Miracle-dz/UniFAR-FTUs huggingface dataset:
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations

The general sequential decision-making problem, which includes Markov decision processes (MDPs) and partially observable MDPs (POMDPs) as special cases, aims at maximizing a cumulative reward by making a sequence of decisions based on a history of observations and actions over time. Recent studies have shown that the sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs). Despite these advancements, existing approaches typically involve oracles or steps that are computationally intractable. On the other hand, the upper confidence bound (UCB) based approaches, which have served successfully as computationally efficient methods in bandits and MDPs, have not been investigated for more general PSRs, due to the difficulty of optimistic bonus design in these more challenging settings. This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models. We further characterize the sample complexity bounds for our designed UCB-type algorithms for both online and offline PSRs. In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational tractability, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.
Persistent homological Quillen-McCord theorem

The Quillen-McCord theorem (aka Quillen fiber lemma) gives a sufficient condition on a map between classifying spaces of posetal categories to be a homotopy equivalence. Jonathan Ariel Barmak in his paper [arXiv:1005.0538] gives an elementary topological proof and proves a homological version of the theorem. Following his scheme of the proof, we formulate and prove the homological Quillen-McCord theorem, stable with respect to interleaving distances. To formulate the theorem and apply the scheme, we introduce persistence objects as objects in appropriate functor categories, describe a-la barcode decompositions of persistence posets, and prove several results, e.g. order extension principle for objects in Fun(I, Pos) and approximate triviality of left derived functors of approximately trivial objects in Fun(I, R-Mod).
Neural Polytopes

We find that simple neural networks with ReLU activation generate polytopes as an approximation of a unit sphere in various dimensions. The species of polytopes are regulated by the network architecture, such as the number of units and layers. For a variety of activation functions, generalization of polytopes is obtained, which we call neural polytopes. They are a smooth analogue of polytopes, exhibiting geometric duality. This finding initiates research of generative discrete geometry to approximate surfaces by machine learning.
Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews

We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews. To this end, we use a GCN for modeling non-consecutive and long-distance semantics to classify the transcriptions into depressed or control subjects. The proposed method aims to mitigate the limiting assumptions of locality and the equal importance of self-connections vs. edges to neighboring nodes in GCNs, while preserving attractive features such as low computational cost, data agnostic, and interpretability capabilities. We perform an exhaustive evaluation in two benchmark datasets. Results show that our approach consistently outperforms the vanilla GCN model as well as previously reported results, achieving an F1=0.84 on both datasets. Finally, a qualitative analysis illustrates the interpretability capabilities of the proposed approach and its alignment with previous findings in psychology.
JoinBoost: Grow Trees Over Normalized Data Using Only SQL

Although dominant for tabular data, ML libraries that train tree models over normalized databases (e.g., LightGBM, XGBoost) require the data to be denormalized as a single table, materialized, and exported. This process is not scalable, slow, and poses security risks. In-DB ML aims to train models within DBMSes to avoid data movement and provide data governance. Rather than modify a DBMS to support In-DB ML, is it possible to offer competitive tree training performance to specialized ML libraries.with only SQL? We present JoinBoost, a Python library that rewrites tree training algorithms over normalized databases into pure SQL. It is portable to any DBMS, offers performance competitive with specialized ML libraries, and scales with the underlying DBMS capabilities. JoinBoost extends prior work from both algorithmic and systems perspectives. Algorithmically, we support factorized gradient boosting, by updating the Y variable to the residual in the non-materialized join result. Although this view update problem is generally ambiguous, we identify addition-to-multiplication preserving , the key property of variance semi-ring to support rmse the most widely used criterion. System-wise, we identify residual updates as a performance bottleneck. Such overhead can be natively minimized on columnar DBMSes by creating a new column of residual values and adding it as a projection. We validate this with two implementations on DuckDB, with no or minimal modifications to its internals for portability. Our experiment shows that JoinBoost is 3× (1.1×) faster for random forests (gradient boosting) compared to LightGBM, and over an order of magnitude faster than state-of-the-art In-DB ML systems. Further, JoinBoost scales well beyond LightGBM in terms of the # features, DB size (TPC-DS SF=1000), and join graph complexity (galaxy schemas).
Quasi-admissible, raisable nilpotent orbits, and theta representations

