Miracle-dz/UniFAR-FTUs huggingface dataset:
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable Convolution

The application of 3D ViTs to medical image segmentation has seen remarkable strides, somewhat overshadowing the budding advancements in Convolutional Neural Network (CNN)-based models. Large kernel depthwise convolution has emerged as a promising technique, showcasing capabilities akin to hierarchical transformers and facilitating an expansive effective receptive field (ERF) vital for dense predictions. Despite this, existing core operators, ranging from global-local attention to large kernel convolution, exhibit inherent trade-offs and limitations (e.g., global-local range trade-off, aggregating attentional features). We hypothesize that deformable convolution can be an exploratory alternative to combine all advantages from the previous operators, providing long-range dependency, adaptive spatial aggregation and computational efficiency as a foundation backbone. In this work, we introduce 3D DeformUX-Net, a pioneering volumetric CNN model that adeptly navigates the shortcomings traditionally associated with ViTs and large kernel convolution. Specifically, we revisit volumetric deformable convolution in depth-wise setting to adapt long-range dependency with computational efficiency. Inspired by the concepts of structural re-parameterization for convolution kernel weights, we further generate the deformable tri-planar offsets by adapting a parallel branch (starting from $1\times1\times1$ convolution), providing adaptive spatial aggregation across all channels. Our empirical evaluations reveal that the 3D DeformUX-Net consistently outperforms existing state-of-the-art ViTs and large kernel convolution models across four challenging public datasets, spanning various scales from organs (KiTS: 0.680 to 0.720, MSD Pancreas: 0.676 to 0.717, AMOS: 0.871 to 0.902) to vessels (e.g., MSD hepatic vessels: 0.635 to 0.671) in mean Dice.
Third order nonlinear correlation of the electromagnetic vacuum at near-infrared frequencies

In recent years, electro-optic sampling, which is based on Pockel's effect between an electromagnetic mode and a copropagating, phase-matched ultrashort probe, has been largely used for the investigation of broadband quantum states of light, especially in the mid-infrared and terahertz frequency range. The use of two mutually delayed femtosecond pulses at near-infrared frequencies allows the measurement of quantum electromagnetic radiation in different space-time points. Their correlation allows therefore direct access to the spectral content of a broadband quantum state at terahertz frequencies after Fourier transformation. In this work, we will prove experimentally and theoretically that when using strongly focused coherent ultrashort probes, the electro-optic sampling technique can be affected by the presence of a third-order nonlinear mixing of the probes' electric field at near-infrared frequencies. Moreover, we will show that these third-order nonlinear phenomena can also influence correlation measurements of the quantum electromagnetic radiation. We will prove that the four-wave mixing of the coherent probes' electric field with their own electromagnetic vacuum at near-infrared frequencies results in the generation of a higher-order nonlinear correlation term. The latter will be characterized experimentally, proving its local nature requiring the physical overlap of the two probes. The parameters regime where higher order nonlinear correlation results predominant with respect to electro-optic correlation of terahertz radiation is provided.
StreetNav: Leveraging Street Cameras to Support Precise Outdoor Navigation for Blind Pedestrians

Blind and low-vision (BLV) people rely on GPS-based systems for outdoor navigation. GPS's inaccuracy, however, causes them to veer off track, run into unexpected obstacles, and struggle to reach precise destinations. While prior work has made precise navigation possible indoors via additional hardware installations, enabling precise navigation outdoors remains a challenge. Ironically, many outdoor environments of interest such as downtown districts are already instrumented with hardware such as street cameras. In this work, we explore the idea of repurposing street cameras for outdoor navigation, and investigate the effectiveness of such an approach. Our resulting system, StreetNav, processes the cameras' video feeds using computer vision and gives BLV pedestrians real-time navigation assistance. Our user evaluations in the COSMOS testbed with eight BLV pedestrians show that StreetNav guides them more precisely than GPS, but its performance is sensitive to lighting conditions and environmental occlusions. We discuss future implications for deploying such systems at scale.
Are Graph Neural Networks Optimal Approximation Algorithms?

