Miracle-dz/UniFAR-FTUs huggingface dataset:
Extended analysis of the effects of the Sumatra topography on downstream low-level vortex development over the Indian Ocean

Fine et al. (2016, hereafter F16) investigated the potential role of Sumatra Island, as well as the Malay Peninsula and Java, in creating terrain-induced circulations over the Indian Ocean (IO) that subsequently develop into tropical cyclones (TCs). Applying sophisticated vortex tracking software to 2.5 yrs of model analyses, F16 found four regions downstream of topographic features in the Maritime Continent to be prolific generators of low-level cyclonic vortices (123/yr). This present study extends the limited analyses of F16 by applying a similar approach to 10 yrs (2008-2017) of ERA5 analyses. While the 2.5-yr period which F16 studied was slightly (8%) more active in terms of vortex production than the 10-yr period, in general the findings of F16 are representative of the longer-term record with 80% of all shed vortices occurring with easterly low-level (925 hPa) flow over the hotspot regions. Additional analysis of the 10-yr record found that vortex counts are highest near MJO phase 1 when low-level easterlies are strongest over the maritime continent region. A secondary peak in vortex counts occurs during MJO phase 4 when low-level westerlies were present near the equator west of Sumatra. This suggests that low-level westerly surges on the equator impinging on Sumatra associated with the MJO contribute to an increase in wake vortex development. While the frequency of vortex genesis over the four hotspot regions is strongly tied to the annual cycle of winds, periods with anomalous zonal flow are shown to impact vortex counts. This is most apparent in the region off the southern tip of Sumatra where vortex counts were 4 times higher during periods of anomalous easterlies compared to periods with anomalous westerlies. In this region positive (negative) ENSO and Indian-Ocean Dipole conditions drive anomalous easterlies (westerlies) which impact vortex formation rates.
Inhomogeneous superconducting state in quasi-one-dimensional systems

We report on results of theoretical study of non-uniform superconducting states in quasi-one-dimensional systems, with attractive interactions and Zeeman splitting between electron spins. Using bosonization to treat intrachain electron-electron interactions, and a combination of renormalization group and mean-field approximation to tackle interchain couplings, we obtain the phase diagram of the system, and show that the transition between the uniform and non-uniform superconducting phases is a continuous transition of the commensurate-incommensurate type.
M3ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design

Multi-task learning (MTL) encapsulates multiple learned tasks in a single model and often lets those tasks learn better jointly. However, when deploying MTL onto those real-world systems that are often resource-constrained or latency-sensitive, two prominent challenges arise: (i) during training, simultaneously optimizing all tasks is often difficult due to gradient conflicts across tasks; (ii) at inference, current MTL regimes have to activate nearly the entire model even to just execute a single task. Yet most real systems demand only one or two tasks at each moment, and switch between tasks as needed: therefore such all tasks activated inference is also highly inefficient and non-scalable. In this paper, we present a model-accelerator co-design framework to enable efficient on-device MTL. Our framework, dubbed M$^3$ViT, customizes mixture-of-experts (MoE) layers into a vision transformer (ViT) backbone for MTL, and sparsely activates task-specific experts during training. Then at inference with any task of interest, the same design allows for activating only the task-corresponding sparse expert pathway, instead of the full model. Our new model design is further enhanced by hardware-level innovations, in particular, a novel computation reordering scheme tailored for memory-constrained MTL that achieves zero-overhead switching between tasks and can scale to any number of experts. When executing single-task inference, M$^{3}$ViT achieves higher accuracies than encoder-focused MTL methods, while significantly reducing 88% inference FLOPs. When implemented on a hardware platform of one Xilinx ZCU104 FPGA, our co-design framework reduces the memory requirement by 2.4 times, while achieving energy efficiency up to 9.23 times higher than a comparable FPGA baseline. Code is available at: https://github.com/VITA-Group/M3ViT.
Visual-Based Kinematics and Pose Estimation for Skid-Steering Robots

