Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Learning of Nonlinear Dynamical Systems with Safety and Stability Properties

—The paper presents a robust parameter learning methodology for identiﬁcation of nonlinear dynamical system from data while satisfying safety and stability constraints in the context of learning from demonstration (LfD) methods. Extreme Learning Machines (ELM) is used to approximate the system model, whose parameters are learned subject to the safety and stability constraints obtained using zeroing barrier and Lyapunov-based stability analysis in the presence of model uncertainties and external disturbances. A constrained Quadratic Program (QP) is developed, which accounts for the ELM function reconstruction error, to estimate the ELM parameters. Furthermore, a robustness lemma is presented, which proves that the learned system model guarantees safety and stability in the presence of disturbances. The method is tested in simulations. Trajectory reconstruction accuracy of the method is compared against state-of-the-art LfD methods using swept error area (SEA) metric. Robustness of the learned model is tested by conducting Monte Carlo tests. The proposed method is implemented on a Baxter robot for a pick-and-place task where the robot is constrained to an ellipsoidal safety region.
Weak Cosmic Censorship and Second Law of Black Hole Thermodynamics in Higher Derivative Gravity

Infalling matter may destroy a black hole and expose the naked singularity. Thus, Penrose proposed the weak cosmic censorship conjecture to avoid such a possibility. On the other hand, if the black hole is not destroyed by infalling matter, from the second law of black hole thermodynamics, the black hole entropy should increase due to the information carried by the infalling matter. In this work, we demonstrate by examples of perturbative near-extremal black holes in higher derivative gravity theories that the second law implies weak cosmic censorship. We also compare our proposal to the one developed by Sorce and Wald based on the first law of black hole thermodynamics and show that the latter fails to yield weak cosmic censorship in such cases. Finally, we give proof of our proposal for generic gravity theories.
Extreme Audio Time Stretching Using Neural Synthesis

A deep neural network solution for time-scale modification (TSM) focused on large stretching factors is proposed, targeting environmental sounds. Traditional TSM artifacts such as transient smearing, loss of presence, and phasiness are heavily accentuated and cause poor audio quality when the TSM factor is four or larger. The weakness of established TSM methods, often based on a phase vocoder structure, lies in the poor description and scaling of the transient and noise components, or nuances, of a sound. Our novel solution combines a sines-transients-noise decomposition with an independent WaveNet synthesizer to provide a better description of the noise component and an improve sound quality for large stretching factors. Results of a subjective listening test against four other TSM algorithms are reported, showing the proposed method to be often superior. The proposed method is stereo compatible and has a wide range of applications related to the slow motion of media content.
Physics‐Based Machine‐Learning Approach for Modeling the Temperature‐Dependent Yield Strength of Superalloys

In the pursuit of developing high‐temperature alloys with improved properties for meeting the performance requirements of next‐generation energy and aerospace demands, integrated computational materials engineering has played a crucial role. Herein, a machine learning approach is presented, capable of predicting the temperature‐dependent yield strengths of superalloys utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature, Tbreak, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black‐box approaches, our model is based on the underlying fundamental physics built directly into the model. A technique of global optimization, one allowing the concurrent optimization of model parameters over the low‐ and high‐temperature regimes, is presented. The results presented extend previous work on high‐entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature‐dependent strength behavior of superalloys as well as HEAs.
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets

Short text classification is a crucial and challenging aspect of Natural Language Processing. For this reason, there are numerous highly specialized short text classifiers. However, in recent short text research, State of the Art (SOTA) methods for traditional text classification, particularly the pure use of Transformers, have been unexploited. In this work, we examine the performance of a variety of short text classifiers as well as the top performing traditional text classifier. We further investigate the effects on two new real-world short text datasets in an effort to address the issue of becoming overly dependent on benchmark datasets with a limited number of characteristics. Our experiments unambiguously demonstrate that Transformers achieve SOTA accuracy on short text classification tasks, raising the question of whether specialized short text techniques are necessary.
Background determination for the LUX-ZEPLIN dark matter experiment

The LUX-ZEPLIN experiment recently reported limits on WIMP-nucleus interactions from its initial science run, down to $9.2\times10^{-48}$ cm$^2$ for the spin-independent interaction of a 36 GeV/c$^2$ WIMP at 90% confidence level. In this paper, we present a comprehensive analysis of the backgrounds important for this result and for other upcoming physics analyses, including neutrinoless double-beta decay searches and effective field theory interpretations of LUX-ZEPLIN data. We confirm that the in-situ determinations of bulk and fixed radioactive backgrounds are consistent with expectations from the ex-situ assays. The observed background rate after WIMP search criteria were applied was $(6.3\pm0.5)\times10^{-5}$ events/keV$_{ee}$/kg/day in the low-energy region, approximately 60 times lower than the equivalent rate reported by the LUX experiment.
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction

Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction. However, the heterogeneity nature of tabular features makes such features relatively independent, and developing effective methods to promote tabular feature interaction still remains an open problem. In this paper, we propose a novel Graph Estimator, which automatically estimates the relations among tabular features and builds graphs by assigning edges between related features. Such relation graphs organize independent tabular features into a kind of graph data such that interaction of nodes (tabular features) can be conducted in an orderly fashion. Based on our proposed Graph Estimator, we present a bespoke Transformer network tailored for tabular learning, called T2G-Former, which processes tabular data by performing tabular feature interaction guided by the relation graphs. A specific Cross-level Readout collects salient features predicted by the layers in T2G-Former across different levels, and attains global semantics for final prediction. Comprehensive experiments show that our T2G-Former achieves superior performance among DNNs and is competitive with non-deep Gradient Boosted Decision Tree models. The code and detailed results are available at https://github.com/jyansir/t2g-former.
Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN

: Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients. Deep learning models help radiologists to accurately and efficiently analyze tumors without manual intervention. However, brain tumor analysis is challenging because of its complex structure, texture, size, location, and appearance. Therefore, a novel deep residual and regional-based Res-BRNet Convolutional Neural Network (CNN) is developed for effective brain tumor (Magnetic Resonance Imaging) MRI classification. The developed Res-BRNet employed Regional and boundary-based operations in a systematic order within the modified spatial and residual blocks. Moreover, the spatial block extract homogeneity and boundary-defined features at the abstract level. Furthermore, the residual blocks employed at the target level significantly learn local and global texture variations of different classes of brain tumors. The efficiency of the developed Res-BRNet is evaluated on a standard dataset; collected from Kaggle and Figshare containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Experiments prove that the developed Res-BRNet outperforms the standard CNN models and attained excellent performances (accuracy: 98.22%, sensitivity: 0.9811, F-score: 0.9841, and precision: 0.9822) on challenging datasets. Additionally, the performance of the proposed Res-BRNet indicates a strong potential for medical image-based disease analyses.
Maximum Likelihood Estimation for a Markov-Modulated Jump-Diffusion Model

We propose a method for obtaining maximum likelihood estimates (MLEs) of a Markov-Modulated Jump-Diffusion Model (MMJDM) when the data is a discrete time sample of the diffusion process, the jumps follow a Laplace distribution, and the parameters of the diffusion are controlled by a Markov Jump Process (MJP). The data can be viewed as incomplete observation of a model with a tractable likelihood function. Therefore we use the EM-algorithm to obtain MLEs of the parameters. We validate our method with simulated data. The motivation for obtaining estimates of this model is that stock prices have distinct drift and volatility at distinct periods of time. The assumption is that these phases are modulated by macroeconomic environments whose changes are given by discontinuities or jumps in prices. This model improves on the stock prices representation of classical models such as the model of Black and Scholes or Merton's Jump-Diffusion Model (JDM). We fit the model to the stock prices of Amazon and Netflix during a 15-years period and use our method to estimate the MLEs.
Testing GLOM's ability to infer wholes from ambiguous parts

The GLOM architecture proposed by Hinton [2021] is a recurrent neural network for parsing an image into a hierarchy of wholes and parts. When a part is ambiguous, GLOM assumes that the ambiguity can be resolved by allowing the part to make multi-modal predictions for the pose and identity of the whole to which it belongs and then using attention to similar predictions coming from other possibly ambiguous parts to settle on a common mode that is predicted by several different parts. In this study, we describe a highly simplified version of GLOM that allows us to assess the effectiveness of this way of dealing with ambiguity. Our results show that, with supervised training, GLOM is able to successfully form islands of very similar embedding vectors for all of the locations occupied by the same object and it is also robust to strong noise injections in the input and to out-of-distribution input transformations.
Disentangling longitudinal and transverse modes of the 
ϕ
 meson through dilepton and kaon decays

Angular distributions of $\phi$ meson decay amplitudes of $e^+e^-$ and $K^+K^-$ channels are computed using both specific interaction Lagrangians and simple arguments relying on angular momentum conservation. Based on the obtained results, we assess methods to experimentally disentangle the longitudinal and transverse polarization modes of the $\phi$ meson and discuss advantages and disadvantages of employing either the leptonic or hadronic decay modes for this task.
Knowledge Distillation based Degradation Estimation for Blind Super-Resolution