In this work we design graph neural network architectures that capture optimal approximation algorithms for a large class of combinatorial optimization problems, using powerful algorithmic tools from semidefinite programming (SDP). Concretely, we prove that polynomial-sized message-passing algorithms can represent the most powerful polynomial time algorithms for Max Constraint Satisfaction Problems assuming the Unique Games Conjecture. We leverage this result to construct efficient graph neural network architectures, OptGNN, that obtain high-quality approximate solutions on landmark combinatorial optimization problems such as Max-Cut, Min-Vertex-Cover, and Max-3-SAT. Our approach achieves strong empirical results across a wide range of real-world and synthetic datasets against solvers and neural baselines. Finally, we take advantage of OptGNN's ability to capture convex relaxations to design an algorithm for producing bounds on the optimal solution from the learned embeddings of OptGNN.
Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation

We propose a new semi-supervised learning design for human pose estimation that revisits the popular dual-student framework and enhances it two ways. First, we introduce a denoising scheme to generate reliable pseudo-heatmaps as targets for learning from unlabeled data. This uses multi-view augmentations and a threshold-and-refine procedure to produce a pool of pseudo-heatmaps. Second, we select the learning targets from these pseudo-heatmaps guided by the estimated cross-student uncertainty. We evaluate our proposed method on multiple evaluation setups on the COCO benchmark. Our results show that our model outperforms previous state-of-the-art semi-supervised pose estimators, especially in extreme low-data regime. For example with only 0.5K labeled images our method is capable of surpassing the best competitor by 7.22 mAP (+25% absolute improvement). We also demonstrate that our model can learn effectively from unlabeled data in the wild to further boost its generalization and performance.
Active Anomaly Detection in Confined Spaces Using Ergodic Traversal of Directed Region Graphs

We provide the first step toward developing a hierarchical control-estimation framework to actively plan robot trajectories for anomaly detection in confined spaces. The space is represented globally using a directed region graph, where a region is a landmark that needs to be visited (inspected). We devise a fast mixing Markov chain to find an ergodic route that traverses this graph so that the region visitation frequency is proportional to its anomaly detection uncertainty, while satisfying the edge directionality (region transition) constraint(s). Preliminary simulation results show fast convergence to the ergodic solution and confident estimation of the presence of anomalies in the inspected regions.
Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference

Estimation and inference in statistics pose significant challenges when data are collected adaptively. Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to exhibit asymptotic normality for single coordinate estimation and have inflated error. This issue is highlighted by a recent minimax lower bound, which shows that the error of estimating a single coordinate can be enlarged by a multiple of $\sqrt{d}$ when data are allowed to be arbitrarily adaptive, compared with the case when they are i.i. d. Our work explores this striking difference in estimation performance between utilizing i.i. d. and adaptive data. We investigate how the degree of adaptivity in data collection impacts the performance of estimating a low-dimensional parameter component in high-dimensional linear models. We identify conditions on the data collection mechanism under which the estimation error for a low-dimensional parameter component matches its counterpart in the i.i. d. setting, up to a factor that depends on the degree of adaptivity. We show that OLS or OLS on centered data can achieve this matching error. In addition, we propose a novel estimator for single coordinate inference via solving a Two-stage Adaptive Linear Estimating equation (TALE). Under a weaker form of adaptivity in data collection, we establish an asymptotic normality property of the proposed estimator.
Kinetic relaxation and nucleation of Bose stars in self-interacting wave dark matter