We study the quasi-admissibility and raisablility of some nilpotent orbits of a covering group. In particular, we determine the degree of the cover such that a given split nilpotent orbit is quasi-admissible and non-raisable. The speculated wavefront sets of theta representations are also computed explicitly, and are shown to be quasi-admissible and non-raisable. Lastly, we determine the leading coefficients in the Harish-Chandra character expansion of theta representations of covers of the general linear groups.
Discovery of the missing intermediate-mass helium stars stripped in binaries

The theory of binary evolution predicts that many massive stars should lose their hydrogen-rich envelopes via interaction with a companion -- revealing hot helium stars with masses of $\sim$2--8M$_{\odot}$. However, only one candidate system had been identified, leaving a large discrepancy between theory and observation. Here, we present a new sample of stars -- identified via excess ultraviolet emission -- whose luminosities, colors, and spectral morphologies are consistent with predictions for the missing population. We detect radial velocity variations indicative of binary motion and measure high temperatures ($T_{\rm eff}\sim60-100$kK), high surface gravities ($\log(g)\sim5$) and depleted surface hydrogen mass fractions ($X_{\rm{H,surf}}\lesssim0.3$), which match expectations for stars with initial masses between 8--25 M$_{\odot}$ that have been stripped via binary interaction. These systems fill the helium star mass gap between subdwarfs and Wolf-Rayet stars, and are thought to be of large astrophysical significance as ionizing sources, progenitors of stripped-envelope supernovae and merging double neutron stars.
Global synchronization analysis of non-diffusively coupled networks through Contraction Theory

Synchronization of coupled dynamical systems is a widespread phenomenon in both biological and engineered networks, and understanding this behavior is crucial for controlling such systems. Considerable research has been dedicated to identifying the conditions that promote synchronization in diffusively coupled systems, where coupling relies on the difference between the states of neighboring systems and vanishes on the synchronization manifold. In particular, contraction theory provides an elegant method for analyzing synchronization patterns in diffusively coupled networks. However, these approaches do not fully explain the emergence of synchronization behavior in non-diffusively coupled networks where the coupling does not vanish on the synchronization manifold and hence the dynamics on the synchronization manifold differ from the uncoupled systems. Inspired by neuronal networks connected via non-diffusive chemical synapses, we extend contraction theory to establish sufficient conditions for global synchronization in general non-diffusively coupled nonlinear networks. We demonstrate the theoretical results on a network of Hindmarsh-Rose oscillators connected via chemical synapses and networks of FitzHugh-Nagumo oscillators connected via chemical synapses and additive coupling.
Rapid mixing of global Markov chains via spectral independence: the unbounded degree case

We consider spin systems on general $n$-vertex graphs of unbounded degree and explore the effects of spectral independence on the rate of convergence to equilibrium of global Markov chains. Spectral independence is a novel way of quantifying the decay of correlations in spin system models, which has significantly advanced the study of Markov chains for spin systems. We prove that whenever spectral independence holds, the popular Swendsen--Wang dynamics for the $q$-state ferromagnetic Potts model on graphs of maximum degree $\Delta$, where $\Delta$ is allowed to grow with $n$, converges in $O((\Delta \log n)^c)$ steps where $c>0$ is a constant independent of $\Delta$ and $n$. We also show a similar mixing time bound for the block dynamics of general spin systems, again assuming that spectral independence holds. Finally, for monotone spin systems such as the Ising model and the hardcore model on bipartite graphs, we show that spectral independence implies that the mixing time of the systematic scan dynamics is $O(\Delta^c \log n)$ for a constant $c>0$ independent of $\Delta$ and $n$. Systematic scan dynamics are widely popular but are notoriously difficult to analyze. Our result implies optimal $O(\log n)$ mixing time bounds for any systematic scan dynamics of the ferromagnetic Ising model on general graphs up to the tree uniqueness threshold. Our main technical contribution is an improved factorization of the entropy functional: this is the common starting point for all our proofs. Specifically, we establish the so-called $k$-partite factorization of entropy with a constant that depends polynomially on the maximum degree of the graph.
On polariton condensation and polariton-mediated superconductivity in novel types of planar microcavities

