Miracle-dz/UniFAR-FTUs huggingface dataset:
The thermodynamic limit of an ideal Bose gas by asymptotic expansions and spectral ζ-functions

We analyze the thermodynamic limit—modeled as the open-trap limit of an isotropic harmonic potential—of an ideal, non-relativistic Bose gas with a special emphasis on the phenomenon of Bose–Einstein condensation. This is accomplished by the use of an asymptotic expansion of the grand potential, which is derived by ζ-regularization techniques. Herewith, we can show that the singularity structure of this expansion is directly interwoven with the phase structure of the system: In the non-condensation phase, the expansion has a form that resembles usual heat kernel expansions. By this, thermodynamic observables are directly calculable. In contrast, the expansion exhibits a singularity of infinite order above a critical density, and a renormalization of the chemical potential is needed to ensure well-defined thermodynamic observables. Furthermore, the renormalization procedure forces the system to exhibit condensation. In addition, we show that characteristic features of the thermodynamic limit, such as the critical density or the internal energy, are entirely encoded in the coefficients of the asymptotic expansion.
Total stability and integral action for discrete-time nonlinear systems

Robustness guarantees are important properties to be looked for during control design. They ensure stability of closed-loop systems in face of uncertainties, unmodeled eﬀects and bounded disturbances. While the theory on robust stability is well established in the continuous-time nonlinear framework, the same cannot be stated for its discrete-time counterpart. In this paper, we propose the discrete-time parallel of total stability results for continuous-time nonlinear system. This enables the analysis of robustness properties via simple model diﬀerence in the discrete-time context. First, we study how existence of equilibria for a nominal model transfers to suﬃciently similar ones. Then, we provide results on the propagation of stability guarantees to perturbed systems. Finally, we relate such properties to the constant output regulation problem by motivating the use of discrete-time integral action in discrete-time nonlinear systems.
Conservation laws and scattering for de Sitter classical particles

Starting from an intrinsic geometric characterization of de Sitter timelike and lightlike geodesics we give a new description of the conserved quantities associated with classical free particles on the de Sitter manifold. These quantities allow for a natural discussion of classical pointlike scattering and decay processes. We also provide an intrinsic definition of the energy of a classical de Sitter particle and discuss its different expressions in various local coordinate systems and their relations with earlier definitions found in the literature.
Event Extraction: A Survey

Extracting the reported events from text is one of the key research themes in natural language processing. This process includes several tasks such as event detection, argument extraction, role labeling. As one of the most important topics in natural language processing and natural language understanding, the applications of event extraction spans across a wide range of domains such as newswire, biomedical domain, history and humanity, and cyber security. This report presents a comprehensive survey for event detection from textual documents. In this report, we provide the task definition, the evaluation method, as well as the benchmark datasets and a taxonomy of methodologies for event extraction. We also present our vision of future research direction in event detection.
VIRDO++: Real-World, Visuo-tactile Dynamics and Perception of Deformable Objects

Deformable objects manipulation can benefit from representations that seamlessly integrate vision and touch while handling occlusions. In this work, we present a novel approach for, and real-world demonstration of, multimodal visuo-tactile state-estimation and dynamics prediction for deformable objects. Our approach, VIRDO++, builds on recent progress in multimodal neural implicit representations for deformable object state-estimation [1] via a new formulation for deformation dynamics and a complementary state-estimation algorithm that (i) maintains a belief over deformations, and (ii) enables practical real-world application by removing the need for privileged contact information. In the context of two real-world robotic tasks, we show:(i) high-fidelity cross-modal state-estimation and prediction of deformable objects from partial visuo-tactile feedback, and (ii) generalization to unseen objects and contact formations.
Lasso Monte Carlo, a Variation on Multi Fidelity Methods for High Dimensional Uncertainty Quantification

Uncertainty quantification (UQ) is an active area of research, and an essential technique used in all fields of science and engineering. The most common methods for UQ are Monte Carlo and surrogate-modelling. The former method is dimensionality independent but has slow convergence, while the latter method has been shown to yield large computational speedups with respect to Monte Carlo. However, surrogate models suffer from the so-called curse of dimensionality, and become costly to train for high-dimensional problems, where UQ might become computationally prohibitive. In this paper we present a new technique, Lasso Monte Carlo (LMC), which combines a Lasso surrogate model with the multifidelity Monte Carlo technique, in order to perform UQ in high-dimensional settings, at a reduced computational cost. We provide mathematical guarantees for the unbiasedness of the method, and show that LMC can be more accurate than simple Monte Carlo. The theory is numerically tested with benchmarks on toy problems, as well as on a real example of UQ from the field of nuclear engineering. In all presented examples LMC is more accurate than simple Monte Carlo and other multifidelity methods. Thanks to LMC, computational costs are reduced by more than a factor of 5 with respect to simple MC, in relevant cases.
On Routing Optimization in Networks with Embedded Computational Services

