Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Federated Minimax Optimization with Lower complexities

Federated learning is a popular distributed and privacy-preserving learning paradigm in machine learning. Recently, some federated learning algorithms have been proposed to solve the distributed minimax problems. However, these federated minimax algorithms still suffer from high gradient or communication complexity. Meanwhile, few algorithm focuses on using adaptive learning rate to accelerate these algorithms. To fill this gap, in the paper, we study a class of nonconvex minimax optimization, and propose an efficient adaptive federated minimax optimization algorithm (i.e., AdaFGDA) to solve these distributed minimax problems. Specifically, our AdaFGDA builds on the momentum-based variance reduced and local-SGD techniques, and it can flexibly incorporate various adaptive learning rates by using the unified adaptive matrices. Theoretically, we provide a solid convergence analysis framework for our AdaFGDA algorithm under non-i.i. d. setting. Moreover, we prove our AdaFGDA algorithm obtains a lower gradient (i.e., stochastic first-order oracle, SFO) complexity of $\tilde{O}(\epsilon^{-3})$ with lower communication complexity of $\tilde{O}(\epsilon^{-2})$ in finding $\epsilon$-stationary point of the nonconvex minimax problems. Experimentally, we conduct some experiments on the deep AUC maximization and robust neural network training tasks to verify efficiency of our algorithms.
Grid-Forming Control of Three-Phase and Single-Phase Converters Across Unbalanced Transmission and Distribution Systems

In this work, we investigate grid-forming control for power systems containing three-phase and single-phase converters connected to unbalanced distribution and transmission networks, investigate self-balancing between single-phase converters, and propose a novel balancing feedback for grid-forming control that explicitly allows to trade-off unbalances in voltage and power. We develop a quasi-steady-state power network model that allows to analyze the interactions between three-phase and single-phase power converters across transmission, distribution, and standard transformer interconnections. We first investigate conditions under which this general network admits a well-posed kron-reduced quasi-steady-state network model. Our main contribution leverages this reduced-order model to develop analytical conditions for stability of the overall network with grid-forming three-phase and single-phase converters connected through standard transformer interconnections. Specifically, we provide conditions on the network topology under which (i) single-phase converters autonomously self-synchronize to a phase-balanced operating point and (ii) single-phase converters phase-balance through synchronization with three-phase converters. Moreover, we establish that the conditions can be relaxed if a phase-balancing feedback control is used. Finally, case studies combining detailed models of transmission systems (i.e., IEEE 9-bus) and distribution systems (i.e., IEEE 13-bus) are used to illustrate the results for (i) a power system containing a mix of transmission and distribution connected converters and, (ii) a power system solely using distribution-connected converters at the grid edge.
A Pipeline for Business Intelligence and Data-Driven Root Cause Analysis on Categorical Data

Business intelligence (BI) is any knowledge derived from existing data that may be strategically applied within a business. Data mining is a technique or method for extracting BI from data using statistical data modeling. Finding relationships or correlations between the various data items that have been collected can be used to boost business performance or at the very least better comprehend what is going on. Root cause analysis (RCA) is discovering the root causes of problems or events to identify appropriate solutions. RCA can show why an event occurred and this can help in avoiding occurrences of an issue in the future. This paper proposes a new clustering + association rule mining pipeline for getting business insights from data. The results of this pipeline are in the form of association rules having consequents, antecedents, and various metrics to evaluate these rules. The results of this pipeline can help in anchoring important business decisions and can also be used by data scientists for updating existing models or while developing new ones. The occurrence of any event is explained by its antecedents in the generated rules. Hence this output can also help in data-driven root cause analysis.
Mr-Nom: Multi-Scale Resolution of Neuronal Cells in Nissl-Stained Histological Slices Via Deliberate over-Segmentation and Merging

In comparative neuroanatomy, the characterization of brain cytoarchitecture is critical to a better understanding of brain structure and function, as it helps to distill information on the development, evolution, and distinctive features of different populations. The automatic segmentation of individual brain cells is a primary prerequisite and yet remains challenging. A new method (MR-NOM) was developed for the instance segmentation of cells in Nissl-stained histological images of the brain. MR-NOM exploits a multi-scale approach to deliberately over-segment the cells into superpixels and subsequently merge them via a classifier based on shape, structure, and intensity features. The method was tested on images of the cerebral cortex, proving successful in dealing with cells of varying characteristics that partially touch or overlap, showing better performance than two state-of-the-art methods.
Variable Step Size Multiscale Methods for Stiff and Highly Oscillatory Dynamical Systems