We revisit kinetic relaxation and soliton/Boson star nucleation in fuzzy scalar dark matter featuring short-ranged self-interactions $\mathcal{H}_{\rm int} = -\lambda|\psi|^4/2m^2$, alongside gravitational self-interactions. We map out the full curve of nucleation timescale for both repulsive ($\lambda<0$) and attractive ($\lambda>0$) short-ranged self-interaction strength, and in doing so reveal two new points. Firstly, besides the two usual terms, $\propto G^2$ and $\propto \lambda^2$, in the total relaxation rate $\Gamma_{\rm relax}$, there is an additional cross term $\propto G\lambda$ arising due to interference between gravitational and short-ranged self-interaction scattering amplitudes. This yields a critical repulsive interaction strength $\lambda_{\rm cr} \simeq - 2\pi Gm^2/v_{0}^2$, at which the relaxation rate is smallest and serves as the transition point between typical net attractive self-interaction ($\lambda \gtrsim \lambda_{\rm cr}$), and net repulsive self-interaction ($-\lambda \gtrsim -\lambda_{\rm cr}$). Secondly, while in the net attractive regime, nucleation time scale is similar to inverse relaxation time scale $\tau_{\rm nuc} \sim \Gamma^{-1}_{\rm relax}$, in the net repulsive regime nucleation occurs at a delayed time $\tau_{\rm nuc} \sim (\lambda/\lambda_{\rm cr})\Gamma^{-1}_{\rm relax}$. We confirm our analytical understanding by performing 3D field simulations with varying average mass density $\bar{\rho}$, box size $L$ and grid size $N$.
Suspected Bronchiectasis and Mortality in Adults With a History of Smoking Who Have Normal and Impaired Lung Function

BACKGROUND Bronchiectasis in adults with chronic obstructive pulmonary disease (COPD) is associated with greater mortality. However, whether suspected bronchiectasis-defined as incidental bronchiectasis on computed tomography (CT) images plus clinical manifestation-is associated with increased mortality in adults with a history of smoking with normal spirometry and preserved ratio impaired spirometry (PRISm) is unknown. OBJECTIVE To determine the association between suspected bronchiectasis and mortality in adults with normal spirometry, PRISm, and obstructive spirometry. DESIGN Prospective, observational cohort. SETTING The COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) study. PARTICIPANTS 7662 non-Hispanic Black or White adults, aged 45 to 80 years, with 10 or more pack-years of smoking history. Participants who were former and current smokers were stratified into normal spirometry (n = 3277), PRISm (n = 986), and obstructive spirometry (n = 3399). MEASUREMENTS Bronchiectasis identified by CT was ascertained using artificial intelligence-based measurements of an airway-to-artery ratio (AAR) greater than 1 (AAR >1), a measure of bronchial dilatation. The primary outcome of "suspected bronchiectasis" was defined as an AAR >1 of greater than 1% plus 2 of the following: cough, phlegm, dyspnea, and history of 2 or more exacerbations. RESULTS Among the 7662 participants (mean age, 60 years; 52% women), 1352 (17.6%) had suspected bronchiectasis. During a median follow-up of 11 years, 2095 (27.3%) died. Ten-year mortality risk was higher in participants with suspected bronchiectasis, compared with those without suspected bronchiectasis (normal spirometry: difference in mortality probability [Pr], 0.15 [95% CI, 0.09 to 0.21]; PRISm: Pr, 0.07 [CI, -0.003 to 0.15]; obstructive spirometry: Pr, 0.06 [CI, 0.03 to 0.09]). When only CT was used to identify bronchiectasis, the differences were attenuated in the normal spirometry (Pr, 0.04 [CI, -0.001 to 0.08]). LIMITATIONS Only 2 racial groups were studied. Only 1 measurement was used to define bronchiectasis on CT. Symptoms of suspected bronchiectasis were nonspecific. CONCLUSION Suspected bronchiectasis was associated with a heightened risk for mortality in adults with normal and obstructive spirometry. PRIMARY FUNDING SOURCE National Heart, Lung, and Blood Institute.
Spectral properties of land surfaces in the thermal infrared: 1. Laboratory measurements of absolute spectral emissivity signatures

