Miracle-dz/UniFAR-FTUs huggingface dataset:
Monster Radiative Shocks in the Perturbed Magnetospheres of Neutron Stars

Magnetospheres of neutron stars can be perturbed by star quakes, interaction in a binary system, or sudden collapse of the star. The perturbations are typically in the kilohertz band and excite magnetohydrodynamic waves. We show that compressive magnetospheric waves steepen into monster shocks, possibly the strongest shocks in the Universe. The shocks are radiative, i.e., the plasma energy is radiated before it crosses the shock. As the kilohertz wave with the radiative shock expands through the magnetosphere, it produces a bright X-ray burst. Then, it launches an approximately adiabatic blast wave, which will expand far from the neutron star. These results suggest a new mechanism for X-ray bursts from magnetars and support the connection of magnetar X-ray activity with fast radio bursts. Similar shocks may occur in magnetized neutron-star binaries before they merge, generating an X-ray precursor of the merger. Powerful radiative shocks are also predicted in the magnetosphere of a neutron star when it collapses into a black hole, producing a bright X-ray transient.
Opportunistic Episodic Reinforcement Learning

In this paper, we propose and study opportunistic reinforcement learning - a new variant of reinforcement learning problems where the regret of selecting a suboptimal action varies under an external environmental condition known as the variation factor. When the variation factor is low, so is the regret of selecting a suboptimal action and vice versa. Our intuition is to exploit more when the variation factor is high, and explore more when the variation factor is low. We demonstrate the benefit of this novel framework for finite-horizon episodic MDPs by designing and evaluating OppUCRL2 and OppPSRL algorithms. Our algorithms dynamically balance the exploration-exploitation trade-off for reinforcement learning by introducing variation factor-dependent optimism to guide exploration. We establish an $\tilde{O}(HS \sqrt{AT})$ regret bound for the OppUCRL2 algorithm and show through simulations that both OppUCRL2 and OppPSRL algorithm outperform their original corresponding algorithms.
Enabling Quantum Speedup of Markov Chains using a Multi-level Approach

Quantum speedup for mixing a Markov chain can be achieved based on the construction of slowly-varying $r$ Markov chains where the initial chain can be easily prepared and the spectral gaps have uniform lower bound. The overall complexity is proportional to $r$. We present a multi-level approach to construct such a sequence of $r$ Markov chains by varying a resolution parameter $h. $ We show that the density function of a low-resolution Markov chain can be used to warm start the Markov chain with high resolution. We prove that in terms of the chain length the new algorithm has $O(1)$ complexity rather than $O(r). $
Speeding Up Question Answering Task of Language Models via Inverted Index

Natural language processing applications, such as conversational agents and their question-answering capabilities, are widely used in the real world. Despite the wide popularity of large language models (LLMs), few real-world conversational agents take advantage of LLMs. Extensive resources consumed by LLMs disable developers from inte-grating them into end-user applications. In this study, we leverage an inverted indexing mechanism combined with LLMs to improve the ef-ﬁciency of question-answering models for closed-domain questions. Our experiments show that using the index improves the average response time by 97.44%. In addition, due to the reduced search scope, the average BLEU score improved by 0.23 while using the inverted index.
Axioms for retrodiction: achieving time-reversal symmetry with a prior

We propose a category-theoretic definition of retrodiction and use it to exhibit a time-reversal symmetry for all quantum channels. We do this by introducing retrodiction families and functors, which capture many intuitive properties that retrodiction should satisfy and are general enough to encompass both classical and quantum theories alike. Classical Bayesian inversion and all rotated and averaged Petz recovery maps define retrodiction families in our sense. However, averaged rotated Petz recovery maps, including the universal recovery map of Junge-Renner-Sutter-Wilde-Winter, do not define retrodiction functors, since they fail to satisfy some compositionality properties. Among all the examples we found of retrodiction families, the original Petz recovery map is the only one that defines a retrodiction functor. In addition, retrodiction functors exhibit an inferential time-reversal symmetry consistent with the standard formulation of quantum theory. The existence of such a retrodiction functor seems to be in stark contrast to the many no-go results on time-reversal symmetry for quantum channels. One of the main reasons is because such works defined time-reversal symmetry on the category of quantum channels alone, whereas we define it on the category of quantum channels and quantum states. This fact further illustrates the importance of a prior in time-reversal symmetry.
Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings

The problem of sequentially maximizing the expectation of a function seeks to maximize the expected value of a function of interest without having direct control on its features. Instead, the distribution of such features depends on a given context and an action taken by an agent. In contrast to Bayesian optimization, the arguments of the function are not under agent's control, but are indirectly determined by the agent's action based on a given context. If the information of the features is to be included in the maximization problem, the full conditional distribution of such features, rather than its expectation only, needs to be accounted for. Furthermore, the function is itself unknown, only counting with noisy observations of such function, and potentially requiring the use of unmatched data sets. We propose a novel algorithm for the aforementioned problem which takes into consideration the uncertainty derived from the estimation of both the conditional distribution of the features and the unknown function, by modeling the former as a Bayesian conditional mean embedding and the latter as a Gaussian process. Our algorithm empirically outperforms the current state-of-the-art algorithm in the experiments conducted.
Extending Lagrangian transformations to nonconvex scalar conservation laws

This article studies a method of finding Lagrangian transformations, in the form of particle paths, for all scalar conservation laws having a smooth flux. These are found using the notion of weak diffeomorphisms. More precisely, from any given scalar conservation law, we derive a Temple system having one linearly degenerate and one genuinely nonlinear family. We modify the system to make it strictly hyperbolic and prove an existence result for it. Finally we establish that entropy admissible weak solutions to this system are equivalent to those of the scalar equation. This method also determines the associated weak diffeomorphism.
Contrastive Search Is What You Need For Neural Text Generation

Generating text with autoregressive language models (LMs) is of great importance to many natural language processing (NLP) applications. Previous solutions for this task often produce text that contains degenerative expressions or lacks semantic consistency. Recently, Su et al. introduced a new decoding method, contrastive search, based on the isotropic representation space of the language model and obtained new state of the art on various benchmarks. Additionally, Su et al. argued that the representations of autoregressive LMs (e.g. GPT-2) are intrinsically anisotropic which is also shared by previous studies. Therefore, to ensure the language model follows an isotropic distribution, Su et al. proposed a contrastive learning scheme, SimCTG, which calibrates the language model's representations through additional training. In this study, we first answer the question:"Are autoregressive LMs really anisotropic? ". To this end, we extensively evaluate the isotropy of LMs across 16 major languages. Surprisingly, we find that the anisotropic problem only exists in the two specific English GPT-2-small/medium models. On the other hand, all other evaluated LMs are naturally isotropic which is in contrast to the conclusion drawn by previous studies. Based on our findings, we further assess the contrastive search decoding method using off-the-shelf LMs on four generation tasks across 16 languages. Our experimental results demonstrate that contrastive search significantly outperforms previous decoding methods without any additional training. More notably, on 12 out of the 16 evaluated languages, contrastive search performs comparably with human-level performances as judged by human evaluations. Our code and other related resources are publicly available at https://github.com/yxuansu/Contrastive_Search_Is_What_You_Need.
Endpoint of the Gregory-Laflamme instability of black strings revisited

We reproduce and extend the previous studies of Lehner and Pretorius of the endpoint of the Gregory-Laflamme instability of black strings in five space-time dimensions. We consider unstable black strings of fixed thickness and different lengths, and in all cases we confirm that at the intermediate stages of the evolution the horizon can be interpreted as a quasistationary self-similar sequence of black strings connecting spherical black holes on different scales. However, we do not find any evidence for a global timescale relating subsequent generations. The endpoint of the instability is the pinch off of the horizon in finite asymptotic time, thus confirming the violation of the weak cosmic censorship conjecture around black string spacetimes.
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.
Classification, Pattern Recognition and Reduction of Dimensionality