We present a new numerical multiscale integrator for stiff and highly oscillatory dynamical systems. The new algorithm can be seen as an improved version of the seamless Heterogeneous Multiscale Method by E, Ren, and Vanden-Eijnden and the method FLAVORS by Tao, Owhadi, and Marsden. It approximates slowly changing quantities in the solution with higher accuracy than these other methods while maintaining the same computational complexity. To achieve higher accuracy, it uses variable mesoscopic time steps which are determined by a special function satisfying moment and regularity conditions. Detailed analytical and numerical comparison between the different methods are given.
Magnetic Feshbach resonances between atoms in $^2$S and $^3$P$_0$ states: mechanisms and dependence on atomic properties

Magnetically tunable Feshbach resonances exist in ultracold collisions between atoms in $^2$S and $^3$P$_0$ states, such as an alkali-metal atom colliding with Yb or Sr in a clock state. We investigate the mechanisms of these resonances and identify the terms in the collision Hamiltonian responsible for them. They involve indirect coupling between the open and closed channels, via intermediate channels involving atoms in $^3$P$_1$ states. The resonance widths are generally proportional to the square of the magnetic field and are strongly enhanced when the magnitude of the background scattering length is large. For any given pair of atoms, the scattering length can be tuned discretely by choosing different isotopes of the $^3$P$_0$ atom. For each combination of an alkali-metal atom and either Yb or Sr, we consider the prospects of finding an isotopic combination that has both a large background scattering length and resonances at high but experimentally accessible field. We conclude that $^{87}$Rb+Yb, Cs+Yb and $^{85}$Rb+Sr are particularly promising combinations.
Warm inflation with a general form of the dissipative coefficient

We propose and investigate a general form of the dissipative coefficient Gamma = C(phi)T(m)/phi(m-1) in warm inflation. We focus on discussing the strong dissipative processes r = Gamma/3H >> 1 in the thermal state of approximate equilibrium. To this toy model, we give the slow-roll conditions, the amplitude and the index of the power spectrum under the general form of dissipative coefficient. Furthermore, the monomial potential and the hybrid-like potential are analyzed specifically. We conclude that the m = 0, 3 cases are worthy further investigation especially.
Sentiment Recognition of Italian Elderly through Domain Adaptation on Cross-corpus Speech Dataset

The aim of this work is to define a speech emotion recognition (SER) model able to recognize positive, neutral and negative emotions in natural conversations of Italian elderly people. Several datasets for SER are available in the literature. However most of them are in English or Chinese, have been recorded while actors and actresses pronounce short phrases and thus are not related to natural conversation. Moreover only few speeches among all the databases are related to elderly people. Therefore, in this work, a multi-language and multi-age corpus is considered merging a dataset in English, that includes also elderly people, with a dataset in Italian. A general model, trained on young and adult English actors and actresses is proposed, based on XGBoost. Then two strategies of domain adaptation are proposed to adapt the model either to elderly people and to Italian speakers. The results suggest that this approach increases the classification performance, underlining also that new datasets should be collected.
Crowdsourcing Work as Mining: A Decentralized Computation and Storage Paradigm

In this paper, we propose a novel and energy-efficient blockchain system, CrowdMine, which exploits useful crowdsourcing computation to achieve decentralized consensus. CrowdMine solves user-proposed computing tasks and utilizes the computation committed to the task solving process to secure decentralized on-chain storage. With our designed “Proof of Crowdsourcing Work” (PoCW) protocol, our system provides an efficient paradigm for computation and storage in a trustless and decentralized environment. We also implement the system with 40 distributed nodes to demonstrate its performance and robustness.
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103,193 event coreference chains, 1,216,217 temporal relations, 57,992 causal relations, and 15,841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://github.com/THU-KEG/MAVEN-ERE.
PALOMA: A MAGNETIC CV BETWEEN POLARS AND INTERMEDIATE POLARS

We present analyses of archival X-ray data obtained from the XMM-Newton satellite and optical photometric data obtained from 1 m class telescopes of ARIES, Nainital of a magnetic cataclysmic variable (MCV) Paloma. Two persistent periods at 156 ± 1 minutes and 130 ± 1 minutes are present in the X-ray data, which we interpret as the orbital and spin periods, respectively. These periods are similar to those obtained from the previous as well as new optical photometric observations. The soft-X-ray excess seen in the X-ray spectrum of Paloma and the averaged X-ray spectra are well fitted by two-temperature plasma models with temperatures of and keV with an Fe Kα line and an absorbing column density of 4.6 × 1022 cm−2. This material partially covers 60 ± 2% of the X-ray source. We also present the orbital and spin-phase-resolved spectroscopy of Paloma in the energy band and find that the X-ray spectral parameters show orbital and spin-phase dependencies. New results obtained from optical and X-ray studies of Paloma indicate that it belongs to a class of a few magnetic CVs that seem to have the characteristics of both the polars and the intermediate polars.
Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network

