Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Onset of Robust Overfitting in Adversarial Training

Adversarial Training (AT) is a widely-used algorithm for building robust neural networks, but it suffers from the issue of robust overfitting, the fundamental mechanism of which remains unclear. In this work, we consider normal data and adversarial perturbation as separate factors, and identify that the underlying causes of robust overfitting stem from the normal data through factor ablation in AT. Furthermore, we explain the onset of robust overfitting as a result of the model learning features that lack robust generalization, which we refer to as non-effective features. Specifically, we provide a detailed analysis of the generation of non-effective features and how they lead to robust overfitting. Additionally, we explain various empirical behaviors observed in robust overfitting and revisit different techniques to mitigate robust overfitting from the perspective of non-effective features, providing a comprehensive understanding of the robust overfitting phenomenon. This understanding inspires us to propose two measures, attack strength and data augmentation, to hinder the learning of non-effective features by the neural network, thereby alleviating robust overfitting. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed methods in mitigating robust overfitting and enhancing adversarial robustness.
Text-image Alignment for Diffusion-based Perception

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current SOTA in diffusion-based semantic segmentation on ADE20K and the current overall SOTA in depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting; we use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: https://www.vision. caltech.edu/tadp/
Prospects for Taiji to detect a gravitational-wave background from cosmic strings

Recently, multiple pulsar timing array collaborations have presented compelling evidence for a stochastic signal at nanohertz frequencies, potentially originating from cosmic strings. Cosmic strings are linear topological defects that can arise during phase transitions in the early Universe or as fundamental strings in superstring theory. This paper focuses on investigating the detection capabilities of Taiji, a planned space-based gravitational wave detector, for the gravitational wave background generated by cosmic strings. By analyzing simulated Taiji data and utilizing comprehensive Bayesian parameter estimation techniques, we demonstrate a significant improvement in precision compared to the NANOGrav 15-year data set, surpassing it by an order of magnitude. This highlights the enhanced measurement capabilities of Taiji. Consequently, Taiji can serve as a valuable complementary tool to pulsar timing arrays in validating and exploring the physics of cosmic strings in the early Universe.
Neuroadaptation in Physical Human-Robot Collaboration

Robots for physical Human-Robot Collaboration (pHRC) systems need to change their behavior and how they operate in consideration of several factors, such as the performance and intention of a human co-worker and the capabilities of different human-co-workers in collision avoidance and singularity of the robot operation. As the system's admittance becomes variable throughout the workspace, a potential solution is to tune the interaction forces and control the parameters based on the operator's requirements. To overcome this issue, we have demonstrated a novel closed-loop-neuroadaptive framework for pHRC. We have applied cognitive conflict information in a closed-loop manner, with the help of reinforcement learning, to adapt to robot strategy and compare this with open-loop settings. The experiment results show that the closed-loop-based neuroadaptive framework successfully reduces the level of cognitive conflict during pHRC, consequently increasing the smoothness and intuitiveness of human-robot collaboration. These results suggest the feasibility of a neuroadaptive approach for future pHRC control systems through electroencephalogram (EEG) signals.
Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method

While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still under-explored in the misinformation domain. In this paper, we examine LLMs with in-context learning (ICL) for news claim verification, and find that only with 4-shot demonstration examples, the performance of several prompting methods can be comparable with previous supervised models. To further boost performance, we introduce a Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to separate a claim into several subclaims and then verify each of them via multiple questions-answering steps progressively. Experiment results on two public misinformation datasets show that HiSS prompting outperforms state-of-the-art fully-supervised approach and strong few-shot ICL-enabled baselines.
Thompson Exploration with Best Challenger Rule in Best Arm Identification

This paper studies the fixed-confidence best arm identification (BAI) problem in the bandit framework in the canonical single-parameter exponential models. For this problem, many policies have been proposed, but most of them require solving an optimization problem at every round and/or are forced to explore an arm at least a certain number of times except those restricted to the Gaussian model. To address these limitations, we propose a novel policy that combines Thompson sampling with a computationally efficient approach known as the best challenger rule. While Thompson sampling was originally considered for maximizing the cumulative reward, we demonstrate that it can be used to naturally explore arms in BAI without forcing it. We show that our policy is asymptotically optimal for any two-armed bandit problems and achieves near optimality for general $K$-armed bandit problems for $K\geq 3$. Nevertheless, in numerical experiments, our policy shows competitive performance compared to asymptotically optimal policies in terms of sample complexity while requiring less computation cost. In addition, we highlight the advantages of our policy by comparing it to the concept of $\beta$-optimality, a relaxed notion of asymptotic optimality commonly considered in the analysis of a class of policies including the proposed one.
Federated Learning with Differential Privacy for End-to-End Speech Recognition