To build commercial robots, skid-steering mechanical design is of increased popularity due to its manufacturing simplicity and unique mechanism. However, these also cause significant challenges on software and algorithm design, especially for the pose estimation (i.e., determining the robot’s rotation and position) of skid-steering robots, since they change their orientation with an inevitable skid. To tackle this problem, we propose a probabilistic sliding-window estimator dedicated to skid-steering robots, using measurements from a monocular camera, the wheel encoders, and optionally an inertial measurement unit (IMU). Specifically, we explicitly model the kinematics of skid-steering robots by both track instantaneous centers of rotation (ICRs) and correction factors, which are capable of compensating for the complexity of track-to-terrain interaction, the imperfectness of mechanical design, terrain conditions and smoothness, etc. To prevent performance reduction in robots’ long-term missions, the time- and location- varying kinematic parameters are estimated online along with pose estimation states in a tightly-coupled manner. More importantly, we conduct in-depth observability analysis for different sensors and design configurations in this paper, which provides us with theoretical tools in making the correct choice when building real commercial robots. In our experiments, we validate the proposed method by both simulation tests and real-world experiments, which demonstrate that our method outperforms competing methods by wide margins. Note to Practitioners—This paper was motivated by the problem of long-term pose estimation of the commonly commercial-used skid-steering robots with only low-cost sensors. Skid-steering robots change their orientation with a skid, which poses a significant challenge for pose estimation when using the wheel encoders. We propose to online estimate the robot’s kinematics, which succeeds in compensating for the complexity of track-to-terrain interaction, due to the slippage, the imperfectness of mechanical design, terrain conditions and smoothness. It is critical to estimate the kinematics and poses jointly to prevent performance reduction in robots’ long-term missions. We further theoretically analyze whether the kinematics parameters can be estimated under different sensor configurations, and find out the special degrade motions that make the parameters unobservable.
A Late Multi-Modal Fusion Model for Detecting Hybrid Spam E-mail

In recent years, spammers are now trying to obfuscate their intents by introducing hybrid spam e-mail combining both image and text parts, which is more challenging to detect in comparison to e-mails containing text or image only. The motivation behind this research is to design an effective approach filtering out hybrid spam e-mails to avoid situations where traditional text-based or image-baesd only filters fail to detect hybrid spam e-mails. To the best of our knowledge, a few studies have been conducted with the goal of detecting hybrid spam e-mails. Ordinarily, Optical Character Recognition (OCR) technology is used to eliminate the image parts of spam by transforming images into text. However, the research questions are that although OCR scanning is a very successful technique in processing text-and-image hybrid spam, it is not an effective solution for dealing with huge quantities due to the CPU power required and the execution time it takes to scan e-mail files. And the OCR techniques are not always reliable in the transformation processes. To address such problems, we propose new late multi-modal fusion training frameworks for a text-and-image hybrid spam e-mail filtering system compared to the classical early fusion detection frameworks based on the OCR method. Convolutional Neural Network (CNN) and Continuous Bag of Words were implemented to extract features from image and text parts of hybrid spam respectively, whereas generated features were fed to sigmoid layer and Machine Learning based classifiers including Random Forest (RF), Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM) to determine the e-mail ham or spam.
Access Service Records Based Trust Management Scheme for Internet of Things

—The distributed structure of the Internet of things has gradually replaced the centralized structure because of its scalability, security and single point of failure. The huge scale of information recording of the Internet of things brings challenges and opportunities to the trust management of the Internet of things. Through the analysis of a variety of existing trust management schemes, this paper proposes a unified data structure for distributed access service records, TokenChain, in which triple DES data encryption is used to ensure privacy and traceability, in order to achieve a unified trust management scheme. Based on TokenChain, a three-tier trust management architecture (TokenChain-Based Trust Management, TBTM) is implemented in the data layer, computing layer and control layer. Trust evaluation is affected by four statistics and finally converges to a real value under certain conditions. Based on TBTM, we carry out theoretical analysis, malicious attack resistance, simulation and performance evaluation in a variety of complex scenarios. The results show that TBTM satisfies service prediction, global trust analysis, high security and excellent performance in multi-domain complex scenarios. Compared with the existing trust management schemes, TBTM realizes cross-scenario interaction and two-way trust management, and simultaneously meets the characteristics of traceability, tamper-proof, attack resistance and distribution. Finally, this paper is summarized.
Spherical collapse of non-top-hat profiles in the presence of dark energy with arbitrary sound speed

We study the spherical collapse of non-top-hat matter fluctuations in the presence of dark energy with arbitrary sound speed. The model is described by a system of partial differential equations solved using a pseudo-spectral method with collocation points. This method can reproduce the known analytical solutions in the linear regime with an accuracy better than 10-6% and better than 10-2% for the virialization threshold given by the usual spherical collapse model. We show the impact of nonlinear dark energy fluctuations on matter profiles, matter peculiar velocity and gravitational potential. We also show that phantom dark energy models with low sound speed can develop a pathological behaviour around matter halos, namely negative energy density. The dependence of the virialization threshold density for collapse on the dark energy sound speed is also computed, confirming and extending previous results in the limit for homogeneous and clustering dark energy.
Drinfeld's lemma for F-isocrystals, II: Tannakian approach

We prove a Tannakian form of Drinfeld's lemma for isocrystals on a variety over a finite field, equipped with actions of partial Frobenius operators. This provides an intermediate step towards transferring V. Lafforgue's work on the Langlands correspondence over function fields from $\ell$-adic to $p$-adic coefficients. We also discuss a motivic variant and a local variant of Drinfeld's lemma.
Nonlinear periodic and solitary rolling waves in falling two-layer viscous liquid films

