Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasiparticle dynamics in a superconducting qubit irradiated by a localized infrared source

A known source of decoherence in superconducting qubits is the presence of broken Cooper pairs, or quasiparticles. These can be generated by high-energy radiation, either present in the environment or purposefully introduced, as in the case of some hybrid quantum devices. Here, we systematically study the properties of a transmon qubit under illumination by focused infrared radiation with various powers, durations, and spatial locations. Despite the high energy of incident photons, our observations agree well with a model of low-energy quasiparticle dynamics dominated by trapping. This technique can be used for understanding and potentially mitigating the effects of high-energy radiation on superconducting circuits with a variety of geometries and materials.
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding

Power-law scaling indicates that large-scale training with uniform sampling is prohibitively slow. Active learning methods aim to increase data efficiency by prioritizing learning on the most relevant examples. Despite their appeal, these methods have yet to be widely adopted since no one algorithm has been shown to a) generalize across models and tasks b) scale to large datasets and c) yield overall FLOP savings when accounting for the overhead of data selection. In this work we propose a method which satisfies these three properties, leveraging small, cheap proxy models to estimate"learnability"scores for datapoints, which are used to prioritize data for the training of much larger models. As a result, our models require 46% and 51% fewer training updates and up to 25% less total computation to reach the same performance as uniformly trained visual classifiers on JFT and multimodal models on ALIGN. Finally, we find our data-prioritization scheme to be complementary with recent data-curation and learning objectives, yielding a new state-of-the-art in several multimodal transfer tasks.
CorresNeRF: Image Correspondence Priors for Neural Radiance Fields

Neural Radiance Fields (NeRFs) have achieved impressive results in novel view synthesis and surface reconstruction tasks. However, their performance suffers under challenging scenarios with sparse input views. We present CorresNeRF, a novel method that leverages image correspondence priors computed by off-the-shelf methods to supervise NeRF training. We design adaptive processes for augmentation and filtering to generate dense and high-quality correspondences. The correspondences are then used to regularize NeRF training via the correspondence pixel reprojection and depth loss terms. We evaluate our methods on novel view synthesis and surface reconstruction tasks with density-based and SDF-based NeRF models on different datasets. Our method outperforms previous methods in both photometric and geometric metrics. We show that this simple yet effective technique of using correspondence priors can be applied as a plug-and-play module across different NeRF variants. The project page is at https://yxlao.github. io/corres-nerf.
Multi-granularity Causal Structure Learning

Unveiling, modeling, and comprehending the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines. Meanwhile, new knowledge emerges when discovering causal relationships from data. Existing causal learning algorithms predominantly focus on the isolated effects of variables, overlook the intricate interplay of multiple variables and their collective behavioral patterns. Furthermore, the ubiquity of high-dimensional data exacts a substantial temporal cost for causal algorithms. In this paper, we develop a novel method called MgCSL (Multi-granularity Causal Structure Learning), which first leverages sparse auto-encoder to explore coarse-graining strategies and causal abstractions from micro-variables to macro-ones. MgCSL then takes multi-granularity variables as inputs to train multilayer perceptrons and to delve the causality between variables. To enhance the efficacy on high-dimensional data, MgCSL introduces a simplified acyclicity constraint to adeptly search the directed acyclic graph among variables. Experimental results show that MgCSL outperforms competitive baselines, and finds out explainable causal connections on fMRI datasets.
Computational workflow for investigating hydrogen permeation in novel hydrogen storage materials