While federated learning (FL) has recently emerged as a promising approach to train machine learning models, it is limited to only preliminary explorations in the domain of automatic speech recognition (ASR). Moreover, FL does not inherently guarantee user privacy and requires the use of differential privacy (DP) for robust privacy guarantees. However, we are not aware of prior work on applying DP to FL for ASR. In this paper, we aim to bridge this research gap by formulating an ASR benchmark for FL with DP and establishing the first baselines. First, we extend the existing research on FL for ASR by exploring different aspects of recent $\textit{large end-to-end transformer models}$: architecture design, seed models, data heterogeneity, domain shift, and impact of cohort size. With a $\textit{practical}$ number of central aggregations we are able to train $\textbf{FL models}$ that are \textbf{nearly optimal} even with heterogeneous data, a seed model from another domain, or no pre-trained seed model. Second, we apply DP to FL for ASR, which is non-trivial since DP noise severely affects model training, especially for large transformer models, due to highly imbalanced gradients in the attention block. We counteract the adverse effect of DP noise by reviving per-layer clipping and explaining why its effect is more apparent in our case than in the prior work. Remarkably, we achieve user-level ($7.2$, $10^{-9}$)-$\textbf{DP}$ (resp. ($4.5$, $10^{-9}$)-$\textbf{DP}$) with a 1.3% (resp. 4.6%) absolute drop in the word error rate for extrapolation to high (resp. low) population scale for $\textbf{FL with DP in ASR}$.
Hierarchical Adaptation with Hypernetworks for Few-shot Molecular Property Prediction

Molecular property prediction (MPP) is important in biomedical applications, which naturally suffers from a lack of labels, thus forming a few-shot learning problem. State-of-the-art approaches are usually based on gradient-based meta learning strategy, which ignore difference in model parameter and molecule's learning difficulty. To address above problems, we propose a novel hierarchical adaptation mechanism for few-shot MPP (HiMPP). The model follows a encoder-predictor framework. First, to make molecular representation property-adaptive, we selectively adapt encoder's parameter by designing a hypernetwork to modulate node embeddings during message propagation. Next, we make molecule-level adaptation by design another hypernetwork, which assigns larger propagating steps for harder molecules in predictor. In this way, molecular representation is transformed by HiMPP hierarchically from property-level to molecular level. Extensive results show that HiMPP obtains the state-of-the-art performance in few-shot MPP problems, and our proposed hierarchical adaptation mechanism is rational and effective.
Kinematically constrained vortex dynamics in charge density waves

We build a minimal model of dissipative vortex dynamics in two spatial dimensions, subject to a kinematic constraint: dipole conservation. The additional conservation law implies anomalously slow decay rates for vortices. We argue that this model of vortex dynamics is relevant for a broad range of time scales during a quench into a uniaxial charge density wave state. Our predictions are consistent with recent experiments on uniaxial charge density wave formation in $\mathrm{LaTe}_3$.
Pb$_9$Cu(PO$_4$)$_6$O is a charge-transfer semiconductor

By means of density functional theory and constrained random phase approximation we analyze the bandstructure of Pb$_9$Cu(PO$_4$)$_6$O (named LK-99). Our data show that the lead-phosphate apatite LK-99 in the proposed Cu-doped structure is a semiconductor with predominant charge-transfer nature, a result incompatible with a superconducting behaviour. In order to understand the interesting electronic and magnetic properties of this compound, it will be necessary to study the actual response to doping, the possibility of alternative structural and stoichiometric (dis)orders and to clarify the magnetic interactions as well as their impact on the electronic structure.
Predicting Swarm Equatorial Plasma Bubbles Via Supervised Machine Learning

The prediction has an accuracy of 88% and performs well across the EPB speciﬁc spatiotemporal scales. This proves that the XGBoost method is able to successfully capture the climatological and daily variability of SWARM EPBs. Capturing the daily variance has long evaded researchers because of local and stochastic features within the ionosphere. We take advantage of Shapley values to explain the model and to gain insight into the physics of EPBs. We ﬁnd that as the solar wind speed increases the probability of an EPB decreases. We also identify a spike in EPB probability around the Earth-Sun perihelion. Both of these insights were derived directly from the XGBoost and Shapley technique.
Response of low-latitude ionospheric total electron content to the geomagnetic storm of 24 August 2005

