Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles

Humans' internal states play a key role in human-machine interaction, leading to the rise of human state estimation as a prominent field. Compared to swift state changes such as surprise and irritation, modeling gradual states like trust and satisfaction are further challenged by label sparsity: long time-series signals are usually associated with a single label, making it difficult to identify the critical span of state shifts. Windowing has been one widely-used technique to enable localized analysis of long time-series data. However, the performance of downstream models can be sensitive to the window size, and determining the optimal window size demands domain expertise and extensive search. To address this challenge, we propose a Selective Windowing Attention Network (SWAN), which employs window prompts and masked attention transformation to enable the selection of attended intervals with flexible lengths. We evaluate SWAN on the task of trust prediction on a new multimodal driving simulation dataset. Experiments show that SWAN significantly outperforms an existing empirical window selection baseline and neural network baselines including CNN-LSTM and Transformer. Furthermore, it shows robustness across a wide span of windowing ranges, compared to the traditional windowing approach.
DETER: Detecting Edited Regions for Deterring Generative Manipulations

Generative AI capabilities have grown substantially in recent years, raising renewed concerns about potential malicious use of generated data, or"deep fakes". However, deep fake datasets have not kept up with generative AI advancements sufficiently to enable the development of deep fake detection technology which can meaningfully alert human users in real-world settings. Existing datasets typically use GAN-based models and introduce spurious correlations by always editing similar face regions. To counteract the shortcomings, we introduce DETER, a large-scale dataset for DETEcting edited image Regions and deterring modern advanced generative manipulations. DETER includes 300,000 images manipulated by four state-of-the-art generators with three editing operations: face swapping (a standard coarse image manipulation), inpainting (a novel manipulation for deep fake datasets), and attribute editing (a subtle fine-grained manipulation). While face swapping and attribute editing are performed on similar face regions such as eyes and nose, the inpainting operation can be performed on random image regions, removing the spurious correlations of previous datasets. Careful image post-processing is performed to ensure deep fakes in DETER look realistic, and human studies confirm that human deep fake detection rate on DETER is 20.4% lower than on other fake datasets. Equipped with the dataset, we conduct extensive experiments and break-down analysis using our rich annotations and improved benchmark protocols, revealing future directions and the next set of challenges in developing reliable regional fake detection models.
Data-Efficient Multimodal Fusion on a Single GPU

The goal of multimodal alignment is to learn a single latent space that is shared between multimodal inputs. The most powerful models in this space have been trained using massive datasets of paired inputs and large-scale computational resources, making them prohibitively expensive to train in many practical scenarios. We surmise that existing unimodal encoders pre-trained on large amounts of unimodal data should provide an effective bootstrap to create multimodal models from unimodal ones at much lower costs. We therefore propose FuseMix, a multimodal augmentation scheme that operates on the latent spaces of arbitrary pre-trained unimodal encoders. Using FuseMix for multimodal alignment, we achieve competitive performance -- and in certain cases outperform state-of-the art methods -- in both image-text and audio-text retrieval, with orders of magnitude less compute and data: for example, we outperform CLIP on the Flickr30K text-to-image retrieval task with $\sim \! 600\times$ fewer GPU days and $\sim \! 80\times$ fewer image-text pairs. Additionally, we show how our method can be applied to convert pre-trained text-to-image generative models into audio-to-image ones. Code is available at: https://github.com/layer6ai-labs/fusemix.
Decomposing Hard SAT Instances with Metaheuristic Optimization

