected to some restrictions: 1) It is assumed that data of different modalities are fully paired, which is impractical in real applications due to sample missing and false data alignment, and 2) binary regression targets including the label matrix and binary codes are too rigid to effectively learn semantic-preserving hash codes and hash functions. To address these problems, this paper proposes an Adaptive Marginalized Semantic Hashing (AMSH) method which not only enhances the discrimination of latent representations and hash codes by adaptive margins, but can also be used for both paired and unpaired CMR. As a two-step method, in the first step, AMSH generates semantic-aware modality-specific latent representations with adaptively marginalized labels, thereby enlarging the distances between different classes, and exploiting the labels to preserve the inter-modal and intra-modal semantic similarities into latent representations and hash codes. In the second step, adaptive margin matrices are embedded into the hash codes, and enlarge the gaps between positive and negative bits, which improves the discrimination and robustness of hash functions. On this basis, AMSH generates similarity-preserving hash codes and robust hash functions without the strict one-to-one data correspondence requirement. Experiments are conducted on several benchmark datasets to demonstrate the superiority and flexibility of AMSH over some state-of-the-art CMR methods.
Inference of regulatory networks through temporally sparse data

A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean networks have emerged as a successful class of models for capturing the behavior of GRNs. In most practical settings, inference of GRNs should be achieved through limited and temporally sparse genomics data. A large number of genes in GRNs leads to a large possible topology candidate space, which often cannot be exhaustively searched due to the limitation in computational resources. This paper develops a scalable and efficient topology inference for GRNs using Bayesian optimization and kernel-based methods. Rather than an exhaustive search over possible topologies, the proposed method constructs a Gaussian Process (GP) with a topology-inspired kernel function to account for correlation in the likelihood function. Then, using the posterior distribution of the GP model, the Bayesian optimization efficiently searches for the topology with the highest likelihood value by optimally balancing between exploration and exploitation. The performance of the proposed method is demonstrated through comprehensive numerical experiments using a well-known mammalian cell-cycle network.
Bayesian Chain Graph LASSO Models to Learn Sparse Microbial Networks with Predictors

Microbiome data require statistical models that can simultaneously decode microbes’ reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is flawed given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes as it does not represent the adjacency matrix. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence and thus, agrees with the experimenter’s intuition on the average behavior of nodes under treatment. The solution to our model is sparse via Bayesian LASSO. In addition, we propose an adaptive extension so that different shrinkage can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting and compositional responses via appropriate hierarchical structure. We apply our model to a human gut and a soil microbial compositional datasets and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data. The CG-LASSO software is available as an R package at https://github.com/YunyiShen/CAR-LASSO. ∗Depts. of Statistics & Forest and Wildlife Ecology, University of Wisconsin–Madison †Wisconsin Institute for Discovery & Dept. of Plan Pathology, University of Wisconsin–Madison, Correspondence to: solislemus@wisc.edu
Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium

We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures, together with deformations of cell shape, size, and atomic positions. The resulting potentials turn out to be unbiased and generically applicable to studies of bulk defects without including any defect structures in the training set or employing any additional Active Learning. Using this approach we construct transferable potentials for pure Magnesium that reproduce the properties of hexagonal closed packed (hcp) and body centered cubic (bcc) polymorphs very well. In the process we investigate how different types of training structures impact the properties and the predictive power of the resulting potential.
Frequency dependence of near-surface oceanic kinetic energy from drifter observations and global high-resolution models

The geographical variability, frequency content, and vertical structure of near-surface oceanic kinetic energy (KE) are important for air-sea interaction, marine ecosystems, operational oceanography, pollutant tracking, and interpreting remotely sensed velocity measurements. Here, KE in high-resolution global simulations (HYbrid Coordinate Ocean Model; HYCOM, and Massachusetts Institute of Technology general circulation model; MITgcm), at the sea surface (0 m) and 15 m, are respectively compared with KE from undrogued and drogued surface drifters. Global maps and zonal averages are computed for low-frequency ($<$ 0.5 cpd), near-inertial, diurnal, and semi-diurnal bands. Both models exhibit low-frequency equatorial KE that is low relative to drifter values. HYCOM near-inertial KE is higher than in MITgcm, and closer to drifter values, probably due to more frequently updated atmospheric forcing. HYCOM semi-diurnal KE is lower than in MITgcm, and closer to drifter values, likely due to inclusion of a parameterized topographic internal wave drag. A concurrent tidal harmonic analysis in the diurnal band demonstrates that much of the diurnal flow is non-tidal. We compute a simple proxy of near-surface vertical structure, the ratio of 0 m KE to 0 m KE plus 15 m KE in model outputs, and undrogued KE to undrogued KE plus drogued KE in drifter observations. Over most latitudes and frequency bands, model ratios track the drifter ratios to within error bars. Values of this ratio demonstrate significant vertical structure in all frequency bands except the semidiurnal band. Latitudinal dependence in the ratio is greatest in diurnal and low-frequency bands.
Orientation-Aware Model Predictive Control with Footstep Adaptation for Dynamic Humanoid Walking