A laboratory procedure to obtain absolute spectral emissivity signatures of soils in the thermal infrared is developed and discussed. Particular attention is given to correction for environmental radiation and to the estimation of the final accuracy attainable. The study covers the [3.0–5.5] μm and the [8.0–13.5] μm bands with a spectral resolution of 2.5% of λ. The method does not require any a priori assumption on the physical parameters, nor does it require any direct temperature measurement. Absolute emissivity signature eλ can be measured within ±0.6%. Bidirectional reflectivity signatures, ρbλ(0, 0), are obtained as well, against a reflectivity standard: a flame-sprayed aluminum diffusor. It is spectrally flat and has been calibrated. SiC powder, SiO2 sand, a number of soil samples, montmorillonite, and kaolinite have been measured. Good correlation is found between eλ and ρbλ(0, 0) as to the spectral information involved. An attempt to calculate the form factor Fλ(0) from the emissivity and reflectivity signatures is made. Fλ(0) is related to the bidirectional reflectivity. The accuracy, however, is poor. No spectral information can be inferred from the data. Agricultural soils have very similar signatures in the [8.0–13.5] μm band, dominated by the SiO2 content. Above 10 μm, eλ is rather flat and of the order of 0.97±0.01. Emissivity of clays is very flat and high above 8 μm. In the [3.0–5.5] μm band, emissivity ratios can be large. Characteristic spectral features can be resolved (CaCO3). Thus simultaneous use of both thermal infrared atmospheric windows would certainly improve the interpretation of remotely sensed data.
Engineering Autonomous Self-Driving Networks

Networking infrastructure, e.g. wide area networks (WAN) connecting data centers worldwide, or wireless 5G and beyond, are all witnessing unprecedented traffic demand, due to massive data-explosion in software applications involving text, image and video transfers and demand for high quality connectivity. The infrastructure itself is often limited by budget; and providing optimum performance with limited resources such as high bandwidth or low latency for user quality experience, is a challenge. Continually upgrading to expensive high-end switches and optical fibers is not a long-term feasible solution.
Transient states during the annealing of mismatched and bulged oligonucleotides

Oligonucleotide hybridization is crucial in various biological, prebiotic and nanotechnological processes, including gene regulation, non-enzymatic primer extension and DNA nanodevice assembly. Although extensive research has focused on the thermodynamics and kinetics of nucleic acid hybridization, the behavior of complex mixtures and the outcome of competition for target binding remain less well understood. In this study, we investigate the impact of mismatches and bulges in a 12bp DNA or RNA duplex on its association (kon) and dissociation (koff) kinetics. We find that such defects have relatively small effects on the association kinetics, while the dissociation kinetics vary in a position-dependent manner by up to 6 orders of magnitude. Building upon this observation, we explored a competition scenario involving multiple oligonucleotides, and observed a transient low specificity of probe hybridization to fully vs. partially complementary targets in solution. We characterize these long-lived metastable states and their evolution toward equilibrium, and show that sufficiently long-lived mis-paired duplexes can serve as substrates for prebiotically relevant chemical copying reactions. Our results suggest that transient low accuracy states may spontaneously emerge within all complex nucleic acid systems comprising a large enough number of competing strands, with potential repercussions for gene regulation in the realm of modern biology and the prebiotic preservation of genetic information.
Development of LIBRA-seq for the guinea pig model system as a tool for the evaluation of antibody responses to multivalent HIV-1 vaccines

