Miracle-dz/UniFAR-FTUs huggingface dataset:
ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer

Representation learning aims to discover individual salient features of a domain in a compact and descriptive form that strongly identifies the unique characteristics of a given sample respective to its domain. Existing works in visual style representation literature have tried to disentangle style from content during training explicitly. A complete separation between these has yet to be fully achieved. Our paper aims to learn a representation of visual artistic style more strongly disentangled from the semantic content depicted in an image. We use Neural Style Transfer (NST) to measure and drive the learning signal and achieve state-of-the-art representation learning on explicitly disentangled metrics. We show that strongly addressing the disentanglement of style and content leads to large gains in style-specific metrics, encoding far less semantic information and achieving state-of-the-art accuracy in downstream multimodal applications.
Image Deformation by User-Defined Force Fields

We present a new method for image deformation. The warping technique provides smooth distortion with intuitive and easy manipulation. Driven by a restrained force field, the input image is deformed gradually and continuously. The method allows us to customize the force fields and the region of interest manually through some simple steps. Experimental results demonstrate the effectiveness and convenience of the approach.
RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction

It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estimate the impact of its decisions. Recent approaches for conditional behavior prediction rely on a regression decoder, meaning that coordinates or polynomial coefficients are regressed. In this work we revisit set-based trajectory prediction, where the probability of each trajectory in a predefined trajectory set is determined by a classification model, and first-time employ it to the task of conditional behavior prediction. We propose RESET, which combines a new metric-driven algorithm for trajectory set generation with a graph-based encoder. For unconditional prediction, RESET achieves comparable performance to a regression-based approach. Due to the nature of set-based approaches, it has the advantageous property of being able to predict a flexible number of trajectories without influencing runtime or complexity. For conditional prediction, RESET achieves reasonable results with late fusion of the planned trajectory, which was not observed for regression-based approaches before. This means that RESET is computationally lightweight to combine with a planner that proposes multiple future plans of the autonomous vehicle, as large parts of the forward pass can be reused.
Filler Word Detection with Hard Category Mining and Inter-Category Focal Loss

Filler words like "um" or "uh" are common in spontaneous speech. It is desirable to automatically detect and remove them in recordings, as they affect the fluency, confidence, and professionalism of speech. Previous studies and our preliminary experiments reveal that the biggest challenge in filler word detection is that fillers can be easily confused with other hard categories like "a" or "I". In this paper, we propose a novel filler word detection method that effectively addresses this challenge by adding auxiliary categories dynamically and applying an additional inter-category focal loss. The auxiliary categories force the model to explicitly model the confusing words by mining hard categories. In addition, inter-category focal loss adaptively adjusts the penalty weight between "filler" and "non-filler" categories to deal with other confusing words left in the "non-filler" category. Our system achieves the best results, with a huge improvement compared to other methods on the PodcastFillers dataset.
A Novel Sparse Regularizer

$L_p$-norm regularization schemes such as $L_0$, $L_1$, and $L_2$-norm regularization and $L_p$-norm-based regularization techniques such as weight decay, LASSO, and elastic net compute a quantity which depends on model weights considered in isolation from one another. This paper introduces a regularizer based on minimizing a novel measure of entropy applied to the model during optimization. In contrast with $L_p$-norm-based regularization, this regularizer is concerned with the spatial arrangement of weights within a weight matrix. This novel regularizer is an additive term for the loss function and is differentiable, simple and fast to compute, scale-invariant, requires a trivial amount of additional memory, and can easily be parallelized. Empirically this method yields approximately a one order-of-magnitude improvement in the number of nonzero model parameters required to achieve a given level of test accuracy when training LeNet300 on MNIST.
Comparison of Bayesian Spatio-Temporal Models for Chronic Diseases

This paper discusses a comprehensive statistical approach that will be useful in answering health-related questions concerning mortality and incidence rates of chronic diseases such as cancer and hypertension. The developed spatio-temporal models will be useful to explain the patterns of mortality rates of chronic disease in terms of environmental changes and social-economic conditions. In addition to age and time effects, models include two components of normally distributed residual effects and spatial effects, one to represent average regional effects and another to represent changes of subgroups within region over time. Numerical analysis is based on male lung cancer mortality data from the state of Missouri. Gibbs sampling is used to obtain the posterior quantities. As a result, all models discussed in this article fit well in stabilizing the mortality rates, especially in the less populated areas. Due to the richness of hierarchical settings, easy interpretation of parameters and ease of implementation, any models proposed in this paper can be applied generally to other sets of data.
Large-Signal Behavior of Ferroelectric Micro-Electromechanical Transducers