Modern communication networks are increasingly equipped with in-network computational capabilities and services. Routing in such networks is significantly more complicated than the traditional routing. A legitimate route for a flow not only needs to have enough communication and computation resources, but also has to conform to various application-specific routing constraints. This paper presents a comprehensive study on routing optimization problems in networks with embedded computational services. We develop a set of routing optimization models and derive low-complexity heuristic routing algorithms for diverse computation scenarios. For dynamic demands, we also develop an online routing algorithm with performance guarantees. Through evaluations over emerging applications on real topologies, we demonstrate that our models can be flexibly customized to meet the diverse routing requirements of different computation applications. Our proposed heuristic algorithms significantly outperform baseline algorithms and can achieve close-to-optimal performance in various scenarios.
Label Propagation with Weak Supervision

Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications. In this paper, we introduce a novel analysis of the classical label propagation algorithm (LPA) (Zhu&Ghahramani, 2002) that moreover takes advantage of useful prior information, specifically probabilistic hypothesized labels on the unlabeled data. We provide an error bound that exploits both the local geometric properties of the underlying graph and the quality of the prior information. We also propose a framework to incorporate multiple sources of noisy information. In particular, we consider the setting of weak supervision, where our sources of information are weak labelers. We demonstrate the ability of our approach on multiple benchmark weakly supervised classification tasks, showing improvements upon existing semi-supervised and weakly supervised methods.
Hate Speech and Offensive Language Detection in Bengali

Social media often serves as a breeding ground for various hateful and offensive content. Identifying such content on social media is crucial due to its impact on the race, gender, or religion in an unprejudiced society. However, while there is extensive research in hate speech detection in English, there is a gap in hateful content detection in low-resource languages like Bengali. Besides, a current trend on social media is the use of Romanized Bengali for regular interactions. To overcome the existing research’s limitations, in this study, we develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Romanized Bengali tweets. We implement several baseline models for the classification of such hateful posts. We further explore the interlingual transfer mechanism to boost classification performance. Finally, we perform an in-depth error analysis by looking into the misclassified posts by the models. While training actual and Romanized datasets separately, we observe that XLM-Roberta performs the best. Further, we witness that on joint training and few-shot training, MuRIL outperforms other models by interpreting the semantic expressions better. We make our code and dataset public for others.
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting*

Feature engineering is required to obtain better results for time series forecasting, and decomposition is a crucial one. One decomposition approach often cannot be used for numerous forecasting tasks since the standard time series decomposition lacks flexibility and robustness. Traditional feature selection relies heavily on preexisting domain knowledge, has no generic methodology, and requires a lot of labor. However, most time series prediction models based on deep learning typically suffer from interpretability issues, so the “black box” results lead to a lack of confidence. Dealing with the above matters forms the motivation of the thesis. In the paper, we propose TSDFNet as a neural network with a self-decomposition mechanism and an attentive feature fusion mechanism. It abandons feature engineering as a preprocessing convention and creatively integrates it as an internal module with the deep model. The self-decomposition mechanism empowers TSDFNet with extensible and adaptive de-composition capabilities for any time series and is more effective in predicting problems than traditional decomposition methods. Users can choose their own basis functions to decompose the sequence into temporal and generalized spatial dimensions. The attentive feature fusion mechanism has the ability to capture the importance of external variables and the causality with target variables. It can automatically suppress the unimportant features while enhancing the effective ones so that users do not have to struggle with feature selection. The basic idea of TDFNNet is straightforward. It integrates feature decomposition, feature selection, and prediction into one model. Moreover, TSDFNet can quickly look into the “black box” of the deep neural network by feature visualization and analyzing the prediction results. We demonstrate performance improvements over existing widely accepted models on more than a dozen datasets, and three experiments showcase the interpretability of TSDFNet.
Explainable AI Based Glaucoma Detection Using Transfer Learning and LIME