ABSTRACT Consistent elicitation of serum antibody responses that neutralize diverse clades of HIV-1 remains a primary goal of HIV-1 vaccine research. Prior work has defined key features of soluble HIV-1 Envelope (Env) immunogen cocktails that influence the neutralization breadth and potency of multivalent vaccine-elicited antibody responses including the number of Env strains in the regimen. We designed immunization groups that consisted of different numbers of SOSIP Env strains to be used in a cocktail immunization strategy: the smallest cocktail (group 2) consisted of a set of two Env strains, which were a subset of the three Env strains that made up group 3, which, in turn, were a subset of the six Env strains that made up group 4. Serum neutralizing titers were modestly broader in guinea pigs that were immunized with a cocktail of three Envs compared to cocktails of two and six, suggesting that multivalent Env immunization could provide a benefit but may be detrimental when the cocktail size is too large. We then adapted the LIBRA-seq platform for antibody discovery to be compatible with guinea pigs, and isolated several tier 2 neutralizing monoclonal antibodies. Three antibodies isolated from two separate guinea pigs were similar in their gene usage and CDR3s, establishing evidence for a guinea pig public clonotype elicited through vaccination. Taken together, this work investigated multivalent HIV-1 Env immunization strategies and provides a novel methodology for screening guinea pig B cell receptor antigen specificity at a high-throughput level using LIBRA-seq. IMPORTANCE Multivalent vaccination with soluble Env immunogens is at the forefront of HIV-1 vaccination strategies but little is known about the influence of the number of Env strains included in vaccine cocktails. Our results suggest that adding more strains is sometimes beneficial but may be detrimental when the number of strains is too high. In addition, we adapted the LIBRA-seq platform to be compatible with guinea pig samples and isolated several tier 2 neutralizing monoclonal antibodies, some of which share V and J gene usage and >70% CDR3 identity, thus establishing the existence of public clonotypes in guinea pigs elicited through vaccination. Multivalent vaccination with soluble Env immunogens is at the forefront of HIV-1 vaccination strategies but little is known about the influence of the number of Env strains included in vaccine cocktails. Our results suggest that adding more strains is sometimes beneficial but may be detrimental when the number of strains is too high. In addition, we adapted the LIBRA-seq platform to be compatible with guinea pig samples and isolated several tier 2 neutralizing monoclonal antibodies, some of which share V and J gene usage and >70% CDR3 identity, thus establishing the existence of public clonotypes in guinea pigs elicited through vaccination.
The herpesvirus UL49.5 protein hijacks a cellular C-degron pathway to drive TAP transporter degradation

The transporter associated with antigen processing (TAP) is a key player in the MHC class I-restricted antigen presentation and an attractive target for immune evasion by viruses. Bovine herpesvirus 1 (BoHV-1) impairs TAP-dependent antigenic peptide transport through a two-pronged mechanism in which binding of the UL49.5 gene product to TAP both inhibits peptide transport and promotes its proteasomal degradation. How UL49.5 promotes TAP degradation is unknown. Here, we use high-content siRNA and genome-wide CRISPR-Cas9 screening to identify CLR2KLHDC3 as the E3 ligase responsible for UL49.5-triggered TAP disposal in human cells. We propose that the C-terminus of UL49.5 mimics a C-end rule degron that recruits the E3 to TAP and engages the CRL2 E3 in ER-associated degradation. SIGNIFICANCE Herpesviruses are masters of immune evasion. Most often, they hijack host cellular pathways to modulate the antiviral immune response. Varicellovirus UL49.5 orthologs have evolved as inhibitors of the transporter associated with antigen processing (TAP) and, this way, major modulators of the MHC class I-restricted antigen presentation. This study identifies the long-sought molecular mechanism exploited by bovine herpesvirus 1-encoded UL49.5 to trigger proteasomal degradation of TAP. Our findings demonstrate that the viral protein hijacks host cell CRL2-ubiquitin conjugation and ER-associated degradation pathways to promote TAP degradation. These findings advance the understanding of how herpesviruses can manipulate the cellular machinery.
Logistics Services and Firm Performance: Evidence from Indonesia, Malaysia and Thailand

Abstract:This paper examines whether logistics upgrading activities undertaken by firms can compensate for the poor quality of logistics infrastructure at the national level. To do so, the study makes use of the World Bank's Enterprises Surveys (ESs) of Indonesia, Malaysia and Thailand for the 2015–16 period. This is the first study to analyse the logistics upgrading efforts of firms across multiple Southeast Asian countries. The key finding is that, while Indonesia lags behind the other two ASEAN members in terms of physical infrastructure investment, cumbersome regulatory procedures are the common challenge in all three countries. The inter-country, firm-level cross-sectional econometric analysis suggests that there is ample room for firms to improve their logistics capabilities to mitigate related bottlenecks at the national level. Moreover, certain forms of subsidies or tax incentives might be needed to offset the additional costs incurred by the firms.