The many-body phenomena in photonic structures are of great interest due to the vast area of potential applications. This thesis is devoted to the theoretical investigations of collective phenomena in different types of semiconductor structures, in particular, parabolic quantum wells, hybrid Bose-Fermi structures, and transition metal dichalcogenide monolayers. It is shown that the experimentally obtained integrated photoluminescence spectra from the parabolic quantum well in a microcavity can be described with the use of the semiclassical Boltzmann equations. However, contrary to expectations, the ladder mechanism of exciton relaxation does not describe the experimental data, as the relaxation processes in the PQW involve transitions from all levels to the ground state. The theoretical investigation of the light-mediated superconductivity in hybrid Bose-Fermi systems is performed. The critical temperature of the phase transition is shown to increase with the density of the polariton condensate and to decrease with the density of electron gas in the superconducting layer. Also, a possible mechanism of suppression of superconductivity by the external magnetic field is discussed, which differs in the considered case from the conventional Meissner effect, because the magnetic field penetration depth is longer than the thickness of the superconducting layer. The possibility of polariton condensation in microcavities with embedded monolayers of transition metal dichalcogenides is discussed. It is shown that these structures are highly promising for the room temperature polaritonics. The first observation of the strong coupling regime and exciton-polariton modes in WSe2 was realised by our collaborators from the Wurzburg University. These results have been described by the coupled harmonic oscillator approach.
MoVie: Visual Model-Based Policy Adaptation for View Generalization

Visual Reinforcement Learning (RL) agents trained on limited views face significant challenges in generalizing their learned abilities to unseen views. This inherent difficulty is known as the problem of $\textit{view generalization}$. In this work, we systematically categorize this fundamental problem into four distinct and highly challenging scenarios that closely resemble real-world situations. Subsequently, we propose a straightforward yet effective approach to enable successful adaptation of visual $\textbf{Mo}$del-based policies for $\textbf{Vie}$w generalization ($\textbf{MoVie}$) during test time, without any need for explicit reward signals and any modification during training time. Our method demonstrates substantial advancements across all four scenarios encompassing a total of $\textbf{18}$ tasks sourced from DMControl, xArm, and Adroit, with a relative improvement of $\mathbf{33}$%, $\mathbf{86}$%, and $\mathbf{152}$% respectively. The superior results highlight the immense potential of our approach for real-world robotics applications. Videos are available at https://yangsizhe.github. io/MoVie/ .
REAL: A Representative Error-Driven Approach for Active Learning

Given a limited labeling budget, active learning (AL) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, AL typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborhood error density, which have great potential to improve the model performance. To address this limitation, we propose $REAL$, a novel approach to select data instances with $\underline{R}$epresentative $\underline{E}$rrors for $\underline{A}$ctive $\underline{L}$earning. It identifies minority predictions as \emph{pseudo errors} within a cluster and allocates an adaptive sampling budget for the cluster based on estimated error density. Extensive experiments on five text classification datasets demonstrate that $REAL$ consistently outperforms all best-performing baselines regarding accuracy and F1-macro scores across a wide range of hyperparameter settings. Our analysis also shows that $REAL$ selects the most representative pseudo errors that match the distribution of ground-truth errors along the decision boundary. Our code is publicly available at https://github.com/withchencheng/ECML_PKDD_23_Real.
Reionisation time field reconstruction from 21cm signal ma

During the Epoch of reionisation, the intergalactic medium is reionised by the UV radiation from the first generation of stars and galaxies. One tracer of the process is the 21 cm line of hydrogen that will be observed by the Square Kilometre Array (SKA) at low frequencies, thus imaging the distribution of ionised and neutral regions and their evolution. To prepare for these upcoming observations, we investigate a deep learning method to predict from 21 cm maps the reionisation time field (treion(r)), i.e. the time at which each location has been reionised. treion(r) encodes the propagation of ionisation fronts in a single field, gives access to times of local reionisation or to the extent of the radiative reach of early sources. Moreover it gives access to the time evolution of ionisation on the plane of sky, when such evolution is usually probed along the line-of-sight direction. We trained a convolutional neural network (CNN) using simulated 21 cm maps and reionisation times fields produced by the simulation code 21cmFAST . We also investigate the performance of the CNN when adding instrumental effects. Globally, we find that without instrumental effects the 21 cm maps can be used to reconstruct the associated reionisation times field in a satisfying manner: the quality of the reconstruction is dependent on the redshift at which the 21 cm observation is being made and in general it is found that small scale (<10cMpc/h) features are smoothed in the reconstructed field, while larger scale features are well recovered. When instrumental effects are included, the scale dependance of reconstruction is even further pronounced, with significant smoothing on small and intermediate scales.
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems

