Miracle-dz/UniFAR-FTUs huggingface dataset:
Polynomial worst-case iteration complexity of quasi-Newton primal-dual interior point algorithms for linear programming

Quasi-Newton methods are well known techniques for large-scale numerical optimization. They use an approximation of the Hessian in optimization problems or the Jacobian in system of nonlinear equations. In the Interior Point context, quasi-Newton algorithms compute low-rank updates of the matrix associated with the Newton systems, instead of computing it from scratch at every iteration. In this work, we show that a simplified quasi-Newton primal-dual interior point algorithm for linear programming enjoys polynomial worst-case iteration complexity. Feasible and infeasible cases of the algorithm are considered and the most common neighborhoods of the central path are analyzed. To the best of our knowledge, this is the first attempt to deliver polynomial worst-case iteration complexity bounds for these methods. Unsurprisingly, the worst-case complexity results obtained when quasi-Newton directions are used are worse than their counterparts when Newton directions are employed. However, quasi-Newton updates are very attractive for large-scale optimization problems where the cost of factorizing the matrices is much higher than the cost of solving linear systems.
Choquet regularization for reinforcement learning

We propose \emph{Choquet regularizers} to measure and manage the level of exploration for reinforcement learning (RL), and reformulate the continuous-time entropy-regularized RL problem of Wang et al. (2020, JMLR, 21(198)) in which we replace the differential entropy used for regularization with a Choquet regularizer. We derive the Hamilton--Jacobi--Bellman equation of the problem, and solve it explicitly in the linear--quadratic (LQ) case via maximizing statically a mean--variance constrained Choquet regularizer. Under the LQ setting, we derive explicit optimal distributions for several specific Choquet regularizers, and conversely identify the Choquet regularizers that generate a number of broadly used exploratory samplers such as $\epsilon$-greedy, exponential, uniform and Gaussian.
Dynamic State Estimation-Based Protection for Induction Motor Loads

Ensuring protective device coordination is critical to maintain the resilience and improve the reliability of large microgrids. Inverter-interfaced generation, however, poses significant challenges when designing protection systems. Traditional time-overcurrent protective devices are unsuitable on account of the lack of fault current. Present industry practice is to force all inverters to shut down during faults, which prevents large microgrids from operating in a resilient and reliable manner. Dynamic state estimation (DSE) has been proposed for line protection, and more recently for the protection of load buses or downstream radial portions of microgrids. However, only passive loads with series resistive-inductive loads have been tested with DSE, even though the behavior of dynamic loads – such as induction motors or power electronics – may differ significantly during faults. This paper considers the case of applying DSE to protecting a load bus serving a three-phase induction motor.
Event-triggered Finite-time Control Using Inverse-optimal Implicit Lyapunov Function

—This work deals with the event-triggered ﬁnite-time control for high-order systems based on an implicit Lyapunov function (ILF). With the construction of an inverse optimal problem, a novel expression of ILF is obtained. By designing the event-triggering mechanism elaborately, it is guaranteed that the trivial solution of the closed-loop system is globally ﬁnite-time stable and there exists no Zeno phenomenon. Extensions to the scenario with a multi-agent system are studied where a ﬁnite-time tracking control drives all the agents to reach a consensus. The obtained theoretical results are supported by numerical simulations.
Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics

Spatiotemporal forecasting is an imperative topic in data science due to its diverse and critical applications in smart cities. Existing works mostly perform consecutive predictions of following steps with observations completely and continuously obtained, where nearest observations can be exploited as key knowledge for instantaneous status estimation. However, the practical issues of early activity planning and sensor failures elicit a brand-new task, i.e., non-consecutive forecasting. In this paper, we define spatiotemporal learning systems with missing observation as Grey Spatiotemporal Systems (G2S) and propose a Factor-Decoupled learning framework for G2S (FDG2S), where the core idea is to hierarchically decouple multi-level factors and enable both flexible aggregations and disentangled uncertainty estimations. Firstly, to compensate for missing observations, a generic semantic-neighboring sequence sampling is devised, which selects representative sequences to capture both periodical regularity and instantaneous variations. Secondly, we turn the predictions of non-consecutive statuses into inferring statuses under expected combined exogenous factors. In particular, a factor-decoupled aggregation scheme is proposed to decouple factor-induced predictive intensity and region-wise proximity by two energy functions of conditional random field. To infer region-wise proximity under flexible factor-wise combinations and enable dynamic neighborhood aggregations, we further disentangle compounded influences of exogenous factors on region-wise proximity and learn to aggregate them. Given the inherent incompleteness and critical applications of G2S, a DisEntangled Uncertainty Quantification is put forward, to identify two types of uncertainty for reliability guarantees and model interpretations.
Target Selection and Validation of DESI Emission Line Galaxies