Discriminant Analysis for Time Series (R.H. Shumway). Optimum Rules for Classification into Two Multivariate Normal Populations with the Same Covariance Matrix (S. D. Gupta). Large Sample Approximations and Asymptotic Expansions of Classification Statistics (M. Siotani). Bayesian Discrimination (S. Geisser). Classification of Growth Curves (J.C. Lee). Nonparametric Classification (J.D. Broffitt). Logistic Discrimination (J.A. Anderson). Nearest Neighbor Methods in Discrimination (L. Devroye, T.J. Wagner). The Classification and Mixture Maximum Likelihood Approaches to Cluster Analysis (G.J. McLachlan). Graphical Techniques for Multivariate Data and for Clustering (J.M. Chambers, B. Kleiner). Cluster Analysis Software (R.K. Blashfield, M.S. Aldenderfer, L.C. Morey). Single-link Clustering Algorithms (F.J. Rohlf). Theory of Multidimensional Scaling (J. de Leeuw, W. Heiser). Multidimensional Scaling and its Applications (M. Wish, J.D. Carroll). Intrinsic Dimensionality Extraction (K. Fukunaga). Structural Methods in Image Analysis and Recognition (L.N. Kanal, B.A. Lambird, D. Lavine). Image Models (N. Ahuja, A. Rosenfeld). Image Texture Survey (R.M. Haralick). Applications of Stochastic Languages (K.S. Fu). A Unifying Viewpoint on Pattern Recognition (J.C. Simon, E. Backer, J. Sallentin). Logical Functions in the Problems of Empirical Prediction (G.S. Lbov). Inference and Data Tables with Missing Values (N.G. Zagoruiko, V.N. Yolkina). Recognition of Electrocardiographic Patterns (J.H. van Bemmel). Waveform Parsing Systems (C.G. Stockman). Continuous Speech Recognition: Statistical Methods (F. Jelinek, R.L. Mercer, L.R. Bahl). Applications of Pattern Recognition in Radar (A. Grometstein, W.H. Schoendorf). White Blood Cell Recognition (E.S. Gelsema, G.H. Landeweerd). Pattern Recognition Techniques for Remote Sensing Applications (P.H. Swain). Optical Character Recognition - Theory and Practice (G. Nagy). Computer and Statistical Considerations for Oil Spill Identification (Y.T. Chien, T.J. Killeen). Pattern Recognition in Chemistry (B.R. Kowalski, S. Wold). Covariance Matrix Representation and Object-Predicate Symmetry (T. Kaminuma, S. Tomita, S. Watanabe). Multivariate Morphometrics (P.A. Reyment). Multivariate Analysis with Latent Variables (P.M. Bentler, D.G. Weeks). Use of Distance Measures, Information Measures and Error Bounds in Feature Evaluation (M. Ben-Bassat). Topics in Measurement Selection (J.M. Van Campenhout). Selection of Variables under Univariate Regression Models (P.R. Krishnaiah). On the Selection of Variables under Regression Models using Krishnaiah's Finite Intersection Tests (J.L. Schmidhammer). Dimensionality and Sample Size Considerations in Pattern Recognition Practice (A.K. Jain, B. Chandrasekaran). Selecting Variables in Discriminant Analysis for Improving upon Classical Procedures (W. Schaafsma). Selection of Variables in Discriminant Analysis (P.R. Krishnaiah). Index.
Self-Reset Schemes for Magnetic Domain Wall-Based Neuron

Spintronic artificial spiking neurons are promising due to their ability to closely mimic the leaky integrate-and-fire (LIF) dynamics of the biological LIF spiking neuron. However, the neuron needs to be reset after firing. Few of the spintronic neurons that have been proposed in the literature discuss the reset process in detail. In this article, we discuss the various schemes to achieve this reset in a magnetic domain wall (DW)-based spintronic neuron in which the position of the DW represents the membrane potential. In all the spintronic neurons studied, the neuron enters a refractory period and is reset when the DW reaches a particular position. We show that the self-reset operation in the neuron devices consumes energy that can vary from several pJ to a few fJ, which highlights the importance of the reset strategy in improving the energy efficiency of spintronic artificial spiking neurons.
Detection of Single-base Mismatches in DNA Sequences by using Electrochemically-gated Graphene Microwave Waveguides

Identiﬁcation of non-ampliﬁed DNA sequences and single-base mutations is essential for molecular biology and genetic diagnostics. This paper reports a novel sensor consisting of electrochemically-gated graphene coplanar waveguides coupled with a microﬂuidic channel. Upon exposure to analytes, propagation of electromagnetic waves in the waveguides is modiﬁed as a result of interactions with the fringing ﬁeld and modulation of graphene dynamic conductivity resulting from electrostatic gating. Probe DNA sequences are immobilised on the graphene surface, and the sensor is exposed to DNA sequences either perfectly matching the probe or containing a single-base mismatch. By monitoring the scattering parameters at frequencies between 50 MHz and 50 GHz, unambiguous and reproducible discrimination of single-base mismatch strands is achieved at concentrations as low as 1 attomole per litre (1 a m ). By controlling and synchronising frequency sweeps, electrochemical gating, and liquid ﬂow in the microﬂuidic channel, the sensor generates multidimensional datasets, leveraging the use of advanced data analysis techniques. By applying diﬀerent machine learning methods, a classiﬁcation accuracy > 95% between single-base mismatches and perfect matches is achieved, even in the presence of simulated noise and low signal-to-noise ratios. The sensor exceeds state-of-the-art sensitivity for the identiﬁcation of single-base mismatches of ﬁeld-eﬀect transistors and microwave sensors.
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation

Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Excision of polyps during colonoscopy helps reduce mortality and morbidity for CRC. Powered by deep learning, computer-aided diagnosis (CAD) systems can detect regions in the colon overlooked by physicians during colonoscopy. Lacking high accuracy and real-time speed are the essential obstacles to be overcome for successful clinical integration of such systems. While literature is focused on improving accuracy, the speed parameter is often ignored. Toward this critical need, we intend to develop a novel real-time deep learning-based architecture, DilatedSegNet, to perform polyp segmentation on the fly. DilatedSegNet is an encoder-decoder network that uses pre-trained ResNet50 as the encoder from which we extract four levels of feature maps. Each of these feature maps is passed through a dilated convolution pooling (DCP) block. The outputs from the DCP blocks are concatenated and passed through a series of four decoder blocks that predicts the segmentation mask. The proposed method achieves a real-time operation speed of 33.68 frames per second with an average dice coefficient of 0.90 and mIoU of 0.83. Additionally, we also provide heatmap along with the qualitative results that shows the explanation for the polyp location, which increases the trustworthiness of the method. The results on the publicly available Kvasir-SEG and BKAI-IGH datasets suggest that DilatedSegNet can give real-time feedback while retaining a high \ac{DSC}, indicating high potential for using such models in real clinical settings in the near future. The GitHub link of the source code can be found here: \url{https://github.com/nikhilroxtomar/DilatedSegNet}.
Using physics-informed neural networks to compute quasinormal modes

In recent years there has been an increased interest in neural networks, particularly with regard to their ability to approximate partial diﬀerential equations. In this regard, research has begun on so-called physics-informed neural networks (PINNs) which incorporate into their loss function the boundary conditions of the functions they are attempting to approximate. In this paper, we investigate the viability of obtaining the quasi-normal modes (QNMs) of non-rotating black holes in 4-dimensional space-time using PINNs, and we ﬁnd that it is achievable using a standard approach that is capable of solving eigenvalue problems (dubbed the eigenvalue solver here). In comparison to the QNMs obtained via more established methods (namely, the continued fraction method and the 6th-order Wentzel, Kramer, Brillouin method) the PINN computations share the same degree of accuracy as these counterparts. In other words, our PINN approximations had percentage deviations as low as ( δω Re , δω Im ) = ( < 0 . 01% , < 0 . 01%). In terms of the time taken to compute QNMs to this accuracy, however, the PINN approach falls short, leading to our conclusion that the method is currently not to be recommended when considering overall performance.
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective

Designing neural networks with bounded Lipschitz constant is a promising way to obtain certifiably robust classifiers against adversarial examples. However, the relevant progress for the important $\ell_\infty$ perturbation setting is rather limited, and a principled understanding of how to design expressive $\ell_\infty$ Lipschitz networks is still lacking. In this paper, we bridge the gap by studying certified $\ell_\infty$ robustness from a novel perspective of representing Boolean functions. We derive two fundamental impossibility results that hold for any standard Lipschitz network: one for robust classification on finite datasets, and the other for Lipschitz function approximation. These results identify that networks built upon norm-bounded affine layers and Lipschitz activations intrinsically lose expressive power even in the two-dimensional case, and shed light on how recently proposed Lipschitz networks (e.g., GroupSort and $\ell_\infty$-distance nets) bypass these impossibilities by leveraging order statistic functions. Finally, based on these insights, we develop a unified Lipschitz network that generalizes prior works, and design a practical version that can be efficiently trained (making certified robust training free). Extensive experiments show that our approach is scalable, efficient, and consistently yields better certified robustness across multiple datasets and perturbation radii than prior Lipschitz networks. Our code is available at https://github.com/zbh2047/SortNet.
The Intermediate Polar FO Aquarii Has Not Been the Same Since Recovering from a Series of Low States

FO Aqr is a bright intermediate polar that has long displayed large amplitude photometric variations corresponding to the 20.9 minutes spin period of its white dwarf. Between 2016 and 2020, the system suffered a series of unprecedented low-states, but recent data shows that it has now recovered to its normal optical luminosity. We compare the light curves obtained by K2/Kepler in 2014 with photometry from the TESS mission obtained in 2021. We find that the spin pulse that had been the dominant feature of the light curve in 2014 is now weak over the second half of the binary orbit and that a beat pulse is enhanced in the TESS photometry. Variations at approximately twice the spin frequency are now seen over the second half of the orbit. These photometric properties may be the new normal for FO Aqr now that its white dwarf has begun to spin down.
Machine learning-based approach for online fault Diagnosis of Discrete Event System

The problem considered in this paper is the online diagnosis of Automated Production Systems with sensors and actuators delivering discrete binary signals that can be modeled as Discrete Event Systems. Even though there are numerous diagnosis methods, none of them can meet all the criteria of implementing an efficient diagnosis system (such as an intelligent solution, an average effort, a reasonable cost, an online diagnosis, fewer false alarms, etc.). In addition, these techniques require either a correct, robust, and representative model of the system or relevant data or experts' knowledge that require continuous updates. In this paper, we propose a Machine Learning-based approach of a diagnostic system. It is considered as a multi-class classifier that predicts the plant state: normal or faulty and what fault that has arisen in the case of failing behavior.