Imbalanced data poses a significant challenge in classification as model performance is affected by insufficient learning from minority classes. Balancing methods are often used to address this problem. However, such techniques can lead to problems such as overfitting or loss of information. This study addresses a more challenging aspect of balancing methods - their impact on model behavior. To capture these changes, Explainable Artificial Intelligence tools are used to compare models trained on datasets before and after balancing. In addition to the variable importance method, this study uses the partial dependence profile and accumulated local effects techniques. Real and simulated datasets are tested, and an open-source Python package edgaro is developed to facilitate this analysis. The results obtained show significant changes in model behavior due to balancing methods, which can lead to biased models toward a balanced distribution. These findings confirm that balancing analysis should go beyond model performance comparisons to achieve higher reliability of machine learning models. Therefore, we propose a new method performance gain plot for informed data balancing strategy to make an optimal selection of balancing method by analyzing the measure of change in model behavior versus performance gain.
Cross-Modal Zero-Shot-Learning for Tactile Object Recognition

In this paper, we address the learning problem of classifying untouched tactile instance with the help of visual modality. The proposed method is based on dictionary learning and we impose different penalty terms on coding vectors between visual and tactile modalities. Using such structured coding vectors, the visual-tactile cross-modal transfer can be achieved. A set of optimization algorithms are developed to obtain the solutions of the proposed optimization problems. After then, we can use the obtained dictionary to predict the coding vectors of the new untouched tactile samples and further determine its label. Finally, we perform extensive experimental evaluations on publicly available datasets to show the effectiveness of the proposed method.
Phenological diversity among sub-tropical moist forest trees of north-eastern India

Abstract Analysing phenological diversity of tropical trees provides a potential tool to detect climate change effects and devise forest management options. In this study, the leaf phenological activity of 28 dominant tree species in a moist sub-tropical hill forest of north-eastern India was examined for a period of 2 years and related to functional traits (i.e. leaf mass per area (LMA) and wood density (WD)). The peak phase of leaf fall occurred in the cool dry period (November to January) with leaf flush peaking in the pre-monsoon period (February to March), but variation was found between species as influenced by their phenological strategy, i.e. evergreen, leaf-exchanging or deciduous (<4 months leafless). Photoperiod and minimum temperature were the environmental factors most strongly correlated with phenological activity, and the synchrony index within species for both phenophases was 0.81. LMA was less in the deciduous species compared with the evergreen species, whereas WD did not differ. LMA was negatively correlated with the length of deciduousness as well as timing of leaf flush and fall indicating that LMA may be more important than WD in influencing phenological patterns in this forest. The study revealed that the phenological diversity of tropical trees is related to changes in environmental variables and has implication for forest management under changing climate. Further study will help in understanding the phenological response of trees to climatic factors and their potential future changes.
Near-continuum, hypersonic oxygen flow over a double cone simulated by direct simulation Monte Carlo informed from quantum chemistry

Abstract A large-scale, fully resolved direct simulation Monte Carlo (DSMC) computation of a non-equilibrium, reactive flow of pure oxygen over a double cone is presented. Under the simulated near-continuum conditions, the computational demands are shown to be significant because of the wide range of length scales that must be resolved. Therefore, robust grid adaption capabilities and efficient parallelization of the Stochastic PArallel Rarefied-gas Time-accurate Analyzer (SPARTA) code that is utilized in this work are essential. The thermochemical and transport collision models were selected for efficiency and simplicity. First-principles data, obtained from the highly accurate direct molecular simulation method, were used to inform the collision models’ parameters. Importantly, because SPARTA implements molecular collision models using collision-specific energies, the resulting macroscopic relaxation rates were evaluated a posteriori via zero-dimensional heat bath simulations. The comparisons of surface properties, namely heat flux and pressure, show very close agreement with previous computational fluid dynamics (CFD) results. Differences with the measurements were found to be similar to the CFD simulations. The unresolved discrepancy with the measurements could be due to inconsistent free stream conditions with the actual experimental data or missing physical phenomena altogether, for example atomic and molecular oxygen electronically excited states, three-dimensional effects, or more complex gas–surface interactions. As shown in this work, the advantages of obtaining a DSMC particle solution for these flows reside in the method's ability to be directly informed from first principles and to seamlessly describe internal energy non-equilibrium for all modes. With the advent of exascale computing and beyond, particle methods will be an increasingly important tool to verify the validity of physical assumptions in reduced-order models via fully resolved, experimental-scale simulations, down to the level of molecular-level distributions.