We develop a Synthetic Fusion Pyramid Network (SPF-Net) with a scale-aware loss function design for accurate crowd counting. Existing crowd-counting methods assume that the training annotation points were accurate and thus ignore the fact that noisy annotations can lead to large model-learning bias and counting error, especially for counting highly dense crowds that appear far away. To the best of our knowledge, this work is the first to properly handle such noise at multiple scales in end-to-end loss design and thus push the crowd counting state-of-the-art. We model the noise of crowd annotation points as a Gaussian and derive the crowd probability density map from the input image. We then approximate the joint distribution of crowd density maps with the full covariance of multiple scales and derive a low-rank approximation for tractability and efficient implementation. The derived scale-aware loss function is used to train the SPF-Net. We show that it outperforms various loss functions on four public datasets: UCF-QNRF, UCF CC 50, NWPU and ShanghaiTech A-B datasets. The proposed SPF-Net can accurately predict the locations of people in the crowd, despite training on noisy training annotations.
Life after eruption – IV. Spectroscopy of 13 old novae

We present data on 13 post-nova systems. This includes the recovery via UBVR photometry of the five post-novae X Cir, V2104 Oph, V363 Sgr, V928 Sgr and V1274 Sgr and their spectroscopic confirmation. We provide accurate coordinates and finding charts for those objects. Additional first-time or improved spectroscopic data are presented for V356 Aql, V500 Aql, V604 Aql, V1370 Aql, MT Cen, V693 CrA, V697 Sco and MU Ser. Investigating the behaviour of a few easily accessible parameters yields (limited) information on the accretion state and the system inclination. We predict that X Cir and V697 Sco are likely to reveal their orbital period via time series photometry and that long-term photometric monitoring of V356 Aql, V500 Aql, V1370 Aql and X Cir has a good chance of discovering outburst-like behaviour in these systems.
THE SELF-SIMILARITY OF THE CIRCUMGALACTIC MEDIUM WITH GALAXY VIRIAL MASS: IMPLICATIONS FOR COLD-MODE ACCRETION

We apply halo abundance matching to obtain galaxy virial masses, M h, and radii, Rvir, for 183 “isolated” galaxies from the “Mg ii Absorber-Galaxy Catalog. ” All galaxies have spectroscopic redshifts (0.07 ⩽ z ⩽ 1.12) and their circumgalactic medium (CGM) is probed in Mg ii absorption within projected galactocentric distances D ⩽ 200 kpc. We examine the behavior of equivalent width, Wr(2796), and covering fraction, fc, as a function of D, D/Rvir, and M h. Bifurcating the sample at the median mass log M h/M☉ = 12, we find (1) systematic segregation of M h on the Wr(2796)–D plane (4.0σ); high-mass halos are found at higher D with larger Wr(2796) compared to low-mass halos. On the Wr(2796)–D/Rvir plane, mass segregation vanishes and we find Wr(2796)∝(D/Rvir)−2 (8.9σ). (2) High-mass halos have larger fc at a given D, whereas fc is independent of M h at all D/Rvir. (3) fc is constant with M h over the range 10.7 ⩽ log M h/M☉ ⩽ 13.9 within a given D or D/Rvir. The combined results suggest the Mg ii absorbing CGM is self-similar with halo mass, even above log M h/M☉ ≃ 12, where cold mode accretion is predicted to be quenched. If theory is correct, either outflows or sub-halos must contribute to absorption in high-mass halos such that low- and high-mass halos are observationally indistinguishable using Mg ii absorption strength once impact parameter is scaled by halo mass. Alternatively, the data may indicate predictions of a universal shut down of cold-mode accretion in high-mass halos may require revision.
Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control

We consider control of dynamical systems through the lens of competitive analysis. Most prior work in this area focuses on minimizing regret, that is, the loss relative to an ideal clairvoyant policy that has noncausal access to past, present, and future disturbances. Motivated by the observation that the optimal cost only provides coarse information about the ideal closed-loop behavior, we instead propose directly minimizing the tracking error relative to the optimal trajectories in hindsight, i.e., imitating the clairvoyant policy. By embracing a system level perspective, we present an efficient optimization-based approach for computing follow-the-clairvoyant (FTC) safe controllers. We prove that these attain minimal regret if no constraints are imposed on the noncausal benchmark. In addition, we present numerical experiments to show that our policy retains the hallmark of competitive algorithms of interpolating between classical $\mathcal{H}_2$ and $\mathcal{H}_\infty$ control laws - while consistently outperforming regret minimization methods in constrained scenarios thanks to the superior ability to chase the clairvoyant.
Multi-Finger Grasping Like Humans

Robots with multi-fingered grippers could perform advanced manipulation tasks for us if we were able to properly specify to them what to do. In this study, we take a step in that direction by making a robot grasp an object like a grasping demonstration performed by a human. We propose a novel optimization-based approach for transferring human grasp demonstrations to any multi-fingered grippers, which produces robotic grasps that mimic the human hand orientation and the contact area with the object, while alleviating interpenetration. Extensive experiments with the Allegro and BarrettHand grippers show that our method leads to grasps more similar to the human demonstration than existing approaches, without requiring any gripper-specific tuning. We confirm these findings through a user study and validate the applicability of our approach on a real robot.
Mass ratio of composite Higgs bosons from bottom-up holographic Wilson loop modeling of beyond the Standard Model strong sector