Blind image super-resolution (Blind-SR) aims to recover a high-resolution (HR) image from its corresponding low-resolution (LR) input image with unknown degradations. Most of the existing works design an explicit degradation estimator for each degradation to guide SR. However, it is infeasible to provide concrete labels of multiple degradation combinations (e.g., blur, noise, jpeg compression) to supervise the degradation estimator training. In addition, these special designs for certain degradation, such as blur, impedes the models from being generalized to handle different degradations. To this end, it is necessary to design an implicit degradation estimator that can extract discriminative degradation representation for all degradations without relying on the supervision of degradation ground-truth. In this paper, we propose a Knowledge Distillation based Blind-SR network (KDSR). It consists of a knowledge distillation based implicit degradation estimator network (KD-IDE) and an efficient SR network. To learn the KDSR model, we first train a teacher network: KD-IDE$_{T}$. It takes paired HR and LR patches as inputs and is optimized with the SR network jointly. Then, we further train a student network KD-IDE$_{S}$, which only takes LR images as input and learns to extract the same implicit degradation representation (IDR) as KD-IDE$_{T}$. In addition, to fully use extracted IDR, we design a simple, strong, and efficient IDR based dynamic convolution residual block (IDR-DCRB) to build an SR network. We conduct extensive experiments under classic and real-world degradation settings. The results show that KDSR achieves SOTA performance and can generalize to various degradation processes. The source codes and pre-trained models will be released.
Bayesian inference for aggregated Hawkes processes

The Hawkes process, a self-exciting point process, has a wide range of applications in modeling earthquakes, social networks and stock markets. The established estimation process requires that researchers have access to the exact time stamps and spatial information. However, available data are often rounded or aggregated. We develop a Bayesian estimation procedure for the parameters of a Hawkes process based on aggregated data. Our approach is developed for temporal, spatio-temporal, and mutually exciting Hawkes processes where data are available over discrete time periods and regions. We show theoretically that the parameters of the Hawkes process under different specifications are identifiable from aggregated data, and demonstrate the method on simulated temporal and spatio-temporal data with various model specifications in the presence of one or more interacting processes, and under varying coarseness of data aggregation. Finally, we analyze spatio-temporal point pattern data of insurgent attacks in Iraq from October to December 2006, and we find coherent results across different time and space aggregations.
Testing parity symmetry of gravity with gravitational waves

The examination of parity symmetry in gravitational interactions has drawn increasing attention. Although Einstein’s General Relativity is parity-conserved, numerous theories of parity-violating (PV) gravity in different frameworks have recently been proposed for different motivations. In this review, we briefly summarize the recent progress of these theories, and focus on the observable effects of PV terms in the gravitational waves (GWs), which are mainly reflected in the difference between the left-hand and right-hand polarization modes. We are primarily concerned with the implications of these theories for GWs generated by the compact binary coalescences and the primordial GWs generated in the early Universe. The deviation of GW waveforms and/or primordial power spectrum can always be quantified by the energy scale of parity violation of the theory. Applying the current and future GW observation from laser interferometers and cosmic microwave background radiation, the current and potential constraints on the PV energy scales are presented, which indicates that the parity symmetry of gravity can be tested in high energy scale in this new era of gravitational waves.
Quadapter: Adapter for GPT-2 Quantization

Transformer language models such as GPT-2 are difficult to quantize because of outliers in activations leading to a large quantization error. To adapt to the error, one must use quantization-aware training, which entails a fine-tuning process based on the dataset and the training pipeline identical to those for the original model. Pretrained language models, however, often do not grant access to their datasets and training pipelines, forcing us to rely on arbitrary ones for fine-tuning. In that case, it is observed that quantization-aware training overfits the model to the fine-tuning data. For quantization without overfitting, we introduce a quantization adapter (Quadapter), a small set of parameters that are learned to make activations quantization-friendly by scaling them channel-wise. It keeps the model parameters unchanged. By applying our method to the challenging task of quantizing GPT-2, we demonstrate that it effectively prevents the overfitting and improves the quantization performance.
Tumor containment for Norton-Simon models

Some clinical and pre-clinical data suggests that treating some tumors at a mild, patient-specific dose might delay resistance to treatment and increase survival time. A recent mathematical model with sensitive and resistant tumor cells identified conditions under which a treatment aiming at tumor containment rather than eradication is indeed optimal. This model however neglected mutations from sensitive to resistant cells, and assumed that the growth-rate of sensitive cells is non-increasing in the size of the resistant population. The latter is not true in standard models of chemotherapy. This article shows how to dispense with this assumption and allow for mutations from sensitive to resistant cells. This is achieved by a novel mathematical analysis comparing tumor sizes across treatments not as a function of time, but as a function of the resistant population size.
SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene

Generative models have shown great promise in synthesizing photorealistic 3D objects, but they require large amounts of training data. We introduce SinGRAF, a 3D-aware generative model that is trained with a few input images of a single scene. Once trained, SinGRAF generates different realizations of this 3D scene that preserve the appearance of the input while varying scene layout. For this purpose, we build on recent progress in 3D GAN architectures and introduce a novel progressive-scale patch discrimination approach during training. With several experiments, we demonstrate that the results produced by Sin-GRAF outperform the closest related works in both quality and diversity by a large margin.
Where did you tweet from? Inferring the origin locations of tweets based on contextual information

Public conversations on Twitter comprise many pertinent topics including disasters, protests, politics, propaganda, sports, climate change, epidemics/pandemic outbreaks, etc., that can have both regional and global aspects. Spatial discourse analysis rely on geographical data. However, today less than 1% of tweets are geotagged; in both cases—point location or bounding place information. A major issue with tweets is that Twitter users can be at location A and exchange conversations specific to location B, which we call the Location A/B problem. The problem is considered solved if location entities can be classified as either origin locations (Location As) or non-origin locations (Location Bs). In this work, we propose a simple yet effective framework—the True Origin Model—to address the problem that uses machine-level natural language understanding to identify tweets that conceivably contain their origin location information. The model achieves promising accuracy at country (80%), state (67%), city (58%), county (56%) and district (64%) levels with support from a Location Extraction Model as basic as the CoNLL-2003-based RoBERTa. We employ a tweet contexualizer (locBERT) which is one of the core components of the proposed model, to investigate multiple tweets’ distributions for understanding Twitter users’ tweeting behavior in terms of mentioning origin and non-origin locations. We also highlight a major concern with the currently regarded gold standard test set (ground truth) methodology, introduce a new data set, and identify further research avenues for advancing the area.
Visual–Tactile Fusion for Transparent Object Grasping in Complex Backgrounds

The grasping of transparent objects is challenging but of significance to robots. In this article, a visual–tactile fusion framework for transparent object grasping in complex backgrounds is proposed, which synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. First, we propose a multiscene synthetic grasping dataset named SimTrans12 K together with a Gaussian-mask annotation method. Next, based on the TaTa gripper, we propose a grasping network named transparent object-grasping convolutional neural network for grasping position detection, which shows good performance in both synthetic and real scenes. Inspired by human grasping, a tactile calibration method and a visual–tactile fusion classification method are designed, which improve the grasping success rate by 36.7% compared with direct grasping and the classification accuracy by 39.1%. Furthermore, the tactile height sensing module and the tactile position exploration module are added to solve the problem of grasping transparent objects in irregular and visually undetectable scenes. The experimental results demonstrate the validity of the framework.
Multi-level Parareal algorithm with Averaging

The present study is an extension of the work done in Parareal convergence for oscillatory pdes with finite time-scale separation (2019), A. G. Peddle, T. Haut, and B. Wingate, [16], and An asymptotic parallel-in-time method for highly oscillatory pdes (2014), T. Haut and B. Wingate, [10], where a two-level Parareal method with averaging is examined. The method proposed in this paper is a multi-level Parareal method with arbitrarily many levels, which is not restricted to the two-level case. We give an asymptotic error estimate which reduces to the two-level estimate for the case when only two levels are considered. Introducing more than two levels has important consequences for the averaging procedure, as we choose separate averaging windows for each of the different levels, which is an additional new feature of the present study. The different averaging windows make the proposed method especially appropriate for multi-scale problems, because we can introduce a level for each intrinsic scale of the problem and adapt the averaging procedure such that we reproduce the behavior of the model on the particular scale resolved by the level. The computational complexity of the new method is investigated and the efficiency is studied on several examples.
Compound Multivariate Hawkes Processes: Large Deviations and Rare Event Simulation

In this paper, we establish a large deviations principle for a multivariate compound process induced by a multivariate Hawkes process with random marks. Our proof hinges on showing essential smoothness of the limiting cumulant of the multivariate compound process, resolving the inherent complication that this cumulant is implicitly characterized through a fixed-point representation. We employ the large deviations principle to derive logarithmic asymptotic results on the marginal ruin probabilities of the associated multivariate risk process. We also show how to conduct rare event simulation in this multivariate setting using importance sampling and prove the asymptotic efficiency of our importance sampling based estimator. The paper is concluded with a systematic assessment of the performance of our rare event simulation procedure.