This paper proposes a novel model predictive control (MPC) for humanoid locomotion that reasons about orientation dynamics and footstep placement in a unified optimization framework. This work employs the augmented single rigid body model (aSRBM) to enable the MPC to leverage stepping strategy and orientation dynamics simultaneously, hence the name orientation-aware MPC (OA-MPC). Since step location is part of the decision variables, this MPC ensures that foot placement satisfies the kinematic reachability constraint and conforms to terrain slope. The OA-MPC produces the desired body pose, ground reaction wrench, and foot swing targets that are tracked by a task-space controller (TSC), which utilizes the full-order dynamics of the humanoid and authorizes arm movements. The proposed control framework is suitable for real-time execution since both MPC and TSC are transcribed as quadratic programs. Simulation investigations show that the OA-MPC is more robust against external torque disturbance compared to controllers using the point mass model, especially when the torso undergoes large angular excursion. The OA-MPC also enables the MIT Humanoid to traverse the wave field.
Benchmarking GNN-Based Recommender Systems on Intel Optane Persistent Memory

Graph neural networks (GNNs), which have emerged as an effective method for handling machine learning tasks on graphs, bring a new approach to building recommender systems, where the task of recommendation can be formulated as the link prediction problem on user-item bipartite graphs. Training GNN-based recommender systems (GNNRecSys) on large graphs incurs a large memory footprint, easily exceeding the DRAM capacity on a typical server. Existing solutions resort to distributed subgraph training, which is inefficient due to the high cost of dynamically constructing subgraphs and significant redundancy across subgraphs. The emerging Intel Optane persistent memory allows a single machine to have up to 6 TB of memory at an affordable cost, thus making single-machine GNNRecSys training feasible, which eliminates the inefficiencies in distributed training. One major concern of using Optane for GNNRecSys is Optane’s relatively low band-width compared with DRAMs. This limitation can be particularly detrimental to achieving high performance for GNNRecSys work-loads since their dominant compute kernels are sparse and memory access intensive. To understand whether Optane is a good fit for GNNRecSys training, we perform an in-depth characterization of GNNRecSys workloads and a comprehensive benchmarking study. Our benchmarking results show that when properly configured, Optane-based single-machine GNNRecSys training outperforms distributed training by a large margin, especially when handling deep GNN models. We analyze where the speedup comes from, provide guidance on how to configure Optane for GNNRecSys
Federated Graph Contrastive Learning

—Graph learning models are critical tools for researchers to explore graph-structured data. To train a capable graph learning model, a conventional method uses sufﬁcient training data to train a graph model on a single device. However, it is prohibitive to do so in real-world scenarios due to privacy concerns. Federated learning provides a feasible solution to address such limitations via introducing various privacy-preserving mechanisms, such as differential privacy on graph edges. Nevertheless, differential privacy in federated graph learning secures the classiﬁed information maintained in graphs. It degrades the performances of the graph learning models. In this paper, we investigate how to implement differential privacy on graph edges and observe the performances decreasing in the experiments. We also note that the differential privacy on graph edges introduces noises to perturb graph proximity, which is one of the graph augmentations in graph contrastive learning. Inspired by that, we propose to leverage the advantages of graph contrastive learning to alleviate the performance dropping caused by differential privacy. Extensive experiments are conducted with several representative graph models and widely-used datasets, showing that contrastive learning indeed alleviates the models’ performance dropping caused by differential privacy.
Learning to Sell a Focal-ancillary Combination

A number of products are sold in the following sequence: First a focal product is shown, and if the customer purchases, one or more ancillary products are displayed for purchase. A prominent example is the sale of an airline ticket, where ﬁrst the ﬂight is shown, and when chosen, a number of ancillaries such as cabin or hold bag options, seat selection, insurance etc. are presented. The ﬁrm has to decide on a sale format—whether to sell them in sequence unbundled, or together as a bundle—and how to price the focal and ancillary products, separately or as a bundle. Since the ancillary is considered by the customer only after the purchase of the focal product, the sale strategy chosen by the ﬁrm creates an information and learning dependency between the products: for instance, oﬀering only a bundle would preclude learning customers’ valuation for the focal and ancillary products individually. In this paper we study learning strategies for such focal and ancillary item combinations under the following scenarios: (a) pure unbundling to all customers, (b) personalized mechanism, where, depending on some observed features of the customers, the two products are presented and priced as a bundle or in sequence, (c) initially unbundling (for all customers), and switch to bundling (if more proﬁtable) permanently once during the horizon. We design pricing and decisions algorithms for all three scenarios, with regret upper bounded by O ( d √ T log T ) , and an optimal switching time for the third scenario.
High Selectivity Balanced-to-Unbalanced Filtering Power Dividers Using Dual-Mode Ring Resonators

Two novel high selectivity balanced-to-unbalanced filtering power dividers using ring resonators are proposed. The even-/odd-mode analysis method is applied to obtain the design equations at the operating frequency. The ring resonators can bring two transmission zeros in each side of the passband for the differential mode for the filtering power divider (structure I). Moreover, the three-quarter wavelength transmission lines are applied to produce the additional two transmission zeros of differential mode for structure II, which can further improve the differential-mode harmonic suppression and common-mode suppression. Two fabricated prototypes with 4-dB bandwidths of 11.2% and 11.6% are designed and measured to prove the proposed methods. All the structures show good performances for differential-mode power division and common-mode suppression.