The Dark Energy Spectroscopic Instrument (DESI) will precisely constrain cosmic expansion and the growth of structure by collecting ∼40 million extragalactic redshifts across ∼80% of cosmic history and one-third of the sky. The Emission Line galaxy (ELG) sample, which will comprise about one-third of all DESI tracers, will be used to probe the universe over the 0.6 < z < 1.6 range, including the 1.1 < z < 1.6 range, which is expected to provide the tightest constraints. We present the target selection for the DESI Survey Validation (SV) and Main Survey ELG samples, which relies on the imaging of the Legacy Surveys. The Main ELG selection consists of a g-band magnitude cut and a (g − r) versus (r − z) color box, while the SV selection explores extensions of the Main selection boundaries. The Main ELG sample is composed of two disjoint subsamples, which have target densities of about 1940 deg−2 and 460 deg−2, respectively. We first characterize their photometric properties and density variations across the footprint. We then analyze the DESI spectroscopic data that have been obtained from 2020 December to 2021 December in the SV and Main Survey. We establish a preliminary criterion for selecting reliable redshifts, based on the [O ii] flux measurement, and assess its performance. Using this criterion, we are able to present the spectroscopic efficiency of the Main ELG selection, along with its redshift distribution. We thus demonstrate that the Main selection 1940 deg−2 subsample alone should provide 400 deg−2 and 460 deg−2 reliable redshifts in the 0.6 < z < 1.1 and the 1.1 < z < 1.6 ranges, respectively.
Atomistic simulation of Mott transition in fluid metals: Combining molecular dynamics with dynamical mean-field theory

We present a new quantum molecular dynamics (MD) method where the electronic structure and atomic forces are solved by a real-space dynamical mean-field theory (DMFT). Contrary to most quantum MD methods that are based on effective single-particle wave functions, the DMFT approach is able to describe correlation-induced Mott metal-insulator transitions and the associated incoherent electronic excitations in an atomic liquid. We apply the DMFT-MD method to study Mott transitions in an atomic liquid model which can be viewed as the liquid-state generalization of the Hubbard model. The half-filled Hubbard liquids also provide a minimum model for alkali fluid metals. Our simulations uncover two distinct types of Mott transition depending on the atomic bonding and short-range structures in the electronically delocalized phase. In the first scenario where atoms tend to form dimers, increasing the Hubbard repulsion gives rise to a transition from a molecular insulator to an atomic insulator with a small window of enhanced metallicity in the vicinity of the localization transition. On the other hand, for Hubbard liquids with atoms forming large conducting clusters, the localization of electrons leads to the fragmentation of clusters and is intimately related to the liquid-gas transition of atoms. Implications of our results for metal-insulator transitions in fluid alkali metals are discussed.
Observation of Fermi liquid phase with broken symmetry in a single crystalline nanorod of Pr$_2$Ir$_2$O$_7$

We report experimental evidence of emergent broken symmetry Fermi liquid state in an isolated single crystalline nanorod of $\rm Pr_2 Ir_2 O_7$. We find clear signature of the onset of the Fermi liquid behavior at low temperature marked by the sign inversion of magnetoresistance from negative at high temperature, characteristic of incoherent Kondo scattering, to positive as well as a $\rm T^2$ dependence of resistivity at low temperature. A resistive anomaly is observed, which is accompanied by thermal hysteresis in the presence of magnetic field, suggesting itinerant metamagnetism. The observed high field negative magnetoresistance with quadratic field dependence at low temperature, which is most likely due to suppression of itinerant spin fluctuation, and the irreversibility of the magneto-resistive properties in the Fermi liquid regime suggest existence of an unusual state with broken spin rotation and time reversal symmetry, hallmark of `hastatic' order. The major features of such temperature dependence of resistivity and magnetoresistance can be explained in a phenomenological model incorporating two distinct hybridization channels, which is physically consistent with the possibility of the formation of the `hastatic' Fermi liquid phase.
Pixel-Wise Prediction based Visual Odometry via Uncertainty Estimation

This paper introduces pixel-wise prediction based visual odometry (PWVO), which is a dense prediction task that evaluates the values of translation and rotation for every pixel in its input observations. PWVO employs uncertainty estimation to identify the noisy regions in the input observations, and adopts a selection mechanism to integrate pixel-wise predictions based on the estimated uncertainty maps to derive the final translation and rotation. In order to train PWVO in a comprehensive fashion, we further develop a data generation workflow for generating synthetic training data. The experimental results show that PWVO is able to deliver favorable results. In addition, our analyses validate the effectiveness of the designs adopted in PWVO, and demonstrate that the uncertainty maps estimated by PWVO is capable of capturing the noises in its input observations.
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution

KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context fac-tors. The evaluation metrics contain RMSE and MAE. This paper describes the solution of Team 88VIP, which mainly comprises two types of models: a gradient boosting decision tree to memorize the basic data patterns and a recurrent neural network to capture the deep and latent probabilistic transitions. Ensembling these models contributes to tackle the fluctuation of wind power, and training submodels targets on the distinguished properties in heterogeneous timescales of forecasting, from minutes to days. In addition, feature engineering, imputation techniques and the design of offline evaluation are also described in details. The proposed solution achieves an overall online score of -45.213 in Phase 3.
To profile or to marginalize - A SMEFT case study

Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. This agreement is encoded in measured Wilson coefficients and their uncertainties. A technical challenge of global analyses are correlations. We compare, for the first time, results from a profile likelihood and a Bayesian marginalization for a given data set with a comprehensive uncertainty treatment. Using the validated Bayesian framework we analyse a series of new kinematic measurements. For the updated dataset we find and explain differences between the marginalization and profile likelihood treatments.
Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels

Many two-sample network hypothesis testing methodologies operate under the implicit assumption that the vertex correspondence across networks is a priori known. In this paper, we consider the degradation of power in two-sample graph hypothesis testing when there are misaligned/label-shuffled vertices across networks. In the context of random dot product and stochastic block model networks, we theoretically explore the power loss due to shuffling for a pair of hypothesis tests based on Frobenius norm differences between estimated edge probability matrices or between adjacency matrices. The loss in testing power is further reinforced by numerous simulations and experiments, both in the stochastic block model and in the random dot product graph model, where we compare the power loss across multiple recently proposed tests in the literature. Lastly, we demonstrate the impact that shuffling can have in real-data testing in a pair of examples from neuroscience and from social network analysis.
A Framework for Understanding and Visualizing Strategies of RL Agents

Recent years seen signiﬁcant advances in explainable AI as the need to understand deep learning models has gained importance with the increased emphasis on trust and ethics in AI. Comprehensible models for sequential decision tasks are a particular challenge as they require understanding not only individual predictions but a series of predictions that interact with environmental dynamics. We present a framework for learning comprehensible models of sequential decision tasks in which agent strategies are characterized using temporal logic formulas. Given a set of agent traces, we ﬁrst cluster the traces using a novel embedding method that captures frequent action patterns. We then search for logical formulas that explain the agent strategies in the different clusters. We evaluate our framework on combat scenarios in StarCraft II (SC2), using traces from a handcrafted expert policy and a trained reinforcement learning agent. We implemented a feature extractor for SC2 environments that extracts traces as sequences of high-level features describing both the state of the environment and the agent’s local behavior from agent replays. We further designed a visualization tool depicting the movement of units in the environment that helps understand how different task conditions lead to distinct agent behavior patterns in each trace cluster. Experimental results show that our framework is capable of separating agent traces into distinct groups of behaviors for which our approach to strategy inference produces consistent, meaningful, and easily understood strategy descriptions.
Combinatorial Reduction of Set Functions through Minor Invertible Product Assignment

We introduce an algebraic model, based on the determinantal expansion of the product of two matrices, to test combinatorial reductions of set functions. Each term of the determinantal expansion is deformed through a monomial factor in d indeterminates, whose exponents deﬁne a Z d -valued set function. By combining the Grassmann-Plücker relations for the two matrices, we derive a family of sparse polynomials, whose factorization properties in a Laurent polynomial ring are studied and related to information-theoretic notions. We prove that, under a given genericity condition, such a deformation returns a determinantal expansion if and only if it is induced by a diagonal matrix of units in C ( t ) acting as a kernel in the original determinant expression. This characterization supports the deﬁnition of a new method for checking and recovering combinatorial reductions for permutations of matroids.
Towards Automated Process Planning and Mining

AI Planning, Machine Learning and Process Mining have so far developed into separate research fields. At the same time, many interesting concepts and insights have been gained at the intersection of these areas in recent years. For example, the behavior of future processes is now comprehensively predicted with the aid of Machine Learning. For the practical application of these findings, however, it is also necessary not only to know the expected course, but also to give recommendations and hints for the achievement of goals, i.e. to carry out comprehensive process planning. At the same time, an adequate integration of the aforementioned research fields is still lacking. In this article, we present a research project in which researchers from the AI and BPM field work jointly together. Therefore, we discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models from executional process data, derive subsequent planning problems and conduct automated planning in order to adaptively plan and execute business processes using real-time forecasts.
Five-Year Community Surveillance Study for Acute Respiratory Infections Using Text Messaging: Findings From the MoSAIC Study