The United States Department of Energy (DOE) has set ambitious targets for hydrogen storage materials for onboard light-duty cars which are to be achieved by 2027. One of the major problems in solid hydrogen storage materials is the sluggish uptake/release kinetics. Much attention has been focused on understanding kinetics. Hydrogen solid-state diffusion is a rate-controlling step in the majority of metal hydrides. Here we dis-cuss computational workflow which can be used to estimate hydrogen diffusivity. A de-tailed study of hydrogen concentration, hydrogen neighbors is performed on nickel hy-dride (NiH) fcc materials to understand their effect on H diffusion. The nudged elastic band (NEB) method is used to determine hydrogen diffusion barrier with various hydro-gen concentrations in presence of hydrogen neighbors. The energy barriers for hydrogen hopping were calculated for few million different configurations with various local chemical environments. Two paths for H hopping from one octahedral site to a vacant neighbor octahedral site are identified: one path is straight, and the other is curved via tetrahedral site. The curved path shows diffusion faster than the straight path. This study demonstrates H diffusion is faster at higher hydrogen concentration, as the concomitant volume expansion lowers the energy barrier.
Using Think-Aloud Data to Understand Relations between Self-Regulation Cycle Characteristics and Student Performance in Intelligent Tutoring Systems

Numerous studies demonstrate the importance of self-regulation during learning by problem-solving. Recent work in learning analytics has largely examined students' use of SRL concerning overall learning gains. Limited research has related SRL to in-the-moment performance differences among learners. The present study investigates SRL behaviors in relationship to learners' moment-by-moment performance while working with intelligent tutoring systems for stoichiometry chemistry. We demonstrate the feasibility of labeling SRL behaviors based on AI-generated think-aloud transcripts, identifying the presence or absence of four SRL categories (processing information, planning, enacting, and realizing errors) in each utterance. Using the SRL codes, we conducted regression analyses to examine how the use of SRL in terms of presence, frequency, cyclical characteristics, and recency relate to student performance on subsequent steps in multi-step problems. A model considering students' SRL cycle characteristics outperformed a model only using in-the-moment SRL assessment. In line with theoretical predictions, students' actions during earlier, process-heavy stages of SRL cycles exhibited lower moment-by-moment correctness during problem-solving than later SRL cycle stages. We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data.
Stellar Population Properties in the Stellar Streams around SPRC047

We have investigated the properties (e.g., age, metallicity) of the stellar populations of a ringlike tidal stellar stream (or streams) around the edge-on galaxy SPRC047 (z = 0.031) using spectral energy distribution (SED) fits to integrated broadband aperture flux densities. We used visual images in six different bands and Spitzer/IRAC 3.6 μm data. We have attempted to derive best-fit stellar population parameters (metallicity, age) in three noncontiguous segments of the stream. Due to the very low surface brightness of the stream, we have performed a deconvolution with a Richardson–Lucy–type algorithm of the low spatial resolution 3.6 μm IRAC image, thereby reducing the effect of the point-spread function aliased emission from the bright edge-on central galaxy at the locations of our three stream segments. Our SED fits that used several different star formation (SF) history priors, from an exponentially decaying SF burst to continuous SF, indicate that the age–metallicity–dust degeneracy is not resolved, most likely because of inadequate wavelength coverage and low signal-to-noise ratios of the low surface brightness features. We also discuss how future deep visual–near-infrared observations, combined with absolute flux calibration uncertainties at or below the 1% level, complemented by equally well absolute flux-calibrated observations in ultraviolet and mid-infrared bands, would improve the accuracy of broadband SED fitting results for low surface brightness targets, such as stellar streams around nearby galaxies that are not resolved into stars.
Hyperresolution for Multi-step Fuzzy Inference in Goedel Logic

This paper is a continuation of our work concerning the logical and computational foundations of multi-step fuzzy inference. We bring further results on the implementation of the Mamdani-Assilian type of fuzzy rules and inference in Goedel logic with truth constants. In our previous work, we have provided translation of Mamdani-Assilian fuzzy rules to formulae of Goedel logic, and subsequently, to suitable clausal form. Moreover, we have outlined a class of problems regarding general properties of fuzzy inference and shown its reduction to a class of deduction/unsatisfiability problems. We now focus on solving such problems using an adapted hyperresolution calculus.
Dzyaloshinskii-Moriya interaction from unquenched orbital angular momentum