We investigate nonlinear periodic and solitary two-dimensional rolling waves in a falling two-layer liquid film in the regime of non-zero Reynolds numbers. At any flow rate, a falling two-layer liquid film is known to be linearly unstable with respect to long-wave deformations of the liquid-air surface and liquid-liquid interface. Two different types of zero-amplitude neutrally stable waves propagate downstream without growing or shrinking: a zig-zag surface mode and a thinning varicose interface mode. Using a boundary-layer reduction of the Navier-Stokes equation, we investigate the onset, possible bifurcations and interactions of nonlinear periodic travelling waves. Periodic waves are obtained by continuation as stationary periodic solutions in the co-moving reference frame starting from small-amplitude neutrally stable waves. We find a variety of solitary waves that appear when a periodic solution approaches a homoclinic loop. Wave interactions are studied using direct numerical simulations of the boundary-layer model. We reveal that in the early stages of temporal evolution coarsening is dominated by an inelastic collision and merging of waves that travel at different speeds. Eventually, coarsening becomes arrested when the waves have reached the largest admissible amplitude. Bifurcation analysis confirms the existence of an upper bound of possible solitary wave amplitudes, thus explaining the arrest of coarsening. In the mixed regime, when both mode types are unstable, the temporal dynamics becomes highly irregular due to the competition between a faster-travelling zig-zag mode and a slower-travelling varicose mode. A quintessentially two-layer dynamical regime is found, which corresponds to a ruptured second layer. In this r
Modelling Correlation Matrices in Multivariate Dyadic Data: Latent Variable Models for Intergenerational Exchanges of Family Support

We define a model for the joint distribution of multiple continuous latent variables which includes a model for how their correlations depend on explanatory variables. This is motivated by and applied to social scientific research questions in the analysis of intergenerational help and support within families, where the correlations describe reciprocity of help between generations and complementarity of different kinds of help. We propose an MCMC procedure for estimating the model which maintains the positive definiteness of the implied correlation matrices, and describe theoretical results which justify this approach and facilitate efficient implementation of it. The model is applied to data from the UK Household Longitudinal Study to analyse exchanges of practical and financial support between adult individuals and their non-coresident parents.
CLIP-FLow: Contrastive Learning by {s}emi-supervised Iterative Pseudo {l}abeling for Optical Flow Estimation

Synthetic datasets are often used to pretrain end-to-end optical flow networks, due to the lack of a large amount of labeled, real-scene data. But major drops in accuracy occur when moving from synthetic to real scenes. How do we better transfer the knowledge learned from synthetic to real domains? To this end, we propose CLIP-FLow, a semi-supervised iterative pseudo-labeling framework to transfer the pretraining knowledge to the target real domain. We leverage large-scale, unlabeled real data to facilitate transfer learning with the supervision of iteratively updated pseudo-ground truth labels, bridging the domain gap between the synthetic and the real. In addition, we propose a contrastive flow loss on reference features and the warped features by pseudo ground truth flows, to further boost the accurate matching and dampen the mismatching due to motion, occlusion, or noisy pseudo labels. We adopt RAFT as the backbone and obtain an F1-all error of 4.11%, i.e. a 19% error reduction from RAFT (5.10%) and ranking 2$^{nd}$ place at submission on the KITTI 2015 benchmark. Our framework can also be extended to other models, e.g. CRAFT, reducing the F1-all error from 4.79% to 4.66% on KITTI 2015 benchmark.
ADR-Lite: A Low-Complexity Adaptive Data Rate Scheme for the LoRa Network

The long-range and low energy consumption re-quirements in Internet of Things (IoT) applications have led to a new wireless communication technology known as Low Power Wide Area Network (LPWANs). In recent years, the Long Range (LoRa) protocol has gained a lot of attention as one of the most promising technologies in LPWAN. Choosing the right combination of transmission parameters is a major challenge in the LoRa networks. In LoRa, an Adaptive Data Rate (ADR) mechanism is executed to configure each End Device's (ED) trans-mission parameters, resulting in improved performance metrics. In this paper, we propose a link-based ADR approach that aims to configure the transmission parameters of EDs by making a decision without taking into account the history of the last received packets, resulting in a relatively low space complexity approach. In this study, we present four different scenarios for assessing performance, including a scenario where mobile EDs are considered. Our simulation results show that in a mobile scenario with high channel noise, our proposed algorithm's Packet Delivery Ratio (PDR) is 2.8 times outperforming the original ADR and 1.35 times that of other relevant algorithms.
Two-parameter sums signatures and corresponding quasisymmetric functions

Quasisymmetric functions have recently been used in time series analysis as polynomial features that are invariant under, so-called, dynamic time warping. We extend this notion to data indexed by two parameters and thus provide warping invariants for images. We show that two-parameter quasisymmetric functions are complete in a certain sense, and provide a two-parameter quasi-shuffle identity. A compatible coproduct is based on diagonal concatenation of the input data, leading to a (weak) form of Chen’s identity.
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select

