Miracle-dz/UniFAR-FTUs huggingface dataset:
Curriculum Meta-Learning for Few-shot Classification

We propose an adaptation of the curriculum training framework, applicable to state-of-the-art meta learning techniques for few-shot classification. Curriculum-based training popularly attempts to mimic human learning by progressively increasing the training complexity to enable incremental concept learning. As the meta-learner's goal is learning how to learn from as few samples as possible, the exact number of those samples (i.e. the size of the support set) arises as a natural proxy of a given task's difficulty. We define a simple yet novel curriculum schedule that begins with a larger support size and progressively reduces it throughout training to eventually match the desired shot-size of the test setup. This proposed method boosts the learning efficiency as well as the generalization capability. Our experiments with the MAML algorithm on two few-shot image classification tasks show significant gains with the curriculum training framework. Ablation studies corroborate the independence of our proposed method from the model architecture as well as the meta-learning hyperparameters
On Complexity of Computing Bottleneck and Lexicographic Optimal Cycles in a Homology Class

Homology features of spaces which appear in applications, for instance 3D meshes, are among the most important topological properties of these objects. Given a non-trivial cycle in a homology class, we consider the problem of computing a representative in that homology class which is optimal. We study two measures of optimality, namely, the lexicographic order of cycles (the lex-optimal cycle) and the bottleneck norm (a bottleneck-optimal cycle). We give a simple algorithm for computing the lex-optimal cycle for a 1-homology class in a closed orientable surface. In contrast to this, our main result is that, in the case of 3-manifolds of size n2 in the Euclidean 3-space, the problem of finding a bottleneck optimal cycle cannot be solved more efficiently than solving a system of linear equations with an n × n sparse matrix. From this reduction, we deduce several hardness results. Most notably, we show that for 3-manifolds given as a subset of the 3-space of size n2, persistent homology computations are at least as hard as rank computation (for sparse matrices) while ordinary homology computations can be done in O(n2 log n) time. This is the first such distinction between these two computations. Moreover, it follows that the same disparity exists between the height persistent homology computation and general sub-level set persistent homology computation for simplicial complexes in the 3-space. 2012 ACM Subject Classification Mathematics of computing → Algebraic topology; Mathematics of computing → Geometric topology; Theory of computation → Computational geometry
A Low-cost Robot with Autonomous Recharge and Navigation for Weed Control in Fields with Narrow Row Spacing

Modern herbicide application in agricultural set-tings typically relies on either large scale sprayers that dispense herbicide over crops and weeds alike or portable sprayers that require labor intensive manual operation. The former method results in overuse of herbicide and reduction in crop yield while the latter is often untenable in large scale operations. This paper presents the first fully autonomous robot for weed management for row crops capable of computer vision based navigation, weed detection, complete field coverage, and automatic recharge for under $400. The target application is autonomous inter-row weed control in crop fields, e.g. flax and canola, where the spacing between croplines is as small as one foot. The proposed robot is small enough to pass between croplines at all stages of plant growth while detecting weeds and spraying herbicide. A recharging system incorporates newly designed robotic hardware, a ramp, a robotic charging arm, and a mobile charging station. An integrated vision algorithm is employed to assist with charger alignment effectively. Combined, they enable the robot to work continuously in the field without access to electricity. In addition, a color-based contour algorithm combined with preprocessing techniques is applied for robust navigation relying on the input from the onboard monocular camera. Incorporating such compact robots into farms could help automate weed control, even during late stages of growth, and reduce herbicide use by targeting weeds with precision. The robotic platform is field-tested in the flaxseed fields of North Dakota.
Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding

With the great success of pre-trained models, the pretrain-then-fine tune paradigm has been widely adopted on downstream tasks for source code understanding. However, compared to costly training a large-scale model from scratch, how to effectively adapt pre-trained models to a new task has not been fully explored. In this paper, we propose an approach to bridge pre-trained models and code-related tasks. We exploit semantic-preserving transformation to enrich downstream data diversity, and help pre-trained models learn semantic features invariant to these semantically equivalent transformations. Further, we introduce curriculum learning to or-ganize the transformed data in an easy-to-hard manner to fine-tune existing pre-trained models. We apply our approach to a range of pre-trained models, and they significantly outperform the state-of-the-art models on tasks for source code understanding, such as algorithm classification, code clone detection, and code search. Our experiments even show that without heavy pre-training on code data, natural language pre-trained model RoBERTa fine-tuned with our lightweight approach could outperform or rival existing code pre-trained models fine-tuned on the above tasks, such as CodeBERT and GraphCodeBERT. This finding suggests that there is still much room for improvement in code pre-trained models.
Temperature-driven reorganization of electronic order in CsV$_3$Sb$_5$

We report a temperature dependent x-ray diffraction study of the electronic ordering instabilities in the kagome material CsV$_3$Sb$_5$. Our measurements between 10 K and 120 K reveal an unexpected reorganization of the three-dimensional electronic order in the bulk of CsV$_3$Sb$_5$: At 10 K, a 2x2x2 superstructure modulation due to electronic order is observed, which upon warming changes to a 2x2x4 superstructure at 60 K. The electronic order-order transition discovered here involves a change in the stacking of electronically ordered V$_3$Sb$_5$-layers and agrees perfectly with anomalies previously observed in magneto-transport measurements. This implies that the temperature dependent three-dimensional electronic order plays a decisive role for transport properties, which are related to the Berry-curvature of the V-bands. Our data also show that the bulk electronic order in CsV$_3$Sb$_5$ breaks the 6-fold rotational symmetry of the underlying $P6/mmm$ lattice structure.
Bone mineral density, kidney function and participant‐reported outcome measures in women who switch from tenofovir disoproxil emtricitabine and a nonnucleoside reverse transcriptase inhibitor to abacavir, lamivudine and dolutegravir

Tenofovir disoproxil fumarate (TDF) is associated with reduced bone mineral density (BMD). The aim of the study was to evaluate changes in BMD in women who switched from TDF, emtricitabine and a nonnucleoside reverse transcriptase inhibitor (TDF/FTC/NNRTI) to abacavir, lamivudine and dolutegravir (ABC/3TC/DTG).
Coronal Rain in Randomly Heated Arcades

Adopting the MPI-AMRVAC code, we present a 2.5-dimensional magnetohydrodynamic simulation, which includes thermal conduction and radiative cooling, to investigate the formation and evolution of the coronal rain phenomenon. We perform the simulation in initially linear force-free magnetic fields that host chromospheric, transition-region, and coronal plasma, with turbulent heating localized on their footpoints. Due to thermal instability, condensations start to occur at the loop top, and rebound shocks are generated by the siphon inflows. Condensations fragment into smaller blobs moving downwards, and as they hit the lower atmosphere, concurrent upflows are triggered. Larger clumps show us clear coronal rain showers as dark structures in synthetic EUV hot channels and as bright blobs with cool cores in the 304 Å channel, well resembling real observations. Following coronal rain dynamics for more than 10 hr, we carry out a statistical study of all coronal rain blobs to quantify their widths, lengths, areas, velocity distributions, and other properties. The coronal rain shows us continuous heating–condensation cycles, as well as cycles in EUV emissions. Compared to the previous studies adopting steady heating, the rain happens faster and in more erratic cycles. Although most blobs are falling downward, upward-moving blobs exist at basically every moment. We also track the movement of individual blobs to study their dynamics and the forces driving their movements. The blobs have a prominence-corona transition-region-like structure surrounding them, and their movements are dominated by the pressure evolution in the very dynamic loop system.
Salt loading decreases urinary excretion and increases intracellular accumulation of uromodulin in stroke-prone spontaneously hypertensive rats