In the article, within the framework of the Boolean Satisfiability problem (SAT), the problem of estimating the hardness of specific Boolean formulas w.r. t. a specific complete SAT solving algorithm is considered. Based on the well-known Strong Backdoor Set (SBS) concept, we introduce the notion of decomposition hardness (d-hardness). If $B$ is an arbitrary subset of the set of variables occurring in a SAT formula $C$, and $A$ is an arbitrary complete SAT solver , then the d-hardness expresses an estimate of the hardness of $C$ w.r. t. $A$ and $B$. We show that the d-hardness of $C$ w.r. t. a particular $B$ can be expressed in terms of the expected value of a special random variable associated with $A$, $B$, and $C$. For its computational evaluation, algorithms based on the Monte Carlo method can be used. The problem of finding $B$ with the minimum value of d-hardness is formulated as an optimization problem for a pseudo-Boolean function whose values are calculated as a result of a probabilistic experiment. To minimize this function, we use evolutionary algorithms. In the experimental part, we demonstrate the applicability of the concept of d-hardness and the methods of its estimation to solving hard unsatisfiable SAT instances.
ESDMR-Net: A Lightweight Network With Expand-Squeeze and Dual Multiscale Residual Connections for Medical Image Segmentation

Segmentation is an important task in a wide range of computer vision applications, including medical image analysis. Recent years have seen an increase in the complexity of medical image segmentation approaches based on sophisticated convolutional neural network architectures. This progress has led to incremental enhancements in performance on widely recognised benchmark datasets. However, most of the existing approaches are computationally demanding, which limits their practical applicability. This paper presents an expand-squeeze dual multiscale residual network (ESDMR-Net), which is a fully convolutional network that is particularly well-suited for resource-constrained computing hardware such as mobile devices. ESDMR-Net focuses on extracting multiscale features, enabling the learning of contextual dependencies among semantically distinct features. The ESDMR-Net architecture allows dual-stream information flow within encoder-decoder pairs. The expansion operation (depthwise separable convolution) makes all of the rich features with multiscale information available to the squeeze operation (bottleneck layer), which then extracts the necessary information for the segmentation task. The Expand-Squeeze (ES) block helps the network pay more attention to under-represented classes, which contributes to improved segmentation accuracy. To enhance the flow of information across multiple resolutions or scales, we integrated dual multiscale residual (DMR) blocks into the skip connection. This integration enables the decoder to access features from various levels of abstraction, ultimately resulting in more comprehensive feature representations. We present experiments on seven datasets from five distinct examples of applications. Our model achieved the best results despite having significantly fewer trainable parameters, with a reduction of two or even three orders of magnitude.
Resilient Federated Learning Under Byzantine Attack in Distributed Nonconvex Optimization with 2-$f$ Redundancy

We study the problem of Byzantine fault tolerance in a distributed optimization setting, where there is a group of $N$ agents communicating with a trusted centralized coordinator. Among these agents, there is a subset of $f$ agents that may not follow a prescribed algorithm and may share arbitrarily incorrect information with the coordinator. The goal is to find the optimizer of the aggregate cost functions of the honest agents. We will be interested in studying the local gradient descent method, also known as federated learning, to solve this problem. However, this method often returns an approximate value of the underlying optimal solution in the Byzantine setting. Recent work showed that by incorporating the so-called comparative elimination (CE) filter at the coordinator, one can provably mitigate the detrimental impact of Byzantine agents and precisely compute the true optimizer in the convex setting. The focus of the present work is to provide theoretical results to show the convergence of local gradient methods with the CE filter in a nonconvex setting. We will also provide a number of numerical simulations to support our theoretical results.
One step closer to unbiased aleatoric uncertainty estimation

Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties are usually categorized into aleatoric (data) and epistemic (model) uncertainty. In this paper, we point out that the existing popular variance attenuation method highly overestimates aleatoric uncertainty. To address this issue, we proposed a new estimation method by actively de-noising the observed data. By conducting a broad range of experiments, we demonstrate that our proposed approach provides a much closer approximation to the actual data uncertainty than the standard method.
Collisional flavor pendula and neutrino quantum thermodynamics

Neutrinos in core-collapse supernovae and neutron-star mergers are susceptible to flavor instabilities of three kinds: slow, fast, and collisional. Prior work has established mappings of the first two onto abstract mechanical systems in flavor space, respectively named the slow and fast flavor pendula. Here we introduce and analyze the flavor pendulum associated with the third class. We explain our results in terms of the recently developed theory of neutrino quantum thermodynamics. Perhaps our most surprising finding is that there exists a limit in which decoherent interactions drive perfectly coherent flavor conversion.
Finiteness properties of generalized Thompson groups via expansion sets