[1] Response of low-latitude ionosphere to the geomagnetic storm of 24 August 2005 has been studied using total electron content (TEC) data obtained from the Global Positioning System (GPS) receivers. These studies were carried out using the receivers that were located (1) near the northern crest (∼15°N mag. Lat.) of the equatorial ionization anomaly around 56°E, 74°E, and 102°E longitude and (2) from the northern crest of the ionization anomaly down to the magnetic equator in the longitude belt 75°E ± 3°E. These studies have been substantiated with the ground-based magnetometer data at Tirunelveli and Alibag, an equatorial and off equatorial station, respectively. The ground-based ionosonde data at New Delhi, a low-latitude station, have also been used to substantiate the TEC observations. The storm day TEC shows two well-defined humps at all stations wherein enhancements of the order of 80%–100% have been observed. While the first of the enhancements has been attributed to the prompt penetration electric field associated with an interplanetary electric field (IEF Ey) of about 35 mV/m, the other one has been attributed to the second episode of the prompt penetration electric field (IEF Ey ∼ 20 mV/m) and abnormal equatorial plasma fountain in late evening hours, respectively. During the unsteady ring current conditions when the IMF Bz was still southward, penetration of a westward electric field has been inferred. Two peaks in foF2 have been observed whose time of occurrence coincides with those of the humps in the low-latitude TEC. Results from stations having nearly the same magnetic latitude show that the ionospheric response, in terms of GPS TEC, to the prompt penetration electric fields is longitudinally independent. Formation of the first hump in TEC is progressively delayed in time from low to near-equatorial latitudes for stations in different magnetic latitudes along nearly the same longitude. However, its time of appearance at Diego Garcia, a station magnetically conjugate to Udaipur, is the same as that at Udaipur. The results also reveal the poleward expansion of the ionization anomaly due to the storm. Disturbance dynamoelectric fields have been inferred to be responsible for the suppressed plasma fountain, resulting in suppressed TEC values and equatorward contraction of the ionization anomaly on 25 August, compared to a reference quiet day.
NS4AR: A new, focused on sampling areas sampling method in graphical recommendation Systems

The effectiveness of graphical recommender system depends on the quantity and quality of negative sampling. This paper selects some typical recommender system models, as well as some latest negative sampling strategies on the models as baseline. Based on typical graphical recommender model, we divide sample region into assigned-n areas and use AdaSim to give different weight to these areas to form positive set and negative set. Because of the volume and significance of negative items, we also proposed a subset selection model to narrow the core negative samples.
Improved methods for daily streamflow estimates at ungauged sites

In this paper, improved flow duration curve (FDC) and area ratio (AR) based methods are developed to obtain better daily streamflow estimation at ungauged sites. A regression based logarithmic interpolation method which makes no assumption on the distribution or shape of a FDC is introduced in this paper to estimate regional FDCs. The estimated FDC is combined with a spatial interpolation algorithm to obtain daily streamflow estimates. Multiple source sites based AR methods, especially the geographical distance weighted AR (GWAR) method, are introduced in an effort to maximize the use of regional information and improve the standard AR method (SAR). Performances of the proposed approaches are evaluated using a jackknife procedure. The application to 109 stations in the province of Quebec, Canada indicates that the FDC based methods outperform AR based methods in all the summary statistics including Nash, root mean squared error (RMSE), and Bias. The number of sites that show better performances using the FDC based approaches is also significantly larger than the number of sites showing better performances using AR based methods. Using geographical distance weighted multiple sources sites based approaches can improve the performance at the majority of the catchments comparing with using the single source site based approaches.
Large submarine groundwater discharge and benthic eutrophication in Bangdu Bay on volcanic Jeju Island, Korea

We estimated the submarine discharge of groundwater (SGD) and associated nutrients into the semienclosed Bangdu Bay on a volcanic island, Jeju, Korea, by analyzing 222Rn, Ra isotopes (224Ra and 226Ra), and nutrients in seawater, pore water, and coastal groundwater. The submarine inputs of groundwater into Bangdu Bay of 120–180 m3 m−2 yr−1 (on the basis of 222Rn, 224Ra, 226Ra, and Si mass balances) were much higher than those reported from typical continental margins. The nutrient fluxes from SGD were about 90%, 20%, and 80% of the total input (except from open ocean waters) for dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved inorganic silicate (DSi), respectively. These excess nutrient inputs from SGD are the major sources of “new nutrients” in this bay. On the basis of photosynthetic pigments and benthic algal distributions, we suggest that the large fluxes of excess nutrients from SGD can cause benthic eutrophication in a semienclosed bay on this highly permeable volcanic island.
Differential compaction and early rock fracturing in high‐relief carbonate platforms: numerical modelling of a Triassic case study (Esino Limestone, Central Southern Alps, Italy)