Glaucoma is the second driving reason for partial or complete blindness among all the visual deficiencies which mainly occurs because of excessive pressure in the eye due to anxiety or depression which damages the optic nerve and creates complications in vision. Traditional glaucoma screening is a time-consuming process that necessitates the medical professionals' constant attention, and even so time to time due to the time constrains and pressure they fail to classify correctly that leads to wrong treatment. Numerous efforts have been made to automate the entire glaucoma classification procedure however, these existing models in general have a black box characteristics that prevents users from understanding the key reasons behind the prediction and thus medical practitioners generally can not rely on these system. In this article after comparing with various pre-trained models, we propose a transfer learning model that is able to classify Glaucoma with 94.71% accuracy. In addition, we have utilized Local Interpretable Model-Agnostic Explanations(LIME) that introduces explainability in our system. This improvement enables medical professionals obtain important and comprehensive information that aid them in making judgments. It also lessen the opacity and fragility of the traditional deep learning models.
Adversarial network training using higher-order moments in a modified Wasserstein distance

Generative-adversarial networks (GANs) have been used to produce data closely resembling example data in a compressed, latent space that is close to sufficient for reconstruction in the original vector space. The Wasserstein metric has been used as an alternative to binary cross-entropy, producing more numerically stable GANs with greater mode covering behavior. Here, a generalization of the Wasserstein distance, using higher-order moments than the mean, is derived. Training a GAN with this higher-order Wasserstein metric is demonstrated to exhibit superior performance, even when adjusted for slightly higher computational cost. This is illustrated generating synthetic antibody sequences.
The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning

Social media platforms provide a continuous stream of real-time news regarding crisis events on a global scale. Several machine learning methods utilize the crowd-sourced data for the automated detection of crises and the characterization of their precursors and aftermaths. Early detection and localization of crisis-related events can help save lives and economies. Yet, the applied automation methods introduce ethical risks worthy of investigation - especially given their high-stakes societal context. This work identifies and critically examines ethical risk factors of social media analyses of crisis events focusing on machine learning methods. We aim to sensitize researchers and practitioners to the ethical pitfalls and promote fairer and more reliable designs.
Active Localization using Bernstein Distribution Functions

In this work, we present a framework that enables a vehicle to autonomously localize a target based on noisy range measurements computed from RSSI data. To achieve the mission objectives, we develop a control scheme composed of two main parts: an estimator and a motion planner. At each time step, new estimates of the target's position are computed and used to generate and update distribution functions using Bernstein polynomials. A metric of the efficiency of the estimator is derived based on the Fisher Information Matrix. Finally, the motion planning problem is formulated to react in real time to new information about the target and improve the estimator's performance.
Quasi-classical Ground States. II. Standard Model of Non-relativistic QED

We consider a non-relativistic electron bound by an external potential and coupled to the quantized electromagnetic field in the standard model of non-relativistic QED. We compute the energy functional of product states of the form $u\otimes \Psi_f$, where $u$ is a normalized state for the electron and $\Psi_f$ is a coherent state in Fock space for the photon field. The minimization of this functional yields a Maxwell--Schr{\"o}dinger system up to a trivial renormalization. We prove the existence of a ground state under general conditions on the external potential and the coupling. In particular, neither an ultraviolet cutoff nor an infrared cutoff needs to be imposed. Our results provide the convergence in the ultraviolet limit and the second-order asymptotic expansion in the coupling constant of the ground state energy of Maxwell--Schr\"odinger systems.
Partition function approach to non-Gaussian likelihoods: Formalism and expansions for weakly non-Gaussian cosmological inference

Non-Gaussian likelihoods, ubiquitous throughout cosmology, are a direct consequence of nonlinearities in the physical model. Their treatment requires Monte-Carlo Markov-chain or more advanced sampling methods for the determination of confidence contours. As an alternative, we construct canonical partition functions as Laplace-transforms of the Bayesian evidence, from which MCMC-methods would sample microstates. Cumulants of order n of the posterior distribution follow by direct n-fold differentiation of the logarithmic partition function, recovering the classic Fisher-matrix formalism at second order. We connect this approach for weakly non-Gaussianities to the DALI- and Gram-Charlier expansions and demonstrate the validity with a supernova-likelihood on the cosmological parameters Ωm and w. We comment on extensions of the canonical partition function to include kinetic energies in order to bridge to Hamilton Monte-Carlo sampling, and on ensemble Markov-chain methods, as they would result from transitioning to macrocanonical partition functions depending on a chemical potential. Lastly we demonstrate the relationship of the partition function approach to the Cramér-Rao boundary and to information entropies.
A2: Efficient Automated Attacker for Boosting Adversarial Training