Orbitronics is an emerging and fascinating field that explores the utilization of the orbital degree of freedom of electrons for information processing. An increasing number of orbital phenomena are being currently discovered, with spin-orbit coupling mediating the interplay between orbital and spin effects, thus providing a wealth of control mechanisms and device applications. In this context, the orbital analog of spin Dzyaloshinskii-Moriya interaction (DMI), i.e. orbital DMI, deserves to be explored in depth, since it is believed to be capable of inducing chiral orbital structures. Here, we unveil the main features and microscopic mechanisms of the orbital DMI in a two-dimensional square lattice using a tight-binding model of t2g orbitals in combination with the Berry phase theory. This approach allows us to investigate and transparently disentangle the role of inversion symmetry breaking, strength of orbital exchange interaction and spin-orbit coupling in shaping the properties of the orbital DMI. By scrutinizing the band-resolved contributions we are able to understand the microscopic mechanisms and guiding principles behind the orbital DMI and its anisotropy in two dimensional magnetic materials, and uncover a fundamental relation between the orbital DMI and its spin counterpart, which is currently explored very intensively. The insights gained from our work contribute to advancing our knowledge of orbitalrelated effects and their potential applications in spintronics, providing a path for future research in the field of chiral orbitronics.
The equivariant genera of marked strongly invertible knots associated with $2$-bridge knots

A marked strongly invertible knot is a triple $(K,h,\delta)$ of a knot $K$ in $S^3$, a strong inversion $h$ of $K$, and a subarc $\delta \subset \operatorname{Fix}(h)\cong S^1$ bounded by $\operatorname{Fix}(h)\cap K\cong S^0$. An invariant Seifert surface for $(K,h,\delta)$ is an $h$-invariant Seifert surface for $K$ that intersects $\operatorname{Fix}(h)$ in the arc $\delta$. In this paper, we completely determine the equivariant genus (the minimum of the genera of invariant Seifert surfaces for $(K,h,\delta)$) of every marked strongly invertible knot $(K,h,\delta)$ with $K$ a $2$-bridge knot.
Thermoelectric properties of armchair graphene nanoribbons with array characteristics

The thermoelectric properties of armchair graphene nanoribbons (AGNRs) with array characteristics are investigated theoretically using the tight-binding model and Green's function technique. The AGNR structures with array characteristics are created by embedding a narrow boron nitride nanoribbon (BNNR) into a wider AGNR, resulting in two narrow AGNRs. This system is denoted as w-AGNR/n-BNNR, where ‘w’ and ‘n’ represent the widths of the wider AGNR and narrow BNNR, respectively. We elucidate the coupling effect between two narrow symmetrical AGNRs on the electronic structure of w-AGNR/i-BNNR. A notable discovery is that the power factor of the 15-AGNR/5-BNNR with the minimum width surpasses the quantum limitation of power factor for 1D ideal systems. The energy level degeneracy observed in the first subbands of w-AGNR/n-BNNR structures proves to be highly advantageous in enhancing the electrical power outputs of graphene nanoribbon devices.
Refinements of Strichartz estimates on tori and applications

We show trilinear Strichartz estimates in one and two dimensions on frequency-dependent time intervals. These improve on the corresponding linear estimates of periodic solutions to the Schr\"odinger equation. The proof combines decoupling iterations with bilinear short-time Strichartz estimates. Secondly, we use decoupling to show new linear Strichartz estimates on frequency dependent time intervals. We apply these in case of the Airy propagator to obtain the sharp Sobolev regularity for the existence of solutions to the modified Korteweg-de Vries equation.
Artificial Intelligence in the automatic coding of interviews on Landscape Quality Objectives. Comparison and case study