Abstract Uromodulin (UMOD) is the most abundant renal protein secreted into urine by the thick ascending limb (TAL) epithelial cells of the loop of Henle. Genetic studies have demonstrated an association between UMOD risk variants and hypertension. We aimed to dissect the role of dietary salt in renal UMOD excretion in normotension and chronic hypertension. Normotensive Wistar–Kyoto rats (WKY) and stroke-prone spontaneously hypertensive rats (SHRSP) (n=8/sex/strain) were maintained on 1% NaCl for 3 weeks. A subset of salt-loaded SHRSP was treated with nifedipine. Salt-loading in SHRSP increased blood pressure (ΔSBP 35 ± 5 mmHg, P<0.0001) and kidney injury markers such as kidney injury marker-1 (KIM-1; fold change, FC 3.4; P=0.003), neutrophil gelatinase-associated lipocalin (NGAL; FC, 2.0; P=0.012) and proteinuria. After salt-loading there was a reduction in urinary UMOD excretion in WKY and SHRSP by 26 and 55% respectively, compared with baseline. Nifedipine treatment reduced blood pressure (BP) in SHRSP, however, did not prevent salt-induced reduction in urinary UMOD excretion. In all experiments, changes in urinary UMOD excretion were dissociated from kidney UMOD protein and mRNA levels. Colocalization and ex-vivo studies showed that salt-loading increased intracellular UMOD retention in both WKY and SHRSP. Our study provides novel insights into the interplay among salt, UMOD, and BP. The role of UMOD as a cardiovascular risk marker deserves mechanistic reappraisal and further investigations based on our findings.
Fast and Secure Key Generation with Channel Obfuscation in Slowly Varying Environments

Physical-layer secret key generation has emerged as a promising solution for establishing cryptographic keys by leveraging reciprocal and time-varying wireless channels. However, existing approaches suffer from low key generation rates and vulnerabilities under various attacks in slowly varying environments. We propose a new physical-layer secret key generation approach with channel obfuscation, which improves the dynamic property of channel parameters based on random filtering and random antenna scheduling. Our approach makes one party obfuscate the channel to allow the legitimate party to obtain similar dynamic channel parameters, yet prevents a third party from inferring the obfuscation information. Our approach allows more random bits to be extracted from the obfuscated channel parameters by a joint design of the K-L transform and adaptive quantization. Results from a testbed implementation show that our approach, compared to the existing ones that we evaluate, performs the best in generating high entropy bits at a fast rate and is able to resist various attacks in slowly varying environments. Specifically, our approach can achieve a significantly faster secret bit generation rate at roughly 67 bit/pkt, and the key sequences can pass the randomness tests of the NIST test suite.
Statistical implications of relaxing the homogeneous mixing assumption in time series Susceptible-Infectious-Removed models

Infectious disease epidemiologists routinely fit stochastic epidemic models to time series data to elucidate infectious disease dynamics, evaluate interventions, and forecast epidemic trajectories. To improve computational tractability, many approximate stochastic models have been proposed. In this paper, we focus on one class of such approximations — time series Susceptible-Infectious-Removed (TSIR) models. Infectious disease modeling often starts with a homogeneous mixing assumption, postulating that the rate of disease transmission is proportional to a product of the numbers of susceptible and infectious individuals. One popular way to relax this assumption proceeds by raising the number of susceptible and/or infectious individuals to some positive powers. We show that when this technique is used within the TSIR models they cannot be interpreted as approximate SIR models, which has important implications for TSIR-based statistical inference. Our simulation study shows that TSIR-based estimates of infection and mixing rates are systematically biased in the presence of non-homogeneous mixing, suggesting that caution is needed when interpreting TSIR model parameter estimates when this class of models relaxes the homogeneous mixing assumption.
Improving Intention Detection in Single-Trial Classification Through Fusion of EEG and Eye-Tracker Data

