Miracle-dz/UniFAR-FTUs huggingface dataset:
Computability of extender sets in multidimensional subshifts

Subshifts are colorings of $\mathbb{Z}^d$ defined by families of forbidden patterns. Given a subshift and a finite pattern, its extender set is the set of admissible completions of this pattern. It has been conjectured that the behavior of extender sets, and in particular their growth called extender entropy (arXiv:1711.07515), could provide a way to separate the classes of sofic and effective subshifts. We prove here that both classes have the same possible extender entropies: exactly the $\Pi_3$ real numbers of $[0,+\infty)$. We also consider computational properties of extender entropies for subshifts with some language or dynamical properties: computable language, minimal and some mixing properties.
IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data

Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic resonance imaging (DWI) data shows potential for assessing fetal lung maturation and generating valuable imaging biomarkers. Yet, the clinical utility of DWI data is hindered by unavoidable fetal motion during acquisition. We present IVIM-morph, a self-supervised deep neural network model for motion-corrected quantitative analysis of DWI data using the Intra-voxel Incoherent Motion (IVIM) model. IVIM-morph combines two sub-networks, a registration sub-network, and an IVIM model fitting sub-network, enabling simultaneous estimation of IVIM model parameters and motion. To promote physically plausible image registration, we introduce a biophysically informed loss function that effectively balances registration and model-fitting quality. We validated the efficacy of IVIM-morph by establishing a correlation between the predicted IVIM model parameters of the lung and gestational age (GA) using fetal DWI data of 39 subjects. Our approach was compared against six baseline methods: 1) no motion compensation, 2) affine registration of all DWI images to the initial image, 3) deformable registration of all DWI images to the initial image, 4) deformable registration of each DWI image to its preceding image in the sequence, 5) iterative deformable motion compensation combined with IVIM model parameter estimation, and 6) self-supervised deep-learning-based deformable registration. IVIM-morph exhibited a notably improved correlation with gestational age (GA) when performing in-vivo quantitative analysis of fetal lung DWI data during the canalicular phase. Specifically, over 2 test groups of cases, it achieved an Rf2 of 0.44 and 0.52, outperforming the values of 0.27 and 0.25, 0.25 and 0.00, 0.00 and 0.00, 0.38 and 0.00, and 0.07 and 0.14 obtained by other methods. IVIM-morph shows potential in developing valuable biomarkers for non-invasive assessment of fetal lung maturity with DWI data. Moreover, its adaptability opens the door to potential applications in other clinical contexts where motion compensation is essential for quantitative DWI analysis. The IVIM-morph code is readily available at:https://github.com/TechnionComputationalMRILab/qDWI-Morph.
A Lawson-time-splitting extended Fourier pseudospectral method for the Gross-Pitaevskii equation with time-dependent low regularity potential

We propose a Lawson-time-splitting extended Fourier pseudospectral (LTSeFP) method for the numerical integration of the Gross-Pitaevskii equation with time-dependent potential that is of low regularity in space. For the spatial discretization of low regularity potential, we use an extended Fourier pseudospectral (eFP) method, i.e., we compute the discrete Fourier transform of the low regularity potential in an extended window. For the temporal discretization, to efficiently implement the eFP method for time-dependent low regularity potential, we combine the standard time-splitting method with a Lawson-type exponential integrator to integrate potential and nonlinearity differently. The LTSeFP method is both accurate and efficient: it achieves first-order convergence in time and optimal-order convergence in space in $L^2$-norm under low regularity potential, while the computational cost is comparable to the standard time-splitting Fourier pseudospectral method. Theoretically, we also prove such convergence orders for a large class of spatially low regularity time-dependent potential. Extensive numerical results are reported to confirm the error estimates and to demonstrate the superiority of our method.
Finding Privacy-relevant Source Code

Privacy code review is a critical process that enables developers and legal experts to ensure compliance with data protection regulations. However, the task is challenging due to resource constraints. To address this, we introduce the concept of privacy-relevant methods - specific methods in code that are directly involved in the processing of personal data. We then present an automated approach to assist in code review by identifying and categorizing these privacy-relevant methods in source code. Using static analysis, we identify a set of methods based on their occurrences in 50 commonly used libraries. We then rank these methods according to their frequency of invocation with actual personal data in the top 30 GitHub applications. The highest-ranked methods are the ones we designate as privacy-relevant in practice. For our evaluation, we examined 100 open-source applications and found that our approach identifies fewer than 5% of the methods as privacy-relevant for personal data processing. This reduces the time required for code reviews. Case studies on Signal Desktop and Cal.com further validate the effectiveness of our approach in aiding code reviewers to produce enhanced reports that facilitate compliance with privacy regulations.
RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving

Road scene understanding is crucial in autonomous driving, enabling machines to perceive the visual environment. However, recent object detectors tailored for learning on datasets collected from certain geographical locations struggle to generalize across different locations. In this paper, we present RSUD20K, a new dataset for road scene understanding, comprised of over 20K high-resolution images from the driving perspective on Bangladesh roads, and includes 130K bounding box annotations for 13 objects. This challenging dataset encompasses diverse road scenes, narrow streets and highways, featuring objects from different viewpoints and scenes from crowded environments with densely cluttered objects and various weather conditions. Our work significantly improves upon previous efforts, providing detailed annotations and increased object complexity. We thoroughly examine the dataset, benchmarking various state-of-the-art object detectors and exploring large vision models as image annotators.
Decoupled Spatial and Temporal Processing for Resource Efficient Multichannel Speech Enhancement

We present a novel model designed for resource-efficient multichannel speech enhancement in the time domain, with a focus on low latency, lightweight, and low computational requirements. The proposed model incorporates explicit spatial and temporal processing within deep neural network (DNN) layers. Inspired by frequency-dependent multichannel filtering, our spatial filtering process applies multiple trainable filters to each hidden unit across the spatial dimension, resulting in a multichannel output. The temporal processing is applied over a single-channel output stream from the spatial processing using a Long Short-Term Memory (LSTM) network. The output from the temporal processing stage is then further integrated into the spatial dimension through elementwise multiplication. This explicit separation of spatial and temporal processing results in a resource-efficient network design. Empirical findings from our experiments show that our proposed model significantly outperforms robust baseline models while demanding far fewer parameters and computations, while achieving an ultra-low algorithmic latency of just 2 milliseconds.
Domain Walls in the A 4 flavored NMSSM

In this work, we study the phenomenology of neutrinos and the formation of cosmic domain walls in the NMSSM extended by an $A_4 \times Z_3$ flavor symmetry. Neutrino masses result from the type I seesaw mechanism using only two flavon fields and the NMSSM singlet $\mathcal{S}$ while their mixing is of Trimaximal mixing form. We perform our phenomenological study in the normal mass hierarchy where we find that observables like $m_{\beta\beta}$, $m_\beta$, and $\sum_i m_i$ can be tested by future experiments. Due to the difference between the $A_4$ subgroups that undergo spontaneous breaking in both the charged lepton and neutrino sectors, the resulting domain walls in each sector exhibit distinct structures. We delve into the details of the breaking patterns within these two sectors, and we introduce a nuanced geometric representation for them. To tackle the domain wall problem, we explore a well-established method involving the explicit breaking of the flavor symmetry. This is achieved through the introduction of Planck-suppressed operators induced by supergravity.
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study

In an era where the silent struggle of underdiagnosed depression pervades globally, our research delves into the crucial link between mental health and social media. This work focuses on early detection of depression, particularly in extroverted social media users, using LLMs such as GPT 3.5, GPT 4 and our proposed GPT 3.5 fine-tuned model DepGPT, as well as advanced Deep learning models(LSTM, Bi-LSTM, GRU, BiGRU) and Transformer models(BERT, BanglaBERT, SahajBERT, BanglaBERT-Base). The study categorized Reddit and X datasets into"Depressive"and"Non-Depressive"segments, translated into Bengali by native speakers with expertise in mental health, resulting in the creation of the Bengali Social Media Depressive Dataset (BSMDD). Our work provides full architecture details for each model and a methodical way to assess their performance in Bengali depressive text categorization using zero-shot and few-shot learning techniques. Our work demonstrates the superiority of SahajBERT and Bi-LSTM with FastText embeddings in their respective domains also tackles explainability issues with transformer models and emphasizes the effectiveness of LLMs, especially DepGPT, demonstrating flexibility and competence in a range of learning contexts. According to the experiment results, the proposed model, DepGPT, outperformed not only Alpaca Lora 7B in zero-shot and few-shot scenarios but also every other model, achieving a near-perfect accuracy of 0.9796 and an F1-score of 0.9804, high recall, and exceptional precision. Although competitive, GPT-3.5 Turbo and Alpaca Lora 7B show relatively poorer effectiveness in zero-shot and few-shot situations. The work emphasizes the effectiveness and flexibility of LLMs in a variety of linguistic circumstances, providing insightful information about the complex field of depression detection models.
Assessing the Effectiveness of Binary-Level CFI Techniques