We outline a general procedure that builds classifying spaces for generalized Thompson groups $\Gamma$. The construction depends on a small number of choices: (1) an inverse semigroup $S$ of partial transformations that ``locally determine"$\Gamma$; (2) an equivalence relation on certain pairs $(f,D)$, and (3) an ``expansion"rule $\mathcal{E}$. These choices determine an \emph{expansion set} $\mathcal{B}$, which is a combinatorial device that outputs a simplicial complex $\Delta^{f}_{\mathcal{B}}$ upon which $\Gamma$ acts. Under favorable conditions, often achieved in practice, $\Delta^{f}_{\mathcal{B}}$ is contractible, and the action of $\Gamma$ has small stabilizers. The definition of $\Delta^{f}_{\mathcal{B}}$ is such that ascending and descending links in $\Delta^{f}_{\mathcal{B}}$ can be described via formulas that depend only on the expansion rule $\mathcal{E}$. The result is to facilitate the usual computations of the connectivity of the descending link. Under natural hypotheses, one can prove that the acting group has type $F_{\infty}$. The net effect of our results is to automate results of this kind. Several applications are given; in particular, we sketch unified proofs that $V$, $nV$, R\"{o}ver's group $G$, and the Lodha-Moore group have type $F_{\infty}$.
Two-relaxation-time regularized lattice Boltzmann model for Navier-Stokes equations

In this paper, we propose a novel two-relaxation-time regularized lattice Boltzmann (TRT-RLB) model for simulating weakly compressible isothermal flows. A free relaxation parameter, $\tau_{s,2}$, is employed to relax the regularized non-equilibrium third-order terms. Chapman-Enskog analysis reveals that our model can accurately recover the Navier-Stokes equations (NSEs). Theoretical analysis of the Poiseuille flow problem demonstrates that the slip velocity magnitude in the proposed model is controlled by a magic parameter, which can be entirely eliminated under specific values, consistent with the classical TRT model. Our simulations of the double shear layer problem, Taylor-Green vortex flow, and force-driven Poiseuille flow confirm that the stability and accuracy of our model significantly surpass those of both the regularized lattice Boltzmann (RLB) and two-relaxation-time (TRT) models, even under super-high Reynolds numbers as $Re=10^7$. Concurrently, the TRT-RLB model exhibits superior performance in very high viscosity scenarios. The simulations of creeping flow around a square cylinder demonstrates the model's capability to accurately compute ultra-low Reynolds numbers as $Re=10^{-7}$. This study establishes the TRT-RLB model as a flexible and robust tool in computational fluid dynamics.
Long‐term decrease in phosphate concentrations in the surface layer of the southern Japan Sea

To identify possible causes for the long-term trends in nutrient concentrations in the southern Japan Sea (JS), we studied nutrient concentrations that were obtained by the Japan Meteorological Agency. Our evaluation shows that phosphate concentrations declined in the surface layers in summer (0–20 m and 21–50 m depth) and winter (0–20 m, 21–50 m, and 51–100 m depth) over the last 40 years, while no significant linear trend was observed for nitrate concentrations. The declining trend in the phosphate concentration was quantified as 1.8–3.3 nM year−1. The increase in atmospheric nutrient deposition to the JS could not explain the decline in phosphate concentration. In addition, the mixed layer depth during winter did not demonstrate any significant trend, and an increase in phosphate concentrations was not observed in any layers; therefore, the decrease in nutrient supply from deep JS water was not considered a major possible cause for the decline in the phosphate concentration. In contrast, the phosphate concentration in the surface of the southern JS during winter showed a significant positive correlation with the concentration in the 21–50 m depth layer of the saline East China Sea (ECS) water in the preceding summer, and the surface water of the southern JS was almost entirely replaced by water originating from the ECS during May–October. Therefore, it is concluded that the declining trend in the phosphate concentrations in the southern JS is caused by horizontal advection of ECS water. This article is protected by copyright. All rights reserved.
Identification of Key Genes and Related Drugs of Adrenocortical Carcinoma by Integrated Bioinformatics Analysis