Abstract Background Acute respiratory infections (ARI) are the most common infectious diseases globally. Community surveillance may provide a more comprehensive picture of disease burden than medically attended illness alone. Methods In this longitudinal study conducted from 2012 to 2017 in the Washington Heights/Inwood area of New York City, we enrolled 405 households with 1915 individuals. Households were sent research text messages twice weekly inquiring about ARI symptoms. Research staff confirmed symptoms by follow-up call. If ≥2 criteria for ARI were met (fever/feverish, cough, congestion, pharyngitis, myalgias), staff obtained a mid-turbinate nasal swab in participants’ homes. Swabs were tested using the FilmArray reverse transcription polymerase chain reaction (RT-PCR) respiratory panel. Results Among participants, 43.9% were children, and 12.8% had a chronic respiratory condition. During the 5 years, 114 724 text messages were sent; the average response rate was 78.8% ± 6.8%. Swabs were collected for 91.4% (2756/3016) of confirmed ARI; 58.7% had a pathogen detected. Rhino/enteroviruses (51.9%), human coronaviruses (13.9%), and influenza (13.2%) were most commonly detected. The overall incidence was 0.62 ARI/person-year, highest (1.73) in <2 year-olds and lowest (0.46) in 18–49 year-olds. Approximately one-fourth of those with ARI sought healthcare; percents differed by pathogen, demographic factors, and presence of a chronic respiratory condition. Conclusions Text messaging is a novel method for community-based surveillance that could be used both seasonally as well as during outbreaks, epidemics and pandemics. The importance of community surveillance to accurately estimate disease burden is underscored by the findings of low rates of care-seeking that varied by demographic factors and pathogens.
Comparison of Statistical Experiments

Preface Acknowledgements 1. Statistical experiments within the measure theoretical framework 2. Convexity 3. Two-person, zero-sum games 4. Statistical decision problems 5. Vector lattices 6. Deficiencies 7. Equivalence, representations and functionals of experiments 8. Comparison of linear models 9. Majorisation and approximate majorisation 10. Complements: Further examples, problems and comments List of symbols Author index Additional references Subject index.
Prospective Cohort Study of Depression During Pregnancy and the Postpartum Period in Women With Epilepsy vs Control Groups

Background and Objectives Assess the incidence and factors associated with major depressive episodes (MDEs) and symptoms of depression and anxiety during pregnancy and postpartum periods in pregnant women with epilepsy (PWWE) compared with healthy pregnant women (HPW) and nonpregnant women with epilepsy (NPWWE) in comparable timeframes. Previous studies have reported higher rates of postpartum depression in women with epilepsy compared with women without epilepsy. However, the incidence of MDE using a structured interview during pregnancy and postpartum has not been directly compared with control groups, and the comparison of depression and anxiety symptoms and the role of associated factors remain ambiguous. Methods The Maternal Outcomes and Neurodevelopmental Effects of Antiepileptic Drugs study is a multicenter NIH-funded prospective observational parallel group cohort study of PWWE and their children. This report examines mood disorders. Unlike previous epilepsy pregnancy studies, the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV (SCID) provided lifetime diagnoses, and repeated SCID mood modules assessed for MDE, the a priori primary outcome. Symptoms of depression (Beck Depression Inventory [BDI] and Edinburg Postnatal Depression Scale [EPDS]) and anxiety (Beck Anxiety Inventory [BAI]) were also assessed along with multiple clinical factors. Results This study included PWWE (n = 331) and HPW (n = 102) during pregnancy and postpartum and NPWWE (n = 102) at comparable times. No difference in SCID-diagnosed MDE incidence was found across groups, but BDI depressive symptoms were worse during pregnancy in PWWE vs NPWWE and during postpartum vs HPW and NPWWE. BAI anxiety symptoms were worse during pregnancy in PWWE vs HPW and NPWWE and during postpartum vs HPW. Factors associated with MDE during pregnancy/postpartum for PWWE included >1 seizure/90 days, anticonvulsant polytherapy, unplanned pregnancy, and lifetime history of mood disorder. Suicidal ideation from BDI or EPDS was related to BAI anxiety symptoms. Discussion Although SCID-based MDE did not differ across groups, this prospective study confirms higher rates of psychiatric symptoms in patients with epilepsy during pregnancy and postpartum, provides new data on associated factors, and underscores the importance of anxiety in risk for depression and thoughts of death/dying or suicide. Given the risks, PWWE should be routinely assessed and symptomatic patients should be offered treatment. Trial Registration Information This study is registered at ClinicalTrials.gov as NCT01730170.