CMOS-MEMS resonators seamlessly integrated in advanced integrated circuit (IC) technology have the unique capability to enable unprecedented integration of stable frequency references, acoustic spectral processors, and physical sensors. Demonstrations of transducers leveraging piezoelectric properties of emerging ferroelectric materials such as Hafnium Zirconium Oxide (HZO) and Aluminum Scandium Nitride (AlScN) enable high figure of merit (FOM=k2Q) resonators over a wide range of frequencies. CMOS-integrated ferroelectric transducers using a thickness-scaled variant of these films for low voltage operation are feasible by leveraging advancements in ferroelectric random access memory (FRAM) and ferroelectric field effect transistors (FeFETs). However, until now, there has not been a full treatment of the effects of nonlinear large-signal behaviour on the performance of electromechanical systems built using ferroelectric transducers in the low coercive voltage regime. In this work, CMOS-MEMS resonators in a 130 nm process operating at ~700 MHz have been used as a vehicle for understanding the performance impact of nonlinear piezoelectric transduction on frequency references and acoustic filters. The first nonlinear large signal model for such resonators has been developed and employed to extract the nonlinear characteristics over different biasing and applied power. Operating conditions and design guidelines have also been developed for these applications, which can be extended to all resonators of this class. The crystallized understanding of large-signal operation of ferroelectric transducers presented in this work provides opportunities to design and demonstrate new capabilities of electromechanical devices in monolithic CMOS-MEMS platforms.
Stand-Up Indulgent Gathering on Lines

We consider a variant of the crash-fault gathering problem called stand-up indulgent gathering (SUIG). In this problem, a group of mobile robots must eventually gather at a single location, which is not known in advance. If no robots crash, they must all meet at the same location. However, if one or more robots crash at a single location, all non-crashed robots must eventually gather at that location. The SUIG problem was first introduced for robots operating in a two-dimensional continuous Euclidean space, with most solutions relying on the ability of robots to move a prescribed (real) distance at each time instant. In this paper, we investigate the SUIG problem for robots operating in a discrete universe (i.e., a graph) where they can only move one unit of distance (i.e., to an adjacent node) at each time instant. Specifically, we focus on line-shaped networks and characterize the solvability of the SUIG problem for oblivious robots without multiplicity detection.
Data-Driven Response Regime Exploration and Identification for Dynamical Systems

Data-Driven Response Regime Exploration and Identification (DR$^2$EI) is a novel and fully data-driven method for identifying and classifying response regimes of a dynamical system without requiring human intervention. This approach is a valuable tool for exploring and discovering response regimes in complex dynamical systems, especially when the governing equations and the number of response regimes are unknown, and the system is expensive to sample. Additionally, the method is useful for order reduction, as it can be used to identify the most dominant response regimes of a given dynamical system. DR$^2$EI utilizes unsupervised learning algorithms to transform the system's response into an embedding space that facilitates regime classification. An active sequential sampling approach based on Gaussian Process Regression (GPR) is used to efficiently sample the parameter space, quantify uncertainty, and provide optimal trade-offs between exploration and exploitation. The performance of the DR$^2$EI method was evaluated by analyzing three established dynamical systems: the mathematical pendulum, the Lorenz system, and the Duffing oscillator. The method was shown to effectively identify a variety of response regimes with both similar and distinct topological features and frequency content, demonstrating its versatility in capturing a wide range of behaviors. While it may not be possible to guarantee that all possible regimes will be identified, the method provides an automated and efficient means for exploring the parameter space of a dynamical system and identifying its underlying"sufficiently dominant"response regimes without prior knowledge of the system's equations or behavior.
Amplitude Banded RLS Approach to Time Variant Channel Equalization

SUMMARY This paper proposes a non-linear adaptive algorithm, the amplitude banded RLS (ABRLS) algorithm, as an adaptation procedure for time variant channel equalizers. In the ABRLS algorithm, a coefficient matrix is updated based on the amplitude level of the received sequence. To enhance the capability of tracking for the ABRLS algorithm, a parallel adaptation scheme is utilized which involves the structures of decision feedback equalizer (DFE). Computer simulations demonstrate that the novel ABRLS based equalizer provides a significant improvement relative to the conventional RLS DFE on a rapidly time variant communication channel.
New deformed Heisenberg algebra from the μ-deformed model of dark matter