It is often assumed that the electroweak symmetry breaking is driven by some conﬁning “strong sector” beyond the Standard Model and that the Higgs boson arises from this sector as a composite particle. The underlying strongly coupled gauge theory may be similar to QCD and could be modeled holographically. We construct a bottom-up holographic model for description of the spectrum of composite Higgs particles. The model is based on the holographic Soft Wall model and the Wilson conﬁnement criterion. The constructed model predicts the mass of the second Higgs boson near 0 . 5 TeV that coincides with the maximal branching ratio for decay of SM like Higgs boson into the t ¯ t pair.
An algebraic approach to spike-time neural codes in the hippocampus

Although temporal coding through spike-time patterns has long been of interest in neuroscience, the speciﬁc structures that could be useful for spike-time codes remain highly unclear. Here, we introduce a new analytical approach, using techniques from discrete mathematics, to study spike-time codes. As an initial example, we focus on the phenomenon of “phase precession” in the rodent hippocampus. During navigation and learning on a physical track, speciﬁc cells in a rodent’s brain form a highly structured pattern relative to the oscillation of population activity in this region. Studies of phase precession largely focus on its role in precisely ordering spike times for synaptic plasticity, as the role of phase precession in memory formation is well established. Comparatively less attention has been paid to the fact that phase precession represents one of the best candidates for a spike-time neural code. The precise nature of this code remains an open question. Here, we derive an analytical expression for an operator mapping points in physical space to complex-valued spikes by representing individual spike times as complex numbers. The properties of this operator make explicit a speciﬁc relationship between past and future in spike patterns of the hippocampus. Importantly, this mathematical approach generalizes beyond the speciﬁc phenomenon studied here, providing a new technique to study the neural codes within precise spike-time sequences found during sensory coding and motor behavior. We then introduce a novel spike-based decoding algorithm, based on this operator, that successfully decodes a simulated animal’s trajectory using only the animal’s initial position and a pattern of spike times. This decoder is robust to noise in spike times and works on a timescale almost an order of magnitude shorter than typically used with decoders that work on average ﬁring rate. These results illustrate the utility of a discrete approach, based on the structure and symmetries in spike patterns across ﬁnite sets of cells, to provide insight into the structure and function of neural systems.
The Expertise Problem: Learning from Specialized Feedback

Reinforcement learning from human feedback (RLHF) is a powerful technique for training agents to perform difficult-to-specify tasks. However, human feedback can be noisy, particularly when human teachers lack relevant knowledge or experience. Levels of expertise vary across teachers, and a given teacher may have differing levels of expertise for different components of a task. RLHF algorithms that learn from multiple teachers therefore face an expertise problem: the reliability of a given piece of feedback depends both on the teacher that it comes from and how specialized that teacher is on relevant components of the task. Existing state-of-the-art RLHF algorithms assume that all evaluations come from the same distribution, obscuring this inter- and intra-human variance, and preventing them from accounting for or taking advantage of variations in expertise. We formalize this problem, implement it as an extension of an existing RLHF benchmark, evaluate the performance of a state-of-the-art RLHF algorithm, and explore techniques to improve query and teacher selection. Our key contribution is to demonstrate and characterize the expertise problem, and to provide an open-source implementation for testing future solutions.
Adaptive High-Order A-WENO Schemes Based on a New Local Smoothness Indicator

We develop new adaptive alternative weighted essentially non-oscillatory (A-WENO) schemes for hyperbolic systems of conservation laws. The new schemes employ the recently proposed local characteristic decomposition based central-upwind numerical fluxes, the three-stage third-order strong stability preserving Runge-Kutta time integrator, and the fifth-order WENO-Z interpolation. The adaptive strategy is implemented by applying the limited interpolation only in the parts of the computational domain where the solution is identified as ``rough'' with the help of a smoothness indicator. We develop and use a new simple and robust local smoothness indicator (LSI), which is applied to the solutions computed at each of the three stages of the ODE solver. The new LSI and adaptive A-WENO schemes are tested on the Euler equations of gas dynamics. We implement the proposed LSI using the pressure, which remains smooth at contact discontinuities, while our goal is to detect other ``rough'' areas and apply the limited interpolation mostly in the neighborhoods of the shock waves. We demonstrate that the new adaptive schemes are highly accurate, non-oscillatory, and robust. They outperform their fully limited counterparts (the A-WENO schemes with the same numerical fluxes and ODE solver but with the WENO-Z interpolation employed everywhere) while being less computationally expensive.