Abstract Adrenocortical carcinoma (ACC) is a malignant carcinoma with an extremely poor prognosis, and its pathogenesis remains to be understood to date, necessitating further investigation. This study aims to discover biomarkers and potential therapeutic agents for ACC through bioinformatics, enhancing clinical diagnosis and treatment strategies. Differentially expressed genes (DEGs) between ACC and normal adrenal cortex were screened out from the GSE19750 and GSE90713 datasets available in the GEO database. An online Venn diagram tool was utilized to identify the common DEGs between the two datasets. The identified DEGs were subjected to functional assessment, pathway enrichment, and identification of hub genes by performing the protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The differences in the expressions of hub genes between ACC and normal adrenal cortex were validated at the GEPIA2 website, and the association of these genes with the overall patient survival was also assessed. Finally, on the QuartataWeb website, drugs related to the identified hub genes were determined. A total of 114 DEGs, 10 hub genes, and 69 known drugs that could interact with these genes were identified. The GO and KEGG analyses revealed a close association of the identified DEGs with cellular signal transduction. The 10 hub genes identified were overexpressed in ACC, in addition to being significantly associated with adverse prognosis in ACC. Three genes and the associated known drugs were identified as potential targets for ACC treatment.
Impact of Reanalysis of Nitrogen Multiple-Breath Washout on its Relationship with Chest Magnetic Resonance Imaging Findings in Clinically Stable and Pulmonary Exacerbated Children with Cystic Fibrosis

Abstract Rationale Multiple-breath washout (MBW)-derived lung clearance index (LCI) detects lung disease in children with cystic fibrosis (CF). Correction of a cross-talk error in the software of the MBW device Exhalyzer D in a new software version has generated significant interest regarding its impact on previous MBW findings. Since LCI and chest magnetic resonance imaging (MRI) correlated before in CF children, this study aims to reassess previous MBW data after correction. Patients/Methods Reanalysis of the main findings from a previously published study comparing MBW and MRI in a pediatric CF cohort by reassessment of nitrogen (N 2 ) MBW of 61 stable children with CF, 75 age-matched healthy controls (HC), and 15 CF children with pulmonary exacerbation (PEx) in the corrected software version. Results The corrected LCI (N 2 LCI cor ) decreased in the entire cohort (−17.0 (11.2)%), HC (−8.5 (8.2)%), stable CF children (−22.2 (11.1)%), and within the PEx group at baseline, at PEx and after antibiotic therapy (−21.5 (7.3)%; −22.5 (6.1)%; −21.4 (6.6)%; all P<0.01). N 2 LCI cor and N 2 LCI pre correlated with chest MRI scores in stable CF (r=0.70 to 0.84; all P<0.01) without a significant difference between N 2 LCI cor and N 2 LCI pre . Change in LCI from baseline to PEx and from PEx to after therapy decreased from N 2 LCI pre to N 2 LCI cor , but these changes remained significant (all P=0.001). Discussion/Conclusions Our results indicate that N 2 LCI cor is significantly lower than N 2 LCI pre , but key results published in the original study demonstrating N 2 MBW and MRI as complementary methods for clinical surveillance in children with CF remain unaffected.
Circulating Cytokines and Venous Thromboembolism: A Bidirectional Two-Sample Mendelian Randomization Study

