Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the Physical Origin of Gamma-Ray Bursts with Supervised Machine Learning

The empirical classification of gamma-ray bursts (GRBs) into long and short GRBs based on their durations is already firmly established. This empirical classification is generally linked to the physical classification of GRBs originating from compact binary mergers and GRBs originating from massive star collapses, or Type I and II GRBs, with the majority of short GRBs belonging to Type I and the majority of long GRBs belonging to Type II. However, there is a significant overlap in the duration distributions of long and short GRBs. Furthermore, some intermingled GRBs, i.e., short-duration Type II and long-duration Type I GRBs, have been reported. A multiparameter classification scheme of GRBs is evidently needed. In this paper, we seek to build such a classification scheme with supervised machine-learning methods, chiefly XGBoost. We utilize the GRB Big Table and Greiner’s GRB catalog and divide the input features into three subgroups: prompt emission, afterglow, and host galaxy. We find that the prompt emission subgroup performs the best in distinguishing between Type I and II GRBs. We also find the most important distinguishing features in prompt emission to be T 90, the hardness ratio, and fluence. After building the machine-learning model, we apply it to the currently unclassified GRBs to predict their probabilities of being either GRB class, and we assign the most probable class of each GRB to be its possible physical class.
Stability of time‐splitting schemes for the Stokes problem with stabilized finite elements

The time‐dependent Stokes problem is solved using continuous, piecewise linear finite elements and a classical stabilization procedure. Four order‐one methods are proposed for the time discretization. The first one is nothing but the Euler backward scheme and requires a large linear system involving the velocity and pressure unknowns to be solved. The other three schemes allow velocity and pressure computations to be decoupled, namely the pressure‐matrix method, a method based on an inexact LU factorization, and an operator splitting method. Stability and condition number estimates are derived. Numerical experiments in two space dimensions confirm the theoretical predictions. © 2001 John Wiley & Sons, Inc. Numer Methods Partial Differential Eq 17:632–656, 2001
Universal quantum computer based on carbon nanotube rotators

We propose a universal quantum computer based on a chain of carbon nanotube rotators where one metallic plate is attached to each rotator. The dynamical variable is the rotational angle ϕ. The attached plate connected to ground electrostatically interacts with two fixed plates. Two angle positions ϕ = 0, π are made stable by applying a voltage difference between the attached plate and the two fixed plates. We assign ϕ = 0 and π to the qubit states ∣0〉 and ∣1〉. Then, considering a chain of rotators, we construct the arbitrary phase-shift gate, the NOT gate and the Ising gate, which constitute a set of universal quantum gates. They are executed by controlling the voltage between various plates.
Building Resilience to Out-of-Distribution Visual Data via Input Optimization and Model Finetuning

A major challenge in machine learning is resilience to out-of-distribution data, that is data that exists outside of the distribution of a model’s training data. Training is often performed using limited, carefully curated datasets and so when a model is deployed there is often a significant distribution shift as edge cases and anomalies not included in the training data are encountered. To address this, we propose the Input Optimisation Network, an image preprocessing model that learns to optimise input data for a specific target vision model. In this work we investigate several out-of-distribution scenarios in the context of semantic segmentation for autonomous vehicles, comparing an Input Optimisation based solution to existing approaches of finetuning the target model with augmented training data and an adversarially trained preprocessing model. We demonstrate that our approach can enable performance on such data comparable to that of a finetuned model, and subsequently that a combined approach, whereby an input optimization network is optimised to target a finetuned model, delivers superior performance to either method in isolation. Finally, we propose a joint optimisation approach, in which input optimization network and target model are trained simultaneously, which we demonstrate achieves significant further performance gains, particularly in challenging edge-case scenarios. We also demonstrate that our architecture can be reduced to a relatively compact size without a significant performance impact, potentially facilitating real time embedded applications.
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology

Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the potential of using self-supervised pre-training to overcome scanner-induced domain shifts for the downstream task of tumor segmentation. For this, we present the Barlow Triplets to learn scanner-invariant representations from a multi-scanner dataset with local image correspondences. We show that self-supervised pre-training successfully aligned different scanner representations, which, interestingly only results in a limited benefit for our downstream task. We thereby provide insights into the influence of scanner characteristics for downstream applications and contribute to a better understanding of why established self-supervised methods have not yet shown the same success on histopathology data as they have for natural images.
Double Robust Bayesian Inference on Average Treatment Effects