Recently, the μ-deformation-based approach to modeling dark matter, which exploits μ-deformed thermodynamics, was extended to the study of galaxy halo density profile and of the rotation curves of a number of (dwarf or low brightness) galaxies. For that goal, μ-deformed analogs of the Lane–Emden equation (LEE) have been proposed, and their solutions describing density profiles obtained. There are two seemingly different versions of μ-deformed LEE which possess the same solution, and so we deal with their equivalence. From the latter property we derive new, rather unusual, μ-deformed Heisenberg algebra (HA) for the position and momentum operators, and present the μ-HA in few possible forms (each one at μ → 0 recovers usual HA). The generalized uncertainty relation linked with the new μ-HA is studied, along with its interesting implications including the appearance of the quadruple of both maximal and minimal lengths and momenta.
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training

Sequential recommendation models, models that learn from chronological user-item interactions, outperform traditional recommendation models in many settings. Despite the success of sequential recommendation models, their robustness has recently come into question. Two properties unique to the nature of sequential recommendation models may impair their robustness - the cascade effects induced during training and the model's tendency to rely too heavily on temporal information. To address these vulnerabilities, we propose Cascade-guided Adversarial training, a new adversarial training procedure that is specifically designed for sequential recommendation models. Our approach harnesses the intrinsic cascade effects present in sequential modeling to produce strategic adversarial perturbations to item embeddings during training. Experiments on training state-of-the-art sequential models on four public datasets from different domains show that our training approach produces superior model ranking accuracy and superior model robustness to real item replacement perturbations when compared to both standard model training and generic adversarial training.
On the response distribution of nonparametric probabilistic models for mid- and high-frequency analysis

The local response of built-up structural and acoustic systems, consisting of stiff components with low modal density and flexible components with high modal density, may be very sensitive to uncertainty in spatial variations in the geometry, material properties, and boundary conditions of the flexible components. In this work, this uncertainty is considered by modeling the low modal density master system as deterministic and the high modal density subsystems in a nonparametric stochastic way, and by subsequently computing the response probability density function. The probability distribution of the master system's displacement degrees of freedom and the total subsystem energies is numerically computed by assuming that the distribution of the eigenvalues and eigenvectors of a decoupled subsystem correspond to those of a Gaussian Orthogonal Ensemble matrix. This approach is extensively validated by application to structures, consisting of thin plates attached to stiff structural components. Good agreement between the predicted probability distributions and the results of detailed Monte Carlo simulations is found. The validation examples also illustrate that the numerical procedure agrees better with the Monte Carlo simulations than a closed-form evaluation of the response probability density, which requires additional assumptions. © (2012) by the Katholieke Universiteit Leuven Department of Mechanical Engineering All rights reserved.
Soft Detection Physical Layer Insecurity

We establish that during the execution of any Guessing Random Additive Noise Decoding (GRAND) algorithm, an interpretable, useful measure of decoding confidence can be evaluated. This measure takes the form of a log-likelihood ratio (LLR) of the hypotheses that, should a decoding be found by a given query, the decoding is correct versus its being incorrect. That LLR can be used as soft output for a range of applications and we demonstrate its utility by showing that it can be used to confidently discard likely erroneous decodings in favor of returning more readily managed erasures. We show that feature can be used to compromise the physical layer security of short length wiretap codes by accurately and confidently revealing a proportion of a communication when code-rate is far above the Shannon capacity of the associated hard detection channel.
Analysis of liquid rocket propellant evaporation characteristic in natural environment

The evaporation characteristic in the ground non-burning propellant and UDMH after the explosion of the liquid-propellant carrier rocket was studied in this paper.A theories model of the liquid rocket propellant vaporization rate in natural environment was developed.Experimental result of the evaporation rate of and UDMH in natural environment was obtained for the evaluation of harmfulness of the poisonous gas contamination of the liquid propellant rocket explosion.Its can be applied to evaluate the harmfulness of the liquid propellant rocket explosions for the space vehicle launching site.
A deep learning-based approach for statistical robustness evaluation in proton therapy treatment planning: a feasibility study

