Miracle-dz/UniFAR-FTUs huggingface dataset:
Singularities and asymptotic distribution of resonances for Schr\"odinger operators in one dimension

We obtain new results about the high-energy distribution of resonances for the one-dimensional Schr\"odinger operator. Our primary result is an upper bound on the density of resonances above any logarithmic curve in terms of the singular support of the potential. We also prove results about the distribution of resonances in sectors away from the real axis, and construct a class of potentials producing multiple sequences of resonances along distinct logarithmic curves, explicitly calculating the asymptotic location of these resonances. The results are unified by the use of an integral representation of the reflection coefficients.
Backward Joint Model for Dynamic Prediction using Multivariate Longitudinal and Competing Risk Data

Joint modeling is a useful approach to dynamic prediction of clinical outcomes using longitudinally measured predictors. When the outcomes are competing risk events, fitting the conventional shared random effects joint model often involves intensive computation, especially when multiple longitudinal biomarkers are be used as predictors, as is often desired in prediction problems. Motivated by a longitudinal cohort study of chronic kidney disease, this paper proposes a new joint model for the dynamic prediction of end-stage renal disease with the competing risk of death. The model factorizes the likelihood into the distribution of the competing risks data and the distribution of longitudinal data given the competing risks data. The estimation with the EM algorithm is efficient, stable and fast, with a one-dimensional integral in the E-step and convex optimization for most parameters in the M-step, regardless of the number of longitudinal predictors. The model also comes with a consistent albeit less efficient estimation method that can be quickly implemented with standard software, ideal for model building and diagnotics. This model enables the prediction of future longitudinal data trajectories conditional on being at risk at a future time, a practically significant problem that has not been studied in the statistical literature. We study the properties of the proposed method using simulations and a real dataset and compare its performance with the shared random effects joint model.
High precision calibration setup for loss measurements in electrical steel sheets

We present details on the current measurement setup at PTB used for high precision loss calibrations in the frequency range 50 Hz to 1 kHz. A combination of analog and digital feedback control is utilized in accordance with the standard. A detailed measurement uncertainty (MU) analysis based on a systematic model equation is presented and inter-dependencies of model parameters are discussed. Experimental results obtained at 50 Hz on NO and GO Epstein samples show excellent agreement between statistical and systematic MU estimation and confirm the MU model analysis. Furthermore, we investigate the influence of external parameters on the loss measurements, like sample loading scheme and the value of maximum demagnetization polarization.
Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors

In this paper, we address the Sim2Real gap in the field of vision-based tactile sensors for classifying object surfaces. We train a Diffusion Model to bridge this gap using a relatively small dataset of real-world images randomly collected from unlabeled everyday objects via the DIGIT sensor. Subsequently, we employ a simulator to generate images by uniformly sampling the surface of objects from the YCB Model Set. These simulated images are then translated into the real domain using the Diffusion Model and automatically labeled to train a classifier. During this training, we further align features of the two domains using an adversarial procedure. Our evaluation is conducted on a dataset of tactile images obtained from a set of ten 3D printed YCB objects. The results reveal a total accuracy of 81.9%, a significant improvement compared to the 34.7% achieved by the classifier trained solely on simulated images. This demonstrates the effectiveness of our approach. We further validate our approach using the classifier on a 6D object pose estimation task from tactile data.
Detecting Deepfakes Without Seeing Any

Deepfake attacks, malicious manipulation of media containing people, are a serious concern for society. Conventional deepfake detection methods train supervised classifiers to distinguish real media from previously encountered deepfakes. Such techniques can only detect deepfakes similar to those previously seen, but not zero-day (previously unseen) attack types. As current deepfake generation techniques are changing at a breathtaking pace, new attack types are proposed frequently, making this a major issue. Our main observations are that: i) in many effective deepfake attacks, the fake media must be accompanied by false facts i.e. claims about the identity, speech, motion, or appearance of the person. For instance, when impersonating Obama, the attacker explicitly or implicitly claims that the fake media show Obama; ii) current generative techniques cannot perfectly synthesize the false facts claimed by the attacker. We therefore introduce the concept of"fact checking", adapted from fake news detection, for detecting zero-day deepfake attacks. Fact checking verifies that the claimed facts (e.g. identity is Obama), agree with the observed media (e.g. is the face really Obama's? ), and thus can differentiate between real and fake media. Consequently, we introduce FACTOR, a practical recipe for deepfake fact checking and demonstrate its power in critical attack settings: face swapping and audio-visual synthesis. Although it is training-free, relies exclusively on off-the-shelf features, is very easy to implement, and does not see any deepfakes, it achieves better than state-of-the-art accuracy.
Mineralogical aspects of fluid migration in the Salt Block II experiment