Intention decoding is an indispensable procedure in hands-free human–computer interaction (HCI). A conventional eye-tracker system using a single-model fixation duration may issue commands that ignore users' real expectations. Here, an eye-brain hybrid brain–computer interface (BCI) interaction system was introduced for intention detection through the fusion of multimodal eye-tracker and event-related potential (ERP) [a measurement derived from electroencephalography (EEG)] features. Eye-tracking and EEG data were recorded from 64 healthy participants as they performed a 40-min customized free search task of a fixed target icon among 25 icons. The corresponding fixation duration of eye tracking and ERP were extracted. Five previously-validated linear discriminant analysis (LDA)-based classifiers [including regularized LDA, stepwise LDA, Bayesian LDA, shrinkage linear discriminant analysis (SKLDA), and spatial-temporal discriminant analysis] and the widely-used convolutional neural network (CNN) method were adopted to verify the efficacy of feature fusion from both offline and pseudo-online analysis, and the optimal approach was evaluated by modulating the training set and system response duration. Our study demonstrated that the input of multimodal eye tracking and ERP features achieved a superior performance of intention detection in the single-trial classification of active search tasks. Compared with the single-model ERP feature, this new strategy also induced congruent accuracy across classifiers. Moreover, in comparison with other classification methods, we found that SKLDA exhibited a superior performance when fusing features in offline tests (ACC = 0.8783, AUC = 0.9004) and online simulations with various sample amounts and duration lengths. In summary, this study revealed a novel and effective approach for intention classification using an eye-brain hybrid BCI and further supported the real-life application of hands-free HCI in a more precise and stable manner.
Bounding Wasserstein distance with couplings

Markov chain Monte Carlo (MCMC) provides asymptotically consistent estimates of intractable posterior expectations as the number of iterations tends to infinity. However, in large data applications, MCMC can be computationally expensive per iteration. This has catalyzed interest in approximating MCMC in a manner that improves computational speed per iteration but does not produce asymptotically consistent estimates. In this article, we propose estimators based on couplings of Markov chains to assess the quality of such asymptotically biased sampling methods. The estimators give empirical upper bounds of the Wasserstein distance between the limiting distribution of the asymptotically biased sampling method and the original target distribution of interest. We establish theoretical guarantees for our upper bounds and show that our estimators can remain effective in high dimensions. We apply our quality measures to stochastic gradient MCMC, variational Bayes, and Laplace approximations for tall data and to approximate MCMC for Bayesian logistic regression in 4500 dimensions and Bayesian linear regression in 50000 dimensions.
Sizes of Lensed Lower-luminosity z = 4–8 Galaxies from the Hubble Frontier Field Program

We constrain the rest-UV size–luminosity relation for star-forming galaxies at z ∼ 4 and z ∼ 6, 7, and 8 identified behind clusters from the Hubble Frontier Fields (HFF) program. The size–luminosity relation is key to deriving accurate luminosity functions (LF) for faint galaxies. Making use of the latest lensing models and full data set for these clusters, lensing-corrected sizes and luminosities are derived for 68 z ∼ 4, 184 z ∼ 6, 93 z ∼ 7, and 53 z ∼ 8 galaxies. We show that size measurements can be reliably measured up to linear magnifications of ∼30×, where the lensing models are well calibrated. The sizes we measure span a >1 dex range, from <50 pc to ≳500 pc. Uncertainties are based on both the formal fit errors and systematic differences between the public lensing models. These uncertainties range from ∼10 pc for the smallest sources to 100 pc for the largest. Using a forward-modeling procedure to model the impact of incompleteness and magnification uncertainties, we characterize the size–luminosity relation at both z ∼ 4 and z ∼ 6–8. We find that the source sizes of star-forming galaxies at z ∼ 4 and z ∼ 6–8 scale with luminosity L as L 0.54±0.08 and L 0.40±0.04, respectively, such that lower-luminosity (≳−18 mag) galaxies are smaller than expected from extrapolating the size–luminosity relation at high luminosities (≲−18 mag). The new evidence for a steeper size–luminosity relation (3σ) adds to earlier evidence for small sizes based on the prevalence of highly magnified galaxies in high-shear regions, theoretical arguments against upturns in the LFs, and other independent determinations of the size–luminosity relation from the HFF clusters.
The Lanford-Ruelle theorem for actions of sofic groups