Objective. Robustness evaluation is critical in particle radiotherapy due to its susceptibility to uncertainties. However, the customary method for robustness evaluation only considers a few uncertainty scenarios, which are insufficient to provide a consistent statistical interpretation. We propose an artificial intelligence–based approach that overcomes this limitation by predicting a set of percentile dose values at every voxel and allows for the evaluation of planning objectives at specific confidence levels. Approach. We built and trained a deep learning (DL) model to predict the 5th and 95th percentile dose distributions, which corresponds to the lower and upper bounds of a two-tailed 90% confidence interval (CI), respectively. Predictions were made directly from the nominal dose distribution and planning computed tomography scan. The data used to train and test the model consisted of proton plans from 543 prostate cancer patients. The ground truth percentile values were estimated for each patient using 600 dose recalculations representing randomly sampled uncertainty scenarios. For comparison, we also tested whether a common worst-case scenario (WCS) robustness evaluation (voxel-wise minimum and maximum) corresponding to a 90% CI could reproduce the ground truth 5th and 95th percentile doses. Main results. The percentile dose distributions predicted by DL yielded excellent agreements with the ground truth dose distributions, with mean dose errors below 0.15 Gy and average gamma passing rates (GPR) at 1 mm/1% above 93.9, which were substantially better than the WCS dose distributions (mean dose error above 2.2 Gy and GPR at 1 mm / 1% below 54). We observed similar outcomes in a dose-volume histogram error analysis, where the DL predictions generally yielded smaller mean errors and standard deviations than the WCS evaluation doses. Significance. The proposed method produces accurate and fast predictions (∼2.5 s for one percentile dose distribution) for a given confidence level. Thus, the method has the potential to improve robustness evaluation.
Cauliflower mosaic virus protein P6 is a multivalent node for RNA granule proteins and interferes with stress granule responses during plant infection

Abstract Biomolecular condensation is a multipurpose cellular process that viruses use ubiquitously during their multiplication. Cauliflower mosaic virus replication complexes are condensates that differ from those of most viruses, as they are nonmembranous assemblies that consist of RNA and protein, mainly the viral protein P6. Although these viral factories (VFs) were described half a century ago, with many observations that followed since, functional details of the condensation process and the properties and relevance of VFs have remained enigmatic. Here, we studied these issues in Arabidopsis thaliana and Nicotiana benthamiana. We observed a large dynamic mobility range of host proteins within VFs, while the viral matrix protein P6 is immobile, as it represents the central node of these condensates. We identified the stress granule (SG) nucleating factors G3BP7 and UBP1 family members as components of VFs. Similarly, as SG components localize to VFs during infection, ectopic P6 localizes to SGs and reduces their assembly after stress. Intriguingly, it appears that soluble rather than condensed P6 suppresses SG formation and mediates other essential P6 functions, suggesting that the increased condensation over the infection time-course may accompany a progressive shift in selected P6 functions. Together, this study highlights VFs as dynamic condensates and P6 as a complex modulator of SG responses.
RLTG: Multi-targets directed greybox fuzzing

Directed greybox fuzzing guides fuzzers to explore specific objective code areas and has achieved good performance in some scenarios such as patch testing. However, if there are multiple objective code to explore, existing directed greybox fuzzers, such as AFLGo and Hawkeye, often neglect some targets because they use harmonic means of distance and prefers to test those targets with shorter reachable path. Besides, existing directed greybox fuzzers cannot calculate the accurate distance due to indirect calls in the program. In addition, existing directed greybox fuzzers fail to address the exploration and exploitation problem and have poor efficiency in seed scheduling. To address these problems, we propose a dynamic seed distance calculation scheme, it increase the seed distance dynamically when the reachable path encounter indirect call. Besides, the seed distance calculation can deal with the bias problem in multi-targets scenarios. With the seed distance calculation method, we propose a new seed scheduling algorithm based on the upper confidence bound algorithm to deal with the exploration and exploitation problem in drected greybox fuzzing. We implemented a prototype RLTG and evaluate it on real-world programs. Evaluation of our prototype shows that our approach outperforms a state-of-the-art directed fuzzer AFLGo. On the multi-targets benchmark Magma, RLTG reproduces bugs with 6.9x speedup and finds 66.7% more bugs than AFLGo.