We propose a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness. Our robust Bayesian approach involves two important modifications: first, we adjust the prior distributions of the conditional mean function; second, we correct the posterior distribution of the resulting ATE. Both adjustments make use of pilot estimators motivated by the semiparametric influence function for ATE estimation. We prove asymptotic equivalence of our Bayesian procedure and efficient frequentist ATE estimators by establishing a new semiparametric Bernstein-von Mises theorem under double robustness; i.e., the lack of smoothness of conditional mean functions can be compensated by high regularity of the propensity score and vice versa. Consequently, the resulting Bayesian credible sets form confidence intervals with asymptotically exact coverage probability. In simulations, our double robust Bayesian procedure leads to significant bias reduction of point estimation over conventional Bayesian methods and more accurate coverage of confidence intervals compared to existing frequentist methods. We illustrate our method in an application to the National Supported Work Demonstration.
Perception : From Video Environment to Game Environment

Training self-driving cars is often challenging since they require a vast amount of labeled data in multiple real-world contexts, which is computationally and memory intensive. Researchers often resort to driving simulators to train the agent and transfer the knowledge to a real-world setting. Since simulators lack realistic behavior, these methods are quite inefficient. To address this issue, we introduce a framework (perception, planning, and control) in a real-world driving environment that transfers the real-world environments into gaming environments by setting up a reliable Markov Decision Process (MDP). We propose variations of existing Reinforcement Learning (RL) algorithms in a multi-agent setting to learn and execute the discrete control in real-world environments. Experiments show that the multi-agent setting outperforms the single-agent setting in all the scenarios. We also propose reliable initialization, data augmentation, and training techniques that enable the agents to learn and generalize to navigate in a real-world environment with minimal input video data, and with minimal training. Additionally, to show the efficacy of our proposed algorithm, we deploy our method in the virtual driving environment TORCS.
Scaling-aware rating of count forecasts

Forecasts crave a rating that reflects the forecast's quality in the context of what is possible in theory and what is reasonable to expect in practice. Granular forecasts in the regime of low count rates - as they often occur in retail, for which an intermittent demand of a handful might be observed per product, day, and location - are dominated by the inevitable statistical uncertainty of the Poisson distribution. This makes it hard to judge whether a certain metric value is dominated by Poisson noise or truly indicates a bad prediction model. To make things worse, every evaluation metric suffers from scaling: Its value is mostly defined by the predicted selling rate and the resulting rate-dependent Poisson noise, and only secondarily by the quality of the forecast. For any metric, comparing two groups of forecasted products often yields"the slow movers are performing worse than the fast movers"or vice versa - the na\"ive scaling trap. To distill the intrinsic quality of a forecast, we stratify predictions into buckets of approximately equal rate and evaluate metrics for each bucket separately. By comparing the achieved value per bucket to benchmarks, we obtain a scaling-aware rating of count forecasts. Our procedure avoids the na\"ive scaling trap, provides an immediate intuitive judgment of forecast quality, and allows to compare forecasts for different products or even industries.
Tensor Formulation of Ensemble-Based Background Error Covariance Matrix Factorization

AbstractFactorization of a background error covariance matrix (B factorization) constructed from localization matrices and a small ensemble that obeys background error statistics is an efficient method for introducing flow-dependent background error statistics into variational form data assimilation systems. Although there are four types of matrix formulations of B factorization, their derivation processes and relationships are not clarified, and mathematical operability is limited because of their complex matrix forms. In this paper, B factorization in the tensor (component) form is formulated to overcome these shortcomings. The tensor formulation is very simple and directly connects the background error covariance matrix with its factorization. All existing matrix formulations are derived from the tensor formulation as their specific matrix form representations. Using the simplicity of the tensor formulation, the relationships between the strong-constraint four-dimensional variational data assimilation .
Score-Based Calibration Testing for Multivariate Forecast Distributions

Calibration tests based on the probability integral transform (PIT) are routinely used to assess the quality of univariate distributional forecasts. However, PIT-based calibration tests for multivariate distributional forecasts face various challenges. We propose two new types of tests based on proper scoring rules, which overcome these challenges. They arise from a general framework for calibration testing in the multivariate case, introduced in this work. The new tests have good size and power properties in simulations and solve various problems of existing tests. We apply the tests to forecast distributions for macroeconomic and financial time series data.
Spectrum of the $\bar{\partial}$-Laplace operator on zero forms for the quantum quadric $\mathcal{O}_q(\textbf{Q}_N)$