Let $\Gamma$ be a sofic group, $\Sigma$ be a sofic approximation sequence of $\Gamma$ and $X$ be a $\Gamma$-subshift with nonnegative sofic topological entropy with respect to $\Sigma$. Further assume that $X$ is a shift of finite type, or more generally, that $X$ satisfies the topological Markov property. We show that for any sufficiently regular potential $f \colon X \to \mathbb{R}$, any translation-invariant Borel probability measure on $X$ which maximizes the measure-theoretical sofic pressure of $f$ with respect to $\Sigma$, is a Gibbs state with respect to $f$. This extends a classical theorem of Lanford and Ruelle, as well as previous generalizations of Moulin Ollagnier, Pinchon, Tempelman and others, to the case where the group is sofic. As applications of our main result we present a criterion for uniqueness of an equilibrium measure, as well as sufficient conditions for having that the equilibrium states do not depend upon the chosen sofic approximation sequence. We also prove that for any group-shift over a sofic group, the Haar measure is the unique measure of maximal sofic entropy for every sofic approximation sequence, as long as the homoclinic group is dense. On the expository side, we present a short proof of Chung's variational principle for sofic topological pressure.
Combining Cortical Voltage Imaging and Hippocampal Electrophysiology for Investigating Global, Multi-Timescale Activity Interactions in the Brain

A new generation of optogenetic tools for analyzing neural activity has been contributing to the elucidation of classical open questions in neuroscience. Specifically, voltage imaging technologies using enhanced genetically encoded voltage indicators have been increasingly used to observe the dynamics of large circuits at the mesoscale. Here, we describe how to combine cortical wide-field voltage imaging with hippocampal electrophysiology in awake, behaving mice. Furthermore, we highlight how this method can be useful for different possible investigations, using the characterization of hippocampal–neocortical interactions as a case study.
Text2Mesh: Text-Driven Neural Stylization for Meshes

In this work, we develop intuitive controls for editing the style of 3D objects. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which conform to a target text prompt. We consider a disentangled representation of a 3D object using a fixed mesh input (content) coupled with a learned neural network, which we term a neural style field network (NSF). In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP. Text2Mesh requires neither a pre-trained generative model nor a specialized 3D mesh dataset. It can handle low-quality meshes (non-manifold, boundaries, etc.) with arbitrary genus, and does not require UV parameterization. We demonstrate the ability of our technique to synthesize a myriad of styles over a wide variety of 3D meshes. Our code and results are available in our project webpage: https://threedle.github. io/text2meshl.
Exploring the S-process History in the Galactic Disk: Cerium Abundances and Gradients in Open Clusters from the OCCAM/APOGEE Sample

The APOGEE Open Cluster Chemical Abundances and Mapping survey is used to probe the chemical evolution of the s-process element cerium in the Galactic disk. Cerium abundances were derived from measurements of Ce ii lines in the APOGEE spectra using the Brussels Automatic Code for Characterizing High Accuracy Spectra in 218 stars belonging to 42 open clusters. Our results indicate that, in general, for ages < 4 Gyr, younger open clusters have higher [Ce/Fe] and [Ce/α-element] ratios than older clusters. In addition, metallicity segregates open clusters in the [Ce/X]–age plane (where X can be H, Fe, or the α-elements O, Mg, Si, or Ca). These metallicity-dependent relations result in [Ce/Fe] and [Ce/α] ratios with ages that are not universal clocks. Radial gradients of [Ce/H] and [Ce/Fe] ratios in open clusters, binned by age, were derived for the first time, with d[Ce/H]/d R GC being negative, while d[Ce/Fe]/d R GC is positive. [Ce/H] and [Ce/Fe] gradients are approximately constant over time, with the [Ce/Fe] gradient becoming slightly steeper, changing by ∼+0.009 dex kpc−1 Gyr−1. Both the [Ce/H] and [Ce/Fe] gradients are shifted to lower values of [Ce/H] and [Ce/Fe] for older open clusters. The chemical pattern of Ce in open clusters across the Galactic disk is discussed within the context of s-process yields from asymptotic giant branch (AGB) stars, gigayear time delays in Ce enrichment of the interstellar medium, and the strong dependence of Ce nucleosynthesis on the metallicity of its AGB stellar sources.