Based on the significant improvement of model robustness by AT (Adversarial Training), various variants have been proposed to further boost the performance. Well-recognized methods have focused on different components of AT (e.g., designing loss functions and leveraging additional unlabeled data). It is generally accepted that stronger perturbations yield more robust models. However, how to generate stronger perturbations efficiently is still missed. In this paper, we propose an efficient automated attacker called A2 to boost AT by generating the optimal perturbations on-the-fly during training. A2 is a parameterized automated attacker to search in the attacker space for the best attacker against the defense model and examples. Extensive experiments across different datasets demonstrate that A2 generates stronger perturbations with low extra cost and reliably improves the robustness of various AT methods against different attacks.
Brief Introduction to Contrastive Learning Pretext Tasks for Visual Representation

To improve performance in visual feature representation from photos or videos for practical applications, we generally require large-scale human-annotated labeled data while training deep neural networks. However, the cost of gathering and annotating human-annotated labeled data is expensive. Given that there is a lot of unlabeled data in the actual world, it is possible to introduce self-defined pseudo labels as supervisions to prevent this issue. Self-supervised learning, specifically contrastive learning, is a subset of unsupervised learning methods that has grown popular in computer vision, natural language processing, and other domains. The purpose of contrastive learning is to embed augmented samples from the same sample near to each other while pushing away those that are not. In the following sections, we will introduce the regular formulation among different learnings. In the next sections, we will discuss the regular formulation of various learnings. Furthermore, we offer some strategies from contrastive learning that have recently been published and are focused on pretext tasks for visual representation.
Time-dependent Backgrounds in String Theory and Dualities

This thesis consists of two parts. The first part deals with gauge/gravity duality in the context of anti de Sitter (AdS) spacetimes with de Sitter (dS) boundary, which can be used to study issues concerning strongly coupled field theory on de Sitter space, such as the issue of vacuum ambiguity. By calculating the symmetric two point function of the strongly coupled ${\cal N} = 4$ supersymmetric Yang-Mills theory on de Sitter space, we show that the vacuum ambiguity persists at strong coupling. Furthermore, the extra ambiguity in the strong coupling correlator seems to suggest that transition between two different vacua is allowed. The second part of this thesis deals with the duality between the rolling tachyon backgrounds in superstring theory and the Dyson gas systems. This duality can be interpreted as a reformulation of non-BPS D-branes in superstring theory in terms of statistical systems in thermal equilibrium, whose description does not include time. We argue that even though the concept of time is absent in the statistical dual sitting at equilibrium, the notion of time can emerge at the large number of particles $N \rightarrow \infty$ limit.
Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization

Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge slowly even collapse when the potential observation noise misdirects the optimization. In this paper, we propose a novel BO algorithm called Neighbor Regularized Bayesian Optimization (NRBO) to solve the problem. We first propose a neighbor-based regularization to smooth each sample observation, which could reduce the observation noise efficiently without any extra training cost. Since the neighbor regularization highly depends on the sample density of a neighbor area, we further design a density-based acquisition function to adjust the acquisition reward and obtain more stable statistics. In addition, we design a adjustment mechanism to ensure the framework maintains a reasonable regularization strength and density reward conditioned on remaining computation resources. We conduct experiments on the bayesmark benchmark and important computer vision benchmarks such as ImageNet and COCO. Extensive experiments demonstrate the effectiveness of NRBO and it consistently outperforms other state-of-the-art methods.
Parallel Computation of functions of matrices and their action on vectors

We present a novel class of methods to compute functions of matrices or their action on vectors that are suitable for parallel programming. Solving appropriate simple linear systems of equations in parallel (or computing the inverse of several matrices) and with a proper linear combination of the results, allows us to obtain new high order approximations to the desired functions of matrices. An error analysis to obtain forward and backward error bounds is presented. The coefficients of each method, which depends on the number of processors, can be adjusted to improve the accuracy, the stability or to reduce round off errors of the methods. We illustrate this procedure by explicitly constructing some methods which are then tested on several numerical examples.
On Bloch’s map for torsion cycles over non-closed fields

Abstract We generalize Bloch’s map on torsion cycles from algebraically closed fields to arbitrary fields. While Bloch’s map over algebraically closed fields is injective for zero-cycles and for cycles of codimension at most two, we show that the generalization to arbitrary fields is only injective for cycles of codimension at most two but, in general, not for zero-cycles. Our result implies that Jannsen’s cycle class map in integral $\ell $ -adic continuous étale cohomology is, in general, not injective on torsion zero-cycles over finitely generated fields. This answers a question of Scavia and Suzuki.