We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and modalities of the document. Our proposed method ReSel decomposes this task into a two-stage procedure that first retrieves the most relevant paragraph/table and then selects the target entity from the retrieved component. For the high-level retrieval stage, ReSel designs a simple and effective feature set, which captures multi-level lexical and semantic similarities between the query and components. For the low-level selection stage, ReSel designs a cross-modal entity correlation graph along with a multi-view architecture, which models both semantic and document-structural relations between entities. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
Converting dark matter to dark radiation does not solve cosmological tensions

Tensions between cosmological parameters (in particular the local expansion rate $H_0$ and the amplitude of matter clustering $S_8$) inferred from low-redshift data and data from the cosmic microwave background (CMB) and large-scale structure (LSS) experiments have inspired many extensions to the standard cosmological model, $\Lambda$CDM. Models which simultaneously lessen both tensions are of particular interest. We consider one scenario with the potential for such a resolution, in which some fraction of the dark matter has converted into dark radiation since the release of the CMB. Such a scenario encompasses and generalizes the more standard"decaying dark matter"model, allowing additional flexibility in the rate and time at which the dark matter converts into dark radiation. In this paper, we constrain this scenario with a focus on exploring whether it can solve (or reduce) these tensions. We find that such a model is effectively ruled out by low-$\ell$ CMB data, in particular by the reduced peak-smearing due to CMB lensing and the excess Integrated Sachs--Wolfe (ISW) signal caused by the additional dark energy density required to preserve flatness after dark matter conversion into dark radiation. Thus, such a model does not have the power to reduce these tensions without further modifications. This conclusion extends and generalizes related conclusions derived for the standard decaying dark matter model.
Adversarially Robust Medical Classification via Attentive Convolutional Neural Networks

Convolutional neural network-based medical image classifiers have been shown to be especially susceptible to adversarial examples. Such instabilities are likely to be unacceptable in the future of automated diagnoses. Though statistical adversarial example detection methods have proven to be effective defense mechanisms, additional research is necessary that investigates the fundamental vulnerabilities of deep-learning-based systems and how best to build models that jointly maximize traditional and robust accuracy. This paper presents the inclusion of attention mechanisms in CNN-based medical image classifiers as a reliable and effective strategy for increasing robust accuracy without sacrifice. This method is able to increase robust accuracy by up to 16% in typical adversarial scenarios and up to 2700% in extreme cases.
CEERS Key Paper. III. The Diversity of Galaxy Structure and Morphology at z = 3–9 with JWST

We present a comprehensive analysis of the evolution of the morphological and structural properties of a large sample of galaxies at z = 3–9 using early James Webb Space Telescope (JWST) CEERS NIRCam observations. Our sample consists of 850 galaxies at z > 3 detected in both Hubble Space Telescope (HST)/WFC3 and CEERS JWST/NIRCam images, enabling a comparison of HST and JWST morphologies. We conduct a set of visual classifications, with each galaxy in the sample classified three times. We also measure quantitative morphologies across all NIRCam filters. We find that galaxies at z > 3 have a wide diversity of morphologies. Galaxies with disks make up 60% of galaxies at z = 3, and this fraction drops to ∼30% at z = 6–9, while galaxies with spheroids make up ∼30%–40% across the redshift range, and pure spheroids with no evidence for disks or irregular features make up ∼20%. The fraction of galaxies with irregular features is roughly constant at all redshifts (∼40%–50%), while those that are purely irregular increases from ∼12% to ∼20% at z > 4.5. We note that these are apparent fractions, as many observational effects impact the visibility of morphological features at high redshift. On average, Spheroid-only galaxies have a higher Sérsic index, smaller size, and higher axis ratio than disk or irregular galaxies. Across all redshifts, smaller spheroid and disk galaxies tend to be rounder. Overall, these trends suggest that galaxies with established disks and spheroids exist across the full redshift range of this study, and further work with large samples at higher redshift is needed to quantify when these features first formed.
Drinfeld's lemma for $F$-isocrystals, I

We prove that in either the convergent or overconvergent setting, an absolutely irreducible $F$-isocrystal on the absolute product of two or more smooth schemes over perfect fields of characteristic $p$, further equipped with actions of the partial Frobenius maps, is an external product of $F$-isocrystals over the multiplicands. The corresponding statement for lisse $\bar{\mathbb{Q}}_\ell$-sheaves, for $\ell \neq p$ a prime, is a consequence of Drinfeld's lemma on the fundamental groups of absolute products of schemes in characteristic $p$. The latter plays a key role in V. Lafforgue's approach to the Langlands correspondence for reductive groups with $\ell$-adic coefficients; the $p$-adic analogue will be considered in subsequent work with Daxin Xu.