Memory corruption is an important class of vulnerability that can be leveraged to craft control flow hijacking attacks. Control Flow Integrity (CFI) provides protection against such attacks. Application of type-based CFI policies requires information regarding the number and type of function arguments. Binary-level type recovery is inherently speculative, which motivates the need for an evaluation framework to assess the effectiveness of binary-level CFI techniques compared with their source-level counterparts, where such type information is fully and accurately accessible. In this work, we develop a novel, generalized and extensible framework to assess how the program analysis information we get from state-of-the-art binary analysis tools affects the efficacy of type-based CFI techniques. We introduce new and insightful metrics to quantitatively compare source independent CFI policies with their ground truth source aware counterparts. We leverage our framework to evaluate binary-level CFI policies implemented using program analysis information extracted from the IDA Pro binary analyzer and compared with the ground truth information obtained from the LLVM compiler, and present our observations.
A Visually Attentive Splice Localization Network with Multi-Domain Feature Extractor and Multi-Receptive Field Upsampler

Image splice manipulation presents a severe challenge in today's society. With easy access to image manipulation tools, it is easier than ever to modify images that can mislead individuals, organizations or society. In this work, a novel,"Visually Attentive Splice Localization Network with Multi-Domain Feature Extractor and Multi-Receptive Field Upsampler"has been proposed. It contains a unique"visually attentive multi-domain feature extractor"(VA-MDFE) that extracts attentional features from the RGB, edge and depth domains. Next, a"visually attentive downsampler"(VA-DS) is responsible for fusing and downsampling the multi-domain features. Finally, a novel"visually attentive multi-receptive field upsampler"(VA-MRFU) module employs multiple receptive field-based convolutions to upsample attentional features by focussing on different information scales. Experimental results conducted on the public benchmark dataset CASIA v2.0 prove the potency of the proposed model. It comfortably beats the existing state-of-the-arts by achieving an IoU score of 0.851, pixel F1 score of 0.9195 and pixel AUC score of 0.8989.
Subsonic Euler flows in a three-dimensional finitely long cylinder with arbitrary cross section

This paper concerns the well-posedness of subsonic flows in a three-dimensional finitely long cylinder with arbitrary cross section. We establish the existence and uniqueness of subsonic flows in the Sobolev space by prescribing the normal component of the momentum, the vorticity, the entropy, the Bernoulli's quantity at the entrance and the normal component of the momentum at the exit. One of the key points in the analysis is to utilize the deformation-curl decomposition for the steady Euler system introduced in \cite{WX19} to deal with the hyperbolic and elliptic modes. Another one is to employ the separation of variables to improve the regularity of solutions to a deformation-curl system near the intersection between the entrance and exit with the cylinder wall.
Laser-field detuning assisted optimization of exciton valley dynamics in monolayer WSe$_2$: Geometric quantum speed limit

Optimizing valley dynamics is an effective instrument towards precisely manipulating qubit in the context of two-dimensional semiconductor. In this work, we construct a comprehensive model, involving both intra- and intervalley channels of excitons in monolayer WSe$_2$, and simultaneously takes the light-matter interaction into account, to investigate the optimal control of valley dynamics with an initial coherent excitonic state. Based on the quantum speed limit (QSL) theory, we propose two optimal control schemes aiming to reduce the evolution time of valley dynamics reaching the target state, along with to boost the evolution speed over a period of time. Further, we emphasize that the implementation of dynamical optimization is closely related to the detuning difference -- the difference of exciton-laser field detunings between the K and K' valleys -- which is determined by the optical excitation mode and magnetically-induced valley splitting. In particular, we reveal that a small detuning difference drives the actual dynamical path to converge towards the geodesic length between the initial and final states, allowing the system to evolve with the least time. Especially, in the presence of valley coherence, the actual evolution time and the calculated QSL time almost coincide, facilitating high fidelity in information transmission based on the valley qubit. Remarkably, we demonstrate an intriguing enhancement in evolution speed of valley dynamics, by adopting a large detuning difference, which induces an emerging valley polarization even without initial polarization. Our work opens a new paradigm for optically tuning excitonic physics in valleytronic applications, and may also offer solutions to some urgent problems such as speed limit of information transmission in qubit.
Counterfactual Slope and Its Applications to Social Stratification

This paper addresses two prominent theses in social stratification research, the great equalizer thesis and Mare's (1980) school transition thesis. Both theses are premised on a descriptive regularity: the association between socioeconomic background and an outcome variable changes when conditioning on an intermediate treatment. The interpretation of this descriptive regularity is complicated by social actors' differential selection into treatment based on their potential outcomes under treatment. In particular, if the descriptive regularity is driven by selection, then the theses do not have a substantive interpretation. We propose a set of novel counterfactual slope estimands, which capture the two theses under the hypothetical scenario where differential selection into treatment is eliminated. Thus, we use the counterfactual slopes to construct selection-free tests for the two theses. Compared with the existing literature, we are the first to provide explicit, nonparametric, and causal estimands, which enable us to conduct principled selection-free tests. We develop efficient and robust estimators by deriving the efficient influence functions of the estimands. We apply our framework to a nationally representative dataset in the United States and re-evaluate the two theses. Findings from our selection-free tests show that the descriptive regularity of the two theses is misleading for substantive interpretations.
GD-CAF: Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting

Accurate precipitation nowcasting is essential for various applications, including flood prediction, disaster management, optimizing agricultural activities, managing transportation routes and renewable energy. While several studies have addressed this challenging task from a sequence-to-sequence perspective, most of them have focused on a single area without considering the existing correlation between multiple disjoint regions. In this paper, we formulate precipitation nowcasting as a spatiotemporal graph sequence nowcasting problem. In particular, we introduce Graph Dual-stream Convolutional Attention Fusion (GD-CAF), a novel approach designed to learn from historical spatiotemporal graph of precipitation maps and nowcast future time step ahead precipitation at different spatial locations. GD-CAF consists of spatio-temporal convolutional attention as well as gated fusion modules which are equipped with depthwise-separable convolutional operations. This enhancement enables the model to directly process the high-dimensional spatiotemporal graph of precipitation maps and exploits higher-order correlations between the data dimensions. We evaluate our model on seven years of precipitation maps across Europe and its neighboring areas collected from the ERA5 dataset, provided by Copernicus Climate Change Services. The experimental results reveal the superior performance of the GD-CAF model compared to the other examined models. Additionally, visualizations of averaged seasonal spatial and temporal attention scores across the test set offer valuable insights into the most robust connections between diverse regions or time steps.
Unsupervised learning of quantum many-body scars using intrinsic dimension

Quantum many-body scarred systems contain both thermal and non-thermal scar eigenstates in their spectra. When these systems are quenched from special initial states which share high overlap with scar eigenstates, the system undergoes dynamics with atypically slow relaxation and periodic revival. This scarring phenomenon poses a potential avenue for circumventing decoherence in various quantum engineering applications. Given access to an unknown scar system, current approaches for identification of special states leading to non-thermal dynamics rely on costly measures such as entanglement entropy. In this work, we show how two dimensionality reduction techniques, multidimensional scaling and intrinsic dimension estimation, can be used to learn structural properties of dynamics in the PXP model and distinguish between thermal and scar initial states. The latter method is shown to be robust against limited sample sizes and experimental measurement errors.
Phenotypic switching mechanisms determine the structure of cell migration into extracellular matrix under the `go-or-grow' hypothesis

A fundamental feature of collective cell migration is phenotypic heterogeneity which, for example, influences tumour progression and relapse. While current mathematical models often consider discrete phenotypic structuring of the cell population, in-line with the `go-or-grow' hypothesis \cite{hatzikirou2012go, stepien2018traveling}, they regularly overlook the role that the environment may play in determining the cells' phenotype during migration. Comparing a previously studied volume-filling model for a homogeneous population of generalist cells that can proliferate, move and degrade extracellular matrix (ECM) \cite{crossley2023travelling} to a novel model for a heterogeneous population comprising two distinct sub-populations of specialist cells that can either move and degrade ECM or proliferate, this study explores how different hypothetical phenotypic switching mechanisms affect the speed and structure of the invading cell populations. Through a continuum model derived from its individual-based counterpart, insights into the influence of the ECM and the impact of phenotypic switching on migrating cell populations emerge. Notably, specialist cell populations that cannot switch phenotype show reduced invasiveness compared to generalist cell populations, while implementing different forms of switching significantly alters the structure of migrating cell fronts. This key result suggests that the structure of an invading cell population could be used to infer the underlying mechanisms governing phenotypic switching.
Auto-Regressive Model with Exogenous Input-ARX-based traffic-flow prediction

Traffic flow prediction is widely used in travel decision making, traffic control, roadway system planning, business sectors, and government agencies. ARX models have proved to be highly effective and versatile. In this research, we investigated the applications of ARX models in prediction for real traffic flow in New York City. The ARX models were constructed by linear/polynomial or neural networks. Comparative studies were carried out based on the results by the efficiency, accuracy, and training computational demand of the algorithms.
AI Hallucinations: A Misnomer Worth Clarifying

As large language models continue to advance in Artificial Intelligence (AI), text generation systems have been shown to suffer from a problematic phenomenon termed often as"hallucination. "However, with AI's increasing presence across various domains including medicine, concerns have arisen regarding the use of the term itself. In this study, we conducted a systematic review to identify papers defining"AI hallucination"across fourteen databases. We present and analyze definitions obtained across all databases, categorize them based on their applications, and extract key points within each category. Our results highlight a lack of consistency in how the term is used, but also help identify several alternative terms in the literature. We discuss implications of these and call for a more unified effort to bring consistency to an important contemporary AI issue that can affect multiple domains significantly.