Numerical models were used to investigate the effects of differential compaction on strain development and early fracturing in an early cemented high‐relief Triassic carbonate platform prograding onto basinal sediments, whose thickness increases basinward. Results show that basinal sediment compaction induces stretching of internal platform and slope strata in prograding platforms. When sediments are early cemented, such extensional strain is accommodated by the generation of syndepositional fractures. The amount of stretching is predicted to increase from the oldest to the youngest layers, due to the thickening of the compactable basinal sequences towards the external parts of the platform. Stretching is also controlled by the characteristics of the basin: the thicker and the more compactable the basinal sediments, the larger will be the stretching. Numerical modelling has been applied to the Ladinian–Early Carnian carbonate platform of the Esino Limestone (Central Southern Alps of Italy). This case study is favourable for numerical modelling, as it is well exposed and both its internal geometry (inner platform, reef and prograding clinostratified slope deposits) and the relationship with the adjacent basin can be fully reconstructed, as the Alpine tectonic overprint is weak in the study area. Evidence for early fracturing (fractures filled by fibrous cements coeval with the platform development) is described and the location, orientation and width of the fractures measured. The fractures are mainly steeply dipping and oriented perpendicularly to the direction of progradation of the platform, mimicking local platform‐margin trends. The integration of numerical models with field data gives the opportunity to quantify the extension triggered by differential compaction and predict the possible distribution of early fractures in carbonate platforms of known geometry and thickness, whereas the interpretation of early fractures as the effects of differential compaction can be supported or rejected by the comparison with the results of ad hoc numerical modelling.
A coupled hygro-elastic 3D model for steady-state analysis of functionally graded plates and shells

Abstract This 3D coupled hygro-elastic model proposes the three-dimensional (3D) equilibrium equations associated with the 3D Fick diffusion equation for spherical shells. The primary unknowns of the problem are the displacements and the moisture content. This coupled 3D exact shell model allows to understand the effects of the moisture field in relation with the elastic field on stresses and deformations in different plates and shells. This model is specifically developed for configurations including functionally graded material (FGM) layers. Four different geometries are analyzed using an orthogonal mixed curvilinear reference system. The main advantage of this reference system for spherical shells is the degeneration of the equations to those for simpler geometries. The solving method is the exponential matrix method in the thickness direction. The closed-form solution is possible because of simply supported sides and harmonic forms for displacements and moisture content. The moisture content amplitudes are directly applied at the top and bottom outer faces through steady-state hypotheses. The final system is based on a set of coupled homogeneous second-order differential equations. The moisture field effects are evaluated for the static analysis in terms of displacement, strain, and stress components. After preliminary validations, used to better understand how to properly define the calculation of the curvature-related terms and FGM properties, four new benchmarks are proposed for several thickness ratios, geometrical data, FGM configurations, and moisture values imposed at the external surfaces. From the results, it is clear the accordance between the uncoupled hygro-elastic model and this new coupled hygro-elastic model when the 3D Fick diffusion law is employed. Both effects connected with the thickness layer and the embedded material are included in the 3D hygro-elastic analyses proposed. The 3D coupled hygro-elastic model is simpler than the uncoupled one because the 3D Fick diffusion law does not have to be separately solved.
The origin, divergence and inter-subspecies hybridization of house mice (Mus musculus)

House mice (Mus musculus) are the ancestors of laboratory mouse strains and an excellent model for evolutionary biology. The origin, divergence and inter-subspecies hybridization of the three main subspecies (Mus musculus domesticus, M. m. musculus, M. m. castaneus) were not well-resolved. Population genomic analyses with 349 samples from Eurasia confirmed the Himalayas-India-Pakistan junctional region as the origin centre. The divergence of M. m. domesticus, M. m. musculus and M. m. castaneus occurred ∼333.6, 308.3 and 134.4 thousand years ago, respectively, which located in different interglacial stages in Pleistocene. Hybridization among M. m. domesticus, M. m. musculus, and M. m. castaneus after secondary contact during spread and gene flow between the ancestral population and different subspecies produced distinct genomic mixtures in populations from different regions of Eurasia. Long-distance introgression from M. m. musculus into M. m. castaneus and from M. m. castaneus into M. m. musculus happened in southern China and northern China, respectively. And extensive genomic introgression occurred on both autosomes and the X chromosomes. Genomic introgression in hybrids from East Asia and Europe revealed that the level of genomic differentiation, recombination rate and benefits for the survival and reproduction of hybrids determined the introgression capability of distinct genomic regions in inter-subspecies hybridization of house mice. Unidirectional introgression of the Y chromosome made all wild mice in East Asia hold the musculus-type Y chromosome. Our study provided comprehensive insights into the origin, divergence and inter-subspecies’ genomic introgression of house mice.