BACKGROUND Epidemiological evidence has linked circulating cytokines to venous thromboembolism (VTE). However, it remains uncertain whether these associations are causal due to confounding factors or reverse causality. We aim to explore the causality between circulating cytokines and VTE, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). METHODS In the current bidirectional Mendelian randomization (MR) study, instrumental variables of 41 circulating cytokines were obtained from the genome-wide association study meta-analyses (8,293 individuals). Summary statistics for the association of VTE (17,048 cases and 325,451 controls), DVT (8,077 cases and 295,014 controls), and PE (8,170 cases and 333,487 controls) were extracted from the FinnGen Study. A multivariable MR study was conducted to adjust for potential confounders. The inverse-variance weighted method was employed as the main analysis, and comprehensive sensitivity analyses were conducted in the supplementary analyses. RESULTS The MR analysis indicated stromal cell-derived factor-1α was suggestively associated with a reduced risk of VTE (odds ratio [OR]: 0.90; 95% confidence interval [CI]: 0.81-0.99; p = 0.033) and DVT (OR: 0.85; 95% CI: 0.75-0.97; p = 0.015). In addition, suggestive association of granulocyte colony-stimulating factor with PE (OR: 1.20; 95% CI: 1.06-1.37; p = 0.005) was observed. Multivariable MR analysis showed that the effect of cytokines on VTE was partly mediated through hemoglobin A1c and systolic blood pressure. Reverse MR analysis revealed that VTE was linked to decreased levels of several cytokines. CONCLUSION We provide suggestive genetic evidence supporting the bidirectional causal effect between circulating cytokines and VTE, highlighting the importance of targeting circulating cytokines to reduce the incidence of VTE.
Investment in Energy Efficiency , Adoption of Renewable Energy and Household Behaviour : Evidence from OECD countries February

There exist some possible synergies between energy efficiency measures and renewable energy adoption in the sense that the former reduces the energy demand so that the latter can begin to cut future GHG emissions with a high potential in residential sector. In this residential sector, many works have been done either on demand for clean energy or on investment in energy efficiency, but to our knowledge there is no specific study that investigates the interaction between the two decisions. This paper fills this gap in literature and first theoretically shows that there exist interactions between the two decisions depending on a threshold on the pro-environmental index of the consumer. Second, the paper empirically shows that the two decisions are positively interrelated and cannot be estimated independently. As a result, univariate methods that estimate separately the two decisions of renewable energy adoption and investment in energy efficiency potentially produce biased results because it may exist unobserved characteristics that determine both decisions. Third, the paper investigates characteristics of the household that significantly affect the interaction between the two decisions by using generalized ordered logit model. More precisely, the paper provides evidence on factors that affect the joint probability of adopting renewable energy and investing in energy efficiency and the probability of doing nothing. This contribution can serve to define incentive policies to boost the energy transition.
Long-term evaluation of faecal calprotectin levels in a European cohort of children with cystic fibrosis

Objective Intestinal inflammation with contradictory data on faecal calprotectin (fCP) levels is documented in patients with cystic fibrosis (CF). The aim of this study was to longitudinally evaluate fCP in a cohort of children with CF and their relationship with clinical variables. Design Prospective observational study to assess evolution of fCP levels, primary aimed at improving fat absorption. Along 1.5 years of follow-up (November 2016–May 2018) with four study visits pertaining to a pilot study (two of four) and to a clinical trial (two of four), the study outcomes were measured. Setting Six European CF centres in the context of MyCyFAPP Project. Subjects Children with CF and pancreatic insufficiency (2–18 years old). Main outcome measurements fCP levels, pulmonary function (percentage of forced expiratory volume in 1 s (FEV1%)) and coefficient of fat absorption (CFA). Additionally, in the last two visits, gastrointestinal (GI) symptoms were evaluated through the PedsQL-GI Questionnaire. Linear mixed regression models were applied to assess association between fCP and FEV1, CFA and GI symptoms. Results Twenty-nine children with CF and pancreatic insufficiency were included. fCP levels were inversely associated with total modified specific PedsQL-GI score (p=0.04) and positively associated with diarrhoea (p=0.03), but not with CFA. Along the four study visits, fCP significantly increased (from 62 to 256 µg/g) and pulmonary function decreased (from 97% to 87%), with a significant inverse association between the two study outcomes (p<0.001). Conclusions In children with CF, fCP levels are inversely associated with pulmonary function and thus the specificity of fCP as a marker of intestinal inflammation in paediatric patients with CF warrants further investigation.