We study the Laplacian operator $\Delta_{\bar{\partial}}$ associated to a K\"ahler structure $(\Omega^{(\bullet, \bullet)}, \kappa)$ for the Heckenberger--Kolb differential calculus of the quantum quadrics $\mathcal{O}_q(\textbf{Q}_N)$, which is to say, the irreducible quantum flag manifolds of types $B_n$ and $D_n$. We show that the eigenvalues of $\Delta_{\bar{\partial}}$ on zero forms tend to infinity and have finite multiplicity.
Dual adaptive MPC using an exact set-membership reformulation

Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work. Strong duality is used to reformulate the set-membership equations exactly within the MPC optimization. A predicted worst-case cost is then used to enable performance-oriented exploration. The proposed approach guarantees robust constraint satisfaction and recursive feasibility. It is shown that method can be implemented using homothetic tube and flexible tube parameterizations of state tubes, and a simulation study demonstrates performance improvement over state-of-the-art controllers.
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators

Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a policy is trained to maximize a learned value function while constrained by the behavior policy to avoid a significant distributional shift. In this paper, we propose our closed-form policy improvement operators. We make a novel observation that the behavior constraint naturally motivates the use of first-order Taylor approximation, leading to a linear approximation of the policy objective. Additionally, as practical datasets are usually collected by heterogeneous policies, we model the behavior policies as a Gaussian Mixture and overcome the induced optimization difficulties by leveraging the LogSumExp's lower bound and Jensen's Inequality, giving rise to a closed-form policy improvement operator. We instantiate offline RL algorithms with our novel policy improvement operators and empirically demonstrate their effectiveness over state-of-the-art algorithms on the standard D4RL benchmark. Our code is available at https://cfpi-icml23.github. io/.
The Surprising Effectiveness of Latent World Models for Continual Reinforcement Learning

We study the use of model-based reinforcement learning methods, in particular, world models for continual reinforcement learning. In continual reinforcement learning, an agent is required to solve one task and then another sequentially while retaining performance and preventing forgetting on past tasks. World models offer a task-agnostic solution: they do not require knowledge of task changes. World models are a straight-forward baseline for continual reinforcement learning for three main reasons. Firstly, forgetting in the world model is prevented by persisting existing experience replay buffers across tasks, experience from previous tasks is replayed for learning the world model. Secondly, they are sample efﬁcient. Thirdly and ﬁnally, they offer a task-agnostic exploration strategy through the uncertainty in the trajectories generated by the world model. We show that world models are a simple and effective continual reinforcement learning baseline. We study their effectiveness on Minigrid and Minihack continual reinforcement learning benchmarks and show that it outperforms state of the art task-agnostic continual reinforcement learning methods.
DiffG-RL: Leveraging Difference between State and Common Sense

Taking into account background knowledge as the context has always been an important part of solving tasks that involve natural language. One representative example of such tasks is text-based games, where players need to make decisions based on both description text previously shown in the game, and their own background knowledge about the language and common sense. In this work, we investigate not simply giving common sense, as can be seen in prior research, but also its effective usage. We assume that a part of the environment states different from common sense should constitute one of the grounds for action selection. We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder. DiffG-RL also contains a framework for extracting the appropriate amount and representation of common sense from the source to support the construction of the graph. We validate DiffG-RL in experiments with text-based games that require common sense and show that it outperforms baselines by 17% of scores. The code is available at https://github.com/ibm/diffg-rl
CoNAL: Anticipating Outliers with Large Language Models

In many task settings, text classification models are likely to encounter examples from novel classes on which they cannot predict correctly. Selective prediction, in which models abstain on low-confidence examples, provides a possible solution, but existing models are often overly confident on OOD examples. To remedy this overconfidence, we introduce Contrastive Novelty-Augmented Learning (CoNAL), a two-step method that generates OOD examples representative of novel classes, then trains to decrease confidence on them. First, we generate OOD examples by prompting a large language model twice: we prompt it to enumerate relevant novel labels, then generate examples from each novel class matching the task format. Second, we train our classifier with a novel contrastive objective that encourages lower confidence on generated OOD examples than training examples. When trained with CoNAL, classifiers improve in their ability to detect and abstain on OOD examples over prior methods by an average of 2.3% AUAC and 5.5% AUROC across 4 NLP datasets, with no cost to in-distribution accuracy.1
Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT

Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing strong supervision on what constitutes positive and negative samples. This constitutes a major obstacle for the development of DL models in radiology--in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)--where labels must come from manual annotations by expert radiologists at the image or slice-level. These differ from examination-level annotations, which are coarser but cheaper, and could be extracted from radiology reports using natural language processing techniques. This work studies the question of what kind of labels should be collected for the problem of intracranial hemorrhage detection in brain CT. We investigate whether image-level annotations should be preferred to examination-level ones. By framing this task as a multiple instance learning problem, and employing modern attention-based DL architectures, we analyze the degree to which different levels of supervision improve detection performance. We find that strong supervision (i.e., learning with local image-level annotations) and weak supervision (i.e., learning with only global examination-level labels) achieve comparable performance in examination-level hemorrhage detection (the task of selecting the images in an examination that show signs of hemorrhage) as well as in image-level hemorrhage detection (highlighting those signs within the selected images). Furthermore, we study this behavior as a function of the number of labels available during training. Our results suggest that local labels may not be necessary at all for these tasks, drastically reducing the time and cost involved in collecting and curating datasets.
Cubic fixed point in three dimensions: Monte Carlo simulations of the 
ϕ4
 model on the simple cubic lattice

We study the cubic fixed point for $N=3$ and $4$ by using finite size scaling applied to data obtained from Monte Carlo simulations of the $N$-component $\phi^4$ model on the simple cubic lattice. We generalize the idea of improved models to a two-parameter family of models. The two-parameter space is scanned for the point, where the amplitudes of the two leading corrections to scaling vanish. To this end, a dimensionless quantity is introduced that monitors the breaking of the $O(N)$-invariance. For $N=4$, we determine the correction exponents $\omega_1=0.763(24)$ and $\omega_2=0.082(5)$. In the case of $N=3$, we obtain $Y_4=0.0142(6)$ for the RG-exponent of the cubic perturbation at the $O(3)$-invariant fixed point, while the correction exponent $\omega_2=0.0133(8)$ at the cubic fixed point. Simulations close to the improved point result in the estimates $\nu=0.7202(7)$ and $\eta=0.0371(2)$ of the critical exponents of the cubic fixed point for $N=4$. For $N=3$, at the cubic fixed point, the $O(3)$-symmetry is only mildly broken and the critical exponents differ only by little from those of the $O(3)$-invariant fixed point. We find $-0.00001 \lessapprox \eta_{cubic}- \eta_{O(3)} \lessapprox 0.00007$ and $\nu_{cubic}-\nu_{O(3)} =-0.00061(10)$.
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations

Knowledge of the symmetries of reinforcement learning (RL) systems can be used to create compressed and semantically meaningful representations of a low-level state space. We present a method of automatically detecting RL symmetries directly from raw trajectory data without requiring active control of the system. Our method generates candidate symmetries and trains a recurrent neural network (RNN) to discriminate between the original trajectories and the transformed trajectories for each candidate symmetry. The RNN discriminator's accuracy for each candidate reveals how symmetric the system is under that transformation. This information can be used to create high-level representations that are invariant to all symmetries on a dataset level and to communicate properties of the RL behavior to users. We show in experiments on two simulated RL use cases (a pusher robot and a UAV flying in wind) that our method can determine the symmetries underlying both the environment physics and the trained RL policy.
How Many Tweets DoWe Need?: Efficient Mining of Short-Term Polarized Topics on Twitter: A Case Study From Japan

In recent years, social media has been criticized for yielding polarization. Identifying emerging disagreements and growing polarization is important for journalists to create alerts and provide more balanced coverage. While recent studies have shown the existence of polarization on social media, they primarily focused on limited topics such as politics with a large volume of data collected in the long term, especially over months or years. While these findings are helpful, they are too late to create an alert immediately. To address this gap, we develop a domain-agnostic mining method to identify polarized topics on Twitter in a short-term period, namely 12 hours. As a result, we find that daily Japanese news-related topics in early 2022 were polarized by 31.6% within a 12-hour range. We also analyzed that they tend to construct information diffusion networks with a relatively high average degree, and half of the tweets are created by a relatively small number of people. However, it is very costly and impractical to collect a large volume of tweets daily on many topics and monitor the polarization due to the limitations of the Twitter API. To make it more cost-efficient, we also develop a prediction method using machine learning techniques to estimate the polarization level using randomly collected tweets leveraging the network information. Extensive experiments show a significant saving in collection costs compared to baseline methods. In particular, our approach achieves F-score of 0.85, requiring 4,000 tweets, 4x savings than the baseline. To the best of our knowledge, our work is the first to predict the polarization level of the topics with low-resource tweets. Our findings have profound implications for the news media, allowing journalists to detect and disseminate polarizing information quickly and efficiently.