A block of evaporite rock containing the mineral assemblage halite (88%) - polyhalite (8%) - sylvite (4%) was machined into a cylinder one meter in diameter and one meter high, and was fitted with an axial heater, thermocouples and an off-gas collection system. After about 100 days of heating, identification of mineral efflorescences at the heater hole (carnallite and bischofite) showed that a significant portion of the 111 grams of water recovered (out of around 8500 grams available in the rock) migrated as a liquid, not as a vapor. A microscopic examination of rock slices from within 15 cm of the heater hole (where the temperature was 100 to 200/sup 0/C, and the gradient was 3 to 15/sup 0/C/cm) revealed that: (1) fluid inclusions had migrated, but rarely across grain boundaries; (2) fluid inclusions had not been mobilized at distances greater than about 15 cm from the heater hole; and (3) intergranular liquid had been conspicuously mobilized within 15 cm of the heater hole.
Gaussian Processes on Cellular Complexes

In recent years, there has been considerable interest in developing machine learning models on graphs in order to account for topological inductive biases. In particular, recent attention was given to Gaussian processes on such structures since they can additionally account for uncertainty. However, graphs are limited to modelling relations between two vertices. In this paper, we go beyond this dyadic setting and consider polyadic relations that include interactions between vertices, edges and one of their generalisations, known as cells. Specifically, we propose Gaussian processes on cellular complexes, a generalisation of graphs that captures interactions between these higher-order cells. One of our key contributions is the derivation of two novel kernels, one that generalises the graph Mat\'ern kernel and one that additionally mixes information of different cell types.
Vision-Language Interpreter for Robot Task Planning

Large language models (LLMs) are accelerating the development of language-guided robot planners. Meanwhile, symbolic planners offer the advantage of interpretability. This paper proposes a new task that bridges these two trends, namely, multimodal planning problem specification. The aim is to generate a problem description (PD), a machine-readable file used by the planners to find a plan. By generating PDs from language instruction and scene observation, we can drive symbolic planners in a language-guided framework. We propose a Vision-Language Interpreter (ViLaIn), a new framework that generates PDs using state-of-the-art LLM and vision-language models. ViLaIn can refine generated PDs via error message feedback from the symbolic planner. Our aim is to answer the question: How accurately can ViLaIn and the symbolic planner generate valid robot plans? To evaluate ViLaIn, we introduce a novel dataset called the problem description generation (ProDG) dataset. The framework is evaluated with four new evaluation metrics. Experimental results show that ViLaIn can generate syntactically correct problems with more than 99\% accuracy and valid plans with more than 58\% accuracy. Our code and dataset are available at https://github.com/omron-sinicx/ViLaIn.
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game

While Large Language Models (LLMs) are increasingly being used in real-world applications, they remain vulnerable to prompt injection attacks: malicious third party prompts that subvert the intent of the system designer. To help researchers study this problem, we present a dataset of over 126,000 prompt injection attacks and 46,000 prompt-based"defenses"against prompt injection, all created by players of an online game called Tensor Trust. To the best of our knowledge, this is currently the largest dataset of human-generated adversarial examples for instruction-following LLMs. The attacks in our dataset have a lot of easily interpretable stucture, and shed light on the weaknesses of LLMs. We also use the dataset to create a benchmark for resistance to two types of prompt injection, which we refer to as prompt extraction and prompt hijacking. Our benchmark results show that many models are vulnerable to the attack strategies in the Tensor Trust dataset. Furthermore, we show that some attack strategies from the dataset generalize to deployed LLM-based applications, even though they have a very different set of constraints to the game. We release all data and source code at https://tensortrust.ai/paper
Deep Double Descent for Time Series Forecasting: Avoiding Undertrained Models

Deep learning models, particularly Transformers, have achieved impressive results in various domains, including time series forecasting. While existing time series literature primarily focuses on model architecture modifications and data augmentation techniques, this paper explores the training schema of deep learning models for time series; how models are trained regardless of their architecture. We perform extensive experiments to investigate the occurrence of deep double descent in several Transformer models trained on public time series data sets. We demonstrate epoch-wise deep double descent and that overfitting can be reverted using more epochs. Leveraging these findings, we achieve state-of-the-art results for long sequence time series forecasting in nearly 70% of the 72 benchmarks tested. This suggests that many models in the literature may possess untapped potential. Additionally, we introduce a taxonomy for classifying training schema modifications, covering data augmentation, model inputs, model targets, time series per model, and computational budget.
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification

Class imbalance in graph data presents significant challenges for node classification. While existing methods, such as SMOTE-based approaches, partially mitigate this issue, they still exhibit limitations in constructing imbalanced graphs. Generative self-supervised learning (SSL) methods, exemplified by graph autoencoders (GAEs), offer a promising solution by directly generating minority nodes from the data itself, yet their potential remains underexplored. In this paper, we delve into the shortcomings of SMOTE-based approaches in the construction of imbalanced graphs. Furthermore, we introduce VIGraph, a simple yet effective generative SSL approach that relies on the Variational GAE as the fundamental model. VIGraph strictly adheres to the concept of imbalance when constructing imbalanced graphs and innovatively leverages the variational inference (VI) ability of Variational GAE to generate nodes for minority classes. VIGraph introduces comprehensive training strategies, including cross-view contrastive learning at the decoding phase to capture semantic knowledge, adjacency matrix reconstruction to preserve graph structure, and alignment strategy to ensure stable training. VIGraph can generate high-quality nodes directly usable for classification, eliminating the need to integrate the generated nodes back to the graph as well as additional retraining found in SMOTE-based methods. We conduct extensive experiments, results from which demonstrate the superiority and generality of our approach.
A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Vision

Generative Adversarial Networks (GANs) have been workhorse generative models for last many years, especially in the research field of computer vision. Accordingly, there have been many significant advancements in the theory and application of GAN models, which are notoriously hard to train, but produce good results if trained well. There have been many a surveys on GANs, organizing the vast GAN literature from various focus and perspectives. However, none of the surveys brings out the important chronological aspect: how the multiple challenges of employing GAN models were solved one-by-one over time, across multiple landmark research works. This survey intends to bridge that gap and present some of the landmark research works on the theory and application of GANs, in chronological order.
Controlling high-harmonic generation from strain engineered monolayer phosphorene

Phosphorene, a well-studied 2D allotrope of phosphorus, features unique properties such as widely tunable bandgap, high carrier mobility, and remarkable intrinsic in-plane anisotropy. Utilizing these structural and electronic properties, we investigate ultrafast electron dynamics and high harmonic generation (HHG) from phosphorene subject to band structure engineering through external strain, based on ab initio time-dependent density-functional theory approach. We show that strong field processes in such systems can be optimized and controlled by biaxial tensile and compressive strain engineering, that results in electronic structure modification. While -10% strain resulted in closing of band gap, 2% strain increased the gap by 22% with respect to 0.9 eV in pristine phosphorene, consequently affecting the high harmonic yield. With reduction of gap, by applying strain from 2% to -10%, the valence band near $\Gamma-$point becomes more flat and discreet, resulting in large electronic density of states and enhanced electronic excitation, which reflects in their ultrafast sub-cycle dynamics under laser excitation. Moreover, due to its intrinsic in-plane anisotropy, harmonic yield with laser polarization along the armchair (AC) direction is found to be higher than that of the zigzag (ZZ) direction for all the strain cases. Nearly, an order of magnitude enhancement of harmonic intensity is achieved for -10% strain along AC direction. The current study expands the research possibilities of phosphorene into a previously unexplored domain, indicating its potential for future utilization in extreme-ultraviolet and attosecond nanophotonics, and also for efficient table-top HHG sources.
A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations

The fusion of causal models with deep learning introducing increasingly intricate data sets, such as the causal associations within images or between textual components, has surfaced as a focal research area. Nonetheless, the broadening of original causal concepts and theories to such complex, non-statistical data has been met with serious challenges. In response, our study proposes redefinitions of causal data into three distinct categories from the standpoint of causal structure and representation: definite data, semi-definite data, and indefinite data. Definite data chiefly pertains to statistical data used in conventional causal scenarios, while semi-definite data refers to a spectrum of data formats germane to deep learning, including time-series, images, text, and others. Indefinite data is an emergent research sphere inferred from the progression of data forms by us. To comprehensively present these three data paradigms, we elaborate on their formal definitions, differences manifested in datasets, resolution pathways, and development of research. We summarize key tasks and achievements pertaining to definite and semi-definite data from myriad research undertakings, present a roadmap for indefinite data, beginning with its current research conundrums. Lastly, we classify and scrutinize the key datasets presently utilized within these three paradigms.
A Microlocal Analysis of the L\'evy Generator with Conjugate Points

We analyze the microlocal structure of the infinitesimal generator of a L\'evy process on a closed Riemannian manifold when conjugate points are allowed. We show that if there are no singular conjugate pairs, then the infinitesimal generator can be written as a sum of pseudodifferential operators and Fourier integral operators. This extends and unifies known results for the flat torus, the sphere, and Anosov manifolds.
Dissimilar thermal transport properties in κ-Ga2O3 and β-Ga2O3 revealed by homogeneous nonequilibrium molecular dynamics simulations using machine-learned potentials