In this study, we conducted a comparative analysis of the automated coding provided by three Artificial Intelligence functionalities (At-las.ti, ChatGPT and Google Bard) in relation to the manual coding of 12 research interviews focused on Landscape Quality Objectives for a small island in the north of Cuba (Cayo Santa Mar\'ia). For this purpose, the following comparison criteria were established: Accuracy, Comprehensiveness, Thematic Coherence, Redundancy, Clarity, Detail and Regularity. The analysis showed the usefulness of AI for the intended purpose, albeit with numerous flaws and shortcomings. In summary, today the automatic coding of AIs can be considered useful as a guide towards a subsequent in-depth and meticulous analysis of the information by the researcher. However, as this is such a recently developed field, rapid evolution is expected to bring the necessary improvements to these tools.
Analysis on Mechanism of Soil and Water Conservation Farming Measures

It conducts artificial rainfall tests on the soil and water conservation of contour farming, manual excavation, hoeing and ploughing in Nihegou Watershed of Chunhua County in the gullied loess plateaus by taking straight-line slopes of conventional farming as check. The outcomes show that all measures of cultivation can increase the stable infiltration rate of soil, extend contributing time and reduce runoff velocity and runoff yield of slope surface and erosion modulus, of which, the function of contour farming is the most remarkable.
Sim-GPT: Text Similarity via GPT Annotated Data

Due to the lack of a large collection of high-quality labeled sentence pairs with textual similarity scores, existing approaches for Semantic Textual Similarity (STS) mostly rely on unsupervised techniques or training signals that are only partially correlated with textual similarity, e.g., NLI-based datasets. To tackle this issue, in this paper, we propose the strategy of measuring text similarity via GPT annotated data (Sim-GPT for short). The core idea of Sim-GPT is to generate data with STS labels using GPT-4, based on which an STS model is trained. Sim-GPT framework utilizes LLMs to provide a substantial amount of reliable annotated data filling the gap of the lack of training signals for STS. Sim-GPT is trained on a one-time generated dataset using BERT or RoBERTa as the backbone, which offers long-term savings in cost and speed compared to repeatedly invoking LLMs for each sentence pair. Trained on the examples from GPT-4 (371K), Sim-GPT yields SOTA performances on the widely-used seven STS benchmarks: +0.99 over supervised-SimCSE, and +0.42 over the current SOTA PromCSE model. To encourage further advancements of the field, we release both models and the 371K annotated examples from GPT-4. Code, models and annotated data are available at: https://github.com/ShuheWang1998/Sim-GPT.
New network models facilitate analysis of biological networks

Exponential-family random graph models (ERGMs) are a family of network models originating in social network analysis, which have also been applied to biological networks. Advances in estimation algorithms have increased the practical scope of these models to larger networks, however it is still not always possible to estimate a model without encountering problems of model near-degeneracy, particularly if it is desired to use only simple model parameters, rather than more complex parameters designed to overcome the problem of near-degeneracy. Two new network models related to the ERGM, the Tapered ERGM, and the latent order logistic (LOLOG) model, have recently been proposed to overcome this problem. In this work I illustrate the application of the Tapered ERGM and the LOLOG to a set of biological networks, including protein-protein interaction (PPI) networks, gene regulatory networks, and neural networks. I find that the Tapered ERGM and the LOLOG are able to estimate models for networks for which it was not possible to estimate a conventional ERGM, and are able to do so using only simple model parameters. In the case of two neural networks where data on the spatial position of neurons is available, this allows the estimation of models including terms for spatial distance and triangle structures, allowing triangle motif statistical significance to be estimated while accounting for the effect of spatial proximity on connection probability. For some larger networks, however, Tapered ERGM and LOLOG estimation was not possible in practical time, while conventional ERGM models were able to be estimated only by using the Equilibrium Expectation (EE) algorithm.
Complementing 16S rRNA gene amplicon sequencing with estimates of total bacterial load to infer absolute species concentrations in the vaginal microbiome