The lattice thermal conductivity (LTC) of Ga2O3 is an important property due to the challenge in the thermal management of high-power devices. In this work, we develop machine-learned neuroevolution potentials (NEPs) for single-crystalline β-Ga2O3 and κ-Ga2O3 and demonstrate their accuracy in modeling thermal transport properties. Combining NEP-driven homogeneous non-equilibrium molecular dynamics simulations with tensor analysis, we determine the spatial distributions of LTCs for two Ga2O3 crystals, showing dissimilar thermal behaviors. Specifically, β-Ga2O3 shows isotropic thermal transport properties, with the LTCs along [100], [010], and [001] directions being predicted to be 10.3±0.2, 19.9±0.2, and 12.6±0.2 W/(m K), respectively, consistent with previous experimental measurements. For κ-Ga2O3, our predictions suggest nearly isotropic thermal transport properties, with the LTCs along [100], [010], and [001] being estimated to be 4.5±0.1, 3.9±0.1, and 4.0±0.1 W/(m K). The reduced LTC of κ-Ga2O3 vs β-Ga2O3 stems from its restricted low-frequency phonons up to 5 THz. Furthermore, we find that the β phase exhibits a typical temperature dependence slightly stronger than ∼T−1, whereas the κ phase shows a weaker temperature dependence, ranging from ∼T−0.5 to ∼T−0.7.
Are These the Same Apple? Comparing Images Based on Object Intrinsics

The human visual system can effortlessly recognize an object under different extrinsic factors such as lighting, object poses, and background, yet current computer vision systems often struggle with these variations. An important step to understanding and improving artificial vision systems is to measure image similarity purely based on intrinsic object properties that define object identity. This problem has been studied in the computer vision literature as re-identification, though mostly restricted to specific object categories such as people and cars. We propose to extend it to general object categories, exploring an image similarity metric based on object intrinsics. To benchmark such measurements, we collect the Common paired objects Under differenT Extrinsics (CUTE) dataset of $18,000$ images of $180$ objects under different extrinsic factors such as lighting, poses, and imaging conditions. While existing methods such as LPIPS and CLIP scores do not measure object intrinsics well, we find that combining deep features learned from contrastive self-supervised learning with foreground filtering is a simple yet effective approach to approximating the similarity. We conduct an extensive survey of pre-trained features and foreground extraction methods to arrive at a strong baseline that best measures intrinsic object-centric image similarity among current methods. Finally, we demonstrate that our approach can aid in downstream applications such as acting as an analog for human subjects and improving generalizable re-identification. Please see our project website at https://s-tian.github. io/projects/cute/ for visualizations of the data and demos of our metric.
Revealing the disk-circumgalactic medium transition with emission mapping

Most of a galaxy's mass is located out to hundreds of kiloparsecs beyond its stellar component. This diffuse reservoir of gas, the circumgalactic medium (CGM), acts as the interface between a galaxy and the cosmic web that connects galaxies. We present kiloparsec-scale resolution integral field spectroscopy of emission lines that trace cool ionized gas from the center of a nearby galaxy to 30 kpc into its CGM. We find a smooth surface brightness profile with a break in slope at twice the 90% stellar radius. At this radius, the gas also transitions from being photoionized by HII star-forming regions in the disk to being ionized by shocks or the extragalactic UV background at larger distances. These changes represent the boundary between the interstellar medium (ISM) and the CGM, revealing the shape and extent of the dominant reservoir of baryonic matter in galaxies for the first time.
Unreading Race: Purging Protected Features from Chest X-ray Embeddings

Purpose: To analyze and remove protected feature effects in chest radiograph embeddings of deep learning models. Materials and Methods: An orthogonalization is utilized to remove the influence of protected features (e.g., age, sex, race) in chest radiograph embeddings, ensuring feature-independent results. To validate the efficacy of the approach, we retrospectively study the MIMIC and CheXpert datasets using three pre-trained models, namely a supervised contrastive, a self-supervised contrastive, and a baseline classifier model. Our statistical analysis involves comparing the original versus the orthogonalized embeddings by estimating protected feature influences and evaluating the ability to predict race, age, or sex using the two types of embeddings. Results: Our experiments reveal a significant influence of protected features on predictions of pathologies. Applying orthogonalization removes these feature effects. Apart from removing any influence on pathology classification, while maintaining competitive predictive performance, orthogonalized embeddings further make it infeasible to directly predict protected attributes and mitigate subgroup disparities. Conclusion: The presented work demonstrates the successful application and evaluation of the orthogonalization technique in the domain of chest X-ray classification.