Whereas 16S rRNA gene amplicon sequencing quantifies relative abundances of bacterial taxa, variation in total bacterial load between samples restricts its ability to reflect absolute concentration of individual species. Quantitative PCR (qPCR) can quantify individual species, but it is not practical to develop a suite of qPCR assays for every bacterium present in a diverse sample. We analyzed 1320 samples from 20 women with a history of frequent bacterial vaginosis, who self-collected vaginal swabs daily over 60 days. We inferred bacterial concentrations by taking the product of species relative abundance (assessed by 16S rRNA gene amplicon sequencing) and total bacterial load (measured by broad-range 16S rRNA gene qPCR). Log10-converted inferred concentrations correlated with targeted qPCR (r = 0. 935, p<2.2e-16) for seven key bacterial species. The mean inferred concentration error varied across bacteria, with rarer bacterial vaginosis-associated bacteria associated with larger errors. 92% of errors >0.5 log10 occurred when relative abundance was <10%. Many errors occurred during early bacterial expansion or late contraction. When relative abundance of a species is >10%, inferred concentrations are reliable proxies for targeted qPCR. However, targeted qPCR is required to capture bacteria at low relative abundance, particularly with BV-associated bacteria during the early onset of bacterial vaginosis.
The Counterattack of CNNs in Self-Supervised Learning: Larger Kernel Size might be All You Need

Vision Transformers have been rapidly uprising in computer vision thanks to their outstanding scaling trends, and gradually replacing convolutional neural networks (CNNs). Recent works on self-supervised learning (SSL) introduce siamese pre-training tasks, on which Transformer backbones continue to demonstrate ever stronger results than CNNs. People come to believe that Transformers or self-attention modules are inherently more suitable than CNNs in the context of SSL. However, it is noteworthy that most if not all prior arts of SSL with CNNs chose the standard ResNets as their backbones, whose architecture effectiveness is known to already lag behind advanced Vision Transformers. Therefore, it remains unclear whether the self-attention operation is crucial for the recent advances in SSL - or CNNs can deliver the same excellence with more advanced designs, too? Can we close the SSL performance gap between Transformers and CNNs? To answer these intriguing questions, we apply self-supervised pre-training to the recently proposed, stronger lager-kernel CNN architecture and conduct an apple-to-apple comparison with Transformers, in their SSL performance. Our results show that we are able to build pure CNN SSL architectures that perform on par with or better than the best SSL-trained Transformers, by just scaling up convolutional kernel sizes besides other small tweaks. Impressively, when transferring to the downstream tasks \texttt{MS COCO} detection and segmentation, our SSL pre-trained CNN model (trained in 100 epochs) achieves the same good performance as the 300-epoch pre-trained Transformer counterpart. We hope this work can help to better understand what is essential (or not) for self-supervised learning backbones.
Extended Kohler's rule of magnetoresistance in 
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TaCo$_2$Te$_2$ is recently reported to be an air-stable, high mobility Van der Waals material with probable magnetic order. Here we investigate the scaling behavior of its magnetoresistance. We measured both the longitudinal ($\rho_{xx}$) and Hall ($\rho_{xy}$) magnetoresistivities of TaCo$_2$Te$_2$ crystals in magnetic fields parallel to the c-axis and found that the magnetoresistance violates the Kohler's rule $MR \sim f[H/\rho_0]$ while obeying the extended Kohler's rule $MR \sim f[H/(n_T\rho_0)]$, where $MR \sim [\rho_{xx}(H)-\rho_0]/\rho_0$, $H$ is the magnetic field, $n_T$ is a thermal factor, $\rho_{xx}(H)$ and $\rho_0$ are the resistivities at $H$ and zero field, respectively. While deviating from those of the densities of electrons ($n_e$) and holes ($n_h$) obtained from the two-band model analysis of the magnetoconductivities, the temperature dependence of $n_T$ is close to that of the Hall carrier densities $n_H$ calculated from the slopes of $\rho_{xy}(H)$ curves at low magnetic fields, providing a new way to obtain the thermal factor in the extended Kohler's rule.